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Loaiza-Bonilla A, Thaker N, Chung C, Parikh RB, Stapleton S, Borkowski P. Driving Knowledge to Action: Building a Better Future With Artificial Intelligence-Enabled Multidisciplinary Oncology. Am Soc Clin Oncol Educ Book 2025; 45:e100048. [PMID: 40315375 DOI: 10.1200/edbk-25-100048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2025]
Abstract
Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and treat cancer. From earlier and more accurate diagnoses to personalized treatment planning, AI's impact is evident across radiology, pathology, radiation oncology, and medical oncology. By leveraging vast and diverse data-including imaging, genomic, clinical, and real-world evidence-AI algorithms can uncover complex patterns, accelerate drug discovery, and help identify optimal treatment regimens for each patient. However, realizing the full potential of AI also necessitates addressing concerns regarding data quality, algorithmic bias, explainability, privacy, and regulatory oversight-especially in low- and middle-income countries (LMICs), where disparities in cancer care are particularly pronounced. This study provides a comprehensive overview of how AI is reshaping cancer care, reviews its benefits and challenges, and outlines ethical and policy implications in line with ASCO's 2025 theme, Driving Knowledge to Action. We offer concrete calls to action for clinicians, researchers, industry stakeholders, and policymakers to ensure that AI-driven, patient-centric oncology is accessible, equitable, and sustainable worldwide.
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Affiliation(s)
- Arturo Loaiza-Bonilla
- St Luke's University Health Network, Bethlehem, PA
- Massive Bio, Inc, New York, NY
- Lewis Katz School of Medicine at Temple University, Philadelphia, PA
| | | | - Caroline Chung
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Shawn Stapleton
- The University of Texas MD Anderson Cancer Center, Houston, TX
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Sun Z, Wang K, Gao G, Wang H, Wu P, Li J, Zhang X, Wang X. Assessing the Performance of Artificial Intelligence Assistance for Prostate MRI: A Two-Center Study Involving Radiologists With Different Experience Levels. J Magn Reson Imaging 2025; 61:2234-2245. [PMID: 39540567 DOI: 10.1002/jmri.29660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 10/22/2024] [Accepted: 10/22/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences. PURPOSE To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance. STUDY TYPE Retrospective. POPULATION Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa). FIELD STRENGTH/SEQUENCE 3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image. ASSESSMENT CsPCa regions based on biopsy results served as the reference standard. Ten less-experienced (<500 prostate MRIs) and six experienced (>1000 prostate MRIs) radiologists reviewed each case twice using Prostate Imaging Reporting and Data System v2.1, with and without AI, separated by 4-week intervals. Cases were equally distributed among less-experienced radiologists, and 90 cases were randomly assigned to each experienced radiologist. Reading time and diagnostic confidence were assessed. STATISTICAL TESTS Area under the curve (AUC), sensitivity, specificity, reading time, and diagnostic confidence were compared using the DeLong test, Chi-squared test, Fisher exact test, or Wilcoxon rank-sum test between the two sessions. A P-value <0.05 was considered significant. Adjusting threshold using Bonferroni correction was performed for multiple comparisons. RESULTS For less-experienced radiologists, AI assistance significantly improved lesion-level sensitivity (0.78 vs. 0.88), sextant-level AUC (0.84 vs. 0.93), and patient-level AUC (0.84 vs. 0.89). For experienced radiologists, AI assistance only improved sextant-level AUC (0.82 vs. 0.91). AI assistance significantly reduced median reading time (250 s [interquartile range, IQR: 157, 402] vs. 130 s [IQR: 88, 209]) and increased diagnostic confidence (5 [IQR: 4, 5] vs. 5 [IQR: 4, 5]) irrespective of experience and enhanced consistency among experienced radiologists (Fleiss κ: 0.53 vs. 0.61). DATA CONCLUSION AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhaonan Sun
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Ge Gao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Huihui Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China
| | - Jialun Li
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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Wahab A, Suhail M, Eggers T, Shehzad K, Akakuru OU, Ahmad Z, Sun Z, Iqbal MZ, Kong X. Innovative perspectives on metal free contrast agents for MRI: Enhancing imaging efficacy, and AI-driven future diagnostics. Acta Biomater 2025; 193:83-106. [PMID: 39793747 DOI: 10.1016/j.actbio.2025.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Revised: 12/13/2024] [Accepted: 01/07/2025] [Indexed: 01/13/2025]
Abstract
The U.S. Food and Drug Administration (FDA) has issued a boxed warning and mandated additional safety measures for all gadolinium-based contrast agents (GBCAs) used in clinical magnetic resonance imaging (MRI) due to their prolonged retention in the body and associated adverse health effects. This review explores recent advancements in CAs for MRI, highlighting four innovative probes: ORCAs, CEST CAs, 19F CAs, and HP 13C MRI. ORCAs offer a metal-free alternative that enhances imaging through nitroxides. CEST MRI facilitates the direct detection of specific molecules via proton exchange, aiding in disease diagnosis and metabolic assessment. 19F MRI CAs identify subtle biological changes, enabling earlier detection and tailored treatment approaches. HP 13C MRI improves visualization of metabolic processes, demonstrating potential in cancer diagnosis and monitoring. Finally, this review concludes by addressing the challenges facing the field and outlining future research directions, with a particular focus on leveraging artificial intelligence to enhance diagnostic capabilities and optimize both the performance and safety profiles of these innovative CAs. STATEMENT OF SIGNIFICANCE: The review addresses the urgent need for safer MRI contrast agents in light of FDA warnings about GBCAs. It highlights the key factors influencing the stability and functionality of metal-free CAs and recent advancements in designing ORCAs, CEST CAs, 19F CAs, and HP 13C probes and functionalization that enhance MRI contrast. It also explores the potential of these agents for multimodal imaging and targeted diagnostics while outlining future research directions and the integration of artificial intelligence to optimize their clinical application and safety. This contribution is pivotal for driving innovation in MRI technology and improving patient outcomes in disease detection and monitoring.
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Affiliation(s)
- Abdul Wahab
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China
| | - Muhammad Suhail
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China
| | - Tatiana Eggers
- Department of Physics, University of South Florida, Tampa, FL 33620, USA
| | - Khurram Shehzad
- Institute of Physics, Silesian University of Technology, Konarskiego 22B, Gliwice 44-100, Poland
| | - Ozioma Udochukwu Akakuru
- Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Alberta, Canada
| | - Zahoor Ahmad
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, China
| | - M Zubair Iqbal
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China.
| | - Xiangdong Kong
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China.
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Zabihollahy F, Miao Q, Naim S, Sonni I, Vangala S, Kim H, Hsu W, Sisk A, Reiter R, Raman SS, Sung K. Investigating MRI-Associated Biological Aspects of Racial Disparities in Prostate Cancer for African American and White Men. J Magn Reson Imaging 2025; 61:121-131. [PMID: 38751322 PMCID: PMC11564416 DOI: 10.1002/jmri.29397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 03/30/2024] [Accepted: 04/01/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Understanding the characteristics of multiparametric MRI (mpMRI) in patients from different racial/ethnic backgrounds is important for reducing the observed gaps in clinical outcomes. PURPOSE To investigate the diagnostic performance of mpMRI and quantitative MRI parameters of prostate cancer (PCa) in African American (AA) and matched White (W) men. STUDY TYPE Retrospective. SUBJECTS One hundred twenty-nine patients (43 AA, 86 W) with histologically proven PCa who underwent mpMRI before radical prostatectomy. FIELD STRENGTH/SEQUENCE 3.0 T, T2-weighted turbo spin echo imaging, a single-shot spin-echo EPI sequence diffusion-weighted imaging, and a gradient echo sequence dynamic contrast-enhanced MRI with an ultrafast 3D spoiled gradient-echo sequence. ASSESSMENT The diagnostic performance of mpMRI in AA and W men was assessed using detection rates (DRs) and positive predictive values (PPVs) in zones defined by the PI-RADS v2.1 prostate sector map. Quantitative MRI parameters, including Ktrans and ve of clinically significant (cs) PCa (Gleason score ≥ 7) tumors were compared between AA and W sub-cohorts after matching age, prostate-specific antigen (PSA), and prostate volume. STATISTICAL TESTS Weighted Pearson's chi-square and Mann-Whitney U tests with a statistically significant level of 0.05 were used to examine differences in DR and PPV and to compare parameters between AA and matched W men, respectively. RESULTS A total number of 264 PCa lesions were identified in the study cohort. The PPVs in the peripheral zone (PZ) and posterior prostate of mpMRI for csPCa lesions were significantly higher in AA men than in matched W men (87.8% vs. 68.1% in PZ, and 89.3% vs. 69.6% in posterior prostate). The Ktrans of index csPCa lesions in AA men was significantly higher than in W men (0.25 ± 0.12 vs. 0.20 ± 0.08 min-1; P < 0.01). DATA CONCLUSION This study demonstrated race-related differences in the diagnostic performances and quantitative MRI measures of csPCa that were not reflected in age, PSA, and prostate volume. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Fatemeh Zabihollahy
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Qi Miao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, China
| | - Sohaib Naim
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ida Sonni
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Sitaram Vangala
- Department of Medicine Statistics Core, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Harrison Kim
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - William Hsu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Anthony Sisk
- Department of Pathology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Robert Reiter
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Steven S. Raman
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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Couchoux T, Jaouen T, Melodelima-Gonindard C, Baseilhac P, Branchu A, Arfi N, Aziza R, Barry Delongchamps N, Bladou F, Bratan F, Brunelle S, Colin P, Correas JM, Cornud F, Descotes JL, Eschwege P, Fiard G, Guillaume B, Grange R, Grenier N, Lang H, Lefèvre F, Malavaud B, Marcelin C, Moldovan PC, Mottet N, Mozer P, Potiron E, Portalez D, Puech P, Renard-Penna R, Roumiguié M, Roy C, Timsit MO, Tricard T, Villers A, Walz J, Debeer S, Mansuy A, Mège-Lechevallier F, Decaussin-Petrucci M, Badet L, Colombel M, Ruffion A, Crouzet S, Rabilloud M, Souchon R, Rouvière O. Performance of a Region of Interest-based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database-CAD-FIRST Study. Eur Urol Oncol 2024; 7:1113-1122. [PMID: 38493072 DOI: 10.1016/j.euo.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 03/01/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND AND OBJECTIVE Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI. METHODS The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively. KEY FINDINGS AND LIMITATIONS After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment. CONCLUSIONS AND CLINICAL IMPLICATIONS The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy. PATIENT SUMMARY An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database.
