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Sha M, Wang J, Cao J, Zou ZH, Qu XY, Xi ZF, Shen C, Tong Y, Zhang JJ, Jeong S, Xia Q. Criteria and prognostic models for patients with hepatocellular carcinoma undergoing liver transplantation. Clin Mol Hepatol 2025; 31:S285-S300. [PMID: 39159949 PMCID: PMC11925443 DOI: 10.3350/cmh.2024.0323] [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: 04/30/2024] [Accepted: 08/12/2024] [Indexed: 08/21/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.
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Affiliation(s)
- Meng Sha
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Wang
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Cao
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Hui Zou
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Naval Military Medical University, Shanghai, China
| | - Xiao-Ye Qu
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Feng Xi
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chuan Shen
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ying Tong
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jian-Jun Zhang
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Seogsong Jeong
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea
| | - Qiang Xia
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Huang H, Wu Q, Qiao H, Chen S, Hu S, Wen Q, Zhou G. P53 status combined with MRI findings for prognosis prediction of single hepatocellular carcinoma. Magn Reson Imaging 2025; 116:110293. [PMID: 39631483 DOI: 10.1016/j.mri.2024.110293] [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: 04/11/2024] [Revised: 10/17/2024] [Accepted: 11/30/2024] [Indexed: 12/07/2024]
Abstract
OBJECT To develop and validate a nomogram for predicting recurrence in individuals suffering single hepatocellular carcinoma (HCC) after curative hepatectomy. MATERIAL AND METHODS A retrospective analysis was conducted on 189 patients with single HCC undergoing curative resection in our center were randomized into training and validation cohorts. P53 status was determined using immunohistochemistry. Clinical data, such as age, and gender were collected. MRI findings, such as tumor size, intratumoral arteries, the presence of peritumoral enhancement and intratumoral necrosis were also recorded. Nomograms were established based on the predictors selected in the training cohort, and receiver operating characteristic (ROC) curve analyses were used to compare the predictive ability among single predictors and nomogram model. The Kaplan-Meier method was used to assess the impact of each predictor and nomogram model on HCC recurrence. The results were validated in the validation cohort. RESULTS Multivariate Cox regression analysis showed that P53 (P < 0.001), tumor size (P = 0.009), and intratumoral artery (P = 0.026) were the independent risk factors for HCC recurrence. The nomogram model demonstrated favorable C-index of 0.740 (95 %CI:0.653-0.826) and 0.767 (95 %CI: 0.633-0.900) in the training and validation cohorts, and the areas under the curve was 0.740 and 0.752, which was better than the performance of P53 and MR factors alone. Calibration curves indicated a good agreement between observed actual outcomes and predicted values. Kaplan-Meier curves indicated that nomogram model was powerful in discrimination and clinical usefulness. CONCLUSIONS The integrated nomogram combining P53 status and MRI findings can be a valuable prognostic tool for predicting postoperative recurrence of single HCC.
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Affiliation(s)
- Hong Huang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214122, China; Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China
| | - Qinghua Wu
- Department of Interventional Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Hongyan Qiao
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Sujing Chen
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Qingqing Wen
- GE Healthcare, MR Research China, Beijing, China
| | - Guofeng Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China; Shanghai Institute of Medical Imaging, 180 Fenglin Road, Shanghai 200032, China.
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Nong HY, Cen YY, Lu SJ, Huang RS, Chen Q, Huang LF, Huang JN, Wei X, Liu MR, Li L, Ding K. Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma: A retrospective study. World J Gastrointest Oncol 2025; 17:96439. [PMID: 39817122 PMCID: PMC11664629 DOI: 10.4251/wjgo.v17.i1.96439] [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: 05/08/2024] [Revised: 09/06/2024] [Accepted: 11/07/2024] [Indexed: 12/12/2024] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma (HCC) surgery. Currently, there is a paucity of preoperative evaluation approaches for MVI. AIM To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC. METHODS Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital. Patients were classified into two groups: MVI-positive (n = 57) and MVI-negative (n = 40), based on postoperative pathological results. The correlation between relevant radiological signs and MVI status was analyzed. MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features, which were combined with radiological signs to construct artificial neural network (ANN) models for MVI prediction. The predictive performance of the ANN models was evaluated using area under the curve, sensitivity, and specificity. ANN models with relatively high predictive performance were screened using the DeLong test, and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models' stability. RESULTS The absence of a pseudocapsule, an incomplete pseudocapsule, and the presence of tumor blood vessels were identified as independent predictors of HCC MVI. The ANN model constructed using the dominant features of the combined group (pseudocapsule status + tumor blood vessels + arterial phase + venous phase) demonstrated the best predictive performance for MVI status and was found to be automated, highly operable, and very stable. CONCLUSION The ANN model constructed using the dominant features of the combined group can be recommended as a non-invasive method for preoperative prediction of HCC MVI status.
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Affiliation(s)
- Hai-Yang Nong
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
- Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
- Guangxi Clinical Medical Research Center for Hepatobiliary Diseases, Affiliated Hospital of Youiiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Yong-Yi Cen
- Guangxi Clinical Medical Research Center for Hepatobiliary Diseases, Affiliated Hospital of Youiiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
- Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Shan-Jin Lu
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Rui-Sui Huang
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Qiong Chen
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Li-Feng Huang
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Jian-Ning Huang
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Xue Wei
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Man-Rong Liu
- Department of Ultrasound, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Lin Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Ke Ding
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
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Heo S, Park HJ, Lee SS. Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence. Korean J Radiol 2024; 25:550-558. [PMID: 38807336 PMCID: PMC11136947 DOI: 10.3348/kjr.2024.0070] [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: 01/18/2024] [Revised: 03/13/2024] [Accepted: 03/31/2024] [Indexed: 05/30/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.
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Affiliation(s)
- Subin Heo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Haghshomar M, Rodrigues D, Kalyan A, Velichko Y, Borhani A. Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies. Front Oncol 2024; 14:1362737. [PMID: 38779098 PMCID: PMC11109422 DOI: 10.3389/fonc.2024.1362737] [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: 12/28/2023] [Accepted: 04/12/2024] [Indexed: 05/25/2024] Open
Abstract
Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.
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Affiliation(s)
| | | | | | | | - Amir Borhani
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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Yin Y, de Haas RJ, Alves N, Pennings JP, Ruiter SJS, Kwee TC, Yakar D. Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT. Abdom Radiol (NY) 2024; 49:1122-1131. [PMID: 38289352 PMCID: PMC10955006 DOI: 10.1007/s00261-023-04178-4] [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: 10/19/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 03/22/2024]
Abstract
OBJECTIVES Detecting ablation site recurrence (ASR) after thermal ablation remains a challenge for radiologists due to the similarity between tumor recurrence and post-ablative changes. Radiomic analysis and machine learning methods may show additional value in addressing this challenge. The present study primarily sought to determine the efficacy of radiomic analysis in detecting ASR on follow-up computed tomography (CT) scans. The second aim was to develop a visualization tool capable of emphasizing regions of ASR between follow-up scans in individual patients. MATERIALS AND METHODS Lasso regression and Extreme Gradient Boosting (XGBoost) classifiers were employed for modeling radiomic features extracted from regions of interest delineated by two radiologists. A leave-one-out test (LOOT) was utilized for performance evaluation. A visualization method, creating difference heatmaps (diff-maps) between two follow-up scans, was developed to emphasize regions of growth and thereby highlighting potential ASR. RESULTS A total of 55 patients, including 20 with and 35 without ASR, were included in the radiomic analysis. The best performing model was achieved by Lasso regression tested with the LOOT approach, reaching an area under the curve (AUC) of 0.97 and an accuracy of 92.73%. The XGBoost classifier demonstrated better performance when trained with all extracted radiomic features than without feature selection, achieving an AUC of 0.93 and an accuracy of 89.09%. The diff-maps correctly highlighted post-ablative liver tumor recurrence in all patients. CONCLUSIONS Machine learning-based radiomic analysis and growth visualization proved effective in detecting ablation site recurrence on follow-up CT scans.
