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Laurent-Bellue A, Sadraoui A, Claude L, Calderaro J, Posseme K, Vibert E, Cherqui D, Rosmorduc O, Lewin M, Pesquet JC, Guettier C. Deep Learning Classification and Quantification of Pejorative and Nonpejorative Architectures in Resected Hepatocellular Carcinoma from Digital Histopathologic Images. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:1684-1700. [PMID: 38879083 DOI: 10.1016/j.ajpath.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/17/2024] [Accepted: 05/16/2024] [Indexed: 06/27/2024]
Abstract
Liver resection is one of the best treatments for small hepatocellular carcinoma (HCC), but post-resection recurrence is frequent. Biotherapies have emerged as an efficient adjuvant treatment, making the identification of patients at high risk of recurrence critical. Microvascular invasion (mVI), poor differentiation, pejorative macrotrabecular architectures, and vessels encapsulating tumor clusters architectures are the most accurate histologic predictors of recurrence, but their evaluation is time-consuming and imperfect. Herein, a supervised deep learning-based approach with ResNet34 on 680 whole slide images (WSIs) from 107 liver resection specimens was used to build an algorithm for the identification and quantification of these pejorative architectures. This model achieved an accuracy of 0.864 at patch level and 0.823 at WSI level. To assess its robustness, it was validated on an external cohort of 29 HCCs from another hospital, with an accuracy of 0.787 at WSI level, affirming its generalization capabilities. Moreover, the largest connected areas of the pejorative architectures extracted from the model were positively correlated to the presence of mVI and the number of tumor emboli. These results suggest that the identification of pejorative architectures could be an efficient surrogate of mVI and have a strong predictive value for the risk of recurrence. This study is the first step in the construction of a composite predictive algorithm for early post-resection recurrence of HCC, including artificial intelligence-based features.
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Affiliation(s)
- Astrid Laurent-Bellue
- Department of Pathology, Bicêtre Hospital, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France
| | - Aymen Sadraoui
- Centre de Vision Numérique, Paris-Saclay University, Inria, CentraleSupélec, Gif-sur-Yvette, France
| | - Laura Claude
- Department of Pathology, Charles Nicolle Hospital, Rouen, France
| | - Julien Calderaro
- Department of Pathology, Henri-Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France
| | - Katia Posseme
- Department of Pathology, Bicêtre Hospital, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France
| | - Eric Vibert
- Centre Hépato-Biliaire, Paul-Brousse Hospital, Assistance Publique-Hôpitaux de Paris, Villejuif, France; Faculté de Médecine, Paris-Saclay University, Le Kremlin-Bicêtre, France; Unité Mixte de Recherche 1193, Paris-Saclay University, INSERM, Villejuif, France
| | - Daniel Cherqui
- Centre Hépato-Biliaire, Paul-Brousse Hospital, Assistance Publique-Hôpitaux de Paris, Villejuif, France; Faculté de Médecine, Paris-Saclay University, Le Kremlin-Bicêtre, France; Unité Mixte de Recherche 1193, Paris-Saclay University, INSERM, Villejuif, France
| | - Olivier Rosmorduc
- Centre Hépato-Biliaire, Paul-Brousse Hospital, Assistance Publique-Hôpitaux de Paris, Villejuif, France; Faculté de Médecine, Paris-Saclay University, Le Kremlin-Bicêtre, France; Unité Mixte de Recherche 1193, Paris-Saclay University, INSERM, Villejuif, France
| | - Maïté Lewin
- Centre Hépato-Biliaire, Paul-Brousse Hospital, Assistance Publique-Hôpitaux de Paris, Villejuif, France; Faculté de Médecine, Paris-Saclay University, Le Kremlin-Bicêtre, France; Unité Mixte de Recherche 1193, Paris-Saclay University, INSERM, Villejuif, France
| | - Jean-Christophe Pesquet
- Centre de Vision Numérique, Paris-Saclay University, Inria, CentraleSupélec, Gif-sur-Yvette, France
| | - Catherine Guettier
- Department of Pathology, Bicêtre Hospital, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France.