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Affiliation(s)
- Thibaut Couchoux
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | | | | | - Pierre Baseilhac
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Arthur Branchu
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Nicolas Arfi
- Department of Urology, Hôpital Saint Joseph Saint Luc, Lyon, France
| | - Richard Aziza
- Department of Radiology, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | | | - Franck Bladou
- Department of Urology, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Flavie Bratan
- Department of Diagnostic and Interventional Imaging, Hôpital Saint Joseph Saint Luc, Lyon, France
| | - Serge Brunelle
- Department of Radiology and Medical Imaging, Institut Paoli-Calmettes Cancer Center, Marseille, France
| | - Pierre Colin
- Department of Urology, Hôpital privé La Louvrière, Lille, France
| | - Jean-Michel Correas
- Department of Radiology, Hôpital Necker, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - François Cornud
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Jean-Luc Descotes
- Université Grenoble Alpes, Grenoble, France; Department of Urology, Centre Hospitalier Universitaire de Grenoble, Grenoble, France
| | - Pascal Eschwege
- Department of Urology, Centre Hospitalier Régional et Universitaire de Nancy, Vandoeuvre, France
| | - Gaelle Fiard
- Université Grenoble Alpes, Grenoble, France; Department of Urology, Centre Hospitalier Universitaire de Grenoble, Grenoble, France
| | - Bénédicte Guillaume
- Department of Radiology, Centre Hospitalier Universitaire de Grenoble, Université Grenoble Apes, Grenoble, France
| | - Rémi Grange
- Department of Radiology, University Hospital of Saint-Etienne, Saint-Priest-en-Jarez, France
| | - Nicolas Grenier
- Department of Radiology, Centre Hospitalier Universitaire de Bordeaux, Hôpital Pellegrin, Bordeaux, France
| | - Hervé Lang
- Department of Urology, Centre Hospitalier Universitaire de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
| | - Frédéric Lefèvre
- Department of Radiology, Centre Hospitalier Régional et Universitaire de Nancy, Vandoeuvre, France
| | - Bernard Malavaud
- Department of Urology, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | - Clément Marcelin
- Department of Radiology, Centre Hospitalier Universitaire de Bordeaux, Hôpital Pellegrin, Bordeaux, France
| | - Paul C Moldovan
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Nicolas Mottet
- Department of Urology, University Hospital of Saint-Etienne, Saint-Priest-en-Jarez, France
| | - Pierre Mozer
- Department of Urology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Eric Potiron
- Clinique Urologique de Nantes, Saint-Herblain, France
| | - Daniel Portalez
- Department of Radiology, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | - Philippe Puech
- Department of Radiology, Centre Hospitalier Régional et Universitaire de Lille, Lille, France
| | - Raphaele Renard-Penna
- Department of Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France; GRC no 5, ONCOTYPE-URO, Sorbonne Universités, Paris, France
| | - Matthieu Roumiguié
- Department of Urology, Toulouse-Rangueil University Hospital, Toulouse France
| | - Catherine Roy
- Department of Radiology B, Centre Hospitalier Universitaire de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
| | - Marc-Olivier Timsit
- Department of Urology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Thibault Tricard
- Department of Urology, Centre Hospitalier Universitaire de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
| | - Arnauld Villers
- Department of Urology, Univ. Lille, CHU Lille, Lille, France
| | - Jochen Walz
- Department of Urology, Institut Paoli-Calmettes Cancer Center, Marseille, France
| | - Sabine Debeer
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Adeline Mansuy
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | | | | | - Lionel Badet
- Department of Urology, University Hospital of Saint-Etienne, Saint-Priest-en-Jarez, France; Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France
| | - Marc Colombel
- Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France
| | - Alain Ruffion
- Université Lyon 1, Université de Lyon, Lyon, France; Department of Urology, Centre Hospitalier Lyon Sud, Hospices Cibvils de Lyon, Pierre-Bénite, France
| | - Sébastien Crouzet
- LabTau, INSERM Unit 1032, Lyon, France; Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France
| | - Muriel Rabilloud
- Université Lyon 1, Université de Lyon, Lyon, France; Pôle Santé Publique, Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Lyon, France; CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, Équipe Biostatistique-Santé, Villeurbanne, France
| | | | - Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; LabTau, INSERM Unit 1032, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France.
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Alanezi ST, Almutairi WM, Cronin M, Gobbo O, O'Mara SM, Sheppard D, O'Connor WT, Gilchrist MD, Kleefeld C, Colgan N. Whole-brain traumatic controlled cortical impact to the left frontal lobe: Magnetic resonance image-based texture analysis. J Neuropathol Exp Neurol 2024; 83:94-106. [PMID: 38164986 DOI: 10.1093/jnen/nlad110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Abstract
This research assesses the capability of texture analysis (TA) derived from high-resolution (HR) T2-weighted magnetic resonance imaging to identify primary sequelae following 1-5 hours of controlled cortical impact mild or severe traumatic brain injury (TBI) to the left frontal cortex (focal impact) and secondary (diffuse) sequelae in the right frontal cortex, bilateral corpus callosum, and hippocampus in rats. The TA technique comprised first-order (histogram-based) and second-order statistics (including gray-level co-occurrence matrix, gray-level run length matrix, and neighborhood gray-level difference matrix). Edema in the left frontal impact region developed within 1 hour and continued throughout the 5-hour assessments. The TA features from HR images confirmed the focal injury. There was no significant difference among radiomics features between the left and right corpus callosum or hippocampus from 1 to 5 hours following a mild or severe impact. The adjacent corpus callosum region and the distal hippocampus region (s), showed no diffuse injury 1-5 hours after mild or severe TBI. These results suggest that combining HR images with TA may enhance detection of early primary and secondary sequelae following TBI.
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Affiliation(s)
- Saleh T Alanezi
- Physics Department, Faculty of Science, Northern Border University, ArAr, Saudi Arabia
- School of Natural Sciences, College of Science and Engineering, University of Galway, Galway, Ireland
| | - Waleed M Almutairi
- Medical Imaging Department, King Abdullah bin Abdulaziz University Hospital, Riyadh, Saudi Arabia
- Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Michelle Cronin
- Conway Institute, University College Dublin, Belfield, Dublin, Ireland
| | - Oliviero Gobbo
- School of Pharmacy and Pharmaceutical Sciences & Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Shane M O'Mara
- Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Declan Sheppard
- Department of Radiology, University Hospital Galway, Galway, Ireland
| | - William T O'Connor
- University of Limerick School of Medicine, Castletroy, Limerick, Ireland
| | - Michael D Gilchrist
- School of Mechanical & Materials Engineering, University College Dublin, Belfield, Dublin, Ireland
| | - Christoph Kleefeld
- School of Natural Sciences, College of Science and Engineering, University of Galway, Galway, Ireland
| | - Niall Colgan
- School of Natural Sciences, College of Science and Engineering, University of Galway, Galway, Ireland
- Department of Engineering, Technological University of the Shannon, Athlone, Ireland
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7
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Sun Z, Wang K, Wu C, Chen Y, Kong Z, She L, Song B, Luo N, Wu P, Wang X, Zhang X, Wang X. Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study. Quant Imaging Med Surg 2024; 14:43-60. [PMID: 38223104 PMCID: PMC10784077 DOI: 10.21037/qims-23-791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/07/2023] [Indexed: 01/16/2024]
Abstract
Background An increasing number of patients with suspected clinically significant prostate cancer (csPCa) are undergoing prostate multiparametric magnetic resonance imaging (mpMRI). The role of artificial intelligence (AI) algorithms in interpreting prostate mpMRI needs to be tested with multicenter external data. This study aimed to investigate the diagnostic efficacy of an AI model in detecting and localizing visible csPCa on mpMRI a multicenter external data set. Methods The data of 2,105 patients suspected of having prostate cancer from four hospitals were retrospectively collected to develop an AI model to detect and localize suspicious csPCa. The lesions were annotated based on pathology records by two radiologists. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values were used as the input for the three-dimensional U-Net framework. Subsequently, the model was validated using an external data set comprising the data of 557 patients from three hospitals. Sensitivity, specificity, and accuracy were employed to evaluate the diagnostic efficacy of the model. Results At the lesion level, the model had a sensitivity of 0.654. At the overall sextant level, the model had a sensitivity, specificity, and accuracy of 0.846, 0.884, and 0.874, respectively. At the patient level, the model had a sensitivity, specificity, and accuracy of 0.943, 0.776, and 0.849, respectively. The AI-predicted accuracy for the csPCa patients (231/245, 0.943) was significantly higher than that for the non-csPCa patients (242/312, 0.776) (P<0.001). The lesion number and tumor volume were greater in the correctly diagnosed patients than the incorrectly diagnosed patients (both P<0.001). Among the positive patients, those with lower average ADC values had a higher rate of correct diagnosis than those with higher average ADC values (P=0.01). Conclusions The AI model exhibited acceptable accuracy in detecting and localizing visible csPCa at the patient and sextant levels. However, further improvements need to be made to enhance the sensitivity of the model at the lesion level.
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Affiliation(s)
- Zhaonan Sun
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Chenchao Wu
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zixuan Kong
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Lilan She
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ning Luo
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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Yilmaz EC, Harmon SA, Belue MJ, Merriman KM, Phelps TE, Lin Y, Garcia C, Hazen L, Patel KR, Merino MJ, Wood BJ, Choyke PL, Pinto PA, Citrin DE, Turkbey B. Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI. Eur J Radiol 2023; 168:111095. [PMID: 37717420 PMCID: PMC10615746 DOI: 10.1016/j.ejrad.2023.111095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 09/04/2023] [Accepted: 09/12/2023] [Indexed: 09/19/2023]
Abstract
OBJECTIVE To evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history. MATERIALS AND METHODS This study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles. RESULTS Of the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0-2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively. CONCLUSION The reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.
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Affiliation(s)
- Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Katie M Merriman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Tim E Phelps
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Charisse Garcia
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Lindsey Hazen
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, United States.
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Garg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer 2023; 1878:189026. [PMID: 37980945 DOI: 10.1016/j.bbcan.2023.189026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.
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Affiliation(s)
- Pankaj Garg
- Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India
| | - Atish Mohanty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - David Horne
- Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Department of Medical Oncology, City of Hope, Chicago, IL 60099, USA
| | - Ravi Salgia
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sharad S Singhal
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA.