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Affiliation(s)
- Yunchao Yin
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands
| | - Robbert J de Haas
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands
| | - Natalia Alves
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, 6500 HB, Nijmegen, The Netherlands
| | - Jan Pieter Pennings
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands
| | - Simeon J S Ruiter
- Department of Hepatobiliary Surgery and Liver Transplantation, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands
| | - Thomas C Kwee
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands
| | - Derya Yakar
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
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Wang F, Cheng M, Du B, Li J, Li L, Huang W, Gao J. Predicting microvascular invasion in small (≤ 5 cm) hepatocellular carcinomas using radiomics-based peritumoral analysis. Insights Imaging 2024; 15:90. [PMID: 38530498 DOI: 10.1186/s13244-024-01649-0] [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: 12/02/2023] [Accepted: 02/10/2024] [Indexed: 03/28/2024] Open
Abstract
OBJECTIVE We assessed the predictive capacity of computed tomography (CT)-enhanced radiomics models in determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC) ≤ 5 cm within peritumoral margins of 5 and 10 mm. METHODS Radiomics software was used for feature extraction. We used the least absolute shrinkage and selection operator (LASSO) algorithm to establish an effective model to predict patients' preoperative MVI status. RESULTS The area under the curve (AUC) values in the validation sets for the 5- and 10-mm radiomics models concerning arterial tumors were 0.759 and 0.637, respectively. In the portal vein phase, they were 0.626 and 0.693, respectively. Additionally, the combined radiomics model for arterial tumors and the peritumoral 5-mm margin had an AUC value of 0.820. The decision curve showed that the combined tumor and peritumoral radiomics model exhibited a somewhat superior benefit compared to the traditional model, while the fusion model demonstrated an even greater advantage, indicating its significant potential in clinical application. CONCLUSION The 5-mm peritumoral arterial model had superior accuracy and sensitivity in predicting MVI. Moreover, the combined tumor and peritumoral radiomics model outperformed both the individual tumor and peritumoral radiomics models. The most effective combination was the arterial phase tumor and peritumor 5-mm margin combination. Using a fusion model that integrates tumor and peritumoral radiomics and clinical data can aid in the preoperative diagnosis of the MVI of isolated HCC ≤ 5 cm, indicating considerable practical value. CRITICAL RELEVANCE STATEMENT The radiomics model including a 5-mm peritumoral expansion is a promising noninvasive biomarker for preoperatively predicting microvascular invasion in patients diagnosed with a solitary HCC ≤ 5 cm. KEY POINTS • Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. • Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. • This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals.
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Affiliation(s)
- Fang Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi, Zhengzhou, Henan, 450052, People's Republic of China
| | - Ming Cheng
- Information Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Binbin Du
- Vasculocardiology Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Jing Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi, Zhengzhou, Henan, 450052, People's Republic of China
| | - Wenpeng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi, Zhengzhou, Henan, 450052, People's Republic of China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi, Zhengzhou, Henan, 450052, People's Republic of China.
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Ramírez-Mejía MM, Méndez-Sánchez N. From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring. World J Gastroenterol 2024; 30:631-635. [PMID: 38515945 PMCID: PMC10950631 DOI: 10.3748/wjg.v30.i7.631] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/12/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.
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Affiliation(s)
- Mariana Michelle Ramírez-Mejía
- Plan of Combined Studies in Medicine, Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
| | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
- Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
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Qazi Arisar FA, Salinas-Miranda E, Ale Ali H, Lajkosz K, Chen C, Azhie A, Healy GM, Deniffel D, Haider MA, Bhat M. Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study. Transpl Int 2023; 36:11149. [PMID: 37720416 PMCID: PMC10503435 DOI: 10.3389/ti.2023.11149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 08/09/2023] [Indexed: 09/19/2023]
Abstract
Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag patients at risk of developing future graft fibrosis. CT scans of 254 patients at 3-6 months post-liver transplant were retrospectively analyzed. Volumetric radiomic features were extracted from the portal phase using an Artificial Intelligence-based tool (PyRadiomics). The primary endpoint was clinically significant (≥F2) graft fibrosis. A 10-fold cross-validated LASSO model using clinical and radiomic features was developed. In total, 75 patients (29.5%) developed ≥F2 fibrosis by a median of 19 (4.3-121.8) months. The maximum liver attenuation at the venous phase (a radiomic feature reflecting venous perfusion), primary etiology, donor/recipient age, recurrence of disease, brain-dead donor, tacrolimus use at 3 months, and APRI score at 3 months were predictive of ≥F2 fibrosis. The combination of radiomics and the clinical features increased the AUC to 0.811 from 0.793 for the clinical-only model (p = 0.008) and from 0.664 for the radiomics-only model (p < 0.001) to predict future ≥F2 fibrosis. This pilot study exploring the role of radiomics demonstrates that the addition of radiomic features in a clinical model increased the model's performance. Further studies are required to investigate the generalizability of this experimental tool.
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Affiliation(s)
- Fakhar Ali Qazi Arisar
- Ajmera Transplant Program, Toronto General Hospital, University Health Network, Toronto, ON, Canada
- Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- National Institute of Liver and GI Diseases, Dow University of Health Sciences, Karachi, Pakistan
| | - Emmanuel Salinas-Miranda
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Hamideh Ale Ali
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Katherine Lajkosz
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Catherine Chen
- Ajmera Transplant Program, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Amirhossein Azhie
- Ajmera Transplant Program, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Gerard M. Healy
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Dominik Deniffel
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Masoom A. Haider
- Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network/Sinai Health System, Toronto, ON, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, Toronto General Hospital, University Health Network, Toronto, ON, Canada
- Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute and Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
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10
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Nie P, Zhang J, Miao W, Duan S, Wang T, Zhang J, Gu J, Wang N, Zhang R, Wang X, Yang G, Rao W, Wang Z. Incremental value of radiomics-based heterogeneity to the existing risk criteria in predicting recurrence of hepatocellular carcinoma after liver transplantation. Eur Radiol 2023; 33:6608-6618. [PMID: 37012548 DOI: 10.1007/s00330-023-09591-3] [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: 09/16/2022] [Revised: 01/02/2023] [Accepted: 02/17/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVES The aim of the study was to evaluate the association between the radiomics-based intratumoral heterogeneity (ITH) and the recurrence risk in hepatocellular carcinoma (HCC) patients after liver transplantation (LT), and to assess its incremental to the Milan, University of California San Francisco (UCSF), Metro-Ticket 2.0, and Hangzhou criteria. METHODS A multicenter cohort of 196 HCC patients were investigated. The endpoint was recurrence-free survival (RFS) after LT. A CT-based radiomics signature (RS) was constructed and assessed in the whole cohort and in the subgroups stratified by the Milan, UCSF, Metro-Ticket 2.0, and Hangzhou criteria. The R-Milan, R-UCSF, R-Metro-Ticket 2.0, and R-Hangzhou nomograms which combined RS and the four existing risk criteria were developed respectively. The incremental value of RS to the four existing risk criteria in RFS prediction was evaluated. RESULTS RS was significantly associated with RFS in the training and test cohorts as well as in the subgroups stratified by the existing risk criteria. The four combined nomograms showed better predictive capability than the existing risk criteria did with higher C-indices (R-Milan [training/test] vs. Milan, 0.745/0.765 vs. 0.677; R-USCF vs. USCF, 0.748/0.767 vs. 0.675; R-Metro-Ticket 2.0 vs. Metro-Ticket 2.0, 0.756/0.783 vs. 0.670; R-Hangzhou vs. Hangzhou, 0.751/0.760 vs. 0.691) and higher clinical net benefit. CONCLUSIONS The radiomics-based ITH can predict outcomes and provide incremental value to the existing risk criteria in HCC patients after LT. Incorporating radiomics-based ITH in HCC risk criteria may facilitate candidate selection, surveillance, and adjuvant trial design. KEY POINTS • Milan, USCF, Metro-Ticket 2.0, and Hangzhou criteria may be insufficient for outcome prediction in HCC after LT. • Radiomics allows for the characterization of tumor heterogeneity. • Radiomics adds incremental value to the existing criteria in outcome prediction.
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Affiliation(s)
- Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Juntao Zhang
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Wenjie Miao
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, Shandong, 266061, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Tongyu Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Ju Zhang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, Shandong, 266061, China
| | - Jinyang Gu
- Department of Transplantation, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ning Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Jinan, Shandong, 250021, China
| | - Ran Zhang
- Huiying Medical Technology Co. Ltd, Beijing, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Jinan, Shandong, 250021, China.
| | - Guangjie Yang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, Shandong, 266061, China.
| | - Wei Rao
- Division of Hepatology, Liver Disease Center, Organ Transplantation Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, Shandong, 266061, China.
| | - Zhenguang Wang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, Shandong, 266061, China.