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Parvaiz A, Nasir ES, Fraz MM. From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1728-1751. [PMID: 38429563 PMCID: PMC11300721 DOI: 10.1007/s10278-024-01049-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 03/03/2024]
Abstract
Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.
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Affiliation(s)
- Arshi Parvaiz
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Esha Sadia Nasir
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
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Zhuo X, Deng H, Qiu M, Qiu X. Pathomic model based on histopathological features and machine learning to predict IDO1 status and its association with breast cancer prognosis. Breast Cancer Res Treat 2024; 207:151-165. [PMID: 38780888 PMCID: PMC11230954 DOI: 10.1007/s10549-024-07350-6] [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: 02/07/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To establish a pathomic model using histopathological image features for predicting indoleamine 2,3-dioxygenase 1 (IDO1) status and its relationship with overall survival (OS) in breast cancer. METHODS A pathomic model was constructed using machine learning and histopathological images obtained from The Cancer Genome Atlas database to predict IDO1 expression. The model performance was evaluated based on the area under the curve, calibration curve, and decision curve analysis (DCA). Prediction scores (PSes) were generated from the model and applied to divide the patients into two groups. Survival outcomes, gene set enrichment, immune microenvironment, and tumor mutations were assessed between the two groups. RESULTS Survival analysis followed by multivariate correction revealed that high IDO1 is a protective factor for OS. Further, the model was calibrated, and it exhibited good discrimination. Additionally, the DCA showed that the proposed model provided a good clinical net benefit. The Kaplan-Meier analysis revealed a positive correlation between high PS and improved OS. Univariate and multivariate Cox regression analyses demonstrated that PS is an independent protective factor for OS. Moreover, differentially expressed genes were enriched in various essential biological processes, including extracellular matrix receptor interaction, angiogenesis, transforming growth factor β signaling, epithelial mesenchymal transition, cell junction, tryptophan metabolism, and heme metabolic processes. PS was positively correlated with M1 macrophages, CD8 + T cells, T follicular helper cells, and tumor mutational burden. CONCLUSION These results indicate the potential ability of the proposed pathomic model to predict IDO1 status and the OS of breast cancer patients.
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Affiliation(s)
- Xiaohua Zhuo
- Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China
| | - Hailong Deng
- Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China
| | - Mingzhu Qiu
- Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China
| | - Xiaoming Qiu
- Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China.
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Ding T, Li X, Mo J, Alexander G, Li J. Recurrence risk stratification of hepatocellular carcinomas based on immune gene expression and features extracted from pathological images. PLoS Comput Biol 2023; 19:e1011716. [PMID: 38157378 PMCID: PMC10783785 DOI: 10.1371/journal.pcbi.1011716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 01/11/2024] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Immune-based therapy is a promising type of treatment for hepatocellular carcinoma (HCC) but has only been partially successful due to the high heterogeneity in HCC tumor. The differences in the degree of tumor cell progression and in the activity of tumor immune microenvironment could lead to varied clinical outcome. Accurate subgrouping for recurrence risk is an approach to address the issue of such heterogeneity. It remains under investigation as whether integrating quantitative whole slide image (WSI) features with the expression profile of immune marker genes can improve the risk stratification, and whether clinical outcome prediction can assist in understanding molecular biology that drives the outcome. METHODS We included a total of 231 patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project. For each patient, we extracted 18 statistical metrics corresponding to a global region of interest and 135 features regarding nucleus shape from WSI. A risk score was developed using these image features with high-dimensional survival modeling. We also introduced into the model the expression profile of 66 representative marker genes relevant to currently available immunotherapies. We stratified all patients into higher and lower-risk subgroup based on the final risk score selected from multiple models generated, and further investigated underlying molecular mechanisms associated with the risk stratification. RESULTS One WSI feature and three immune marker genes were selected into the final recurrence-free survival (RFS) prediction model following the best integrated modeling framework. The resultant score showed a significantly improved prediction performance on the test dataset (mean time-dependent AUCs = 0.707) as compared to those of other types (e.g: mean time-dependent AUCs of AJCC tumor stage = 0.525) of input data integration. To assess that the risk score could provide a higher-resolution risk stratification, a lower-risk subgroup (or a higher-risk subgroup) was arbitrarily assigned according to score falling below (or above) the median score. The lower risk subgroup had significantly longer median RFS time than that of the higher-risk patients (median RFS = 903 vs. 265 days, log-rank test p-value< 0.0001). Additionally, the higher-risk subgroup, in contrast to the lower-risk patients were characterized with a significant downregulation of immune checkpoint genes, suppressive signal in tumor immune response pathways, and depletion of CD8 T cells. These observations for the higher-risk subgroup suggest that new targets for adoptive or checkpoint-based combined systemic therapies may be useful. CONCLUSION We developed a novel prognostic model to predict RFS for HCC patients, using one feature that can be automatically extracted from routine histopathological images, as well as the expression profiles of three immune marker genes. The methodology used in this paper demonstrates the feasibility of developing prognostic models that provide both useful risk stratification along with valuable biological insights into the underlying characteristics of the subgroups identified.
