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Mazzetti S, Defeudis A, Nicoletti G, Chiorino G, De Luca S, Faletti R, Gatti M, Gontero P, Manfredi M, Mello-Grand M, Peraldo-Neia C, Zitella A, Porpiglia F, Regge D, Giannini V. Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer. Eur Radiol 2024; 34:5108-5117. [PMID: 38177618 PMCID: PMC11255044 DOI: 10.1007/s00330-023-10542-1] [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: 04/12/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVES The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone. METHODS This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated. RESULTS MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone. CONCLUSION MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results. CLINICAL RELEVANCE STATEMENT The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa. KEY POINTS • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA.
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Affiliation(s)
- Simone Mazzetti
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Arianna Defeudis
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.
- Department of Surgical Sciences, University of Turin, Turin, Italy.
| | - Giulia Nicoletti
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | | | - Stefano De Luca
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Paolo Gontero
- Division of Urology, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Matteo Manfredi
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | | | | | - Andrea Zitella
- Division of Urology, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Francesco Porpiglia
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Valentina Giannini
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
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Mumuni S, O’Donnell C, Doody O. The Experiences and Perspectives of Persons with Prostate Cancer and Their Partners: A Qualitative Evidence Synthesis Using Meta-Ethnography. Healthcare (Basel) 2024; 12:1490. [PMID: 39120193 PMCID: PMC11311449 DOI: 10.3390/healthcare12151490] [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: 04/24/2024] [Revised: 07/18/2024] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
Prostate cancer affects one in nine men, so understanding patients' and their partners experiences is crucial for developing effective treatments. The purpose of this review was to synthesis and report the experiences and views of persons with prostate cancer and their partners. METHODS A qualitative evidence synthesis (QES) was conducted following the eMERGe reporting guideline. Six databases were searched for the relevant literature, and the Critical Appraisal Skills Program (CASP) tool was used for quality appraisal. RESULTS A total of 1372 papers were identified, and 36 met the inclusion criteria. Four themes emerged: quality of life, relationships and dynamics, treatment journey and survivorship and aftercare. CONCLUSIONS Prostate cancer's impact on patients and partners is significant, requiring comprehensive support, holistic care, tailored assistance, and research into therapies to minimize adverse effects and address emotional distress and relationship strain. Prostate cancer treatment causes physical changes, triggering feelings of loss and grief, and affects coping mechanisms. Drawing on emotional support and education is vital for boosting confidence and resilience, as many patients and partners face fears of recurrence and lifestyle changes, highlighting the need for tailored information and presurgery support.
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Affiliation(s)
- Seidu Mumuni
- Department of Nursing and Midwifery, University of Limerick, V94 T9PX Limerick, Ireland; (S.M.); (C.O.)
| | - Claire O’Donnell
- Department of Nursing and Midwifery, University of Limerick, V94 T9PX Limerick, Ireland; (S.M.); (C.O.)
| | - Owen Doody
- Department of Nursing and Midwifery, University of Limerick, V94 T9PX Limerick, Ireland; (S.M.); (C.O.)
- Health Research Institute, University of Limerick, V94 T9PX Limerick, Ireland
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Abudoubari S, Bu K, Mei Y, Maimaitiyiming A, An H, Tao N. Prostate cancer epidemiology and prognostic factors in the United States. Front Oncol 2023; 13:1142976. [PMID: 37901326 PMCID: PMC10603232 DOI: 10.3389/fonc.2023.1142976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023] Open
Abstract
Objective Using the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients. Methods Patients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index. Results There was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model. Conclusions Although there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.