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11
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Chen C, Liu J, Gu Z, Sun Y, Lu W, Liu X, Chen K, Ma T, Zhao S, Zhao H. Integration of Multimodal Computed Tomography Radiomic Features of Primary Tumors and the Spleen to Predict Early Recurrence in Patients with Postoperative Adjuvant Transarterial Chemoembolization. J Hepatocell Carcinoma 2023; 10:1295-1308. [PMID: 37576612 PMCID: PMC10422964 DOI: 10.2147/jhc.s423129] [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: 05/27/2023] [Accepted: 07/28/2023] [Indexed: 08/15/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is one of the most lethal malignancies in the world. Patients with HCC choose postoperative adjuvant transarterial chemoembolization (PA-TACE) after surgical resection to reduce the risk of recurrence. However, many of them have recurrence within a short period. Methods In this retrospective analysis, a total of 173 patients who underwent PA-TACE between September 2016 and March 2020 were recruited. Radiomic features were derived from the arterial and venous phases of each patient. Early recurrence (ER)-related radiomics features of HCC and the spleen were selected to build two rad-scores using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Logistic regression was applied to establish the Radiation (Rad)_score by combining the two regions. We constructed a nomogram containing clinical information and dual-region rad-scores, which was evaluated in terms of discrimination, calibration, and clinical usefulness. Results All three radiological scores showed good performance for ER prediction. The combined Rad_score performed the best, with an area under the curve (AUC) of 0.853 (95% confidence interval [CI], 0.783-0.908) in the training set and 0.929 (95% CI, 0.789-0.988) in the validation set. Multivariate analysis identified total bilirubin (TBIL) and the combined Rad_score as independent prognostic factors for ER. The nomogram was found to be clinically valuable, as determined by the decision curves (DCA) and clinical impact curves (CIC). Conclusion A multimodal dual-region radiomics model combining HCC and the spleen is an independent prognostic tool for ER. The combination of dual-region radiomics features and clinicopathological factors has a good clinical application value.
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Affiliation(s)
- Cong Chen
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Jian Liu
- Dalian Medical University and Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Zhuxin Gu
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Yanjun Sun
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Wenwu Lu
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Xiaokan Liu
- Department of Interventional Radiology, Affiliated Hospital 2 of Nantong University, Nantong, 226001, People’s Republic of China
| | - Kang Chen
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Tianzhi Ma
- Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, People’s Republic of China
| | - Suming Zhao
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
| | - Hui Zhao
- Department of Interventional & Vascular Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, People’s Republic of China
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12
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Kucukkaya AS, Zeevi T, Chai NX, Raju R, Haider SP, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Sci Rep 2023; 13:7579. [PMID: 37165035 PMCID: PMC10172370 DOI: 10.1038/s41598-023-34439-7] [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: 11/13/2022] [Accepted: 04/29/2023] [Indexed: 05/12/2023] Open
Abstract
Tumor recurrence affects up to 70% of early-stage hepatocellular carcinoma (HCC) patients, depending on treatment option. Deep learning algorithms allow in-depth exploration of imaging data to discover imaging features that may be predictive of recurrence. This study explored the use of convolutional neural networks (CNN) to predict HCC recurrence in patients with early-stage HCC from pre-treatment magnetic resonance (MR) images. This retrospective study included 120 patients with early-stage HCC. Pre-treatment MR images were fed into a machine learning pipeline (VGG16 and XGBoost) to predict recurrence within six different time frames (range 1-6 years). Model performance was evaluated with the area under the receiver operating characteristic curves (AUC-ROC). After prediction, the model's clinical relevance was evaluated using Kaplan-Meier analysis with recurrence-free survival (RFS) as the endpoint. Of 120 patients, 44 had disease recurrence after therapy. Six different models performed with AUC values between 0.71 to 0.85. In Kaplan-Meier analysis, five of six models obtained statistical significance when predicting RFS (log-rank p < 0.05). Our proof-of-concept study indicates that deep learning algorithms can be utilized to predict early-stage HCC recurrence. Successful identification of high-risk recurrence candidates may help optimize follow-up imaging and improve long-term outcomes post-treatment.
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Affiliation(s)
- Ahmet Said Kucukkaya
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
- Institute of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Nathan Xianming Chai
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Rajiv Raju
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Stefan Philipp Haider
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Mohamed Elbanan
- Department of Diagnostic Radiology, Bridgeport Hospital, Yale New Haven Health System, 267 Grant Street, Bridgeport, CT, 06610, USA
| | - Alexandra Petukhova-Greenstein
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
- Institute of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
- Visage Imaging, Inc., 12625 High Bluff Drive, Suite 205, San Diego, CA, 92130, USA
| | - John Onofrey
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Michal Nowak
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Kirsten Cooper
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Elizabeth Thomas
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Jessica Santana
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Bernhard Gebauer
- Institute of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - David Mulligan
- Transplantation and Immunology, Department of Surgery, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA
| | - Lawrence Staib
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA
| | - Ramesh Batra
- Transplantation and Immunology, Department of Surgery, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT, 06520-8042, USA.
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13
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Jiang C, Cai YQ, Yang JJ, Ma CY, Chen JX, Huang L, Xiang Z, Wu J. Radiomics in the diagnosis and treatment of hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int 2023:S1499-3872(23)00044-9. [PMID: 37019775 DOI: 10.1016/j.hbpd.2023.03.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023]
Abstract
Hepatocellular carcinoma (HCC) is a common malignant tumor. At present, early diagnosis of HCC is difficult and therapeutic methods are limited. Radiomics can achieve accurate quantitative evaluation of the lesions without invasion, and has important value in the diagnosis and treatment of HCC. Radiomics features can predict the development of cancer in patients, serve as the basis for risk stratification of HCC patients, and help clinicians distinguish similar diseases, thus improving the diagnostic accuracy. Furthermore, the prediction of the treatment outcomes helps determine the treatment plan. Radiomics is also helpful in predicting the HCC recurrence, disease-free survival and overall survival. This review summarized the role of radiomics in the diagnosis, treatment and prognosis of HCC.
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Affiliation(s)
- Chun Jiang
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Yi-Qi Cai
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jia-Jia Yang
- Department of Infection Management, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Can-Yu Ma
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jia-Xi Chen
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Lan Huang
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Ze Xiang
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jian Wu
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China.
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14
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Wang H, Liu R, Mo H, Li R, Lian J, Liu Q, Han S. A novel nomogram predicting the early recurrence of hepatocellular carcinoma patients after R0 resection. Front Oncol 2023; 13:1133807. [PMID: 37007138 PMCID: PMC10063973 DOI: 10.3389/fonc.2023.1133807] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/20/2023] [Indexed: 03/19/2023] Open
Abstract
Background Early tumor recurrence is one of the most significant poor prognostic factors for patients with HCC after R0 resection. The aim of this study is to identify risk factors of early recurrence, in addition, to develop a nomogram model predicting early recurrence of HCC patients. Methods A total of 481 HCC patients after R0 resection were enrolled and divided into a training cohort (n = 337) and a validation cohort (n = 144). Risk factors for early recurrence were determined based on Cox regression analysis in the training cohort. A nomogram incorporating independent risk predictors was established and validated. Results Early recurrence occurred in 37.8% of the 481 patients who underwent curative liver resection of HCC. AFP ≥ 400 ng/mL (HR: 1.662; P = 0.008), VEGF-A among 127.8 to 240.3 pg/mL (HR: 1.781, P = 0.012), VEGF-A > 240.3 pg/mL (HR: 2.552, P < 0.001), M1 subgroup of MVI (HR: 2.221, P = 0.002), M2 subgroup of MVI (HR: 3.120, P < 0.001), intratumor necrosis (HR: 1.666, P = 0.011), surgical margin among 5.0 to 10.0 mm (HR: 1.601, P = 0.043) and surgical margin < 5.0 mm (HR: 1.790, P = 0.012) were found to be independent risk factors for recurrence-free survival in the training cohort and were used for constructing the nomogram. The nomogram indicated good predictive performance with an AUC of 0.781 (95% CI: 0.729-0.832) and 0.808 (95% CI: 0.731-0.886) in the training and validation cohorts, respectively. Conclusions Elevated serum concentrations of AFP and VEGF-A, microvascular invasion, intratumor necrosis, surgical margin were independent risk factors of early intrahepatic recurrence. A reliable nomogram model which incorporated blood biomarkers and pathological variables was established and validated. The nomogram achieved desirable effectiveness in predicting early recurrence in HCC patients.