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Handke AE, Ritter M, Albers P, Noldus J, Radtke JP, Krausewitz P. [Prostate cancer-multiparametric MRI and alternative approaches in intervention and therapy planning]. UROLOGIE (HEIDELBERG, GERMANY) 2023; 62:1160-1168. [PMID: 37666944 DOI: 10.1007/s00120-023-02190-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/10/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND In recent years, multiparametric magnetic resonance imaging (mpMRI) of the prostate has gained importance and plays a crucial role in both personalized diagnostics and increasingly in the treatment planning for patients with prostate cancer. OBJECTIVE The aim of this study is to present established and innovative applications of MRI in the diagnosis and treatment of localized prostate cancer, evaluating their strengths and weaknesses. Furthermore, it will explore alternative approaches and compare them in a comprehensive manner. MATERIALS AND METHODS A systematic literature review on the application of mpMRI for biopsy and therapy planning was conducted. RESULTS The integration of modern imaging techniques, especially mpMRI, into the diagnostic algorithm has revolutionized prostate cancer diagnosis. MRI and MRI-guided biopsy detect more significant prostate cancer, with the potential to reduce unnecessary biopsies and the diagnosis of clinically insignificant carcinomas. In addition, MRI provides crucial information for risk stratification and treatment planning in prostate cancer patients, both before radical prostatectomy and during active surveillance. CONCLUSION Multiparametric MRI offers significant added value for the diagnosis and treatment of localized prostate cancer. The advancement of MRI analysis, such as the implementation of artificial intelligence algorithms, holds the potential for further enhancing imaging diagnostics.
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Affiliation(s)
- Analena Elisa Handke
- Marienhospital Herne, Universitätsklinikum, Ruhr-Universität Bochum, Herne, Deutschland
| | - Manuel Ritter
- Klinik und Poliklinik für Urologie und Kinderurologie, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
| | - Peter Albers
- Klinik für Urologie, Medizinische Fakultät, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
- Abteilung für Personalisierte Früherkennung des Prostatakarzinoms, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland
| | - Joachim Noldus
- Marienhospital Herne, Universitätsklinikum, Ruhr-Universität Bochum, Herne, Deutschland
| | - Jan Philipp Radtke
- Klinik für Urologie, Medizinische Fakultät, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
- Abteilung für Personalisierte Früherkennung des Prostatakarzinoms, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland
- Abteilung Radiologie, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland
| | - Philipp Krausewitz
- Klinik und Poliklinik für Urologie und Kinderurologie, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
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da Silva HEC, Santos GNM, Leite AF, Mesquita CRM, Figueiredo PTDS, Stefani CM, de Melo NS. The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews. PLoS One 2023; 18:e0292063. [PMID: 37796946 PMCID: PMC10553229 DOI: 10.1371/journal.pone.0292063] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 09/12/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND AND PURPOSE In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients. DATA SOURCES The acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately. RESULTS In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis. CONCLUSIONS The detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems. TRIAL REGISTRATION Systematic review registration. Prospero registration number: CRD42022307403.
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Affiliation(s)
| | | | - André Ferreira Leite
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
| | | | | | - Cristine Miron Stefani
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
| | - Nilce Santos de Melo
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
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12
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Sun Z, Wu P, Cui Y, Liu X, Wang K, Gao G, Wang H, Zhang X, Wang X. Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI. J Magn Reson Imaging 2023; 58:1067-1081. [PMID: 36825823 DOI: 10.1002/jmri.28608] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. PURPOSE To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa. STUDY TYPE Retrospective. POPULATION One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N = 1428) and hold-out test (N = 200) datasets. FIELD STRENGTH/SEQUENCE DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T. ASSESSMENT The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level. STATISTICAL TESTS The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant. RESULTS The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P = 0.777; zone-level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863). DATA CONCLUSION The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhaonan Sun
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China
| | - Yingpu Cui
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Xiang Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Ge Gao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Huihui Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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Jaouen T, Souchon R, Moldovan PC, Bratan F, Duran A, Hoang-Dinh A, Di Franco F, Debeer S, Dubreuil-Chambardel M, Arfi N, Ruffion A, Colombel M, Crouzet S, Gonindard-Melodelima C, Rouvière O. Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader. Diagn Interv Imaging 2023; 104:465-476. [PMID: 37345961 DOI: 10.1016/j.diii.2023.04.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/16/2023] [Accepted: 04/18/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE The purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers. MATERIALS AND METHODS ROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets. Two radiologists (R1/R2) retrospectively delineated the lesions targeted at biopsy in test datasets. The CAD area under the receiver operating characteristic curve (AUC) for characterizing ISUP≥2 cancers was compared to that of the Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score prospectively assigned to targeted lesions. RESULTS The best models used the 25th apparent diffusion coefficient (ADC) percentile in transition zone and the 2nd ADC percentile and normalized wash-in rate in peripheral zone. The PI-RADSv2 AUCs were 82% (95% confidence interval [CI]: 74-87) and 86% (95% CI: 81-91) in the internal and external test datasets respectively. They were not different from the CAD AUCs obtained with R1 and R2 delineations, in the internal (82% [95% CI: 76-89], P = 0.95 and 85% [95% CI: 78-91], P = 0.55) and external (82% [95% CI: 74-91], P = 0.41 and 86% [95% CI:78-95], P = 0.98) test datasets. The CAD yielded sensitivities of 86-89% and 90-91%, and specificities of 64-65% and 69-75% in the internal and external test datasets respectively. CONCLUSION The CAD performance for characterizing ISUP grade≥2 prostate cancers on MRI is not different from that of PI-RADSv2 score across two test datasets.
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Affiliation(s)
| | | | - Paul C Moldovan
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Flavie Bratan
- Hôpital Saint Joseph Saint Luc, Department of Radiology, Lyon, 69007, France
| | - Audrey Duran
- Univ Lyon, CNRS, Inserm, INSA Lyon, UCBL, CREATIS, UMR5220, U1294, Villeurbanne, 69100, France
| | - Au Hoang-Dinh
- INSERM, LabTAU, U1032, Lyon, 69003, France; Hanoi Medical University, Department of Radiology, Hanoi, 116001, Vietnam
| | - Florian Di Franco
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Sabine Debeer
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Marine Dubreuil-Chambardel
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Nicolas Arfi
- Hôpital Saint Joseph Saint Luc, Department of Urology, Lyon, 69007, France
| | - Alain Ruffion
- Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Department of Urology, Pierre-Bénite, 69310, France; Equipe 2 - Centre d'Innovation en Cancérologie de Lyon (EA 3738 CICLY), Pierre-Bénite, 69310, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Faculté de Médecine Lyon Sud, Pierre-Bénite, 69310, France
| | - Marc Colombel
- Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France
| | - Sébastien Crouzet
- INSERM, LabTAU, U1032, Lyon, 69003, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France
| | - Christelle Gonindard-Melodelima
- Université Grenoble Alpes, Laboratoire d'Ecologie Alpine, BP 53, Grenoble 38041, France; CNRS, UMR 5553, BP 53, Grenoble, 38041, France
| | - Olivier Rouvière
- INSERM, LabTAU, U1032, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France.
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Mervak BM, Fried JG, Wasnik AP. A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging. Diagnostics (Basel) 2023; 13:2889. [PMID: 37761253 PMCID: PMC10529018 DOI: 10.3390/diagnostics13182889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/23/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.
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Affiliation(s)
| | | | - Ashish P. Wasnik
- Department of Radiology, University of Michigan—Michigan Medicine, 1500 E. Medical Center Dr., Ann Arbor, MI 48109, USA; (B.M.M.); (J.G.F.)
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Froń A, Semianiuk A, Lazuk U, Ptaszkowski K, Siennicka A, Lemiński A, Krajewski W, Szydełko T, Małkiewicz B. Artificial Intelligence in Urooncology: What We Have and What We Expect. Cancers (Basel) 2023; 15:4282. [PMID: 37686558 PMCID: PMC10486651 DOI: 10.3390/cancers15174282] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
INTRODUCTION Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. METHODOLOGY We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. RESULTS Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. CONCLUSIONS AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.
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Affiliation(s)
- Anita Froń
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Alina Semianiuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Uladzimir Lazuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Kuba Ptaszkowski
- Department of Physiotherapy, Wroclaw Medical University, 50-368 Wroclaw, Poland;
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-556 Wroclaw, Poland;
| | - Artur Lemiński
- Department of Urology and Urological Oncology, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Wojciech Krajewski
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Tomasz Szydełko
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Bartosz Małkiewicz
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
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Kim H, Kang SW, Kim JH, Nagar H, Sabuncu M, Margolis DJA, Kim CK. The role of AI in prostate MRI quality and interpretation: Opportunities and challenges. Eur J Radiol 2023; 165:110887. [PMID: 37245342 DOI: 10.1016/j.ejrad.2023.110887] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 05/30/2023]
Abstract
Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.
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Affiliation(s)
- Heejong Kim
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Shin Won Kang
- Research Institute for Future Medicine, Samsung Medical Center, Republic of Korea
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
| | - Himanshu Nagar
- Department of Radiation Oncology, Weill Cornell Medical College, 525 E 68th St, New York, NY 10021, United States
| | - Mert Sabuncu
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Daniel J A Margolis
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States.
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
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17
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Yilmaz EC, Belue MJ, Turkbey B, Reinhold C, Choyke PL. A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging. Can Assoc Radiol J 2023; 74:534-547. [PMID: 36515576 DOI: 10.1177/08465371221135782] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.
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Affiliation(s)
- Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Caroline Reinhold
- McGill University Health Center, McGill University, Montreal, Canada
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
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18
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Chervenkov L, Sirakov N, Kostov G, Velikova T, Hadjidekov G. Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging. World J Radiol 2023; 15:136-145. [PMID: 37275303 PMCID: PMC10236970 DOI: 10.4329/wjr.v15.i5.136] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/21/2023] [Accepted: 04/10/2023] [Indexed: 05/23/2023] Open
Abstract
Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca.
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Affiliation(s)
- Lyubomir Chervenkov
- Department of Diagnostic Imaging, Medical University Plovdiv, Plovdiv 4000, Bulgaria
- Research Complex for Translational Neuroscience, Medical University of Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4002, Bulgaria
| | - Nikolay Sirakov
- Research Complex for Translational Neuroscience, Medical University of Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4002, Bulgaria
- Department of Diagnostic Imaging, Dental Allergology and Physiotherapy, Faculty of Dental Medicine, Medical University Plovdiv, Plovdiv 4000, Bulgaria
| | - Gancho Kostov
- Department of Special Surgery, Medical University Plovdiv, Plovdiv 4000, Bulgaria
| | - Tsvetelina Velikova
- Department of Clinical Immunology, University Hospital Lozenetz, Sofia 1407, Bulgaria
- Department of Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
| | - George Hadjidekov
- Department of Radiology, University Hospital Lozenetz, Sofia 1407, Bulgaria
- Department of Physics, Biophysics and Radiology, Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
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19
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Gibala S, Obuchowicz R, Lasek J, Schneider Z, Piorkowski A, Pociask E, Nurzynska K. Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels. J Clin Med 2023; 12:jcm12082836. [PMID: 37109173 PMCID: PMC10146387 DOI: 10.3390/jcm12082836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. METHODS Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. RESULTS Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. CONCLUSIONS There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.