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Affiliation(s)
- Tao Ding
- Department of Statistical Science, University College London, London, United Kingdom
| | - Xiao Li
- Product Development Personalized Healthcare, Genentech, San Francisco, California, United States of America
| | - Jiu Mo
- Department of Computer Science, Central South University of Forestry and Technology, Changsha, Hunan, People’s Republic of China
| | - Gregory Alexander
- Mathematical Statistician Consultant, San Francisco, California, United States of America
| | - Jialu Li
- Mathematical Statistician Consultant, San Francisco, California, United States of America
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Feng S, Wang J, Wang L, Qiu Q, Chen D, Su H, Li X, Xiao Y, Lin C. Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma. J Clin Transl Hepatol 2023; 11:1184-1191. [PMID: 37577233 PMCID: PMC10412715 DOI: 10.14218/jcth.2022.00077s] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/11/2022] [Accepted: 02/21/2023] [Indexed: 07/03/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.
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Affiliation(s)
- Sijia Feng
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Jianhua Wang
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Liheng Wang
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Qixuan Qiu
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Dongdong Chen
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Huo Su
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Xiaoli Li
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Yao Xiao
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Chiayen Lin
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
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Allaume P, Rabilloud N, Turlin B, Bardou-Jacquet E, Loréal O, Calderaro J, Khene ZE, Acosta O, De Crevoisier R, Rioux-Leclercq N, Pecot T, Kammerer-Jacquet SF. Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13101799. [PMID: 37238283 DOI: 10.3390/diagnostics13101799] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/04/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.
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Affiliation(s)
- Pierre Allaume
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Noémie Rabilloud
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Bruno Turlin
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Edouard Bardou-Jacquet
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Department of Liver Diseases CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Olivier Loréal
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Department of Pathology Henri Mondor, 94000 Créteil, France
- INSERM U955, Team Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers, 94000 Créteil, France
| | - Zine-Eddine Khene
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
- Department of Urology, CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Oscar Acosta
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Renaud De Crevoisier
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
- Department of Radiotherapy, Centre Eugène Marquis, 35033 Rennes, France
| | - Nathalie Rioux-Leclercq
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Thierry Pecot
- Biosit Platform UAR 3480 CNRS US18 INSERM U955, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Solène-Florence Kammerer-Jacquet
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
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Shen X, Wu J, Su J, Yao Z, Huang W, Zhang L, Jiang Y, Yu W, Li Z. Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework. Front Genet 2023; 14:1004481. [PMID: 37007970 PMCID: PMC10064216 DOI: 10.3389/fgene.2023.1004481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/14/2023] [Indexed: 03/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.