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Affiliation(s)
- Huanhuan Wang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Runkun Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Huanye Mo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Runtian Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Jie Lian
- Department of Pathology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Qingguang Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Shaoshan Han
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
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Berbís MA, Paulano Godino F, Royuela del Val J, Alcalá Mata L, Luna A. Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver. World J Gastroenterol 2023; 29:1427-1445. [PMID: 36998424 PMCID: PMC10044858 DOI: 10.3748/wjg.v29.i9.1427] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 01/13/2023] [Accepted: 02/27/2023] [Indexed: 03/07/2023] Open
Abstract
Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.
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Affiliation(s)
- M Alvaro Berbís
- Department of Radiology, HT Médica, San Juan de Dios Hospital, Córdoba 14960, Spain
- Faculty of Medicine, Autonomous University of Madrid, Madrid 28049, Spain
| | | | | | - Lidia Alcalá Mata
- Department of Radiology, HT Médica, Clínica las Nieves, Jaén 23007, Spain
| | - Antonio Luna
- Department of Radiology, HT Médica, Clínica las Nieves, Jaén 23007, Spain
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Liu Z, Zhang T, Lin L, Long F, Guo H, Han L. Applications of radiomics-based analysis pipeline for predicting epidermal growth factor receptor mutation status. Biomed Eng Online 2023; 22:17. [PMID: 36810090 PMCID: PMC9945395 DOI: 10.1186/s12938-022-01049-9] [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: 07/02/2022] [Accepted: 11/04/2022] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND This study aimed to develop a pipeline for selecting the best feature engineering-based radiomic path to predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS The study enrolled 115 lung adenocarcinoma patients with EGFR mutation status from June 2016 and September 2017. We extracted radiomics features by delineating regions-of-interest around the entire tumor in 18F-FDG PET/CT images. The feature engineering-based radiomic paths were built by combining various methods of data scaling, feature selection, and many methods for predictive model-building. Next, a pipeline was developed to select the best path. RESULTS In the paths from CT images, the highest accuracy was 0.907 (95% confidence interval [CI]: 0.849, 0.966), the highest area under curve (AUC) was 0.917 (95% CI: 0.853, 0.981), and the highest F1 score was 0.908 (95% CI: 0.842, 0.974). In the paths based on PET images, the highest accuracy was 0.913 (95% CI: 0.863, 0.963), the highest AUC was 0.960 (95% CI: 0.926, 0.995), and the highest F1 score was 0.878 (95% CI: 0.815, 0.941). Additionally, a novel evaluation metric was developed to evaluate the comprehensive level of the models. Some feature engineering-based radiomic paths obtained promising results. CONCLUSIONS The pipeline is capable of selecting the best feature engineering-based radiomic path. Combining various feature engineering-based radiomic paths could compare their performances and identify paths built with the most appropriate methods to predict EGFR-mutant lung adenocarcinoma in 18FDG PET/CT. The pipeline proposed in this work can select the best feature engineering-based radiomic path.
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Affiliation(s)
- Zefeng Liu
- grid.412645.00000 0004 1757 9434Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052 People’s Republic of China
| | - Tianyou Zhang
- grid.412645.00000 0004 1757 9434Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052 People’s Republic of China
| | - Liying Lin
- grid.265021.20000 0000 9792 1228First Central Clinical College, Tianjin Medical University, 22 Qixiangtai Road, Heping District, Tianjin, 300070 People’s Republic of China
| | - Fenghua Long
- grid.506261.60000 0001 0706 7839Department of Radiology, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300041 People’s Republic of China
| | - Hongyu Guo
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.
| | - Li Han
- School of Medical Imaging, Tianjin Medical University, 9-307, Guangdong Rd. #1, Hexi, Tianjin, 300203, People's Republic of China. .,Department of Radiology, University of Michigan, Ann Arbor, Michigan, 48109, USA.
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Bodard S, Liu Y, Guinebert S, Kherabi Y, Asselah T. Performance of Radiomics in Microvascular Invasion Risk Stratification and Prognostic Assessment in Hepatocellular Carcinoma: A Meta-Analysis. Cancers (Basel) 2023; 15:cancers15030743. [PMID: 36765701 PMCID: PMC9913680 DOI: 10.3390/cancers15030743] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in phenomenal imaging are paving the way for application in diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC. METHODS We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE addressing the value of imaging phenomics for HCC risk stratification and prognostication. Publication information for each were collected using a standardized data extraction form. RESULTS Twenty-seven articles were analyzed. Our study shows the importance of imaging phenomics in HCC MVI prediction. When the training and validation datasets were analyzed separately by the random-effects model, in the training datasets, radiomics had good MVI prediction (AUC of 0.81 (95% CI 0.76-0.86)). Similar results were found in the validation datasets (AUC of 0.79 (95% CI 0.72-0.85)). Using the fixed effects model, the mean AUC of all datasets was 0.80 (95% CI 0.76-0.84). CONCLUSIONS Imaging phenomics is an effective solution to predict microvascular invasion risk, prognosis, and treatment response in patients with HCC.
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Affiliation(s)
- Sylvain Bodard
- Service de Radiologie Adulte, Hôpital Universitaire Necker-Enfants Malades, AP-HP Centre, 75015 Paris, France
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
- CNRS, INSERM, UMR 7371, Laboratoire d’Imagerie Biomédicale, Sorbonne Université, 75006 Paris, France
- Correspondence: ; Tel.: +33-6-18-81-62-10
| | - Yan Liu
- Faculty of Life Science and Medicine, King’s College London, London WC2R 2LS, UK
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France
| | - Sylvain Guinebert
- Service de Radiologie Adulte, Hôpital Universitaire Necker-Enfants Malades, AP-HP Centre, 75015 Paris, France
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
| | - Yousra Kherabi
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
| | - Tarik Asselah
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
- Service d’Hépatologie, INSERM, UMR1149, Hôpital Beaujon, AP-HP.Nord, 92110 Clichy, France
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Wei J, Jiang H, Zhou Y, Tian J, Furtado FS, Catalano OA. Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma. Dig Liver Dis 2023:S1590-8658(22)00863-5. [PMID: 36641292 DOI: 10.1016/j.dld.2022.12.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/16/2023]
Abstract
The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China.
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, PR. China
| | - Yu Zhou
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; School of Life Science and Technology, Xidian University, Xi'an, PR. China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, PR. China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR. China.
| | - Felipe S Furtado
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
| | - Onofrio A Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States.
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Miranda J, Horvat N, Fonseca GM, Araujo-Filho JDAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29:43-60. [PMID: 36683711 PMCID: PMC9850949 DOI: 10.3748/wjg.v29.i1.43] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-010, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | | | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Paulo Herman
- Department of Gastroenterology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
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Park HJ, Kim KW, Lee SS. Artificial intelligence in radiology and its application in liver disease. ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING IN PRECISION MEDICINE IN LIVER DISEASES 2023:53-79. [DOI: 10.1016/b978-0-323-99136-0.00002-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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21
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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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22
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Construction of a prediction model for chronic HBV-associated hepatocellular carcinoma based on ultrasound radiomics. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [DOI: 10.1016/j.jrras.2022.100487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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Wang L, Zhang L, Jiang B, Zhao K, Zhang Y, Xie X. Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review. Br J Radiol 2022; 95:20211136. [PMID: 35816550 PMCID: PMC10162062 DOI: 10.1259/bjr.20211136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 04/26/2022] [Accepted: 07/05/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (MR) imaging. METHODS We searched PubMed and Web of Science for publications, using terms related to deep learning, radiomics, imaging methods (CT or MR), and the liver. Two reviewers independently selected articles and extracted data from each eligible article. The Quality Assessment of Diagnostic Accuracy Studies-AI (QUADAS-AI) tool was used to assess the risk of bias and concerns regarding applicability. RESULTS The screening identified 45 high-quality publications from 235 candidates, including 8 on diffuse liver diseases and 37 on focal liver lesions. Nine studies used deep learning and 36 studies used radiomics. All 45 studies were rated as low risk of bias in patient selection and workflow, but 36 (80%) were rated as high risk of bias in the index test because they lacked external validation. In terms of concerns regarding applicability, all 45 studies were rated as low concerns. These studies demonstrated that deep learning and radiomics can evaluate liver fibrosis, cirrhosis, portal hypertension, and a series of complications caused by cirrhosis, predict the prognosis of malignant hepatic tumors, and differentiate focal hepatic lesions. CONCLUSIONS The latest studies have shown that deep learning and radiomics based on hepatic CT and MR imaging have potential application value in the diagnosis, treatment evaluation, and prognosis prediction of common liver diseases. The AI methods may become useful tools to support clinical decision-making in the future. ADVANCES IN KNOWLEDGE Deep learning and radiomics have shown their potential in the diagnosis, treatment evaluation, and prognosis prediction of a series of common diffuse liver diseases and focal liver lesions.