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Affiliation(s)
- Sebastian Gibala
- Urology Department, Ultragen Medical Center, 31-572 Krakow, Poland
| | - Rafal Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland
| | - Julia Lasek
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Zofia Schneider
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Adam Piorkowski
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Elżbieta Pociask
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Karolina Nurzynska
- Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland
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20
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Di Franco F, Souchon R, Crouzet S, Colombel M, Ruffion A, Klich A, Almeras M, Milot L, Rabilloud M, Rouvière O. Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study). Insights Imaging 2023; 14:49. [PMID: 36939970 PMCID: PMC10027981 DOI: 10.1186/s13244-023-01391-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/15/2023] [Indexed: 03/21/2023] Open
Abstract
OBJECTIVE To assess PI-RADSv2.1 and PI-RADSv2 descriptors across readers with varying experience. METHODS Twenty-one radiologists (7 experienced (≥ 5 years) seniors, 7 less experienced seniors and 7 juniors) assessed 240 'predefined' lesions from 159 pre-biopsy multiparametric prostate MRIs. They specified their location (peripheral, transition or central zone) and size, and scored them using PI-RADSv2.1 and PI-RADSv2 descriptors. They also described and scored 'additional' lesions if needed. Per-lesion analysis assessed the 'predefined' lesions, using targeted biopsy as reference; per-lobe analysis included 'predefined' and 'additional' lesions, using combined systematic and targeted biopsy as reference. Areas under the curve (AUCs) quantified the performance in diagnosing clinically significant cancer (csPCa; ISUP ≥ 2 cancer). Kappa coefficients (κ) or concordance correlation coefficients (CCC) assessed inter-reader agreement. RESULTS At per-lesion analysis, inter-reader agreement on location and size was moderate-to-good (κ = 0.60-0.73) and excellent (CCC ≥ 0.80), respectively. Agreement on PI-RADSv2.1 scoring was moderate (κ = 0.43-0.47) for seniors and fair (κ = 0.39) for juniors. Using PI-RADSv2.1, juniors obtained a significantly lower AUC (0.74; 95% confidence interval [95%CI]: 0.70-0.79) than experienced seniors (0.80; 95%CI 0.76-0.84; p = 0.008) but not than less experienced seniors (0.74; 95%CI 0.70-0.78; p = 0.75). As compared to PI-RADSv2, PI-RADSv2.1 downgraded 17 lesions/reader (interquartile range [IQR]: 6-29), of which 2 (IQR: 1-3) were csPCa; it upgraded 4 lesions/reader (IQR: 2-7), of which 1 (IQR: 0-2) was csPCa. Per-lobe analysis, which included 60 (IQR: 25-73) 'additional' lesions/reader, yielded similar results. CONCLUSIONS Experience significantly impacted lesion characterization using PI-RADSv2.1 descriptors. As compared to PI-RADSv2, PI-RADSv2.1 tended to downgrade non-csPCa lesions, but this effect was small and variable across readers.
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Affiliation(s)
- Florian Di Franco
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France
| | | | - Sébastien Crouzet
- INSERM, LabTau, U1032, Lyon, France
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Est, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Hôpital Edouard Herriot, 69437, Lyon, France
| | - Marc Colombel
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Est, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Hôpital Edouard Herriot, 69437, Lyon, France
| | - Alain Ruffion
- Université de Lyon, Université Lyon 1, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Centre Hospitalier Lyon Sud, Pierre-Bénite, France
- Equipe 2-Centre d'Innovation en Cancérologie de Lyon, 3738, Lyon, EA, France
- Faculté de Médecine Lyon Sud, 69003, Lyon, France
| | - Amna Klich
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Mathilde Almeras
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Laurent Milot
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France
- INSERM, LabTau, U1032, Lyon, France
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Sud, 69003, Lyon, France
| | - Muriel Rabilloud
- Université de Lyon, Université Lyon 1, Lyon, France
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Olivier Rouvière
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France.
- INSERM, LabTau, U1032, Lyon, France.
- Université de Lyon, Université Lyon 1, Lyon, France.
- Faculté de Médecine Lyon Est, Lyon, France.
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21
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Zabihollahy F, Miao Q, Sonni I, Vangala S, Kim H, Hsu W, Sisk A, Reiter R, Raman S, Sung K. Racial Disparities in Quantitative MRI for African American and White Men with Prostate Cancer. RESEARCH SQUARE 2023:rs.3.rs-2547854. [PMID: 36824946 PMCID: PMC9949266 DOI: 10.21203/rs.3.rs-2547854/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
The risk of prostate cancer (PCa) is strongly influenced by race and ethnicity. The purpose of this study is to investigate differences in the diagnostic performance of multiparametric MRI (mpMRI) in African American (AA) and white (W) men. 111 patients (37 AA and 74 W men) were selected from the study's initial cohort of 885 patients after matching age, prostate-specific antigen, and prostate volume. The diagnostic performance of mpMRI was assessed using detection rates (DRs) and positive predictive values (PPVs) with/without combining Ktrans (volume transfer constant) stratified by prostate zones for AA and W sub-cohorts. The DRs of mpMRI for clinically significant PCa (csPCa) lesions in AA and W sub-cohort with PI-RADS scores ≥ 3 were 67.3% vs. 80.3% in the transition zone (TZ; p=0.026) and 81.2% vs. 76.1% in the peripheral zone (PZ; p>0.9). The Ktrans of csPCa in AA men was significantly higher than in W men (0.23±0.08 min-1 vs. 0.19±0.07 min-1; p=0.022). This emphasizes that there are race-related differences in the performance of mpMRI and quantitative MRI measures that are not reflected in age, PSA, and prostate volume.
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Affiliation(s)
- Fatemeh Zabihollahy
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Qi Miao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, China
| | - Ida Sonni
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Sitaram Vangala
- Department of Medicine Statistics Core, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Harrison Kim
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - William Hsu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Anthony Sisk
- Department of Pathology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Robert Reiter
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Steven Raman
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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22
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Belue MJ, Harmon SA, Lay NS, Daryanani A, Phelps TE, Choyke PL, Turkbey B. The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms. J Am Coll Radiol 2023; 20:134-145. [PMID: 35922018 PMCID: PMC9887098 DOI: 10.1016/j.jacr.2022.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research. MATERIALS AND METHODS This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons. RESULTS In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation. CONCLUSION A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.
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Affiliation(s)
- Mason J Belue
- Medical Research Scholars Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Harmon
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Nathan S Lay
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Intramural Research Training Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Tim E Phelps
- Postdoctoral Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Artificial Intelligence Resource, Chief of Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Baris Turkbey
- Senior Clinician/Director, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
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23
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Rouvière O, Jaouen T, Baseilhac P, Benomar ML, Escande R, Crouzet S, Souchon R. Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review. Diagn Interv Imaging 2022; 104:221-234. [PMID: 36517398 DOI: 10.1016/j.diii.2022.11.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE The purpose of this study was to perform a systematic review of the literature on the diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based algorithms aimed at characterizing/detecting prostate cancer on magnetic resonance imaging (MRI). MATERIALS AND METHODS Medline, Embase and Web of Science were searched for studies published between January 2018 and September 2022, using a histological reference standard, and assessing prostate cancer characterization/detection by AI-based MRI algorithms in test cohorts composed of more than 40 patients and with at least one of the following independency criteria as compared to the training cohort: different institution, different population type, different MRI vendor, different magnetic field strength or strict temporal splitting. RESULTS Thirty-five studies were selected. The overall risk of bias was low. However, 23 studies did not use predefined diagnostic thresholds, which may have optimistically biased the results. Test cohorts fulfilled one to three of the five independency criteria. The diagnostic performance of the algorithms used as standalones was good, challenging that of human reading. In the 12 studies with predefined diagnostic thresholds, radiomics-based computer-aided diagnosis systems (assessing regions-of-interest drawn by the radiologist) tended to provide more robust results than deep learning-based computer-aided detection systems (providing probability maps). Two of the six studies comparing unassisted and assisted reading showed significant improvement due to the algorithm, mostly by reducing false positive findings. CONCLUSION Prostate MRI AI-based algorithms showed promising results, especially for the relatively simple task of characterizing predefined lesions. The best management of discrepancies between human reading and algorithm findings still needs to be defined.
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Affiliation(s)
- Olivier Rouvière
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France; Université Lyon 1, Faculté de médecine Lyon Est, Lyon 69003, France; LabTAU, INSERM, U1032, Lyon 69003, France.
| | | | - Pierre Baseilhac
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France
| | - Mohammed Lamine Benomar
- LabTAU, INSERM, U1032, Lyon 69003, France; University of Ain Temouchent, Faculty of Science and Technology, Algeria
| | - Raphael Escande
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France
| | - Sébastien Crouzet
- Université Lyon 1, Faculté de médecine Lyon Est, Lyon 69003, France; LabTAU, INSERM, U1032, Lyon 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon 69003, France
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24
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Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer. Cancers (Basel) 2022; 14:cancers14225595. [PMID: 36428686 PMCID: PMC9688370 DOI: 10.3390/cancers14225595] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/29/2022] [Accepted: 11/01/2022] [Indexed: 11/16/2022] Open
Abstract
As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.