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Affiliation(s)
- Xiaomin Shen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jinxin Wu
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Junwei Su
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Zhenyu Yao
- School of Computer Science, King's College London, London, United Kingdom
| | - Wei Huang
- Department of Gastroenterology II, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Li Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yiheng Jiang
- Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Wei Yu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Zhao Li
- School of Computer Science, Zhejiang University, Hangzhou, China
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Stulpinas R, Zilenaite-Petrulaitiene D, Rasmusson A, Gulla A, Grigonyte A, Strupas K, Laurinavicius A. Prognostic Value of CD8+ Lymphocytes in Hepatocellular Carcinoma and Perineoplastic Parenchyma Assessed by Interface Density Profiles in Liver Resection Samples. Cancers (Basel) 2023; 15:cancers15020366. [PMID: 36672317 PMCID: PMC9857181 DOI: 10.3390/cancers15020366] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/28/2022] [Accepted: 01/04/2023] [Indexed: 01/08/2023] Open
Abstract
Hepatocellular carcinoma (HCC) often emerges in the setting of long-standing inflammatory liver disease. CD8 lymphocytes are involved in both the antitumoral response and hepatocyte damage in the remaining parenchyma. We investigated the dual role of CD8 lymphocytes by assessing density profiles at the interfaces of both HCC and perineoplastic liver parenchyma with surrounding stroma in whole-slide immunohistochemistry images of surgical resection samples. We applied a hexagonal grid-based digital image analysis method to sample the interface zones and compute the CD8 density profiles within them. The prognostic value of the indicators was explored in the context of clinicopathological, peripheral blood testing, and surgery data. Independent predictors of worse OS were a low standard deviation of CD8+ density along the tumor edge, high mean CD8+ density within the epithelial aspect of the perineoplastic liver-stroma interface, longer duration of surgery, a higher level of aspartate transaminase (AST), and a higher basophil count in the peripheral blood. A combined score, derived from these five independent predictors, enabled risk stratification of the patients into three prognostic categories with a 5-year OS probability of 76%, 40%, and 8%. Independent predictors of longer RFS were stage pT1, shorter duration of surgery, larger tumor size, wider tumor-free margin, and higher mean CD8+ density in the epithelial aspect of the tumor-stroma interface. We conclude that (1) our computational models reveal independent and opposite prognostic impacts of CD8+ cell densities at the interfaces of the malignant and non-malignant epithelium interfaces with the surrounding stroma; and (2) together with pathology, surgery, and laboratory data, comprehensive prognostic models can be constructed to predict patient outcomes after liver resection due to HCC.
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Affiliation(s)
- Rokas Stulpinas
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology, Forensic Medicine and Pharmacology, Vilnius University, 03101 Vilnius, Lithuania
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, 08406 Vilnius, Lithuania
- Correspondence:
| | - Dovile Zilenaite-Petrulaitiene
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology, Forensic Medicine and Pharmacology, Vilnius University, 03101 Vilnius, Lithuania
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, 08406 Vilnius, Lithuania
| | - Allan Rasmusson
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology, Forensic Medicine and Pharmacology, Vilnius University, 03101 Vilnius, Lithuania
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, 08406 Vilnius, Lithuania
| | - Aiste Gulla
- Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, 03101 Vilnius, Lithuania
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Agne Grigonyte
- Faculty of Medicine, Vilnius University, 03101 Vilnius, Lithuania
| | - Kestutis Strupas
- Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, 03101 Vilnius, Lithuania
| | - Arvydas Laurinavicius
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology, Forensic Medicine and Pharmacology, Vilnius University, 03101 Vilnius, Lithuania
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, 08406 Vilnius, Lithuania
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Mokhria RK, Singh J. Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma. Artif Intell Gastroenterol 2022; 3:96-104. [DOI: 10.35712/aig.v3.i4.96] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/30/2022] [Accepted: 09/14/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.