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Affiliation(s)
- Saimaitikari Abudoubari
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, Xinjiang, China
- College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Ke Bu
- College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yujie Mei
- College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | | | - Hengqing An
- The First Affiliated Hospital, Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Genitouriary System, Urumqi, Xinjiang, China
| | - Ning Tao
- College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Genitouriary System, Urumqi, Xinjiang, China
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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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Maxey J, Gupta A, Houchens N. Quality and safety in the literature: April 2023. BMJ Qual Saf 2023; 32:235-240. [PMID: 36931631 DOI: 10.1136/bmjqs-2023-015977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 03/19/2023]
Affiliation(s)
- Jordan Maxey
- Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Ashwin Gupta
- Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Nathan Houchens
- Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
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Wang L, Chen X, Zhang L, Li L, Huang Y, Sun Y, Yuan X. Artificial intelligence in clinical decision support systems for oncology. Int J Med Sci 2023; 20:79-86. [PMID: 36619220 PMCID: PMC9812798 DOI: 10.7150/ijms.77205] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.
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Affiliation(s)
- Lu Wang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Xinyi Chen
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Lu Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Long Li
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - YongBiao Huang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Yinan Sun
- Department of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Xianglin Yuan
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
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Vyas N, Brunckhorst O, Fox L, Van Hemelrijck M, Muir G, Stewart R, Dasgupta P, Ahmed K. Undergoing radical treatment for prostate cancer and its impact on wellbeing: A qualitative study exploring men's experiences. PLoS One 2022; 17:e0279250. [PMID: 36525457 PMCID: PMC9757548 DOI: 10.1371/journal.pone.0279250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/03/2022] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Quality of life in prostate cancer survivorship is becoming increasingly important, with mental and social wellbeing recognised as key components. However, limited global evaluation of psychosocial challenges experienced after treatment exists. Therefore, we aimed to explore the lived experiences of men who underwent radical treatment, and its psychosocial impact. MATERIAL AND METHODS This qualitative study was conducted using 19 men who had undergone radical treatment (prostatectomy or radiotherapy) for their cancer. Semi-structured interviews were conducted exploring lived experiences of men after treatment. A Structured thematic analysis of collected data was undertaken, with an inductive co-construction of themes through the lens of the biopsychosocial model. Themes generated were considered within a psychological, social, and physical wellbeing framework. RESULTS An initial knowledge gap meant mental wellbeing was strongly impacted initially leading to a 'Diagnostic Blow and the Search for Clarity'. Doubt over individuals' future resulted in 'An Uncertain Future' in many men. Once treatment was completed a 'Reflective journey' began, with men considering their outcomes and decisions made. Social wellbeing was also impacted with many identifying the 'Emotional Repercussions' on their relationships and the impact their diagnosis had on their partner and family. Many subsequently sought to increase their support through 'The Social Network and Advocacy', while physical changes led to an increased need for 'Social Planning'. Finally, physical wellbeing was highlighted by a continual acknowledgement of the 'Natural process of ageing' leading to a reluctancy to seek help, whilst simultaneously attempting to improve existing health via 'The Health Kick'. CONCLUSIONS Radical treatments have a considerable impact on mental and social wellbeing of individuals. Anxiety after diagnosis and significant uncertainty over individual futures exist, with physical complications of treatment leading to social repercussions. Future research should aim to identify forms of support to improve quality of life of these men.
