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Zhou S, Xie Y, Feng X, Li Y, Shen L, Chen Y. Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications. Cancer Lett 2025; 614:217555. [PMID: 39952597 DOI: 10.1016/j.canlet.2025.217555] [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: 12/04/2024] [Revised: 01/31/2025] [Accepted: 02/11/2025] [Indexed: 02/17/2025]
Abstract
With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.
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Affiliation(s)
- Shengyuan Zhou
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yi Xie
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xujiao Feng
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yanyan Li
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yang Chen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China; Department of Gastrointestinal Cancer, Beijing GoBroad Hospital, Beijing, 102200, China.
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2
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Keshavarz P, Nezami N, Yazdanpanah F, Khojaste-Sarakhsi M, Mohammadigoldar Z, Azami M, Hajati A, Ebrahimian Sadabad F, Chiang J, McWilliams JP, Lu DSK, Raman SS. Prediction of treatment response and outcome of transarterial chemoembolization in patients with hepatocellular carcinoma using artificial intelligence: A systematic review of efficacy. Eur J Radiol 2025; 184:111948. [PMID: 39892373 DOI: 10.1016/j.ejrad.2025.111948] [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: 10/03/2024] [Revised: 01/10/2025] [Accepted: 01/22/2025] [Indexed: 02/03/2025]
Abstract
PURPOSE To perform a systematic literature review of the efficacy of different AI models to predict HCC treatment response to transarterial chemoembolization (TACE), including overall survival (OS) and time to progression (TTP). METHODS This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines until May 2, 2024. RESULTS The systematic review included 23 studies with 4,486 HCC patients. The AI algorithm receiver operator characteristic (ROC) area under the curve (AUC) for predicting HCC response to TACE based on mRECIST criteria ranged from 0.55 to 0.97. Radiomics-models outperformed non-radiomics models (AUCs: 0.79, 95 %CI: 0.75-0.82 vs. 0.73, 0.61-0.77, respectively). The best ML methods used for the prediction of TACE response for HCC patients were CNN, GB, SVM, and RF with AUCs of 0.88 (0.79-0.97), 0.82 (0.71-0.89), 0.8 (0.60-0.87) and 0.8 (0.55-0.96), respectively. Of all predictive feature models, those combining clinic-radiologic features (ALBI grade, BCLC stage, AFP level, tumor diameter, distribution, and peritumoral arterial enhancement) had higher AUCs compared with models based on clinical characteristics alone (0.79, 0.73-0.89; p = 0.04 for CT + clinical, 0.81, 0.75-0.88; p = 0.017 for MRI + clinical versus 0.6, 0.55-0.75 in clinical characteristics alone). CONCLUSION Integrating clinic-radiologic features enhances AI models' predictive performance for HCC patient response to TACE, with CNN, GB, SVM, and RF methods outperforming others. Key predictive clinic-radiologic features include ALBI grade, BCLC stage, AFP level, tumor diameter, distribution, and peritumoral arterial enhancement. Multi-institutional studies are needed to improve AI model accuracy, address heterogeneity, and resolve validation issues.
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Affiliation(s)
- Pedram Keshavarz
- Department of Radiological Sciences, David Geffen School of Medicine at The University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
| | - Nariman Nezami
- Department of Radiology, MedStar Georgetown University Hospital, Washington, DC 20007, USA; Georgetown University School of Medicine, Washington, DC 20007, USA; Lombardi Comprehensive Cancer Center, Washington, DC 20007, USA
| | | | | | - Zahra Mohammadigoldar
- Department of Radiological Sciences, David Geffen School of Medicine at The University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Mobin Azami
- Department of Diagnostic & Interventional Radiology, New Hospitals Ltd., Tbilisi 0114, Georgia
| | - Azadeh Hajati
- Department of Radiology, Division of Abdominal Imaging, Harvard Medical School, Boston, MA 02114, USA
| | | | - Jason Chiang
- Department of Radiological Sciences, David Geffen School of Medicine at The University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Justin P McWilliams
- Department of Radiological Sciences, David Geffen School of Medicine at The University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - David S K Lu
- Department of Radiological Sciences, David Geffen School of Medicine at The University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Steven S Raman
- Department of Radiological Sciences, David Geffen School of Medicine at The University of California, Los Angeles (UCLA), Los Angeles, CA, USA
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Cho EEL, Law M, Yu Z, Yong JN, Tan CS, Tan EY, Takahashi H, Danpanichkul P, Nah B, Soon GST, Ng CH, Tan DJH, Seko Y, Nakamura T, Morishita A, Chirapongsathorn S, Kumar R, Kow AWC, Huang DQ, Lim MC, Law JH. Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review. Dig Dis Sci 2025; 70:533-542. [PMID: 39708260 DOI: 10.1007/s10620-024-08747-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Accepted: 11/06/2024] [Indexed: 12/23/2024]
Abstract
BACKGROUND Major society guidelines recommend transarterial chemoembolization (TACE) as the standard of care for intermediate-stage hepatocellular carcinoma (HCC) patients. However, predicting treatment response remains challenging. AIMS As artificial intelligence (AI) may predict therapeutic responses, this systematic review aims to assess the performance and effectiveness of radiomics and AI-based models in predicting TACE outcomes in patients with HCC. METHODS A systemic search was conducted on Medline and Embase databases from inception to 7th April 2024. Included studies generated a predictive model for TACE response and evaluated its performance by area under the curve (AUC), specificity, or sensitivity analysis. Systematic reviews, meta-analyses, case series and reports, pediatric, and animal studies were excluded. Secondary search of references of included articles ensured comprehensiveness. RESULTS 64 articles, with 13,412 TACE-treated patients, were included. AI in pre-treatment CT scans provided value in predicting the efficacy of TACE in HCC treatment. A positive association was observed for AI in pre-treatment MRI scans. Models incorporating radiomics had numerically better performance than those incorporating manual measured radiological variables. 39 studies demonstrated that combined predictive models had numerically better performance than models based solely on imaging or non-imaging features. CONCLUSION A combined predictive model incorporating clinical features, laboratory investigations, and radiological characteristics may effectively predict response to TACE treatment for HCC.
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Affiliation(s)
- Elina En Li Cho
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Michelle Law
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhenning Yu
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Jie Ning Yong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Claire Shiying Tan
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - En Ying Tan
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
| | | | - Pojsakorn Danpanichkul
- Immunology Unit, Department of Microbiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Benjamin Nah
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Cheng Han Ng
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
- Department of Medicine, Division of Gastroenterology, Kurume University School of Medicine, Fukuoka, Japan
| | - Darren Jun Hao Tan
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Yuya Seko
- Department of Molecular Gastroenterology and Hepatology, Kyoto Prefectural University of Medicine Graduate School of Medical Science, Kyoto, Japan
| | - Toru Nakamura
- Department of Medicine, Division of Gastroenterology, Kurume University School of Medicine, Fukuoka, Japan
| | - Asahiro Morishita
- Department of Gastroenterology and Neurology, Kagawa University School of Medicine, Kagawa, Japan
| | | | - Rahul Kumar
- Department of Gastroenterology, Changi General Hospital, Singapore, Singapore
| | - Alfred Wei Chieh Kow
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- National University Centre for Organ Transplantation, National University Health System, Singapore, Singapore
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, National University Hospital Singapore, Singapore, Singapore
| | - Daniel Q Huang
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Mei Chin Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Diagnostic Imaging, National University Health System, Singapore, Singapore
| | - Jia Hao Law
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, National University Hospital Singapore, Singapore, Singapore.