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Affiliation(s)
- Rajesh Kumar Mokhria
- Government Model Sanskriti Senior Secondary School, Chulkana, 132101, Panipat, Haryana, India
| | - Jasbir Singh
- Department of Biochemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India
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Macias RIR, Cardinale V, Kendall TJ, Avila MA, Guido M, Coulouarn C, Braconi C, Frampton AE, Bridgewater J, Overi D, Pereira SP, Rengo M, Kather JN, Lamarca A, Pedica F, Forner A, Valle JW, Gaudio E, Alvaro D, Banales JM, Carpino G. Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions. Gut 2022; 71:1669-1683. [PMID: 35580963 DOI: 10.1136/gutjnl-2022-327099] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/22/2022] [Indexed: 02/06/2023]
Abstract
Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers.
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Affiliation(s)
- Rocio I R Macias
- Experimental Hepatology and Drug Targeting (HEVEPHARM) group, University of Salamanca, IBSAL, Salamanca, Spain.,Center for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain
| | - Vincenzo Cardinale
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy
| | - Timothy J Kendall
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
| | - Matias A Avila
- Center for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain.,Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Maria Guido
- Department of Medicine - DIMED, University of Padua, Padua, Italy
| | - Cedric Coulouarn
- UMR_S 1242, COSS, Centre de Lutte contre le Cancer Eugène Marquis, INSERM University of Rennes 1, Rennes, France
| | - Chiara Braconi
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Adam E Frampton
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, Surrey, UK
| | - John Bridgewater
- Department of Medical Oncology, UCL Cancer Institute, London, UK
| | - Diletta Overi
- Department of Anatomical, Histological, Forensic Medicine and Orthopaedic Sciences, Sapienza University of Rome, Rome, Italy
| | - Stephen P Pereira
- Institute for Liver & Digestive Health, University College London, London, UK
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Jakob N Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Angela Lamarca
- Medical Oncology/Institute of Cancer Sciences, The Christie NHS Foundation Trust/University of Manchester, Manchester, UK
| | - Federica Pedica
- Department of Pathology, San Raffaele Scientific Institute, Milan, Italy
| | - Alejandro Forner
- Center for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain.,BCLC group, Liver Unit, Hospital Clínic Barcelona. IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Juan W Valle
- Medical Oncology/Institute of Cancer Sciences, The Christie NHS Foundation Trust/University of Manchester, Manchester, UK
| | - Eugenio Gaudio
- Department of Anatomical, Histological, Forensic Medicine and Orthopaedic Sciences, Sapienza University of Rome, Rome, Italy
| | - Domenico Alvaro
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Jesus M Banales
- Center for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain.,Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute, Donostia University Hospital, University of the Basque Country (UPV/EHU), Ikerbasque, San Sebastian, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Guido Carpino
- Department of Movement, Human and Health Sciences, University of Rome 'Foro Italico', Rome, Italy
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11
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Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism. BMC Med Inform Decis Mak 2022; 22:176. [PMID: 35787805 PMCID: PMC9254605 DOI: 10.1186/s12911-022-01919-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 06/23/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. METHODS Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. RESULTS Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. CONCLUSIONS This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.
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12
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Chen WM, Fu M, Zhang CJ, Xing QQ, Zhou F, Lin MJ, Dong X, Huang J, Lin S, Hong MZ, Zheng QZ, Pan JS. Deep Learning-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond. Front Med (Lausanne) 2022; 9:853261. [PMID: 35530044 PMCID: PMC9072864 DOI: 10.3389/fmed.2022.853261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/30/2022] [Indexed: 11/23/2022] Open
Abstract
Background and Aims We aim to develop a diagnostic tool for pathological-image classification using transfer learning that can be applied to diverse tumor types. Methods Microscopic images of liver tissue with and without hepatocellular carcinoma (HCC) were used to train and validate the classification framework based on a convolutional neural network. To evaluate the universal classification performance of the artificial intelligence (AI) framework, histological images from colorectal tissue and the breast were collected. Images for the training and validation sets were obtained from the Xiamen Hospital of Traditional Chinese Medicine, and those for the test set were collected from Zhongshan Hospital Xiamen University. The accuracy, sensitivity, and specificity values for the proposed framework were reported and compared with those of human image interpretation. Results In the human–machine comparisons, the sensitivity, and specificity for the AI algorithm were 98.0, and 99.0%, whereas for the human experts, the sensitivity ranged between 86.0 and 97.0%, while the specificity ranged between 91.0 and 100%. Based on transfer learning, the accuracies of the AI framework in classifying colorectal carcinoma and breast invasive ductal carcinoma were 96.8 and 96.0%, respectively. Conclusion The performance of the proposed AI framework in classifying histological images with HCC was comparable to the classification performance achieved by human experts, indicating that extending the proposed AI’s application to diagnoses and treatment recommendations is a promising area for future investigation.