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25
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Zhang KS, Schelb P, Netzer N, Tavakoli AA, Keymling M, Wehrse E, Hog R, Rotkopf LT, Wennmann M, Glemser PA, Thierjung H, von Knebel Doeberitz N, Kleesiek J, Görtz M, Schütz V, Hielscher T, Stenzinger A, Hohenfellner M, Schlemmer HP, Maier-Hein K, Bonekamp D. Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration. Invest Radiol 2022; 57:601-612. [PMID: 35467572 DOI: 10.1097/rli.0000000000000878] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI). MATERIALS AND METHODS The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric prostate MRI from consecutive men included between November 2019 and September 2020 was fully automatically and individually analyzed by a CNN briefly after image acquisition (pseudoprospective design). Radiology residents performed 2 research Prostate Imaging Reporting and Data System (PI-RADS) assessments of the multiparametric dataset independent from clinical reporting (paraclinical design) before and after review of the CNN results and completed a survey. Presence of clinically significant PC was determined by the presence of an International Society of Urological Pathology grade 2 or higher PC on combined targeted and extended systematic transperineal MRI/transrectal ultrasound fusion biopsy. Sensitivities and specificities on a patient and prostate sextant basis were compared using the McNemar test and compared with the receiver operating characteristic (ROC) curve of CNN. Survey results were summarized as absolute counts and percentages. RESULTS A total of 201 men were included. The CNN achieved an ROC area under the curve of 0.77 on a patient basis. Using PI-RADS ≥3-emulating probability threshold (c3), CNN had a patient-based sensitivity of 81.8% and specificity of 54.8%, not statistically different from the current clinical routine PI-RADS ≥4 assessment at 90.9% and 54.8%, respectively ( P = 0.30/ P = 1.0). In general, residents achieved similar sensitivity and specificity before and after CNN review. On a prostate sextant basis, clinical assessment possessed the highest ROC area under the curve of 0.82, higher than CNN (AUC = 0.76, P = 0.21) and significantly higher than resident performance before and after CNN review (AUC = 0.76 / 0.76, P ≤ 0.03). The resident survey indicated CNN to be helpful and clinically useful. CONCLUSIONS Pseudoprospective paraclinical integration of fully automated CNN-based detection of suspicious lesions on prostate multiparametric MRI was demonstrated and showed good acceptance among residents, whereas no significant improvement in resident performance was found. General CNN performance was preserved despite an observed shift in CNN calibration, identifying the requirement for continuous quality control and recalibration.
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Affiliation(s)
- Kevin Sun Zhang
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | | | | | - Myriam Keymling
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | - Eckhard Wehrse
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | - Robert Hog
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | - Markus Wennmann
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | - Heidi Thierjung
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | | | | | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center
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Turkbey B, Haider MA. Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the AJR Special Series on AI Applications. AJR Am J Roentgenol 2022; 219:188-194. [PMID: 34877870 DOI: 10.2214/ajr.21.26917] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Use of prostate MRI has increased greatly in the past decade, primarily in directing targeted prostate biopsy. However, prostate MRI interpretation remains prone to interreader variation. Artificial intelligence (AI) has the potential to standardize detection of lesions on MRI that are suspicious for prostate cancer (PCa). The purpose of this review is to explore the current status of AI for the automated detection of PCa on MRI. Recent literature describing promising results regarding AI models for PCa detection on MRI is highlighted. Numerous limitations of the existing literature are also described, including biases in model validation, heterogeneity in reporting of performance metrics, and lack of sufficient evidence of clinical translation. Challenges related to AI ethics and data governance are also discussed. An outlook is provided for AI in lesion detection on prostate MRI in the coming years, emphasizing current research needs. Future investigations, incorporating large-scale diverse multiinstitutional training and testing datasets, are anticipated to enable the development of more robust AI models for PCa detection on MRI, though prospective clinical trials will ultimately be required to establish benefit of AI in patient management.
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Affiliation(s)
- Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, Rm B3B85, Bethesda, MD 20892
| | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, To ronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
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27
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Belue MJ, Turkbey B. Tasks for artificial intelligence in prostate MRI. Eur Radiol Exp 2022; 6:33. [PMID: 35908102 PMCID: PMC9339059 DOI: 10.1186/s41747-022-00287-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/18/2022] [Indexed: 11/17/2022] Open
Abstract
The advent of precision medicine, increasing clinical needs, and imaging availability among many other factors in the prostate cancer diagnostic pathway has engendered the utilization of artificial intelligence (AI). AI carries a vast number of potential applications in every step of the prostate cancer diagnostic pathway from classifying/improving prostate multiparametric magnetic resonance image quality, prostate segmentation, anatomically segmenting cancer suspicious foci, detecting and differentiating clinically insignificant cancers from clinically significant cancers on a voxel-level, and classifying entire lesions into Prostate Imaging Reporting and Data System categories/Gleason scores. Multiple studies in all these areas have shown many promising results approximating accuracies of radiologists. Despite this flourishing research, more prospective multicenter studies are needed to uncover the full impact and utility of AI on improving radiologist performance and clinical management of prostate cancer. In this narrative review, we aim to introduce emerging medical imaging AI paper quality metrics such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Field-Weighted Citation Impact (FWCI), dive into some of the top AI models for segmentation, detection, and classification.
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Affiliation(s)
- Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health Bethesda, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health Bethesda, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA.
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28
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Labus S, Altmann MM, Huisman H, Tong A, Penzkofer T, Choi MH, Shabunin I, Winkel DJ, Xing P, Szolar DH, Shea SM, Grimm R, von Busch H, Kamen A, Herold T, Baumann C. A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists. Eur Radiol 2022; 33:64-76. [PMID: 35900376 DOI: 10.1007/s00330-022-08978-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. METHODS In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. RESULTS In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). CONCLUSIONS DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. KEY POINTS • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade.
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Affiliation(s)
- Sandra Labus
- Department of Radiology, Helios Klinikum Berlin-Buch, Schwanebecker Ch 50, 13125, Berlin, Germany.
| | - Martin M Altmann
- Department of Radiology, Helios Klinikum Berlin-Buch, Schwanebecker Ch 50, 13125, Berlin, Germany
| | - Henkjan Huisman
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Angela Tong
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Moon Hyung Choi
- Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | | | - David J Winkel
- Department of Radiology, University Hospital of Basel, Basel, Switzerland
| | - Pengyi Xing
- Department of Radiology, Changhai Hospital, Shanghai, China
| | | | | | - Robert Grimm
- Diagnostic Imaging, Siemens Healthcare, Erlangen, Germany
| | | | - Ali Kamen
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Thomas Herold
- Department of Radiology, Helios Klinikum Berlin-Buch, Schwanebecker Ch 50, 13125, Berlin, Germany
| | - Clemens Baumann
- Department of Radiology, Helios Klinikum Berlin-Buch, Schwanebecker Ch 50, 13125, Berlin, Germany
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Belue MJ, Yilmaz EC, Daryanani A, Turkbey B. Current Status of Biparametric MRI in Prostate Cancer Diagnosis: Literature Analysis. Life (Basel) 2022; 12:804. [PMID: 35743835 PMCID: PMC9224842 DOI: 10.3390/life12060804] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/23/2022] [Accepted: 05/23/2022] [Indexed: 12/19/2022] Open
Abstract
The role of multiparametric MRI (mpMRI) in the detection of prostate cancer is well-established. Based on the limited role of dynamic contrast enhancement (DCE) in PI-RADS v2.1, the risk of potential side effects, and the increased cost and time, there has been an increase in studies advocating for the omission of DCE from MRI assessments. Per PI-RADS v2.1, DCE is indicated in the assessment of PI-RADS 3 lesions in the peripheral zone, with its most pronounced effect when T2WI and DWI are of insufficient quality. The aim of this study was to evaluate the methodology and reporting in the literature from the past 5 years regarding the use of DCE in prostate MRI, especially with respect to the indications for DCE as stated in PI-RADS v2.1, and to describe the different approaches used across the studies. We searched for studies investigating the use of bpMRI and/or mpMRI in the detection of clinically significant prostate cancer between January 2017 and April 2022 in the PubMed, Web of Science, and Google Scholar databases. Through the search process, a total of 269 studies were gathered and 41 remained after abstract and full-text screening. The following information was extracted from the eligible studies: general clinical and technical characteristics of the studies, the number of PI-RADS 3 lesions, different definitions of clinically significant prostate cancer (csPCa), biopsy thresholds, reference standard methods, and number and experience of readers. Forty-one studies were included in the study. Only 51% (21/41) of studies reported the prevalence of csPCa in their equivocal lesion (PI-RADS category 3 lesions) subgroups. Of the included studies, none (0/41) performed a stratified sub-analysis of the DCE benefit versus MRI quality and 46% (19/41) made explicit statements about removing MRI scans based on a range of factors including motion, noise, and image artifacts. Furthermore, the number of studies investigating the role of DCE using readers with varying experience was relatively low. This review demonstrates that a high proportion of the studies investigating whether bpMRI can replace mpMRI did not transparently report information inherent to their study design concerning the key indications of DCE, such as the number of clinically insignificant/significant PI-RADS 3 lesions, nor did they provide any sub-analyses to test image quality, with some removing bad quality MRI scans altogether, or reader-experience-dependency indications for DCE. For the studies that reported on most of the DCE indications, their conclusions about the utility of DCE were heavily definition-dependent (with varying definitions of csPCa and of the PI-RADS category biopsy significance threshold). Reporting the information inherent to the study design and related to the specific indications for DCE as stated in PI-RADS v2.1 is needed to determine whether DCE is helpful or not. With most of the recent literature being retrospective and not including the data related to DCE indications in particular, the ongoing dispute between bpMRI and mpMRI is likely to linger.
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Affiliation(s)
| | | | | | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892-9760, USA; (M.J.B.); (E.C.Y.); (A.D.)
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30
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Rothberg MB, Enders JJ, Kozel Z, Gopal N, Turkbey B, Pinto PA. The role of novel imaging in prostate cancer focal therapy: treatment and follow-up. Curr Opin Urol 2022; 32:231-238. [PMID: 35275101 DOI: 10.1097/mou.0000000000000986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Multiparametric magnetic resonance imaging (mpMRI) has fundamentally changed how intraprostatic lesions are visualized, serving as a highly sensitive means for detecting clinically significant prostate cancer (csPCa) via image-targeted biopsy. However, limitations associated with mpMRI have led to the development of new imaging technologies with the goal of better characterizing intraprostatic disease burden to more accurately guide treatment planning and surveillance for prostate cancer focal therapy. Herein, we review several novel imaging modalities with an emphasis on clinical data reported within the past two years. RECENT FINDINGS 7T MRI, artificial intelligence applied to mpMRI, positron emission tomography combined with either computerized tomography or MRI, contrast-enhanced ultrasound, and micro-ultrasound are novel imaging modalities with the potential to further improve intraprostatic lesion localization for applications in focal therapy for prostate cancer. Many of these technologies have demonstrated equivalent or favorable diagnostic accuracy compared to contemporary mpMRI for identifying csPCa and some have even shown improved capabilities to define lesion borders, to provide volumetric estimates of lesions, and to assess the adequacy of focal ablation of planned treatment zones. SUMMARY Novel imaging modalities with capabilities to better characterize intraprostatic lesions have the potential to improve accuracy in treatment planning, real-time assessment of the ablation zone, and posttreatment surveillance; however, many of these technologies require further validation to determine their clinical utility.