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Affiliation(s)
- Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Keke Zhao
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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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: 4] [Impact Index Per Article: 1.3] [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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25
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Ke Z, Li L, Wang L, Liu H, Lu X, Zeng F, Zha Y. Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19). Acta Radiol 2022; 63:319-327. [PMID: 33601893 DOI: 10.1177/0284185121994695] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. PURPOSE To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. MATERIAL AND METHODS The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. RESULTS Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. CONCLUSION The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.
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Affiliation(s)
- Zan Ke
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Liang Li
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Li Wang
- Department of Infection Prevention and Control, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Huan Liu
- GE Healthcare, Shanghai, PR China
| | - Xuefang Lu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Feifei Zeng
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
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Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions. Eur J Nucl Med Mol Imaging 2022; 49:2917-2928. [PMID: 35230493 PMCID: PMC9206604 DOI: 10.1007/s00259-022-05742-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/17/2022] [Indexed: 12/17/2022]
Abstract
Purpose
This study aimed to investigate whether models built from radiomics features based on multiphase contrast-enhanced MRI can identify microscopic pre-hepatocellular carcinoma lesions. Methods We retrospectively studied 54 small hepatocellular carcinoma (SHCC, diameter < 2 cm) patients and 70 patients with hepatocellular cysts or haemangiomas from September 2018 to June 2021. For the former, two MRI scans were collected within 12 months of each other; the 2nd scan was used to confirm the diagnosis. The volumes of interest (VOIs), including SHCCs and normal liver tissues, were delineated on the 2nd scans, mapped to the 1st scans via image registration, and enrolled into the SHCC and internal-control cohorts, respectively, while those of normal liver tissues from patients with hepatocellular cysts or haemangioma were enrolled in the external-control cohort. We extracted 1132 radiomics features from each VOI and analysed their discriminability between the SHCC and internal-control cohorts for intra-group classification and the SHCC and external-control cohorts for inter-group classification. Five radial basis-function, kernel-based support vector machine (SVM) models (four corresponding single-phase models and one integrated from the four-phase MR images) were established. Results Among the 124 subjects, the multiphase models yielded better performance on the testing set for intra-group and inter-group classification, with areas under the receiver operating characteristic curves of 0.93 (95% CI, 0.85–1.00) and 0.97 (95% CI, 0.92–1.00), accuracies of 86.67% and 94.12%, sensitivities of 87.50% and 94.12%, and specificities of 85.71% and 94.12%, respectively. Conclusion The combined multiphase MRI-based radiomics feature model revealed microscopic pre-hepatocellular carcinoma lesions. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05742-8.
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Wei J, Niu M, Yabo O, Zhou Y, Ma X, Yang X, Jiang H, Hui H, Cao H, Duan B, Li H, Ding D, Tian J. Advances in artificial intelligence techniques drive the application of radiomics in the clinical research of hepatocellular carcinoma. ILIVER 2022; 1:49-54. [DOI: 10.1016/j.iliver.2022.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2025]
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Wang X, Wang S, Yin X, Zheng Y. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med 2021; 141:105058. [PMID: 34836622 DOI: 10.1016/j.compbiomed.2021.105058] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/19/2021] [Accepted: 11/20/2021] [Indexed: 02/07/2023]
Abstract
OBJECT To distinguish combined hepatocellular cholangiocarcinoma (cHCC-CC), hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) before operation using MRI radiomics. METHOD This study retrospectively analyzed 196 liver cancers: 33 cHCC-CC, 88 HCC and 75 CC. They had confirmed by pathological analysis in the Affiliated Hospital of Hebei University. MRI lesions were manually segmented by a radiologist.1316 features were extracted from MRI lesions by Pyradiomics. Useful features were retained through two-level feature selection to establish a classification model. Receiver operating characteristic (ROC), area under curve (AUC) and F1-score were used to evaluate the performance of the model. RESULTS Compared with low-order image features, the performance of the model based on high-order features was improved by about 10%. The model showed better performance in identifying HCC tumors during the delay phase (AUC = 0.91, sensitivity = 0.88, specificity = 0.89, accuracy = 0.89, F1-Score = 0.88). CONCLUSION The classification ability of cHCC-CC, HCC and CC can be further improved by extracting MRI high-order features and using a two-level feature selection method.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China; Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China.
| | - Shuping Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China; Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Baoding, 071000, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100010, China
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Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13:1599-1615. [PMID: 34853638 PMCID: PMC8603458 DOI: 10.4251/wjgo.v13.i11.1599] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/02/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021; 54:890-901. [PMID: 34390014 PMCID: PMC8435007 DOI: 10.1111/apt.16563] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/08/2021] [Accepted: 07/25/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology. AIMS To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation. METHODS We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies. RESULTS A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard. CONCLUSIONS Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.
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Affiliation(s)
- Emily Harding-Theobald
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Jeremy Louissaint
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Bharat Maraj
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Edward Cuaresma
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Whitney Townsend
- Division of Library Sciences, University of Michigan, Ann Arbor, MI, USA
| | | | - Amit G Singal
- Division of Digestive and Liver Diseases, University of Texas Southwestern, Dallas, TX, USA
| | - Grace L Su
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Anna S Lok
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Neehar D Parikh
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
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Fang S, Lai L, Zhu J, Zheng L, Xu Y, Chen W, Wu F, Wu X, Chen M, Weng Q, Ji J, Zhao Z, Tu J. A Radiomics Signature-Based Nomogram to Predict the Progression-Free Survival of Patients With Hepatocellular Carcinoma After Transcatheter Arterial Chemoembolization Plus Radiofrequency Ablation. Front Mol Biosci 2021; 8:662366. [PMID: 34532340 PMCID: PMC8439353 DOI: 10.3389/fmolb.2021.662366] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 08/03/2021] [Indexed: 12/24/2022] Open
Abstract
Objective: The study aims to establish an magnetic resonance imaging radiomics signature-based nomogram for predicting the progression-free survival of intermediate and advanced hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) plus radiofrequency ablation Materials and Methods: A total of 113 intermediate and advanced HCC patients treated with TACE and RFA were eligible for this study. Patients were classified into a training cohort (n = 78 cases) and a validation cohort (n = 35 cases). Radiomics features were extracted from contrast-enhanced T1W images by analysis kit software. Dimension reduction was conducted to select optimal features using the least absolute shrinkage and selection operator (LASSO). A rad-score was calculated and used to classify the patients into high-risk and low-risk groups and further integrated into multivariate Cox analysis. Two prediction models based on radiomics signature combined with or without clinical factors and a clinical model based on clinical factors were developed. A nomogram comcined radiomics signature and clinical factors were established and the concordance index (C-index) was used for measuring discrimination ability of the model, calibration curve was used for measuring calibration ability, and decision curve and clinical impact curve are used for measuring clinical utility. Results: Eight radiomics features were selected by LASSO, and the cut-off of the Rad-score was 1.62. The C-index of the radiomics signature for PFS was 0.646 (95%: 0.582–0.71) in the training cohort and 0.669 (95% CI:0.572–0.766) in validation cohort. The median PFS of the low-risk group [30.4 (95% CI: 19.41–41.38)] months was higher than that of the high-risk group [8.1 (95% CI: 4.41–11.79)] months in the training cohort (log rank test, z = 16.58, p < 0.001) and was verified in the validation cohort. Multivariate Cox analysis showed that BCLC stage [hazard ratio (HR): 2.52, 95% CI: 1.42–4.47, p = 0.002], AFP level (HR: 2.01, 95% CI: 1.01–3.99 p = 0.046), time interval (HR: 0.48, 95% CI: 0.26–0.87, p = 0.016) and radiomics signature (HR 2.98, 95% CI: 1.60–5.51, p = 0.001) were independent prognostic factors of PFS in the training cohort. The C-index of the combined model in the training cohort was higher than that of clinical model for PFS prediction [0.722 (95% CI: 0.657–0.786) vs. 0.669 (95% CI: 0.657–0.786), p＜0.001]. Similarly, The C-index of the combined model in the validation cohort, was higher than that of clinical model [0.821 (95% CI: 0.726–0.915) vs. 0.76 (95% CI: 0.667–0.851), p = 0.004]. The calibration curve, decision curve and clinical impact curve showed that the nomogram can be used to accurately predict the PFS of patients. Conclusion: The radiomics signature was a prognostic risk factor, and a nomogram combined radiomics and clinical factors acts as a new strategy for predicted the PFS of intermediate and advanced HCC treated with TACE plus RFA.