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Lanza C, Ascenti V, Amato GV, Pellegrino G, Triggiani S, Tintori J, Intrieri C, Angileri SA, Biondetti P, Carriero S, Torcia P, Ierardi AM, Carrafiello G. All You Need to Know About TACE: A Comprehensive Review of Indications, Techniques, Efficacy, Limits, and Technical Advancement. J Clin Med 2025; 14:314. [PMID: 39860320 PMCID: PMC11766109 DOI: 10.3390/jcm14020314] [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: 11/10/2024] [Revised: 12/17/2024] [Accepted: 12/28/2024] [Indexed: 01/27/2025] Open
Abstract
Transcatheter arterial chemoembolization (TACE) is a proven and widely accepted treatment option for hepatocellular carcinoma and it is recommended as first-line non-curative therapy for BCLC B/intermediate HCC (preserved liver function, multifocal, no cancer-related symptoms) in patients without vascular involvement. Different types of TACE are available nowadays, including TAE, c-TACE, DEB-TACE, and DSM-TACE, but at present there is insufficient evidence to recommend one TACE technique over another and the choice is left to the operator. This review then aims to provide a comprehensive overview of the current literature on indications, types of procedures, safety, and efficacy of different TACE treatments.
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Affiliation(s)
- Carolina Lanza
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Velio Ascenti
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Gaetano Valerio Amato
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Giuseppe Pellegrino
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Sonia Triggiani
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Jacopo Tintori
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Cristina Intrieri
- Postgraduate School in Diangostic Imaging, Università degli Studi di Siena, 20122 Milan, Italy;
| | - Salvatore Alessio Angileri
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Pierpaolo Biondetti
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Serena Carriero
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Pierluca Torcia
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Anna Maria Ierardi
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Gianpaolo Carrafiello
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
- Faculty of Health Science, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
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Nishida N. Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care. Bioengineering (Basel) 2024; 11:1243. [PMID: 39768061 PMCID: PMC11673237 DOI: 10.3390/bioengineering11121243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2024] [Revised: 11/21/2024] [Accepted: 12/05/2024] [Indexed: 01/03/2025] Open
Abstract
Liver disease can significantly impact life expectancy, making early diagnosis and therapeutic intervention critical challenges in medical care. Imaging diagnostics play a crucial role in diagnosing and managing liver diseases. Recently, the application of artificial intelligence (AI) in medical imaging analysis has become indispensable in healthcare. AI, trained on vast datasets of medical images, has sometimes demonstrated diagnostic accuracy that surpasses that of human experts. AI-assisted imaging diagnostics are expected to contribute significantly to the standardization of diagnostic quality. Furthermore, AI has the potential to identify image features that are imperceptible to humans, thereby playing an essential role in clinical decision-making. This capability enables physicians to make more accurate diagnoses and develop effective treatment strategies, ultimately improving patient outcomes. Additionally, AI is anticipated to become a powerful tool in personalized medicine. By integrating individual patient imaging data with clinical information, AI can propose optimal plans for treatment, making it an essential component in the provision of the most appropriate care for each patient. Current reports highlight the advantages of AI in managing liver diseases. As AI technology continues to evolve, it is expected to advance personalized diagnostics and treatments and contribute to overall improvements in healthcare quality.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 377-2 Ohno-Higashi, Osakasayama 589-8511, Japan
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6
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Xin H, Lai Q, Liu Y, Liao N, Wang Y, Liao B, Zhou K, Zhou Y, Bai Y, Chen Z, Zhou Y. Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study. Pharmacol Res 2024; 210:107535. [PMID: 39626849 DOI: 10.1016/j.phrs.2024.107535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Revised: 10/22/2024] [Accepted: 11/29/2024] [Indexed: 12/06/2024]
Abstract
As the hallmark of cancer, genetic and phenotypic heterogeneity leads to biomarkers that are typically tailored to specific cancer type or subtype. This specificity introduces complexities in facilitating streamlined evaluations across diverse cancer types and optimizing therapeutic outcomes. In this study, we comprehensively characterized the radiological patterns underlying liver cancer (LC) by integrating radiomics profiles from computed tomography (CT) images of hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and colorectal cancer liver metastases (CRLM) through unsupervised clustering analysis. We identified three distinct radiomics clusters, displaying heterogeneity in prognosis. Subsequently, we formulated a shared prognosticator, the liver cancer radiomics signature (LCRS), by discovering and manifesting connectivity among radiomics phenotypes using GGI strategy. We validated that the LCRS is independent prognostic factor after adjusting for clinic-pathologic variables (all P < 0.05), with the LCRS-High group consistently associated with worse survival outcomes across HCC, ICC, and CRLM. However, the LCRS-High group showed clinical benefit from adjuvant chemotherapy, leading to reduced disease recurrence risk and improved survival. By contrast, the LCRS-Low group, including a subset of gastric cancer liver metastases (GCLM), exhibited more favorable response to immune checkpoint inhibitors (ICIs)-based combinational therapy (P = 0.02, hazard ratio (HR): 0.34 [95 % confidence interval (CI): 0.13-0.88]). Further analysis revealed that Notch signaling pathway was enriched in LCRS-High tumors, while LCRS-Low tumors exhibited higher infiltration of natural killer cell. These findings highlight the promise of this universal scoring model to personalize management strategies for patients with LC.
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Affiliation(s)
- Hongjie Xin
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qianwei Lai
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanping Liu
- Department of Gastroenterology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Naying Liao
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ying Wang
- Department of Gastroenterology, The Fourth Hospital of Changsha, Hunan Normal University, Changsha, China
| | - Bihong Liao
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Keyang Zhou
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Yuchen Zhou
- Department of General Surgery, Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Yang Bai
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Zhihua Chen
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
| | - Yuanping Zhou
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
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Loper MR, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging. Tomography 2024; 10:1814-1831. [PMID: 39590942 PMCID: PMC11598375 DOI: 10.3390/tomography10110133] [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: 09/08/2024] [Revised: 11/11/2024] [Accepted: 11/15/2024] [Indexed: 11/28/2024] Open
Abstract
Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.
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Affiliation(s)
| | - Mina S. Makary
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA;
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8
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Bitar R, Salem R, Finn R, Greten TF, Goldberg SN, Chapiro J, Atzen S. Interventional Oncology Meets Immuno-oncology: Combination Therapies for Hepatocellular Carcinoma. Radiology 2024; 313:e232875. [PMID: 39560477 PMCID: PMC11605110 DOI: 10.1148/radiol.232875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 08/15/2024] [Accepted: 08/27/2024] [Indexed: 11/20/2024]
Abstract
The management of hepatocellular carcinoma (HCC) is undergoing transformational changes due to the emergence of various novel immunotherapies and their combination with image-guided locoregional therapies. In this setting, immunotherapy is expected to become one of the standards of care in both neoadjuvant and adjuvant settings across all disease stages of HCC. Currently, more than 50 ongoing prospective clinical trials are investigating various end points for the combination of immunotherapy with both percutaneous and catheter-directed therapies. This review will outline essential tumor microenvironment mechanisms responsible for disease evolution and therapy resistance, discuss the rationale for combining locoregional therapy with immunotherapy, summarize ongoing clinical trials, and report on developing imaging end points and novel biomarkers that are relevant to both diagnostic and interventional radiologists participating in the management of HCC.
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Affiliation(s)
- Ryan Bitar
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Riad Salem
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Richard Finn
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Tim F. Greten
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - S. Nahum Goldberg
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Julius Chapiro
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Sarah Atzen
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
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9
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Bartnik K, Bartczak T, Krzyziński M, Korzeniowski K, Lamparski K, Węgrzyn P, Lam E, Bartkowiak M, Wróblewski T, Mech K, Januszewicz M, Biecek P. WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data. Radiol Artif Intell 2024; 6:e240296. [PMID: 39441110 PMCID: PMC11605144 DOI: 10.1148/ryai.240296] [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: 05/13/2024] [Revised: 08/30/2024] [Accepted: 10/10/2024] [Indexed: 10/25/2024]
Abstract
The WAW-TACE dataset contains baseline multiphase abdominal CT images from 233 treatment-naive patients with hepatocellular carcinoma treated with transarterial chemoembolization and includes 377 handcrafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data. The dataset can be accessed at https://zenodo.org/records/12741586 .