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Affiliation(s)
- Michael B Rothberg
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute
| | - Jacob J Enders
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute
| | - Zachary Kozel
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute
| | - Nikhil Gopal
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute
| | - Baris Turkbey
- Molecular Imaging Branch, Center for Cancer Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter A Pinto
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute
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31
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Hötker AM, Vargas HA, Donati OF. Abbreviated MR Protocols in Prostate MRI. Life (Basel) 2022; 12:life12040552. [PMID: 35455043 PMCID: PMC9029675 DOI: 10.3390/life12040552] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/15/2022] [Accepted: 03/17/2022] [Indexed: 11/16/2022] Open
Abstract
Prostate MRI is an integral part of the clinical work-up in biopsy-naïve patients with suspected prostate cancer, and its use has been increasing steadily over the last years. To further its general availability and the number of men benefitting from it and to reduce the costs associated with MR, several approaches have been developed to shorten examination times, e.g., by focusing on sequences that provide the most useful information, employing new technological achievements, or improving the workflow in the MR suite. This review highlights these approaches; discusses their implications, advantages, and disadvantages; and serves as a starting point whenever an abbreviated prostate MRI protocol is being considered for implementation in clinical routine.
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Affiliation(s)
- Andreas M. Hötker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland;
- Correspondence:
| | - Hebert Alberto Vargas
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY 10065, USA;
| | - Olivio F. Donati
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland;
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Rouvière O, Souchon R, Lartizien C, Mansuy A, Magaud L, Colom M, Dubreuil-Chambardel M, Debeer S, Jaouen T, Duran A, Rippert P, Riche B, Monini C, Vlaeminck-Guillem V, Haesebaert J, Rabilloud M, Crouzet S. Detection of ISUP ≥2 prostate cancers using multiparametric MRI: prospective multicentre assessment of the non-inferiority of an artificial intelligence system as compared to the PI-RADS V.2.1 score (CHANGE study). BMJ Open 2022; 12:e051274. [PMID: 35140147 PMCID: PMC8830410 DOI: 10.1136/bmjopen-2021-051274] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Prostate multiparametric MRI (mpMRI) has shown good sensitivity in detecting cancers with an International Society of Urological Pathology (ISUP) grade of ≥2. However, it lacks specificity, and its inter-reader reproducibility remains moderate. Biomarkers, such as the Prostate Health Index (PHI), may help select patients for prostate biopsy. Computer-aided diagnosis/detection (CAD) systems may also improve mpMRI interpretation. Different prototypes of CAD systems are currently developed under the Recherche Hospitalo-Universitaire en Santé / Personalized Focused Ultrasound Surgery of Localized Prostate Cancer (RHU PERFUSE) research programme, tackling challenging issues such as robustness across imaging protocols and magnetic resonance (MR) vendors, and ability to characterise cancer aggressiveness. The study primary objective is to evaluate the non-inferiority of the area under the receiver operating characteristic curve of the final CAD system as compared with the Prostate Imaging-Reporting and Data System V.2.1 (PI-RADS V.2.1) in predicting the presence of ISUP ≥2 prostate cancer in patients undergoing prostate biopsy. METHODS This prospective, multicentre, non-inferiority trial will include 420 men with suspected prostate cancer, a prostate-specific antigen level of ≤30 ng/mL and a clinical stage ≤T2 c. Included men will undergo prostate mpMRI that will be interpreted using the PI-RADS V.2.1 score. Then, they will undergo systematic and targeted biopsy. PHI will be assessed before biopsy. At the end of patient inclusion, MR images will be assessed by the final version of the CAD system developed under the RHU PERFUSE programme. Key secondary outcomes include the prediction of ISUP grade ≥2 prostate cancer during a 3-year follow-up, and the number of biopsy procedures saved and ISUP grade ≥2 cancers missed by several diagnostic pathways combining PHI and MRI findings. ETHICS AND DISSEMINATION Ethical approval was obtained from the Comité de Protection des Personnes Nord Ouest III (ID-RCB: 2020-A02785-34). After publication of the results, access to MR images will be possible for testing other CAD systems. TRIAL REGISTRATION NUMBER NCT04732156.
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Affiliation(s)
- Olivier Rouvière
- Université Lyon 1, Université de Lyon, Lyon, France
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
- LabTau, INSERM U1032, Lyon, France
| | | | - Carole Lartizien
- CREATIS, INSERM U1294, Villeurbanne, France
- CNRS UMR 5220, INSA-Lyon, Villeurbanne, France
| | - Adeline Mansuy
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Laurent Magaud
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Matthieu Colom
- Direction de la Recherche Clinique et de l'Innovation, Hospices Civils de Lyon, Lyon, France
| | - Marine Dubreuil-Chambardel
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Sabine Debeer
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | | | - Audrey Duran
- CREATIS, INSERM U1294, Villeurbanne, France
- CNRS UMR 5220, INSA-Lyon, Villeurbanne, France
| | - Pascal Rippert
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Benjamin Riche
- Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Laboratoire de Biométrie et Biologie Évolutive CNRS UMR 5558, Équipe Biostatistiques Santé, Université de Lyon, Lyon, France
| | | | - Virginie Vlaeminck-Guillem
- Université Lyon 1, Université de Lyon, Lyon, France
- Service de Biochimie et Biologie Moléculaire Sud, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Julie Haesebaert
- Université Lyon 1, Université de Lyon, Lyon, France
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Lyon, France
| | - Muriel Rabilloud
- Université Lyon 1, Université de Lyon, Lyon, France
- Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Laboratoire de Biométrie et Biologie Évolutive CNRS UMR 5558, Équipe Biostatistiques Santé, Université de Lyon, Lyon, France
| | - Sébastien Crouzet
- Université Lyon 1, Université de Lyon, Lyon, France
- LabTau, INSERM U1032, Lyon, France
- Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
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Bhattacharya I, Khandwala YS, Vesal S, Shao W, Yang Q, Soerensen SJ, Fan RE, Ghanouni P, Kunder CA, Brooks JD, Hu Y, Rusu M, Sonn GA. A review of artificial intelligence in prostate cancer detection on imaging. Ther Adv Urol 2022; 14:17562872221128791. [PMID: 36249889 PMCID: PMC9554123 DOI: 10.1177/17562872221128791] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/30/2022] [Indexed: 11/07/2022] Open
Abstract
A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.
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Affiliation(s)
- Indrani Bhattacharya
- Department of Radiology, Stanford University School of Medicine, 1201 Welch Road, Stanford, CA 94305, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yash S. Khandwala
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sulaiman Vesal
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Wei Shao
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Qianye Yang
- Centre for Medical Image Computing, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Simon J.C. Soerensen
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Richard E. Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Pejman Ghanouni
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian A. Kunder
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - James D. Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yipeng Hu
- Centre for Medical Image Computing, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Mirabela Rusu
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Geoffrey A. Sonn
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
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Mehta P, Antonelli M, Singh S, Grondecka N, Johnston EW, Ahmed HU, Emberton M, Punwani S, Ourselin S. AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning. Cancers (Basel) 2021; 13:6138. [PMID: 34885246 PMCID: PMC8656605 DOI: 10.3390/cancers13236138] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 11/30/2021] [Accepted: 12/03/2021] [Indexed: 11/21/2022] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.
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Affiliation(s)
- Pritesh Mehta
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
- School of Biomedical Engineering Imaging Sciences, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
| | - Michela Antonelli
- School of Biomedical Engineering Imaging Sciences, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London WC1E 6BT, UK; (S.S.); (S.P.)
| | - Natalia Grondecka
- Department of Medical Radiology, Medical University of Lublin, 20-059 Lublin, Poland;
| | | | - Hashim U. Ahmed
- Imperial Prostate, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
| | - Mark Emberton
- Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London WC1E 6BT, UK;
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London WC1E 6BT, UK; (S.S.); (S.P.)
| | - Sébastien Ourselin
- School of Biomedical Engineering Imaging Sciences, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
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Hellmann F, Ren Z, Andre E, Schuller BW. Deformable Dilated Faster R-CNN for Universal Lesion Detection in CT Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2896-2902. [PMID: 34891852 DOI: 10.1109/embc46164.2021.9631021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cancer is a major public health issue and takes the second-highest toll of deaths caused by non-communicable diseases worldwide. Automatically detecting lesions at an early stage is essential to increase the chance of a cure. This study proposes a novel dilated Faster R-CNN with modulated deformable convolution and modulated deformable positive-sensitive region of interest pooling to detect lesions in computer tomography images. A pre-trained VGG-16 is transferred as the backbone of Faster R-CNN, followed by a region proposal network and a region of interest pooling layer to achieve lesion detection. The modulated deformable convolutional layers are employed to learn deformable convolutional filters, while the modulated deformable positive-sensitive region of interest pooling provides an enhanced feature extraction on the feature maps. Moreover, dilated convolutions are combined with the modulated deformable convolutions to fine-tune the VGG-16 model with multi-scale receptive fields. In the experiments evaluated on the DeepLesion dataset, the modulated deformable positive-sensitive region of interest pooling model achieves the highest sensitivity score of 58.8 % on average with dilation of [4, 4, 4] and outperforms state-of-the-art models in the range of [2], [8] average false positives per image. This research demonstrates the suitability of dilation modifications and the possibility of enhancing the performance using a modulated deformable positive-sensitive region of interest pooling layer for universal lesion detectors.
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Mata LA, Retamero JA, Gupta RT, García Figueras R, Luna A. Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics 2021; 41:1676-1697. [PMID: 34597215 DOI: 10.1148/rg.2021210020] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The classic prostate cancer (PCa) diagnostic pathway that is based on prostate-specific antigen (PSA) levels and the findings of digital rectal examination followed by systematic biopsy has shown multiple limitations. The use of multiparametric MRI (mpMRI) is now widely accepted in men with clinical suspicion for PCa. In addition, clinical information, PSA density, risk calculators, and genomic and other "omics" biomarkers are being used to improve risk stratification. On the basis of mpMRI and MRI-targeted biopsies (MRI-TBx), new diagnostic pathways have been established, aiming to improve the limitations of the classic diagnostic approach. However, these pathways still show limitations associated with mpMRI and MRI-TBx. Definitive PCa diagnosis is made on the basis of histopathologic Gleason grading, which has demonstrated an excellent correlation with clinical outcomes. However, Gleason grading is done subjectively by pathologists and involves poor reproducibility, and PCa may have a heterogeneous distribution of histologic patterns. Thus, important discrepancies persist between biopsy tumor grading and final whole-organ pathologic assessment after radical prostatectomy. PCa offers a unique opportunity to establish a real radiologic-pathologic correlation, as whole-mount radical prostatectomy specimens permit a complete spatial relationship with mpMRI. Artificial intelligence is increasingly being applied to radiologic and pathologic images to improve clinical accuracy and efficiency in PCa diagnosis. This review delineates current PCa diagnostic pathways, with a focus on the role of mpMRI, MRI-TBx, and pathologic analysis. An overview of the expected improvements in PCa diagnosis derived from the use of artificial intelligence, integrated radiologic-pathologic systems, and decision support tools for multidisciplinary teams is provided. An invited commentary by Purysko is available online. Online supplemental material is available for this article. ©RSNA, 2021.