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Affiliation(s)
- Shiji Fang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China.,Department of Intervention, Lishui Hospital of Zhejiang University, Lishui, China
| | - Linqiang Lai
- Department of Intervention, Lishui Hospital of Zhejiang University, Lishui, China
| | - Jinyu Zhu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China.,Department of Radiology, Lishui Hospital of Zhejiang University, Lishui, China
| | - Liyun Zheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China.,Department of Intervention, Lishui Hospital of Zhejiang University, Lishui, China
| | - Yuanyuan Xu
- Department of Pathology, Lishui Hospital of Zhejiang University, Lishui, China
| | - Weiqian Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China.,Department of Intervention, Lishui Hospital of Zhejiang University, Lishui, China
| | - Fazong Wu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China.,Department of Intervention, Lishui Hospital of Zhejiang University, Lishui, China
| | - Xulu Wu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China.,Department of Intervention, Lishui Hospital of Zhejiang University, Lishui, China
| | - Zhongwei Zhao
- Intervention of Department, Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China.,Department of Intervention, Lishui Hospital of Zhejiang University, Lishui, China
| | - Jianfei Tu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention, Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, China.,Department of Radiology, Lishui Hospital of Zhejiang University, Lishui, China
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A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis. Diagnostics (Basel) 2021; 11:diagnostics11091650. [PMID: 34573991 PMCID: PMC8471809 DOI: 10.3390/diagnostics11091650] [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: 07/05/2021] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 01/02/2023] Open
Abstract
Liver cirrhosis poses a major risk for the development of hepatocellular carcinoma (HCC). This retrospective study investigated to what extent radiomic features allow the prediction of emerging HCC in patients with cirrhosis in contrast-enhanced computed tomography (CECT). A total of 51 patients with liver cirrhosis and newly detected HCC lesions (n = 82) during follow-up (FU-CT) after local tumor therapy were included. These lesions were not to have been detected by the radiologist in the chronologically prior CECT (PRE-CT). For training purposes, segmentations of 22 patients with liver cirrhosis but without HCC-recurrence were added. A total of 186 areas (82 HCCs and 104 cirrhotic liver areas without HCC) were analyzed. Using univariate analysis, four independent features were identified, and a multivariate logistic regression model was trained to classify the outlined regions as "HCC probable" or "HCC improbable". In total, 60/82 (73%) of segmentations with later detected HCC and 84/104 (81%) segmentations without HCC were classified correctly (AUC of 81%, 95% CI 74-87%), yielding a sensitivity of 72% (95% CI 57-83%) and a specificity of 86% (95% CI 76-96%). In conclusion, the model predicted the occurrence of new HCCs within segmented areas with an acceptable sensitivity and specificity in cirrhotic liver tissue in CECT.
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Zhang L, Hu J, Hou J, Jiang X, Guo L, Tian L. Radiomics-based model using gadoxetic acid disodium-enhanced MR images: associations with recurrence-free survival of patients with hepatocellular carcinoma treated by surgical resection. Abdom Radiol (NY) 2021; 46:3845-3854. [PMID: 33733337 DOI: 10.1007/s00261-021-03034-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a prediction model that combined magnetic resonance images (MRI)-based radiomics features with clinical factors to predict recurrence-free survival (RFS) of hepatocellular carcinoma (HCC) patients treated with surgical resection. METHODS HCC patients treated with surgical resection (n = 153) were randomly divided into training (n = 107) and validation (n = 46) datasets. The volumes of interest were manually outlined around the lesion and additional 2 mm and 5 mm peritumoral areas were created with automated dilatation in MRI to extract tumoral (T) and peritumoral (PT) radiomics features. The radiomics models were constructed using least absolute shrinkage and selection operator Cox regression. The combined model incorporated clinical factors and radiomics features using multivariable Cox regression based on the Akaike information criterion principle. Predictive performance of different models were evaluated by receiver operating characteristic (ROC) curves, decision curves, and calibration curves. RESULTS Among the radiomics models, similar performance was observed in the 2 mm and 5 mm PT models (C-index both 0.657), which were better than the T model or T + PT model (C-index 0.607 and 0.641, respectively) in the validation dataset, whereas the model combined with the three identified clinical risk factors showed the best performance (C-index 0.725). Results of the ROC curves, decision curves, and the calibration curves indicated that the combined model and the derived nomogram had better prediction performance, greater clinical benefits, and fair calibration efficiency. CONCLUSION The prediction model that combined MRI radiomics signatures with clinical factors can effectively predict the prognosis of patients with HCC treated with surgical resection.
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Affiliation(s)
- Ling Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Jianming Hu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Jingyu Hou
- Department of Liver Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Xinhua Jiang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Lei Guo
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
| | - Li Tian
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China.
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Hepatocellular carcinoma: metastatic pathways and extra-hepatic findings. Abdom Radiol (NY) 2021; 46:3698-3707. [PMID: 34091729 DOI: 10.1007/s00261-021-03151-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/10/2023]
Abstract
Although a small portion of patients with hepatocellular carcinoma (HCC) have radiologically evident extrahepatic disease at the initial presentation, a larger number of them develop metastatic disease later during the course of treatment or after definitive treatment. Furthermore, early metastatic disease could be overlooked by imaging due to small size and non-specificity of findings. Extrahepatic spread of HCC occurs via different pathways and is directly fueled by tumor biology and its molecular characteristics. Early and accurate detection of extrahepatic disease in patients with HCC has significant impact on management and selection of treatment options. Additionally, precise staging of disease will allow for better prediction of survival and outcome. Different pathways of regional and systemic spread of HCC with their proposed mechanisms and relevant underlying molecular derangement will be discussed in this article. Potential roles in management of patients with HCC will be discussed and reviewed in this article.
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Abstract
ABSTRACT Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.
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Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021; 11:1194. [PMID: 34209197 PMCID: PMC8307071 DOI: 10.3390/diagnostics11071194] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.
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Affiliation(s)
- Anna Castaldo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Davide Raffaele De Lucia
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy;
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
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Jin ZC, Zhong BY. Application of radiomics in hepatocellular carcinoma: A review. Artif Intell Med Imaging 2021; 2:64-72. [DOI: 10.35711/aimi.v2.i3.64] [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: 05/13/2021] [Revised: 06/19/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with low 5-year survival rate. The high molecular heterogeneity in HCC poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC. However, current clinical practice based on single bioptic or archived tumor tissue has been deficient in identifying useful biomarkers. The concept of radiomics was first proposed in 2012 and is different from the traditional imaging analysis based on the qualitative or semi-quantitative analysis by radiologists. Radiomics refers to high-throughput extraction of large amounts number of high-dimensional quantitative features from medical images through machine learning or deep learning algorithms. Using the radiomics method could quantify tumoral phenotypes and heterogeneity, which may provide benefits in clinical decision-making at a lower cost. Here, we review the workflow and application of radiomics in HCC.