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Affiliation(s)
- Krzysztof Bartnik
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Tomasz Bartczak
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Mateusz Krzyziński
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Krzysztof Korzeniowski
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Krzysztof Lamparski
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Piotr Węgrzyn
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Eric Lam
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Mateusz Bartkowiak
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Tadeusz Wróblewski
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Katarzyna Mech
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Magdalena Januszewicz
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
| | - Przemysław Biecek
- From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.)
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Sobhani N, D'Angelo A, Pittacolo M, Mondani G, Generali D. Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion. Cancers (Basel) 2024; 16:3089. [PMID: 39272947 PMCID: PMC11394064 DOI: 10.3390/cancers16173089] [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: 07/31/2024] [Revised: 08/31/2024] [Accepted: 09/03/2024] [Indexed: 09/15/2024] Open
Abstract
The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy.
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Affiliation(s)
- Navid Sobhani
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Alberto D'Angelo
- Department of Medicine, Northern General Hospital, Sheffield S5 7AT, UK
| | - Matteo Pittacolo
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35122 Padova, Italy
| | - Giuseppina Mondani
- Royal Infirmary Hospital, Foresterhill Health Campus, Aberdeen AB25 2ZN, UK
| | - Daniele Generali
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34100 Trieste, Italy
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11
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Wang G, Ding F, Chen K, Liang Z, Han P, Wang L, Cui F, Zhu Q, Cheng Z, Chen X, Huang C, Cheng H, Wang X, Zhao X. CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment. J Transl Med 2024; 22:683. [PMID: 39218938 PMCID: PMC11367757 DOI: 10.1186/s12967-024-05393-3] [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: 03/20/2024] [Accepted: 06/12/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment. METHODS Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis. RESULTS The areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells. CONCLUSION The CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.
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Affiliation(s)
- Gongzheng Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, China
| | - Feier Ding
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Kaige Chen
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Zhuoshuai Liang
- Department of Epidemiology and Biostatistics, School of Public Health of Jilin University, Changchun, 130021, China
| | - Pengxi Han
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Linxiang Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
| | - Fengyun Cui
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, China
| | - Qiang Zhu
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Zhaoping Cheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Xingzhi Chen
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, 100080, People's Republic of China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, 100080, People's Republic of China
| | - Hongxia Cheng
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, China.
| | - Xinya Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, China.
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12
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Zhang M, Kuang B, Zhang J, Peng J, Xia H, Feng X, Peng L. Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features. Front Med (Lausanne) 2024; 11:1419058. [PMID: 39086938 PMCID: PMC11289890 DOI: 10.3389/fmed.2024.1419058] [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: 04/17/2024] [Accepted: 06/24/2024] [Indexed: 08/02/2024] Open
Abstract
Objective This study aimed to investigate the use of radiomics features and clinical information by four machine learning algorithms for predicting the prognosis of patients with hepatocellular carcinoma (HCC) who have been treated with transarterial chemoembolization (TACE). Methods A total of 105 patients with HCC treated with TACE from 2002 to 2012 were enrolled retrospectively and randomly divided into two cohorts for training (n = 74) and validation (n = 31) according to a ratio of 7:3. The Spearman rank, random forest, and univariate Cox regression were used to select the optimal radiomics features. Univariate Cox regression was used to select clinical features. Four machine learning algorithms were used to develop the models: random survival forest, eXtreme gradient boosting (XGBoost), gradient boosting, and the Cox proportional hazard regression model. The area under the curve (AUC) and C-index were devoted to assessing the performance of the models in predicting HCC prognosis. Results A total of 1,834 radiomics features were extracted from the computed tomography images of each patient. The clinical risk factors for HCC prognosis were age at diagnosis, TNM stage, and metastasis, which were analyzed using univariate Cox regression. In various models, the efficacy of the combined models generally surpassed that of the radiomics and clinical models. Among four machine learning algorithms, XGBoost exhibited the best performance in combined models, achieving an AUC of 0.979 in the training set and 0.750 in the testing set, demonstrating its strong prognostic prediction capability. Conclusion The superior performance of the XGBoost-based combined model underscores its potential as a powerful tool for enhancing the precision of prognostic assessments for patients with HCC.
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Affiliation(s)
- Mingqi Zhang
- Department of Gastroenterology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
- The Second Clinical School of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Bingling Kuang
- Nanshan College, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jingxuan Zhang
- Nanshan College, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jingyi Peng
- The Second Clinical School of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Haoming Xia
- School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Xiaobin Feng
- School of Clinical Medicine, Tsinghua University, Beijing, China
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Liang Peng
- Department of Gastroenterology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
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13
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Glielmo P, Fusco S, Gitto S, Zantonelli G, Albano D, Messina C, Sconfienza LM, Mauri G. Artificial intelligence in interventional radiology: state of the art. Eur Radiol Exp 2024; 8:62. [PMID: 38693468 PMCID: PMC11063019 DOI: 10.1186/s41747-024-00452-2] [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: 09/28/2023] [Accepted: 02/26/2024] [Indexed: 05/03/2024] Open
Abstract
Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.
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Affiliation(s)
- Pierluigi Glielmo
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy.
| | - Stefano Fusco
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giulia Zantonelli
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Via della Commenda, 10, 20122, Milan, Italy
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giovanni Mauri
- Divisione di Radiologia Interventistica, IEO, IRCCS Istituto Europeo di Oncologia, Milan, Italy
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Zhang P, Gao C, Huang Y, Chen X, Pan Z, Wang L, Dong D, Li S, Qi X. Artificial intelligence in liver imaging: methods and applications. Hepatol Int 2024; 18:422-434. [PMID: 38376649 DOI: 10.1007/s12072-023-10630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/18/2023] [Indexed: 02/21/2024]
Abstract
Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.
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Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Chaofei Gao
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Yifei Huang
- Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiangyi Chen
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Zhuoshi Pan
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Southeast University, Nanjing, China.
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15
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Nikzad N, Fuentes DT, Roach M, Chowdhury T, Cagley M, Badawy M, Elkhesen A, Hassan M, Elsayes KM, Beretta L, Koay EJ, Jalal PK. Enhancement Pattern Mapping for Early Detection of Hepatocellular Carcinoma in Patients with Cirrhosis. J Hepatocell Carcinoma 2024; 11:595-606. [PMID: 38525156 PMCID: PMC10961013 DOI: 10.2147/jhc.s449996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 03/07/2024] [Indexed: 03/26/2024] Open
Abstract
Background and Aims Limited methods exist to accurately characterize the risk of malignant progression of liver lesions. Enhancement pattern mapping (EPM) measures voxel-based root mean square deviation (RMSD) of parenchyma and the contrast-to-noise (CNR) ratio enhances in malignant lesions. This study investigates the utilization of EPM to differentiate between HCC versus cirrhotic parenchyma with and without benign lesions. Methods Patients with cirrhosis undergoing MRI surveillance were studied prospectively. Cases (n=48) were defined as patients with LI-RADS 3 and 4 lesions who developed HCC during surveillance. Controls (n=99) were patients with and without LI-RADS 3 and 4 lesions who did not develop HCC. Manual and automated EPM signals of liver parenchyma between cases and controls were quantitatively validated on an independent patient set using cross validation with manual methods avoiding parenchyma with artifacts or blood vessels. Results With manual EPM, RMSD of 0.37 was identified as a cutoff for distinguishing lesions that progress to HCC from background parenchyma with and without lesions on pre-diagnostic scans (median time interval 6.8 months) with an area under the curve (AUC) of 0.83 (CI: 0.73-0.94) and a sensitivity, specificity, and accuracy of 0.65, 0.97, and 0.89, respectively. At the time of diagnostic scans, a sensitivity, specificity, and accuracy of 0.79, 0.93, and 0.88 were achieved with manual EPM with an AUC of 0.89 (CI: 0.82-0.96). EPM RMSD signals of background parenchyma that did not progress to HCC in cases and controls were similar (case EPM: 0.22 ± 0.08, control EPM: 0.22 ± 0.09, p=0.8). Automated EPM produced similar quantitative results and performance. Conclusion With manual EPM, a cutoff of 0.37 identifies quantifiable differences between HCC cases and controls approximately six months prior to diagnosis of HCC with an accuracy of 89%.