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Affiliation(s)
- Lidia Alcalá Mata
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| | - Juan Antonio Retamero
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| | - Rajan T Gupta
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| | - Roberto García Figueras
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| | - Antonio Luna
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
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Retter A, Gong F, Syer T, Singh S, Adeleke S, Punwani S. Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly. Mol Oncol 2021; 15:2565-2579. [PMID: 34328279 PMCID: PMC8486595 DOI: 10.1002/1878-0261.13071] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 07/09/2021] [Accepted: 07/29/2021] [Indexed: 12/24/2022] Open
Abstract
Imaging plays a fundamental role in all aspects of the cancer management pathway. However, conventional imaging techniques are largely reliant on morphological and size descriptors that have well-known limitations, particularly when considering targeted-therapy response monitoring. Thus, new imaging methods have been developed to characterise cancer and are now routinely implemented, such as diffusion-weighted imaging, dynamic contrast enhancement, positron emission technology (PET) and magnetic resonance spectroscopy. However, despite the improvement these techniques have enabled, limitations still remain. Novel imaging methods are now emerging, intent on further interrogating cancers. These techniques are at different stages of maturity along the biomarker pathway and aim to further evaluate the cancer microstructure (vascular, extracellular and restricted diffusion for cytometry in tumours) magnetic resonance imaging (MRI), luminal water fraction imaging] as well as the metabolic alterations associated with cancers (novel PET tracers, hyperpolarised MRI). Finally, the use of machine learning has shown powerful potential applications. By using prostate cancer as an exemplar, this Review aims to showcase these potentially potent imaging techniques and what stage we are at in their application to conventional clinical practice.
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Affiliation(s)
| | | | - Tom Syer
- UCL Centre for Medical ImagingLondonUK
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Liu Y, Zheng H, Liang Z, Miao Q, Brisbane WG, Marks LS, Raman SS, Reiter RE, Yang G, Sung K. Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification. Diagnostics (Basel) 2021; 11:1785. [PMID: 34679484 PMCID: PMC8535024 DOI: 10.3390/diagnostics11101785] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/24/2022] Open
Abstract
The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (n = 239) of patients for training, 10% (n = 42) for validation, and 30% (n = 121) for testing) with 3T multiparametric MRI matched with whole-mount histopathology after radical prostatectomy. For a given suspicious prostate lesion, the volumetric patches of T2-Weighted MRI and apparent diffusion coefficient images were cropped and used as the input to Textured-DL, consisting of a 3D gray-level co-occurrence matrix extractor and a CNN. PI-RADS-CLA by an expert reader served as a baseline to compare classification performance with Textured-DL in differentiating csPCa from non-csPCa. Sensitivity and specificity comparisons were performed using Mcnemar's test. Bootstrapping with 1000 samples was performed to estimate the 95% confidence interval (CI) for AUC. CIs of sensitivity and specificity were calculated by the Wald method. The Textured-DL model achieved an AUC of 0.85 (CI [0.79, 0.91]), which was significantly higher than the PI-RADS-CLA (AUC of 0.73 (CI [0.65, 0.80]); p < 0.05) for PCa classification, and the specificity was significantly different between Textured-DL and PI-RADS-CLA (0.70 (CI [0.59, 0.82]) vs. 0.47 (CI [0.35, 0.59]); p < 0.05). In sub-analyses, Textured-DL demonstrated significantly higher specificities in the peripheral zone (PZ) and solitary tumor lesions compared to the PI-RADS-CLA (0.78 (CI [0.66, 0.90]) vs. 0.42 (CI [0.28, 0.57]); 0.75 (CI [0.54, 0.96]) vs. 0.38 [0.14, 0.61]; all p values < 0.05). Moreover, Textured-DL demonstrated a high negative predictive value of 92% while maintaining a high positive predictive value of 58% among the lesions with a PI-RADS score of 3. In conclusion, the Textured-DL model was superior to the PI-RADS-CLA in the classification of PCa. In addition, Textured-DL demonstrated superior performance in the specificities for the peripheral zone and solitary tumors compared with PI-RADS-based risk assessment.
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Affiliation(s)
- Yongkai Liu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (H.Z.); (Q.M.); (S.S.R.); (K.S.)
- Physics and Biology in Medicine IDP, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Haoxin Zheng
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (H.Z.); (Q.M.); (S.S.R.); (K.S.)
| | - Zhengrong Liang
- Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA;
| | - Qi Miao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (H.Z.); (Q.M.); (S.S.R.); (K.S.)
| | - Wayne G. Brisbane
- Department of Urology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (W.G.B.); (L.S.M.); (R.E.R.)
| | - Leonard S. Marks
- Department of Urology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (W.G.B.); (L.S.M.); (R.E.R.)
| | - Steven S. Raman
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (H.Z.); (Q.M.); (S.S.R.); (K.S.)
| | - Robert E. Reiter
- Department of Urology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (W.G.B.); (L.S.M.); (R.E.R.)
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, South Kensington, London SW7 2AZ, UK;
| | - Kyunghyun Sung
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (H.Z.); (Q.M.); (S.S.R.); (K.S.)
- Physics and Biology in Medicine IDP, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
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Emerging role of multiparametric magnetic resonance imaging in identifying clinically relevant localized prostate cancer. Curr Opin Oncol 2021; 33:244-251. [PMID: 33606404 DOI: 10.1097/cco.0000000000000717] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
PURPOSE OF REVIEW To explore the recent advances and utility of multiparametric magnetic resonance imaging (mpMRI) in the diagnosis and risk-stratification of prostate cancer. RECENT FINDINGS Low-risk, clinically insignificant prostate cancer has a decreased risk of morbidity or mortality. Meanwhile, patients with intermediate and high-risk prostate cancer may significantly benefit from interventions like radiation or surgery. To appropriately risk stratify these patients, MRI has emerged as the imaging modality in the last decade to assist in defining prostate cancer significance, location, and biologic aggressiveness. Traditional 12-core transrectal ultrasound-guided biopsy is associated with over-detection, and ultimately over-treatment of clinically insignificant disease, and the under-detection of clinically significant disease. Biopsy accuracy is improved with MRI-guided targeted biopsy and with the use of standardized risk stratification imaging score systems. Cancer detection accuracy is further improved with combined biopsy techniques that include both systematic and MRI-targeted biopsy that aid in detection of MRI-invisible lesions. SUMMARY mpMRI is an area of expanding innovation that continues to refine the diagnostic accuracy of prostate biopsies. As mpMRI-targeted biopsy in prostate cancer becomes more commonplace, advances like artificial intelligence and less invasive dynamic metabolic imaging will continue to improve the utility of MRI.
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Syer T, Mehta P, Antonelli M, Mallett S, Atkinson D, Ourselin S, Punwani S. Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies. Cancers (Basel) 2021; 13:3318. [PMID: 34282762 PMCID: PMC8268820 DOI: 10.3390/cancers13133318] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/24/2021] [Accepted: 06/30/2021] [Indexed: 11/16/2022] Open
Abstract
Computer-aided diagnosis (CAD) of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), using artificial intelligence (AI), may reduce missed cancers and unnecessary biopsies, increase inter-observer agreement between radiologists, and alleviate pressures caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. However, well-designed evaluation studies are required to prove efficacy above current clinical practice. A systematic search of the MEDLINE, EMBASE, and arXiv electronic databases was conducted for studies that compared CAD for prostate cancer detection or classification on MRI against radiologist interpretation and a histopathological reference standard, in treatment-naïve men with a clinical suspicion of prostate cancer. Twenty-seven studies were included in the final analysis. Due to substantial heterogeneities in the included studies, a narrative synthesis is presented. Several studies reported superior diagnostic accuracy for CAD over radiologist interpretation on small, internal patient datasets, though this was not observed in the few studies that performed evaluation using external patient data. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present. Further work is needed to develop and enforce methodological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.
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Affiliation(s)
- Tom Syer
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - Pritesh Mehta
- Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, Bloomsbury Campus, University College London, London WC1E 6DH, UK;
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, St Thomas’ Campus, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
| | - Sue Mallett
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - David Atkinson
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, St Thomas’ Campus, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
| | - Shonit Punwani
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
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Mehta P, Antonelli M, Ahmed HU, Emberton M, Punwani S, Ourselin S. Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: A patient-level classification framework. Med Image Anal 2021; 73:102153. [PMID: 34246848 DOI: 10.1016/j.media.2021.102153] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 04/03/2021] [Accepted: 06/28/2021] [Indexed: 01/07/2023]
Abstract
Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multiparametric magnetic resonance imaging (mpMRI) is actively being investigated as a means to provide clinical decision support to radiologists. Typically, these systems are trained using lesion annotations. However, lesion annotations are expensive to obtain and inadequate for characterizing certain tumor types e.g. diffuse tumors and MRI invisible tumors. In this work, we introduce a novel patient-level classification framework, denoted PCF, that is trained using patient-level labels only. In PCF, features are extracted from three-dimensional mpMRI and derived parameter maps using convolutional neural networks and subsequently, combined with clinical features by a multi-classifier support vector machine scheme. The output of PCF is a probability value that indicates whether a patient is harboring clinically significant PCa (Gleason score ≥3+4) or not. PCF achieved mean area under the receiver operating characteristic curves of 0.79 and 0.86 on the PICTURE and PROSTATEx datasets respectively, using five-fold cross-validation. Clinical evaluation over a temporally separated PICTURE dataset cohort demonstrated comparable sensitivity and specificity to an experienced radiologist. We envision PCF finding most utility as a second reader during routine diagnosis or as a triage tool to identify low-risk patients who do not require a clinical read.