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Affiliation(s)
- Zhi-Cheng Jin
- Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Bin-Yan Zhong
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
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Budai BK, Frank V, Shariati S, Fejér B, Tóth A, Orbán V, Bérczi V, Kaposi PN. CT texture analysis of abdominal lesions – Part I.: Liver lesions. IMAGING 2021. [DOI: 10.1556/1647.2021.00007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
AbstractArtificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.
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Affiliation(s)
- Bettina Katalin Budai
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Veronica Frank
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Sonaz Shariati
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Bence Fejér
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Ambrus Tóth
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Vince Orbán
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Viktor Bérczi
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Pál Novák Kaposi
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
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Lin W, Que L, Lin G, Chen R, Lu Q, Zhicheng Du MD, Hui Liu MD, Yu Z, Huang M. Using Machine Learning to Predict Five-Year Reintervention Risk in Type B Aortic Dissection Patients After Thoracic Endovascular Aortic Repair. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Purpose: Type B aortic dissection (TBAD) is a high-risk disease, commonly treated with thoracic endovascular aortic repair (TEVAR). However, for the long-term follow-up, it is associated with a high 5-year reintervention rate for patients after TEVAR. There is no accurate definition
of prognostic risk factors for TBAD in medical guidelines, and there is no scientific judgment standard for patients’ quality of life or survival outcome in the next five years in clinical practice. A large amount of medical data features makes prognostic analysis difficult. However,
machine learning (ML) permits lots of objective data features to be considered for clinical risk stratification and patient management. We aimed to predict the 5-year prognosis in TBAD after TEVAR by Ml, based on baseline, stent characteristics and computed tomography angiography (CTA) imaging
data, and provided a certain degree of scientific basis for prognostic risk score and stratification in medical guidelines. Materials and Methods: Dataset we recorded was obtained from 172 TBAD patients undergoing TEVAR. Totally 40 features were recorded, including 14 baseline, 5 stent
characteristics and 21 CTA imaging data. Information gain (IG) was used to select features highly associated with adverse outcome. Then, the Gradient Boost classifier was trained using grid search and stratified 5-fold cross-validation, and Its predictive performance was evaluated by the area
under the curve (AUC) in the receiver operating characteristic (ROC). Results: Totally 60 patients underwent reintervention during follow-up. Combing 24 features selected by IG, ML model predicted prognosis well in TBAD after TEVAR, with an AUC of 0.816 and a 95% confidence interval
of 0.797 to 0.837. Reintervention rate of prediction was slightly higher than the actual (48.2% vs. 34.8%). Conclusion: Machine learning, which combined with baseline, stent characteristics and CTA imaging data for personalized risk computations, effectively predicted reintervention
risk in TBAD patients after TEVAR in 5-year follow-up. The model could be used to efficiently assist the clinical management of TBAD patients and prompt high-risk factors.
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Affiliation(s)
- Weiyuan Lin
- College of Automation Science and Technology, South China University of Technology, Guangzhou, 510640, China
| | - Lifeng Que
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, 518110, China
| | - Guisen Lin
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Rui Chen
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Qiyang Lu
- College of Automation Science and Technology, South China University of Technology, Guangzhou, 510640, China
| | - M. D. Zhicheng Du
- Department of Medical Statistics and Epidemiology, Health Information Research Center, Guangdong Key Laboratory of Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - M. D. Hui Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Zhuliang Yu
- College of Automation Science and Technology, South China University of Technology, Guangzhou, 510640, China
| | - Meiping Huang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
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Sheen H, Kim JS, Lee JK, Choi SY, Baek SY, Kim JY. A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion. Abdom Radiol (NY) 2021; 46:2839-2849. [PMID: 33388805 DOI: 10.1007/s00261-020-02884-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 11/19/2020] [Accepted: 11/25/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE A radiomics nomogram for pretreatment prediction of TACE refractoriness was developed and validated for hepatocellular carcinoma (HCC) without extrahepatic metastasis or macrovascular invasion. MATERIALS AND METHODS This study included 80 patients with HCC without extrahepatic metastasis or macrovascular involvement treated with TACE between July 2016 and November 2018. The datasets were divided into a training set (80%) and a test set (20%) for feature selection and tenfold cross-validation. Forty radiomic features were extracted from arterial-phase computed tomography (CT) using the Local Image Features Extraction software. The Lasso regression model was used for radiomics signature selection. The Lasso regression model was used for radiomics signature selection and the selected signatures were validated using the Mann-Whitney U-test. The radiomics nomogram was developed based on a multivariate logistic regression model incorporating the Rad-score, CT imaging factors, and clinical factors, and it was validated. RESULTS The Rad-score, which consists of the Gray-Level Zone Length Matrix (GLZLM)-Long-Zone Low Gray-Level Emphasis (LZLGE) and GLZLM-Gray-Level Non-Uniformity (GLNU), T-stage, log α-fetoprotein (AFP), and bilobar distribution were significantly associated with TACE refractoriness (p < 0.05). Predictors in the radiomics nomogram were the Rad-score and T-stage (Rad-score + T-stage), Rad-score and bilobar distribution (Rad-score + bilobar distribution), or Rad-score and logAFP (Rad-score + logAFP). The multivariate logistic regression model showed a good predictive performance (Rad-score + T-stage, AUC, 0.95; Rad-score + bilobar distribution, AUC 0.91; and Rad-score + logAFP, AUC, 0.91). CONCLUSION The radiomics nomogram could be used for the pretreatment prediction of TACE refractoriness.
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Affiliation(s)
- Heesoon Sheen
- Department of Radiation Oncology, Samsung Medical Center, #81, Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
- RI Translational Research Team, Division of Applied RI, Korea Institute of Radiological & Medical Sciences, Seoul, 01812, Republic of Korea
| | - Jin Sil Kim
- Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University, Anyangcheon-Ro, 1071, Yangcheon-gu, Seoul, 07985, Republic of Korea.
| | - Jeong Kyong Lee
- Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University, Anyangcheon-Ro, 1071, Yangcheon-gu, Seoul, 07985, Republic of Korea
| | - Sun Young Choi
- Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University, Anyangcheon-Ro, 1071, Yangcheon-gu, Seoul, 07985, Republic of Korea
| | - Seung Yon Baek
- Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University, Anyangcheon-Ro, 1071, Yangcheon-gu, Seoul, 07985, Republic of Korea
| | - Jung Young Kim
- RI Translational Research Team, Division of Applied RI, Korea Institute of Radiological & Medical Sciences, Seoul, 01812, Republic of Korea
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Lang Q, Zhong C, Liang Z, Zhang Y, Wu B, Xu F, Cong L, Wu S, Tian Y. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10023-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Radiomics, Radiogenomics, and Next-Generation Molecular Imaging to Augment Diagnosis of Hepatocellular Carcinoma. ACTA ACUST UNITED AC 2021; 26:108-115. [PMID: 32205534 DOI: 10.1097/ppo.0000000000000435] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Ultrasound, computed tomography, magnetic resonance imaging, and [F]F-fluorodeoxyglucose positron emission tomography are invaluable in the clinical evaluation of human cancers. Radiomics and radiogenomics tools may allow clinicians to standardize interpretation of these conventional imaging modalities, while better linking radiographic hallmarks to disease biology and prognosis. These advances, coupled with next-generation positron emission tomography imaging tracers capable of providing biologically relevant tumor information, may further expand the tools available in our armamentarium against human cancers. We present current imaging methods and explore emerging research that may improve diagnosis and monitoring of local, oligometastatic, and disseminated cancers exhibiting heterogeneous uptake of [F]F-fluorodeoxyglucose, using hepatocellular carcinoma as an example.
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Sung YS, Park B, Park HJ, Lee SS. Radiomics and deep learning in liver diseases. J Gastroenterol Hepatol 2021; 36:561-568. [PMID: 33709608 DOI: 10.1111/jgh.15414] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/17/2021] [Accepted: 01/19/2021] [Indexed: 12/14/2022]
Abstract
Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.
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Affiliation(s)
- Yu Sub Sung
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Bumwoo Park
- Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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Maruyama H, Yamaguchi T, Nagamatsu H, Shiina S. AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound. Diagnostics (Basel) 2021; 11:diagnostics11020292. [PMID: 33673229 PMCID: PMC7918339 DOI: 10.3390/diagnostics11020292] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/03/2021] [Accepted: 02/10/2021] [Indexed: 02/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.