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Affiliation(s)
- Newsha Nikzad
- Department of Medicine and Surgery, Baylor College of Medicine, Houston, TX, USA
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Internal Medicine, The University of Chicago Medical Center, Chicago, IL, USA
| | - David Thomas Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Millicent Roach
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tasadduk Chowdhury
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Matthew Cagley
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mohamed Badawy
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ahmed Elkhesen
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - Manal Hassan
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Beretta
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene Jon Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Prasun Kumar Jalal
- Department of Medicine and Surgery, Baylor College of Medicine, Houston, TX, USA
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16
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Nakao Y, Nishihara T, Sasaki R, Fukushima M, Miuma S, Miyaaki H, Akazawa Y, Nakao K. Investigation of deep learning model for predicting immune checkpoint inhibitor treatment efficacy on contrast-enhanced computed tomography images of hepatocellular carcinoma. Sci Rep 2024; 14:6576. [PMID: 38503827 PMCID: PMC10951210 DOI: 10.1038/s41598-024-57078-y] [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: 08/18/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Although the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable hepatocellular carcinoma (HCC) is promising, individual response variability exists. Therefore, we developed an artificial intelligence (AI)-based model to predict treatment efficacy using pre-ICIs contrast-enhanced computed tomography (CT) imaging characteristics. We evaluated the efficacy of atezolizumab and bevacizumab in 43 patients at the Nagasaki University Hospital from 2020 to 2022 using the modified Response Evaluation Criteria in Solid Tumors. A total of 197 Progressive Disease (PD), 271 Partial Response (PR), and 342 Stable Disease (SD) contrast CT images of HCC were used for training. We used ResNet-18 as the Convolutional Neural Network (CNN) model and YOLOv5, YOLOv7, YOLOv8 as the You Only Look Once (YOLO) model with precision-recall curves and class activation maps (CAMs) for diagnostic performance evaluation and model interpretation, respectively. The 3D t-distributed Stochastic Neighbor Embedding was used for image feature analysis. The YOLOv7 model demonstrated Precision 53.7%, Recall 100%, F1 score 69.8%, mAP@0.5 99.5% for PD, providing accurate and clinically versatile predictions by identifying decisive points. The ResNet-18 model had Precision 100% and Recall 100% for PD. However, the CAMs sites did not align with the tumors, suggesting the CNN model is not predicting that a given CT slice is PD, PR, or SD, but that it accurately predicts Individual Patient's CT slices. Preparing substantial training data for tumor drug effect prediction models is challenging compared to general tumor diagnosis models; hence, large-scale validation using an efficient YOLO model is warranted.
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Affiliation(s)
- Yasuhiko Nakao
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan.
| | - Takahito Nishihara
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
- Department of Gastroenterology and Hepatology, Isahaya General Hospital, 24-1 Eishohigashimachi, Isahaya, Nagasaki, Japan
| | - Ryu Sasaki
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Masanori Fukushima
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Satoshi Miuma
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Hisamitsu Miyaaki
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Yuko Akazawa
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
- Department of Histology and Cell Biology, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Kazuhiko Nakao
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
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Barat M, Pellat A, Hoeffel C, Dohan A, Coriat R, Fishman EK, Nougaret S, Chu L, Soyer P. CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence. Jpn J Radiol 2024; 42:246-260. [PMID: 37926780 DOI: 10.1007/s11604-023-01504-0] [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: 09/13/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023]
Abstract
Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Anna Pellat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Christine Hoeffel
- Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, 51092, Reims, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Romain Coriat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Stéphanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, 34000, Montpellier, France
- PINKCC Lab, IRCM, U1194, 34000, Montpellier, France
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France.
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France.
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Hsieh C, Laguna A, Ikeda I, Maxwell AWP, Chapiro J, Nadolski G, Jiao Z, Bai HX. Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma. Radiology 2023; 309:e222891. [PMID: 37934098 DOI: 10.1148/radiol.222891] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.
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Affiliation(s)
- Celina Hsieh
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Amanda Laguna
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Ian Ikeda
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Aaron W P Maxwell
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Julius Chapiro
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Gregory Nadolski
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Zhicheng Jiao
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Harrison X Bai
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
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Klontzas ME, Leventis D, Spanakis K, Karantanas AH, Kranioti EF. Post-mortem CT radiomics for the prediction of time since death. Eur Radiol 2023; 33:8387-8395. [PMID: 37329460 DOI: 10.1007/s00330-023-09746-2] [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: 02/15/2023] [Revised: 03/27/2023] [Accepted: 04/22/2023] [Indexed: 06/19/2023]
Abstract
OBJECTIVES Post-mortem interval (PMI) estimation has long been relying on sequential post-mortem changes on the body as a function of extrinsic, intrinsic, and environmental factors. Such factors are difficult to account for in complicated death scenes; thus, PMI estimation can be compromised. Herein, we aimed to evaluate the use of post-mortem CT (PMCT) radiomics for the differentiation between early and late PMI. METHODS Consecutive whole-body PMCT examinations performed between 2016 and 2021 were retrospectively included (n = 120), excluding corpses without an accurately reported PMI (n = 23). Radiomics data were extracted from liver and pancreas tissue and randomly split into training and validation sets (70:30%). Following data preprocessing, significant features were selected (Boruta selection) and three XGBoost classifiers were built (liver, pancreas, combined) to differentiate between early (< 12 h) and late (> 12 h) PMI. Classifier performance was assessed with receiver operating characteristics (ROC) curves and areas under the curves (AUC), which were compared by bootstrapping. RESULTS A total of 97 PMCTs were included, representing individuals (23 females and 74 males) with a mean age of 47.1 ± 23.38 years. The combined model achieved the highest AUC reaching 75% (95%CI 58.4-91.6%) (p = 0.03 compared to liver and p = 0.18 compared to pancreas). The liver-based and pancreas-based XGBoost models achieved AUCs of 53.6% (95%CI 34.8-72.3%) and 64.3% (95%CI 46.7-81.9%) respectively (p > 0.05 for the comparison between liver- and pancreas-based models). CONCLUSION The use of radiomics analysis on PMCT examinations differentiated early from late PMI, unveiling a novel image-based method with important repercussions in forensic casework. CLINICAL RELEVANCE STATEMENT This paper introduces the employment of radiomics in forensic diagnosis by presenting an effective automated alternative method of estimating post-mortem interval from targeted tissues, thus paving the way for improvement in speed and quality of forensic investigations. KEY POINTS • A combined liver-pancreas radiomics model differentiated early from late post-mortem intervals (using a 12-h threshold) with an area under the curve of 75% (95%CI 58.4-91.6%). • XGBoost models based on liver-only or pancreas-only radiomics demonstrated inferior performance to the combined model in predicting the post-mortem interval.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Voutes, Heraklion, 71110, Crete, Greece
- Department of Radiology, Medical School, University of Crete, Voutes, Heraklion, 71110, Crete, Greece
- Advanced Hybrid Imaging Systems, Institute of Computer Science - FORTH, Voutes, Heraklion, 71110, Crete, Greece
| | - Dimitrios Leventis
- Department of Medical Imaging, University Hospital of Heraklion, Voutes, Heraklion, 71110, Crete, Greece
| | - Konstantinos Spanakis
- Department of Medical Imaging, University Hospital of Heraklion, Voutes, Heraklion, 71110, Crete, Greece
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Voutes, Heraklion, 71110, Crete, Greece.