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Affiliation(s)
- Neel Vyas
- MRC Centre for Transplantation, Guy’s Hospital Campus, King’s College London, King’s Health Partners, London, United Kingdom
| | - Oliver Brunckhorst
- MRC Centre for Transplantation, Guy’s Hospital Campus, King’s College London, King’s Health Partners, London, United Kingdom
| | - Louis Fox
- Translational Oncology and Urology Research (TOUR), School of Cancer and Pharmaceutical Sciences, King’s College London, London, United Kingdom
| | - Mieke Van Hemelrijck
- Translational Oncology and Urology Research (TOUR), School of Cancer and Pharmaceutical Sciences, King’s College London, London, United Kingdom
| | - Gordon Muir
- Department of Urology, King’s College Hospital, London, United Kingdom
| | - Robert Stewart
- King’s College London Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Prokar Dasgupta
- MRC Centre for Transplantation, Guy’s Hospital Campus, King’s College London, King’s Health Partners, London, United Kingdom
- Urology Centre, Guy’s and St. Thomas’ NHS Foundation Trust, King’s Health Partners, London, United Kingdom
| | - Kamran Ahmed
- MRC Centre for Transplantation, Guy’s Hospital Campus, King’s College London, King’s Health Partners, London, United Kingdom
- Department of Urology, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
- Khalifa University, Abu Dhabi, United Arab Emirates
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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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Thurtle D, Jenkins V, Freeman A, Pearson M, Recchia G, Tamer P, Leonard K, Pharoah P, Aning J, Madaan S, Goh C, Hilman S, McCracken S, Ilie PC, Lazarowicz H, Gnanapragasam V. Clinical Impact of the Predict Prostate Risk Communication Tool in Men Newly Diagnosed with Nonmetastatic Prostate Cancer: A Multicentre Randomised Controlled Trial. Eur Urol 2021; 80:661-669. [PMID: 34493413 DOI: 10.1016/j.eururo.2021.08.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Predict Prostate is a freely available online personalised risk communication tool for men with nonmetastatic prostate cancer. Its accuracy has been assessed in multiple validation studies, but its clinical impact among patients has not hitherto been assessed. OBJECTIVE To assess the impact of the tool on patient decision-making and disease perception. DESIGN, SETTING, AND PARTICIPANTS A multicentre randomised controlled trial was performed across eight UK centres among newly diagnosed men considering either active surveillance or radical treatment. A total of 145 patients were included between 2018 and 2020, with median age 67 yr (interquartile range [IQR] 61-72) and prostate-specific antigen 6.8 ng/ml (IQR 5.1-8.8). INTERVENTION Participants were randomised to either standard of care (SOC) information or SOC and a structured presentation of the Predict Prostate tool. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Validated questionnaires were completed by assessing the impact of the tool on decisional conflict, uncertainty, anxiety, and perception of survival. RESULTS AND LIMITATIONS Mean Decisional Conflict Scale scores were 26% lower in the Predict Prostate group (mean = 16.1) than in the SOC group (mean = 21.7; p = 0.027). Scores on the "support", "uncertainty", and "value clarity" subscales all favoured Predict Prostate (all p < 0.05). There was no significant difference in anxiety scores or final treatment selection between the two groups. Patient perception of 15-yr prostate cancer-specific mortality (PCSM) and overall survival benefit from radical treatment were considerably lower and more accurate among men in the Predict Prostate group (p < 0.001). In total, 57% of men reported that the Predict Prostate estimates for PCSM were lower than expected, and 36% reported being less likely to select radical treatment. Over 90% of patients in the intervention group found it useful and 94% would recommend it to others. CONCLUSIONS Predict Prostate reduces decisional conflict and uncertainty, and shifts patient perception around prognosis to be more realistic. This randomised trial demonstrates that Predict Prostate can directly inform the complex decision-making process in prostate cancer and is felt to be useful by patients. Future larger trials are warranted to test its impact upon final treatment decisions. PATIENT SUMMARY In this national study, we assessed the impact of an individualised risk communication tool, called Predict Prostate, on patient decision-making after a diagnosis of localised prostate cancer. Men were randomly assigned to two groups, which received either standard counselling and information, or this in addition to a structured presentation of the Predict Prostate tool. Men who saw the tool were less conflicted and uncertain in their decision-making, and recommended the tool highly. Those who saw the tool had more realistic perception about their long-term survival and the potential impact of treatment upon this. TAKE HOME MESSAGE The use of an individualised risk communication tool, such as Predict Prostate, reduces patient decisional conflict and uncertainty when deciding about treatment for nonmetastatic prostate cancer. The tool leads to more realistic perceptions about survival outcomes and prognosis.