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Affiliation(s)
- Hitoshi Maruyama
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
- Correspondence: ; Tel.: +81-3-38133111; Fax: +81-3-56845960
| | - Tadashi Yamaguchi
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoicho, Inage, Chiba 263-8522, Japan;
| | - Hiroaki Nagamatsu
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
| | - Shuichiro Shiina
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
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Wei L, Owen D, Rosen B, Guo X, Cuneo K, Lawrence TS, Ten Haken R, El Naqa I. A deep survival interpretable radiomics model of hepatocellular carcinoma patients. Phys Med 2021; 82:295-305. [PMID: 33714190 PMCID: PMC8035300 DOI: 10.1016/j.ejmp.2021.02.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 02/13/2021] [Accepted: 02/19/2021] [Indexed: 02/07/2023] Open
Abstract
This work aims to identify a new radiomics signature using imaging phenotypes and clinical variables for risk prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with stereotactic body radiation therapy (SBRT). 167 patients were retrospectively analyzed with repeated nested cross-validation to mitigate overfitting issues. 56 radiomic features were extracted from pre-treatment contrast-enhanced (CE) CT images. 37 clinical factors were obtained from patients' electronic records. Variational autoencoders (VAE) based survival models were designed for radiomics and clinical features and a convolutional neural network (CNN) survival model was used for the CECT. Finally, radiomics, clinical and raw image deep learning network (DNN) models were combined to predict the risk probability for OS. The final models yielded c-indices of 0.579 (95%CI: 0.544-0.621), 0.629 (95%CI: 0.601-0.643), 0.581 (95%CI: 0.553-0.613) and 0.650 (95%CI: 0.635-0.683) for radiomics, clinical, image input and combined models on nested cross validation scheme, respectively. Integrated gradients method was used to interpret the trained models. Our interpretability analysis of the DNN showed that the top ranked features were clinical liver function and liver exclusive of tumor radiomics features, which suggests a prominent role of side effects and toxicities in liver outside the tumor region in determining the survival rate of these patients. In summary, novel deep radiomic analysis provides improved performance for risk assessment of HCC prognosis compared with Cox survival models and may facilitate stratification of HCC patients and personalization of their treatment strategies. Liver function was found to contribute most to the OS for these HCC patients and radiomics can aid in their management.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Xinzhou Guo
- Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
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Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology). Curr Opin Organ Transplant 2021; 25:426-434. [PMID: 32487887 DOI: 10.1097/mot.0000000000000773] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW To highlight recent efforts in the development and implementation of machine learning in transplant oncology - a field that uses liver transplantation for the treatment of hepatobiliary malignancies - and particularly in hepatocellular carcinoma, the most commonly treated diagnosis in transplant oncology. RECENT FINDINGS The development of machine learning has occurred within three domains related to hepatocellular carcinoma: identification of key clinicopathological variables, genomics, and image processing. SUMMARY Machine-learning classifiers can be effectively applied for more accurate clinical prediction and handling of data, such as genetics and imaging in transplant oncology. This has allowed for the identification of factors that most significantly influence recurrence and survival in disease, such as hepatocellular carcinoma, and thus help in prognosticating patients who may benefit from a liver transplant. Although progress has been made in using these methods to analyse clinicopathological information, genomic profiles, and image processed data (both histopathological and radiomic), future progress relies on integrating data across these domains.
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A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation. A Pilot Study on a Novel Biomarker. Transplantation 2021; 105:2435-2444. [PMID: 33982917 DOI: 10.1097/tp.0000000000003605] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Despite Trans-Arterial Chemo Embolization (TACE) for hepatocellular carcinoma (HCC), a significant number of patients will develop progression on the liver transplant (LT) waiting list or disease recurrence post-LT. We sought to evaluate the feasibility of a pre-TACE radiomic model, an imaging-based tool to predict these adverse outcomes. METHODS We analyzed the pre-TACE computed tomography images of patients waiting for a LT. The primary endpoint was a combined event that included waitlist dropout for tumor progression or tumor recurrence post-LT. The radiomic features were extracted from the largest HCC volume from the arterial and portal venous phase. A third set of features was created, combining the features from these 2 contrast phases. We applied a LASSO feature selection method and a support vector machine classifier. Three prognostic models were built using each feature set. The models' performance was compared using 5-fold cross-validated Area Under the Receiver Operating Characteristic curves (AUC). RESULTS 88 patients were included, of whom 33 experienced the combined event (37.5%). The median time to dropout was 5.6 months (IQR:3.6-9.3), and the median time for post-LT recurrence was 19.2 months (IQR:6.1-34.0). Twenty-four patients (27.3%) dropped out, and 64 (72.7%) patients were transplanted. Of these, 14 (21.9%) had recurrence post-LT. Model performance yielded a mean AUC of 0.70(±0.07), 0.87(±0.06) and 0.81(±0.06) for the arterial, venous and the combined models, respectively. CONCLUSION A pre-TACE radiomics model for HCC patients undergoing LT may be a useful tool for outcome prediction. Further external model validation with a larger sample size is required.
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Cusumano D, Boldrini L, Yadav P, Casà C, Lee SL, Romano A, Piras A, Chiloiro G, Placidi L, Catucci F, Votta C, Mattiucci GC, Indovina L, Gambacorta MA, Bassetti M, Valentini V. Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy. Diagnostics (Basel) 2021; 11:diagnostics11010072. [PMID: 33466307 PMCID: PMC7824764 DOI: 10.3390/diagnostics11010072] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/31/2020] [Accepted: 12/31/2020] [Indexed: 02/07/2023] Open
Abstract
The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann-Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a p-value of 0.005 and an AUC of 0.78 (0.61-0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience.
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Affiliation(s)
- Davide Cusumano
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Luca Boldrini
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Poonam Yadav
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA; (P.Y.); (M.B.)
| | - Calogero Casà
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
- Correspondence: ; Tel.: +39-06-3015-5226
| | - Sangjune Laurence Lee
- Department of Oncology, University of Calgary, 1331 29 Street NW, Calgary, AB T2N 1N4, Canada;
| | - Angela Romano
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Antonio Piras
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Francesco Catucci
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Claudio Votta
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Gian Carlo Mattiucci
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Luca Indovina
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Maria Antonietta Gambacorta
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
| | - Michael Bassetti
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA; (P.Y.); (M.B.)
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, 00168 Rome, Italy; (D.C.); (L.B.); (A.R.); (A.P.); (G.C.); (L.P.); (F.C.); (C.V.); (G.C.M.); (L.I.); (M.A.G.); (V.V.)
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Abstract
The diagnosis of hepatocellular carcinoma relies largely on non-invasive imaging, and is well suited for radiomics analysis. Radiomics is an emerging method for quantification of tumor heterogeneity by mathematically analyzing the spatial distribution and relationships of gray levels in medical images. The published studies on radiomics analysis of HCC provide encouraging data demonstrating potential utility for prediction of tumor biology, molecular profiles, post-therapy response, and outcome. The combination of radiomics data and clinical/laboratory information provides added value in many studies. Radiomics is a multi-step process that requires optimization and standardization, the development of semi-automated or automated segmentation methods, robust data quality control, and refinement of algorithms and modeling approaches for high-throughput data analysis. While radiomics remains largely in the research setting, the strong associations of predictive models and nomograms with certain pathologic, molecular, and immune markers with tumor aggressiveness and patient outcomes, provide great potential for clinical applications to inform optimized treatment strategies and patient prognosis.
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Dreher C, Linde P, Boda-Heggemann J, Baessler B. Radiomics for liver tumours. Strahlenther Onkol 2020; 196:888-899. [PMID: 32296901 PMCID: PMC7498486 DOI: 10.1007/s00066-020-01615-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 03/20/2020] [Indexed: 12/15/2022]
Abstract
Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.
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Affiliation(s)
- Constantin Dreher
- Department of Radiation Oncology, University Hospital Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1–3, 68167 Mannheim, Germany
| | - Philipp Linde
- Department of Radiation Oncology, Medical Faculty and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Judit Boda-Heggemann
- Department of Radiation Oncology, University Hospital Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1–3, 68167 Mannheim, Germany
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
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