- Department of Radiology, Medical School, University of Crete, Voutes, Heraklion, 71110, Crete, Greece.
- Advanced Hybrid Imaging Systems, Institute of Computer Science - FORTH, Voutes, Heraklion, 71110, Crete, Greece.
| | - Elena F Kranioti
- Forensic Medicine Unit, Department of Forensic Sciences, Faculty of Medicine, University of Crete, Voutes, Heraklion, 71110, Greece.
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Radiya K, Joakimsen HL, Mikalsen KØ, Aahlin EK, Lindsetmo RO, Mortensen KE. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. Eur Radiol 2023; 33:6689-6717. [PMID: 37171491 PMCID: PMC10511359 DOI: 10.1007/s00330-023-09609-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? METHODS A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. RESULTS One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. CONCLUSION Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. KEY POINTS • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.
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Affiliation(s)
- Keyur Radiya
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway.
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.
| | - Henrik Lykke Joakimsen
- Institute of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
| | - Karl Øyvind Mikalsen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
- UiT Machine Learning Group, Department of Physics and Technology, UiT the Arctic University of Norway, Tromso, Norway
| | - Eirik Kjus Aahlin
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
| | - Rolv-Ole Lindsetmo
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Head Clinic of Surgery, Oncology and Women Health, University Hospital of North Norway, Tromso, Norway
| | - Kim Erlend Mortensen
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
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Huang K, Wu Y, Fan W, Zhao Y, Xue M, Liu H, Tang Y, Li J. Identification of BRD7 by whole-exome sequencing as a predictor for intermediate-stage hepatocellular carcinoma in patients undergoing TACE. J Cancer Res Clin Oncol 2023; 149:11247-11261. [PMID: 37365429 DOI: 10.1007/s00432-023-04883-z] [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: 04/09/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVE In the present study, we aimed to identify potential predictors of intermediate-stage hepatocellular carcinoma (HCC) using whole-exome sequencing (WES) in patients undergoing transarterial chemoembolization (TACE). MATERIALS AND METHODS In A total of 51 patients, newly diagnosed with intermediate-stage HCC between January 2013 and December 2020, were enrolled. Prior to treatment, histological samples were collected for western blotting and immunohistochemistry. The predictive roles of clinical indicators and genes in patient prognosis were analyzed using univariate and multivariate analyses. Finally, the correlation between imaging features and gene signatures was examined. RESULTS Using WES, we identified that bromodomain-containing protein 7 (BRD7) was significantly mutated in patients with different TACE responses. No significant difference in BRD7 expression was observed between patients with and without BRD7 mutations. HCC tumors exhibited higher BRD7 than normal liver tissues. Multivariate analysis revealed that alpha-fetoprotein (AFP), BRD7 expression, and BRD7 mutations were independent risk factors for progression-free survival (PFS). In addition, Child-Pugh class, BRD7 expression, and BRD7 mutations were independent risk factors for overall survival (OS). Patients with wild-type BRD7 and high BRD7 expression had worse PFS and OS, whereas those with mutated BRD7 and low BRD7 expression exhibited the best PFS and OS. The Kruskal-Wallis test revealed that wash-in enhancement on computed tomography might be an independent risk factor for high BRD7 expression. CONCLUSION BRD7 expression may be an independent risk factor for prognosis in patients with HCC undergoing TACE. Imaging features such as wash-in enhancement are closely related to BRD7 expression.
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Affiliation(s)
- Kun Huang
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, People's Republic of China
- Department of Radiology, Guizhou Provincial People's Hospital, No. 83 East Zhongshan Road, Guiyang, 550002, Guizhou, China
| | - Yanqin Wu
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Wenzhe Fan
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Yue Zhao
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Miao Xue
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Haikuan Liu
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Yiyang Tang
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Jiaping Li
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, No. 58 Zhongshan 2 Road, Guangzhou, 510080, Guangdong, People's Republic of China.
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Dai Y, Liu D, Xin Y, Li Y, Wang D, He B, Zeng X, Li J, Jia F, Jiang H. Efficacy and Interpretability Analysis of Noninvasive Imaging Based on Computed Tomography in Patients with Hepatocellular Carcinoma After Initial Transarterial Chemoembolization. Acad Radiol 2023; 30 Suppl 1:S61-S72. [PMID: 37393179 DOI: 10.1016/j.acra.2023.05.027] [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: 04/25/2023] [Revised: 05/19/2023] [Accepted: 05/24/2023] [Indexed: 07/03/2023]
Abstract
RATIONALE AND OBJECTIVES The objective of this study is to accurately and timely assess the efficacy of patients with hepatocellular carcinoma (HCC) after the initial transarterial chemoembolization (TACE). MATERIALS AND METHODS This retrospective study consisted of 279 patients with HCC in Center 1, who were split into training and validation cohorts in the ratio of 4:1, and 72 patients in Center 2 as an external testing cohort. Radiomics signatures both in the arterial phase and venous phase of contrast-enhanced computed tomography images were selected by univariate analysis, correlation analysis, and least absolute shrinkage and selection operator regression to build the predicting models. The clinical model and combined model were constructed by independent risk factors after univariate and multivariate logistic regression analysis. The biological interpretability of radiomics signatures correlating transcriptome sequencing data was explored using publicly available data sets. RESULTS A total of 31 radiomics signatures in the arterial phase and 13 radiomics signatures in the venous phase were selected to construct Radscore_arterial and Radscore_venous, respectively, which were independent risk factors. After constructing the combined model, the area under the receiver operating characteristic curve in three cohorts was 0.865, 0.800, and 0.745, respectively. Through correlation analysis, 11 radiomics signatures in the arterial phase and 4 radiomics signatures in the venous phase were associated with 8 and 5 gene modules, respectively (All P < .05), which enriched some pathways closely related to tumor development and proliferation. CONCLUSION Noninvasive imaging has considerable value in predicting the efficacy of patients with HCC after initial TACE. The biological interpretability of the radiological signatures can be mapped at the micro level.
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Affiliation(s)
- Yanmei Dai
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Dongmin Liu
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Yanjie Xin
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Yuchong Li
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Y.L., B.H., F.J.)
| | - Dandan Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Baochun He
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Y.L., B.H., F.J.)
| | - Xu Zeng
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Jiaping Li
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China (J.L.)
| | - Fucang Jia
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Y.L., B.H., F.J.); Pazhou Lab, Guangzhou, China (F.J.)
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.).
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Mansur A, Vrionis A, Charles JP, Hancel K, Panagides JC, Moloudi F, Iqbal S, Daye D. The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities. Cancers (Basel) 2023; 15:2928. [PMID: 37296890 PMCID: PMC10251861 DOI: 10.3390/cancers15112928] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.
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Affiliation(s)
- Arian Mansur
- Harvard Medical School, Boston, MA 02115, USA; (A.M.); (J.C.P.)
| | - Andrea Vrionis
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA; (A.V.); (J.P.C.)
| | - Jonathan P. Charles
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA; (A.V.); (J.P.C.)
| | - Kayesha Hancel
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | | | - Farzad Moloudi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | - Shams Iqbal
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.H.); (F.M.); (S.I.)
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Moroney J, Trivella J, George B, White SB. A Paradigm Shift in Primary Liver Cancer Therapy Utilizing Genomics, Molecular Biomarkers, and Artificial Intelligence. Cancers (Basel) 2023; 15:2791. [PMID: 37345129 PMCID: PMC10216313 DOI: 10.3390/cancers15102791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/02/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023] Open
Abstract
Primary liver cancer is the sixth most common cancer worldwide and the third leading cause of cancer-related death. Conventional therapies offer limited survival benefit despite improvements in locoregional liver-directed therapies, which highlights the underlying complexity of liver cancers. This review explores the latest research in primary liver cancer therapies, focusing on developments in genomics, molecular biomarkers, and artificial intelligence. Attention is also given to ongoing research and future directions of immunotherapy and locoregional therapies of primary liver cancers.