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Affiliation(s)
- Pritesh Mehta
- Department of Medical Physics and Biomedical Engineering, University College London, UK.
| | - Michela Antonelli
- Biomedical Engineering & Imaging Sciences School, King's College London, UK
| | - Hashim U Ahmed
- Imperial Prostate, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK
| | - Mark Emberton
- Division of Surgery and Interventional Science, University College London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, UK
| | - Sébastien Ourselin
- Biomedical Engineering & Imaging Sciences School, King's College London, UK
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The potential of AI in cancer care and research. Biochim Biophys Acta Rev Cancer 2021; 1876:188573. [PMID: 34052390 DOI: 10.1016/j.bbcan.2021.188573] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/25/2021] [Accepted: 05/25/2021] [Indexed: 02/08/2023]
Abstract
Current applications of artificial intelligence (AI), machine learning, and deep learning in cancer research and clinical care are highly diverse-from aiding radiologists in reading medical images to predicting oncoprotein folding and dynamics. The list of available AI-based tools is growing rapidly and will only continue to expand. With the immense potential for AI to advance cancer research and clinical care, the National Cancer Institute (NCI) has a responsibility to consider and support the development and evaluation of such technologies. NCI's current involvement in AI research spans the spectrum of development, implementation, and assessment. That includes generating large, publicly available, curated datasets; shifting the culture of data sharing; training the next generation of scientists in both AI and cancer sciences; fostering interdisciplinary collaborations; investing in research to improve AI methods and models that are designed specifically for cancer; widening access to computing power; procuring computer architecture for future developments; and assuring AI research and technologies follow ethical principles. In addition to a broad overview of AI applications in cancer research and care, and NCI's ongoing AI-based activities, this Perspective outlines NCI's four priority areas for future investment of cancer-focused AI development.
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Twilt JJ, van Leeuwen KG, Huisman HJ, Fütterer JJ, de Rooij M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11060959. [PMID: 34073627 PMCID: PMC8229869 DOI: 10.3390/diagnostics11060959] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022] Open
Abstract
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.
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Brodie A, Dai N, Teoh JYC, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021; 39:379-399. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/20/2020] [Accepted: 03/21/2021] [Indexed: 01/16/2023]
Abstract
There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.
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Affiliation(s)
- Andrew Brodie
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Nick Dai
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Prokar Dasgupta
- Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Nikhil Vasdev
- Hertfordshire and Bedfordshire Urological Cancer Centre, Department of Urology, Lister Hospital, Stevenage, United Kingdom; School of Medicine and Life Sciences, University of Hertfordshire, Hatfield, United Kingdom.
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ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging. Eur Radiol 2021; 31:9567-9578. [PMID: 33991226 PMCID: PMC8589789 DOI: 10.1007/s00330-021-08021-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/19/2021] [Accepted: 04/27/2021] [Indexed: 11/06/2022]
Abstract
Abstract Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists’ workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists’ workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.
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Cheung HMC, Rubin D. Challenges and opportunities for artificial intelligence in oncological imaging. Clin Radiol 2021; 76:728-736. [PMID: 33902889 DOI: 10.1016/j.crad.2021.03.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/15/2021] [Indexed: 02/08/2023]
Abstract
Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, and complications of treatment or disease. The current use of clinical oncology is predominantly qualitative in nature with some relatively crude size-based measurements of tumours for assessment of disease progression or treatment response; however, it is increasingly understood that there may be significantly more information about oncological disease that can be obtained from imaging that is not currently utilized. Artificial intelligence (AI) has the potential to harness quantitative techniques to improve oncological imaging. These may include improving the efficiency or accuracy of traditional roles of imaging such as diagnosis or detection. These may also include new roles for imaging such as risk-stratifying patients for different types of therapy or determining biological tumour subtypes. This review article outlines several major areas in oncological imaging where there may be opportunities for AI technology. These include (1) screening and detection of cancer, (2) diagnosis and risk stratification, (3) tumour segmentation, (4) precision oncology, and (5) predicting prognosis and assessing treatment response. This review will also address some of the potential barriers to AI research in oncological imaging.
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Affiliation(s)
- H M C Cheung
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Canada
| | - D Rubin
- Department of Radiology, Stanford University, CA, USA.
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Lai CC, Wang HK, Wang FN, Peng YC, Lin TP, Peng HH, Shen SH. Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks. SENSORS 2021; 21:s21082709. [PMID: 33921451 PMCID: PMC8070192 DOI: 10.3390/s21082709] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/01/2021] [Accepted: 04/09/2021] [Indexed: 12/21/2022]
Abstract
The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in detecting PCa. However, prostate MRI assessment relies on human experts and specialized training with considerable inter-reader variability. Deep learning may be a more robust approach for prostate MRI assessment. Here we present a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model. We used PROSTATEx dataset to train the model and combined different sequences into three channels of a single image. For each subject, all slices that contained the transition zone (TZ), peripheral zone (PZ), and PCa region were selected. The datasets were produced using different combinations of images, including T2-weighted (T2W) images, diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) images. Among these groups, the T2W + DWI + ADC images exhibited the best performance with a dice similarity coefficient of 90.45% for the TZ, 70.04% for the PZ, and 52.73% for the PCa region. Image sequence analysis with a DCNN model has the potential to assist PCa diagnosis.
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Affiliation(s)
- Chih-Ching Lai
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 300044, Taiwan; (C.-C.L.); (F.-N.W.)
| | - Hsin-Kai Wang
- Department of Radiology, Taipei Veterans General Hospital, Taipei 112201, Taiwan;
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan; (Y.-C.P.); (T.-P.L.)
| | - Fu-Nien Wang
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 300044, Taiwan; (C.-C.L.); (F.-N.W.)
| | - Yu-Ching Peng
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan; (Y.-C.P.); (T.-P.L.)
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Tzu-Ping Lin
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan; (Y.-C.P.); (T.-P.L.)
- Department of Urology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Hsu-Hsia Peng
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 300044, Taiwan; (C.-C.L.); (F.-N.W.)
- Correspondence: (H.-H.P.); (S.-H.S.); Tel.: +886-3-571-5131 (ext. 80189) (H.-H.P.); +886-2-28757350 (S.-H.S.)
| | - Shu-Huei Shen
- Department of Radiology, Taipei Veterans General Hospital, Taipei 112201, Taiwan;
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan; (Y.-C.P.); (T.-P.L.)
- Correspondence: (H.-H.P.); (S.-H.S.); Tel.: +886-3-571-5131 (ext. 80189) (H.-H.P.); +886-2-28757350 (S.-H.S.)
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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Udayakumar N, Porter KK. How Fast Can We Go: Abbreviated Prostate MR Protocols. Curr Urol Rep 2020; 21:59. [PMID: 33135121 DOI: 10.1007/s11934-020-01008-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/15/2020] [Indexed: 12/27/2022]
Abstract
PURPOSE OF REVIEW Multiparametric MRI (mpMRI), composed of T2WI, DWI, and DCE sequences, is effective in identifying prostate cancer (PCa), but length and cost preclude its application as a PCa screening tool. Here we review abbreviated MRI protocols that shorten or omit conventional mpMRI components to reduce scan time and expense without forgoing diagnostic accuracy. RECENT FINDINGS The DCE sequence, which plays a limited diagnostic role in PI-RADS, is eliminated in variations of the biparametric MRI (bpMRI). T2WI, the lengthiest sequence, is truncated by only acquiring the axial plane or utilizing 3D acquisition with subsequent 2D reconstruction. DW-EPISMS further accelerates DWI acquisition. The fastest protocol described to date consists of just DW-EPISMS and axial-only 2D T2WI and runs less than 5 min. Abbreviated protocols can mitigate scan expense and increase scan access, allowing prostate MRI to become an efficient PCa screening tool.
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Affiliation(s)
- Neha Udayakumar
- University of Alabama at Birmingham School of Medicine, 1720 2nd Ave S, Birmingham, AL, 35249, USA
| | - Kristin K Porter
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street S, JT N374, Birmingham, AL, 35249, USA.
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Mehralivand S, Harmon SA, Shih JH, Smith CP, Lay N, Argun B, Bednarova S, Baroni RH, Canda AE, Ercan K, Girometti R, Karaarslan E, Kural AR, Purysko AS, Rais-Bahrami S, Tonso VM, Magi-Galluzzi C, Gordetsky JB, Macarenco RSES, Merino MJ, Gumuskaya B, Saglican Y, Sioletic S, Warren AY, Barrett T, Bittencourt L, Coskun M, Knauss C, Law YM, Malayeri AA, Margolis DJ, Marko J, Yakar D, Wood BJ, Pinto PA, Choyke PL, Summers RM, Turkbey B. Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI. AJR Am J Roentgenol 2020; 215:903-912. [PMID: 32755355 PMCID: PMC8974983 DOI: 10.2214/ajr.19.22573] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVE. The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MATERIALS AND METHODS. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. RESULTS. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9% for MRI and 77.5% for AI with no significant difference (p = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4% vs 53.6%, p = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8% vs 50.8%, p = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, p < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7% vs 58.5%, p = 0.966). CONCLUSION. Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time.
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Affiliation(s)
- Sherif Mehralivand
- Department of Urology and Pediatric Urology, University Medical Center, Mainz, Germany
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
| | - Stephanie A Harmon
- Clinical Research Directorate, Leidos Biomedical Research, Inc., Frederick, MD
| | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Rockville, MD
| | - Clayton P Smith
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
| | - Nathan Lay
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
| | - Burak Argun
- Department of Urology, Acibadem University, Istanbul, Turkey
| | | | | | | | - Karabekir Ercan
- Department of Radiology, Ankara City Hospital, Ankara, Turkey
| | | | | | - Ali Riza Kural
- Department of Urology, Acibadem University, Istanbul, Turkey
| | | | - Soroush Rais-Bahrami
- Department of Urology, University of Alabama at Birmingham, Birmingham, AL
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
- O'Neal Comprehensive Cancer Center at UAB, University of Alabama at Birmingham, Birmingham, AL
| | | | | | - Jennifer B Gordetsky
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL
- Present address: Department of Pathology, Vanderbilt University, Nashville, TN
| | | | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Berrak Gumuskaya
- Department of Pathology, Ankara Yildirim Beyazit University, School of Medicine, Ankara, Turkey
| | - Yesim Saglican
- Department of Pathology, Acibadem University, Istanbul, Turkey
| | | | - Anne Y Warren
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Leonardo Bittencourt
- Department of Radiology, Federal Fluminense University, Rio de Janeiro, Brazil
- DASA Company, Rio de Janeiro, Brazil
| | - Mehmet Coskun
- Department of Radiology, University of Health Sciences Dr. Behçet Uz Child Disease and Pediatric Surgery Training and Research Hospital, İzmir, Turkey
| | - Chris Knauss
- Department of Radiology, Walter Reed Medical Center, Bethesda, MD
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | - Ashkan A Malayeri
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | | | - Jamie Marko
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Derya Yakar
- Department of Radiology, Medical Imaging Centre, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute and Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Bethesda, MD
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892-1088
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