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Affiliation(s)
- David Thurtle
- Department of Surgery, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Val Jenkins
- Brighton and Sussex Medical School, Brighton, UK
| | - Alex Freeman
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
| | - Mike Pearson
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
| | - Gabriel Recchia
- Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK
| | - Priya Tamer
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Kelly Leonard
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Paul Pharoah
- Department of Community Medicine, University of Cambridge, Cambridge, UK; Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan Aning
- University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | | | - Chee Goh
- Surrey and Sussex Healthcare NHS Trust, Surrey, UK
| | - Serena Hilman
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | | | | | - Henry Lazarowicz
- Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Vincent Gnanapragasam
- Department of Surgery, University of Cambridge School of Clinical Medicine, Cambridge, UK
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10
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Yıldızlı ÖO, Üntan İ, Demirci D. What is the consistency between the results of needle biopsy and prostatectomy specimen pathology results? A pilot study. Turk J Med Sci 2021; 51:1360-1364. [PMID: 33535735 PMCID: PMC8283461 DOI: 10.3906/sag-2009-73] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 02/03/2021] [Indexed: 11/29/2022] Open
Abstract
Background/aim The aim of this study was to establish the relationship between the needle biopsy and the pathology result after radical prostatectomy administrated for prostate cancer. Materials and methods We retrospectively analyzed 67 patients who had undergone radical prostatectomy from 2016 to 2019. All surgeries and all biopsies were performed in the third author’s urology department. Samples were collected through 12-core biopsy under local anesthesia. All specimens were studied in the pathology department of the third author’s center. The results evaluated were needle biopsies’ Gleason scores and prostatectomy specimens’ Gleason scores. Results Inclusion criteria were not having any neo-adjuvant treatment and being treated with surgery after needle biopsy. Gleason scores obtained from needle biopsies and prostatectomy specimens were evaluated. The comparison revealed that 39% of the tumors were undergraded, 7% were overgraded, and 54% had exact scoring in needle biopsies and prostatectomy specimens according to the detailed Gleason scoring as primary and secondary metrics. The patients were grouped into five categories according to the ISUP 2014 prostate cancer grading system. The relationship was strong with 64% of results staying in the same group after the operation; nevertheless, the correlation remained weak based on the kappa coefficient. Conclusion The information obtained from the needle biopsy is not a strong herald of the pathological result. Urologists should have awareness of this restraint when utilizing the needle biopsy’s Gleason score in decision making and treatment planning.
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Affiliation(s)
| | - İbrahim Üntan
- Department of Urology, Training and Research Hospital, Ahi Evran University, Kırşehir, Turkey
| | - Deniz Demirci
- Department of Urology, Faculty of Medicine, Erciyes University, Kayseri, Turkey
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11
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van Wijk Y, Ramaekers B, Vanneste BGL, Halilaj I, Oberije C, Chatterjee A, Marcelissen T, Jochems A, Woodruff HC, Lambin P. Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost-Utility Study. Cancers (Basel) 2021; 13:cancers13112687. [PMID: 34072509 PMCID: PMC8198879 DOI: 10.3390/cancers13112687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/20/2021] [Accepted: 05/24/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Low–intermediate prostate cancer has a number of viable treatment options, such as radical prostatectomy and radiotherapy, with similar survival outcomes but different treatment-related side effects. The aim of this study is to facilitate patient-specific treatment selection by developing a decision support system (DSS) that incorporates predictive models for cancer-free survival and treatment-related side effects. We challenged this DSS by validating it against randomized clinical trials and assessing the benefit through a cost–utility analysis. We aim to expand upon the applications of this DSS by using it as the basis for an in silico clinical trial for an underrepresented patient group. This modeling study shows that DSS-based treatment decisions will result in a clinically relevant increase in the patients’ quality of life and can be used for in silico trials. Abstract The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection.
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Affiliation(s)
- Yvonka van Wijk
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
- Correspondence:
| | - Bram Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands;
| | - Ben G. L. Vanneste
- Department of Radiation Oncology (MAASTRO), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
| | - Iva Halilaj
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Cary Oberije
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Tom Marcelissen
- Department of Urology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands;
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
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12
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The growing significance of smartphone apps in data-driven clinical decision-making: Challenges and pitfalls. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00010-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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13
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Clinical Decision Support Systems in Breast Cancer: A Systematic Review. Cancers (Basel) 2020; 12:cancers12020369. [PMID: 32041094 PMCID: PMC7072392 DOI: 10.3390/cancers12020369] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 01/29/2020] [Accepted: 01/31/2020] [Indexed: 12/12/2022] Open
Abstract
Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.
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