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Affiliation(s)
- James Moroney
- Division of Vascular and Interventional Radiology, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Juan Trivella
- Division of Gastroenterology and Hepatology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Ben George
- Division of Hematology and Oncology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Sarah B. White
- Division of Vascular and Interventional Radiology, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Chen M, Kong C, Qiao E, Chen Y, Chen W, Jiang X, Fang S, Zhang D, Chen M, Chen W, Ji J. Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI. Insights Imaging 2023; 14:38. [PMID: 36854872 PMCID: PMC9975141 DOI: 10.1186/s13244-023-01380-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/29/2023] [Indexed: 03/02/2023] Open
Abstract
OBJECTIVES This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance. METHODS The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN). RESULTS DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735). CONCLUSIONS DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.
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Affiliation(s)
- Mingzhen Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Chunli Kong
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Enqi Qiao
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Yaning Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Weiyue Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Xiaole Jiang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Shiji Fang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Dengke Zhang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Minjiang Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Weiqian Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. .,Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. .,Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. .,Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. .,Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
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von Ende E, Ryan S, Crain MA, Makary MS. Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology. Diagnostics (Basel) 2023; 13:892. [PMID: 36900036 PMCID: PMC10000832 DOI: 10.3390/diagnostics13050892] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/12/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is capable of using neural networks to extract more complex, high-level data, even from unlabeled data sets, and better emulate, or even exceed, the human brain. Advances in AI have and will continue to revolutionize medicine, especially the field of radiology. Compared to the field of interventional radiology, AI innovations in the field of diagnostic radiology are more widely understood and used, although still with significant potential and growth on the horizon. Additionally, AI is closely related and often incorporated into the technology and programming of augmented reality, virtual reality, and radiogenomic innovations which have the potential to enhance the efficiency and accuracy of radiological diagnoses and treatment planning. There are many barriers that limit the applications of artificial intelligence applications into the clinical practice and dynamic procedures of interventional radiology. Despite these barriers to implementation, artificial intelligence in IR continues to advance and the continued development of machine learning and deep learning places interventional radiology in a unique position for exponential growth. This review describes the current and possible future applications of artificial intelligence, radiogenomics, and augmented and virtual reality in interventional radiology while also describing the challenges and limitations that must be addressed before these applications can be fully implemented into common clinical practice.
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Affiliation(s)
| | | | | | - Mina S. Makary
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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Posa A, Barbieri P, Mazza G, Tanzilli A, Natale L, Sala E, Iezzi R. Technological Advancements in Interventional Oncology. Diagnostics (Basel) 2023; 13:228. [PMID: 36673038 PMCID: PMC9857620 DOI: 10.3390/diagnostics13020228] [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: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023] Open
Abstract
Interventional radiology, and particularly interventional oncology, represents one of the medical subspecialties in which technological advancements and innovations play an utterly fundamental role. Artificial intelligence, consisting of big data analysis and feature extrapolation through computational algorithms for disease diagnosis and treatment response evaluation, is nowadays playing an increasingly important role in various healthcare fields and applications, from diagnosis to treatment response prediction. One of the fields which greatly benefits from artificial intelligence is interventional oncology. In addition, digital health, consisting of practical technological applications, can assist healthcare practitioners in their daily activities. This review aims to cover the most useful, established, and interesting artificial intelligence and digital health innovations and updates, to help physicians become more and more involved in their use in clinical practice, particularly in the field of interventional oncology.
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Affiliation(s)
- Alessandro Posa
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
| | - Pierluigi Barbieri
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
| | - Giulia Mazza
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
| | - Alessandro Tanzilli
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
| | - Luigi Natale
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
- Istituto di Radiodiagnostica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Evis Sala
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
- Istituto di Radiodiagnostica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Roberto Iezzi
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
- Istituto di Radiodiagnostica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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Labinsky H, Ukalovic D, Hartmann F, Runft V, Wichmann A, Jakubcik J, Gambel K, Otani K, Morf H, Taubmann J, Fagni F, Kleyer A, Simon D, Schett G, Reichert M, Knitza J. An AI-Powered Clinical Decision Support System to Predict Flares in Rheumatoid Arthritis: A Pilot Study. Diagnostics (Basel) 2023; 13:148. [PMID: 36611439 PMCID: PMC9818406 DOI: 10.3390/diagnostics13010148] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/11/2022] [Accepted: 12/27/2022] [Indexed: 01/04/2023] Open
Abstract
Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS-Rheuma Care Manager (RCM)-including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients' flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool (20% to 12% for half dosage flare prediction), and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence.
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Affiliation(s)
- Hannah Labinsky
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | | | - Fabian Hartmann
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | | | | | | | - Kira Gambel
- Siemens Healthineers, 91502 Erlangen, Germany
| | | | - Harriet Morf
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | - Jule Taubmann
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | - Filippo Fagni
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | - Arnd Kleyer
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | - David Simon
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | - Georg Schett
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | | | - Johannes Knitza
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
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Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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Boeken T, Feydy J, Lecler A, Soyer P, Feydy A, Barat M, Duron L. Artificial intelligence in diagnostic and interventional radiology: Where are we now? Diagn Interv Imaging 2023; 104:1-5. [PMID: 36494290 DOI: 10.1016/j.diii.2022.11.004] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022]
Abstract
The emergence of massively parallel yet affordable computing devices has been a game changer for research in the field of artificial intelligence (AI). In addition, dramatic investment from the web giants has fostered the development of a high-quality software stack. Going forward, the combination of faster computers with dedicated software libraries and the widespread availability of data has opened the door to more flexibility in the design of AI models. Radiomics is a process used to discover new imaging biomarkers that has multiple applications in radiology and can be used in conjunction with AI. AI can be used throughout the various processes of diagnostic imaging, including data acquisition, reconstruction, analysis and reporting. Today, the concept of "AI-augmented" radiologists is preferred to the theory of the replacement of radiologists by AI in many indications. Current evidence bolsters the assumption that AI-assisted radiologists work better and faster. Interventional radiology becomes a data-rich specialty where the entire procedure is fully recorded in a standardized DICOM format and accessible via standard picture archiving and communication systems. No other interventional specialty can bolster such readiness. In this setting, interventional radiology could lead the development of AI-powered applications in the broader interventional community. This article provides an update on the current status of radiomics and AI research, analyzes upcoming challenges and also discusses the main applications in AI in interventional radiology to help radiologists better understand and criticize articles reporting AI in medical imaging.
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Affiliation(s)
- Tom Boeken
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Vascular and Oncological Interventional Radiology, Hôpital Européen Georges Pompidou, APHP, Paris 75015, France; HeKA team, INRIA, Paris 75012 , France.
| | | | - Augustin Lecler
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Rothschild Foundation Hospital, Paris 75019, France
| | - Philippe Soyer
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Antoine Feydy
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Maxime Barat
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Loïc Duron
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Rothschild Foundation Hospital, Paris 75019, France
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Nguyen HV, Byeon H. LIME-based ensemble machine for predicting performance status of patients with liver cancer. Digit Health 2023; 9:20552076231211636. [PMID: 38025102 PMCID: PMC10631338 DOI: 10.1177/20552076231211636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
Objective The Eastern Cooperative Oncology Group performance status (ECOG PS) is a widely recognized measure used to assess the functional abilities of cancer patients and predict their prognosis. It plays a crucial role in guiding treatment decisions made by physicians. This study aimed to build a stacking ensemble-based prognosis predictor model for predicting the ECOG PS of a liver cancer patient undergoing treatment. Methods We used Light Gradient Boosting Machine (LightGBM) as the meta-model, and five base models, including Random Forest (RF), Extra Trees (ET), AdaBoost (Ada), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). After preprocessing the data and applying feature selection method, the stacking ensemble model was trained using 1622 liver cancer patients' data and 46 variables. We also integrated the stacking ensemble model with a LIME-based explainable model to obtain model prediction explainability. Results According to the research, the best combination of the stacking ensemble model is ET + XGBoost + RF + GBM + Ada + LightGBM and achieved a ROC AUC of 0.9826 on the training set and 0.9675 on the test set. Conclusions This explainable stacking ensemble model can become a helpful tool for objectively predicting ECOG PS in liver cancer patients and aiding healthcare practitioners to adapt their treatment approach more effectively.
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Affiliation(s)
- Hung Viet Nguyen
- Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae, Republic of Korea
| | - Haewon Byeon
- Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae, Republic of Korea
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Artificial intelligence: A review of current applications in hepatocellular carcinoma imaging. Diagn Interv Imaging 2023; 104:24-36. [PMID: 36272931 DOI: 10.1016/j.diii.2022.10.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 01/10/2023]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and currently the third-leading cause of cancer-related death worldwide. Recently, artificial intelligence (AI) has emerged as an important tool to improve clinical management of HCC, including for diagnosis, prognostication and evaluation of treatment response. Different AI approaches, such as machine learning and deep learning, are both based on the concept of developing prediction algorithms from large amounts of data, or big data. The era of digital medicine has led to a rapidly expanding amount of routinely collected health data which can be leveraged for the development of AI models. Various studies have constructed AI models by using features extracted from ultrasound imaging, computed tomography imaging and magnetic resonance imaging. Most of these models have used convolutional neural networks. These tools have shown promising results for HCC detection, characterization of liver lesions and liver/tumor segmentation. Regarding treatment, studies have outlined a role for AI in evaluation of treatment response and improvement of pre-treatment planning. Several challenges remain to fully integrate AI models in clinical practice. Future research is still needed to robustly evaluate AI algorithms in prospective trials, and improve interpretability, generalizability and transparency. If such challenges can be overcome, AI has the potential to profoundly change the management of patients with HCC. The purpose of this review was to sum up current evidence on AI approaches using imaging for the clinical management of HCC.
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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Mokhria RK, Singh J. Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma. Artif Intell Gastroenterol 2022; 3:96-104. [DOI: 10.35712/aig.v3.i4.96] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/30/2022] [Accepted: 09/14/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.
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Affiliation(s)
- Rajesh Kumar Mokhria
- Government Model Sanskriti Senior Secondary School, Chulkana, 132101, Panipat, Haryana, India
| | - Jasbir Singh
- Department of Biochemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India
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Ramachandran A, Bhalla D, Rangarajan K, Pramanik R, Banerjee S, Arora C. Building and evaluating an artificial intelligence algorithm: A practical guide for practicing oncologists. Artif Intell Cancer 2022; 3:42-53. [DOI: 10.35713/aic.v3.i3.42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/09/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
The use of machine learning and deep learning has enabled many applications, previously thought of as being impossible. Among all medical fields, cancer care is arguably the most significantly impacted, with precision medicine now truly being a possibility. The effect of these technologies, loosely known as artificial intelligence, is particularly striking in fields involving images (such as radiology and pathology) and fields involving large amounts of data (such as genomics). Practicing oncologists are often confronted with new technologies claiming to predict response to therapy or predict the genomic make-up of patients. Underst-anding these new claims and technologies requires a deep understanding of the field. In this review, we provide an overview of the basis of deep learning. We describe various common tasks and their data requirements so that oncologists could be equipped to start such projects, as well as evaluate algorithms presented to them.
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Affiliation(s)
- Anupama Ramachandran
- Department of Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Deeksha Bhalla
- Department of Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Krithika Rangarajan
- Department of Radiology, All India Institute of Medical Sciences New Delhi, New Delhi 110029, India
- School of Information Technology, Indian Institute of Technology, Delhi 110016, India
| | - Raja Pramanik
- Department of Medical Oncology, Dr. B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Subhashis Banerjee
- Department of Computer Science, Indian Institute of Technology, Delhi 110016, India
| | - Chetan Arora
- Department of Computer Science, Indian Institute of Technology, Delhi 110016, India
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Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside. J Vasc Interv Radiol 2022; 33:1113-1120. [PMID: 35871021 DOI: 10.1016/j.jvir.2022.06.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/02/2022] [Accepted: 06/04/2022] [Indexed: 11/24/2022] Open
Abstract
Artificial intelligence (AI)-based technologies are the most rapidly growing field of innovation in healthcare with the promise to achieve substantial improvements in delivery of patient care across all disciplines of medicine. Recent advances in imaging technology along with marked expansion of readily available advanced health information, data offer a unique opportunity for interventional radiology (IR) to reinvent itself as a data-driven specialty. Additionally, the growth of AI-based applications in diagnostic imaging is expected to have downstream effects on all image-guidance modalities. Therefore, the Society of Interventional Radiology Foundation has called upon 13 key opinion leaders in the field of IR to develop research priorities for clinical applications of AI in IR. The objectives of the assembled research consensus panel were to assess the availability and understand the applicability of AI for IR, estimate current needs and clinical use cases, and assemble a list of research priorities for the development of AI in IR. Individual panel members proposed and all participants voted upon consensus statements to rank them according to their overall impact for IR. The results identified the top priorities for the IR research community and provide organizing principles for innovative academic-industrial research collaborations that will leverage both clinical expertise and cutting-edge technology to benefit patient care in IR.
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Newbury A, Ferguson C, Valero DA, Kutcher-Diaz R, McIntosh L, Karamanian A, Harman A. Interventional oncology update. Eur J Radiol Open 2022; 9:100430. [PMID: 35761853 PMCID: PMC9233207 DOI: 10.1016/j.ejro.2022.100430] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/13/2022] [Indexed: 12/29/2022] Open
Abstract
Interventional Oncology (IO) is a subspecialty field of Interventional Radiology bridging between diagnostic radiology and the clinical oncology team, addressing the diagnosis and treatment of cancer. There have been many exciting advancements in the field of IO in recent years; far too many to cover in a single paper. To give each topic sufficient attention, we have limited the scope of this review article to four topics which we feel have the potential to drastically change how cancer is treated managed in the immediate future.
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Affiliation(s)
- Alex Newbury
- UMass Memorial Medical Center, Worcester, MA, USA
| | | | | | | | | | | | - Aaron Harman
- UMass Memorial Medical Center, Worcester, MA, USA
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Wu T, Cooper SA, Shah VH. Omics and AI advance biomarker discovery for liver disease. Nat Med 2022; 28:1131-1132. [PMID: 35710988 DOI: 10.1038/s41591-022-01853-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Tiffany Wu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Shawna A Cooper
- Department of Biochemistry and Molecular Biology, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, USA
| | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA.
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Seah J, Boeken T, Sapoval M, Goh GS. Prime Time for Artificial Intelligence in Interventional Radiology. Cardiovasc Intervent Radiol 2022; 45:283-289. [PMID: 35031822 PMCID: PMC8921296 DOI: 10.1007/s00270-021-03044-4,] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/28/2021] [Indexed: 01/27/2025]
Abstract
Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.
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Affiliation(s)
- Jarrel Seah
- Department of Radiology, Alfred Health, Melbourne, VIC, Australia
- Department of Neuroscience, Monash University, Melbourne, VIC, Australia
| | - Tom Boeken
- Vascular and Oncological Interventional Radiology, University of Paris, Hopital Européen Georges Pompidou, Paris, France
| | - Marc Sapoval
- Vascular and Oncological Interventional Radiology, University of Paris, Hopital Européen Georges Pompidou, Paris, France
| | - Gerard S Goh
- Department of Radiology, Alfred Health, Melbourne, VIC, Australia.
- Department of Surgery, Central Clinical School, Monash University, Melbourne, VIC, Australia.
- National Trauma Research Institute, Central Clinical School, Monash University, Melbourne, VIC, Australia.
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Papaconstantinou D, Hewitt DB, Brown ZJ, Schizas D, Tsilimigras DI, Pawlik TM. Patient stratification in hepatocellular carcinoma: impact on choice of therapy. Expert Rev Anticancer Ther 2022; 22:297-306. [PMID: 35157530 DOI: 10.1080/14737140.2022.2041415] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
INTRODUCTION HCC comprises around 60 to 80% of all primary liver cancers and exhibits wide geographical variability. Appropriate treatment allocation needs to include both patient and tumor characteristics. AREAS COVERED Current HCC classification systems to guide therapy are either liver function-centric and evaluate physiologic liver function to guide therapy or prognostic stratification classification systems broadly based on tumor morphologic parameters, patient performance status, and liver reserve assessment. This review focuses on different classification systems for HCC, their strengths, and weaknesses as well as the use of artificial intelligence in improving prognostication in HCC. EXPERT OPINION Future HCC classification systems will need to incorporate clinic-pathologic data from a multitude of sources and emerging therapies to develop patient-specific treatment plans targeting a patient's unique tumor profile.
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Affiliation(s)
- Dimitrios Papaconstantinou
- Third Department of Surgery, Attikon University Hospital, National and Kapodistrian University of Athens, Medical School, Greece
| | - D Brock Hewitt
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio
| | - Zachary J Brown
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio
| | - Dimitrios Schizas
- First Department of Surgery, Laikon General Hospital, National and Kapodistrian University of Athens, Medical School, Greece
| | - Diamantis I Tsilimigras
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio
| | - Timothy M Pawlik
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio
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Li Y, Xu Z, An C, Chen H, Li X. Multi-Task Deep Learning Approach for Simultaneous Objective Response Prediction and Tumor Segmentation in HCC Patients with Transarterial Chemoembolization. J Pers Med 2022; 12:jpm12020248. [PMID: 35207736 PMCID: PMC8875107 DOI: 10.3390/jpm12020248] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/01/2022] [Accepted: 02/08/2022] [Indexed: 12/24/2022] Open
Abstract
This study aimed to develop a deep learning-based model to simultaneously perform the objective response (OR) and tumor segmentation for hepatocellular carcinoma (HCC) patients who underwent transarterial chemoembolization (TACE) treatment. A total of 248 patients from two hospitals were retrospectively included and divided into the training, internal validation, and external testing cohort. A network consisting of an encoder pathway, a prediction pathway, and a segmentation pathway was developed, and named multi-DL (multi-task deep learning), using contrast-enhanced CT images as input. We compared multi-DL with other deep learning-based OR prediction and tumor segmentation methods to explore the incremental value of introducing the interconnected task into a unified network. Additionally, the clinical model was developed using multivariate logistic regression to predict OR. Results showed that multi-DL could achieve the highest AUC of 0.871 in OR prediction and the highest dice coefficient of 73.6% in tumor segmentation. Furthermore, multi-DL can successfully perform the risk stratification that the low-risk and high-risk patients showed a significant difference in survival (p = 0.006). In conclusion, the proposed method may provide a useful tool for therapeutic regime selection in clinical practice.
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Affiliation(s)
- Yuze Li
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
| | - Ziming Xu
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
| | - Chao An
- Department of Minimal Invasive Intervention, Sun Yat-sen University Cancer Center, Guangzhou 510060, China;
| | - Huijun Chen
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
- Correspondence: (H.C.); (X.L.)
| | - Xiao Li
- Department of Interventional Therapy, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Correspondence: (H.C.); (X.L.)
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Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2022; 4:100443. [PMID: 35243281 PMCID: PMC8867112 DOI: 10.1016/j.jhepr.2022.100443] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/26/2021] [Accepted: 01/11/2022] [Indexed: 12/18/2022]
Abstract
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
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Prime Time for Artificial Intelligence in Interventional Radiology. Cardiovasc Intervent Radiol 2022; 45:283-289. [PMID: 35031822 PMCID: PMC8921296 DOI: 10.1007/s00270-021-03044-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/28/2021] [Indexed: 12/16/2022]
Abstract
Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.
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45
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Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities. J Comput Assist Tomogr 2022; 46:78-90. [PMID: 35027520 DOI: 10.1097/rct.0000000000001247] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
ABSTRACT Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.
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Gouda G, Gupta MK, Donde R, Behera L, Vadde R. Metabolic pathway-based target therapy to hepatocellular carcinoma: a computational approach. THERANOSTICS AND PRECISION MEDICINE FOR THE MANAGEMENT OF HEPATOCELLULAR CARCINOMA, VOLUME 2 2022:83-103. [DOI: 10.1016/b978-0-323-98807-0.00003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
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Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13:1977-1990. [PMID: 35070002 PMCID: PMC8727218 DOI: 10.4254/wjh.v13.i12.1977] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/09/2021] [Accepted: 11/25/2021] [Indexed: 02/06/2023] Open
Abstract
The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.
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Affiliation(s)
- Fadl H Veerankutty
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Govind Jayan
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Manish Kumar Yadav
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Krishnan Sarojam Manoj
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Abhishek Yadav
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Sindhu Radha Sadasivan Nair
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - T U Shabeerali
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Varghese Yeldho
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Madhu Sasidharan
- Gastroenterology and Hepatology, Kerala Institute of Medical Sciences, Thiruvananthapuram 695029, India
| | - Shiraz Ahmad Rather
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
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Wei P. Radiomics, deep learning and early diagnosis in oncology. Emerg Top Life Sci 2021; 5:829-835. [PMID: 34874454 PMCID: PMC8786297 DOI: 10.1042/etls20210218] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022]
Abstract
Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists' task and help with challenging cases, computer-aided diagnosis has been developing rapidly in the past decade, pioneered by radiomics early on, and more recently, driven by deep learning. In this mini-review, I use breast cancer as an example and review how medical imaging and its quantitative modeling, including radiomics and deep learning, have improved the early detection and treatment response prediction of breast cancer. I also outline what radiomics and deep learning share in common and how they differ in terms of modeling procedure, sample size requirement, and computational implementation. Finally, I discuss the challenges and efforts entailed to integrate deep learning models and software in clinical practice.
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Affiliation(s)
- Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A
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Dong Z, Lin Y, Lin F, Luo X, Lin Z, Zhang Y, Li L, Li ZP, Feng ST, Cai H, Peng Z. Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography. J Hepatocell Carcinoma 2021; 8:1473-1484. [PMID: 34877267 PMCID: PMC8643205 DOI: 10.2147/jhc.s334674] [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: 08/17/2021] [Accepted: 11/05/2021] [Indexed: 12/20/2022] Open
Abstract
Purpose The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment. Patients and Methods Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model. Results Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively. Conclusion Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.
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Affiliation(s)
- Zhi Dong
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Fangzeng Lin
- Department of Interventional Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Xuyi Luo
- Department of Emergency, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Zhi Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Yinhong Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Lujie Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Zi-Ping Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Huasong Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Zhenpeng Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
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Mazaheri S, Loya MF, Newsome J, Lungren M, Gichoya JW. Challenges of Implementing Artificial Intelligence in Interventional Radiology. Semin Intervent Radiol 2021; 38:554-559. [PMID: 34853501 DOI: 10.1055/s-0041-1736659] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.
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Affiliation(s)
- Sina Mazaheri
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Mohammed F Loya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Janice Newsome
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.,Department of Interventional Radiology, Emory University School of Medicine, Atlanta, Georgia
| | - Mathew Lungren
- LPCH Pediatric Interventional Radiology, Stanford University, Stanford, California
| | - Judy Wawira Gichoya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
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