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Affiliation(s)
- Wei-Ming Chen
- Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- School of Medicine, Xiamen University, Xiamen, China
| | - Min Fu
- School of Aerospace Engineering, Xiamen University, Xiamen, China
| | - Cheng-Ju Zhang
- Department of Anesthesiology, Zhongshan Hospital Xiamen University, Xiamen, China
| | - Qing-Qing Xing
- Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Fei Zhou
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China
| | - Meng-Jie Lin
- Department of Pathology, Zhongshan Hospital Xiamen University, Xiamen, China
| | - Xuan Dong
- Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- School of Medicine, Xiamen University, Xiamen, China
| | - Jiaofeng Huang
- Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Su Lin
- Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Mei-Zhu Hong
- Department of Traditional Chinese Medicine, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Qi-Zhong Zheng
- Department of Pathology, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, China
- *Correspondence: Qi-Zhong Zheng,
| | - Jin-Shui Pan
- Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- Jin-Shui Pan,
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13
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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14
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Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2022; 4:100443. [PMID: 35243281 PMCID: PMC8867112 DOI: 10.1016/j.jhepr.2022.100443] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/26/2021] [Accepted: 01/11/2022] [Indexed: 12/18/2022]
Abstract
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
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15
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Feng S, Yu X, Liang W, Li X, Zhong W, Hu W, Zhang H, Feng Z, Song M, Zhang J, Zhang X. Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma. Front Oncol 2021; 11:762733. [PMID: 34926264 PMCID: PMC8671137 DOI: 10.3389/fonc.2021.762733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 11/08/2021] [Indexed: 11/20/2022] Open
Abstract
Background An accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on the experience of a pathologist. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification. Methods We collected a whole-slide image of hematoxylin and eosin-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noise-specific deep learning model. The model was trained initially with 137 cases cropped into multiple-scaled datasets. Patch screening and dynamic label smoothing strategies are adopted to handle the histopathological liver image with noise annotation from the perspective of input and output. The model was then tested in an independent cohort of 455 cases with comparable tumor types and differentiations. Results Exhaustive experiments demonstrated that our two-step method achieved 87.81% pixel-level accuracy and 98.77% slide-level accuracy in the test dataset. Furthermore, the generalization performance of our model was also verified using The Cancer Genome Atlas dataset, which contains 157 HCC pathological slides, and achieved an accuracy of 87.90%. Conclusions The noise-specific histopathological classification model of HCC based on deep learning is effective for the dataset with noisy annotation, and it significantly improved the pixel-level accuracy of the regular convolutional neural network (CNN) model. Moreover, the model also has an advantage in detecting well-differentiated HCC and microvascular invasion.
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Affiliation(s)
- Shi Feng
- Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaotian Yu
- Department of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Wenjie Liang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Xuejie Li
- Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Weixiang Zhong
- Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Wanwan Hu
- Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Han Zhang
- Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Zunlei Feng
- Department of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Mingli Song
- Department of Computer Science and Technology, Zhejiang University, Hangzhou, China
- *Correspondence: Jing Zhang, ; Mingli Song, ; Xiuming Zhang,
| | - Jing Zhang
- Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Jing Zhang, ; Mingli Song, ; Xiuming Zhang,
| | - Xiuming Zhang
- Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Jing Zhang, ; Mingli Song, ; Xiuming Zhang,
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16
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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: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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17
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Karadag Soylu N. Update on Hepatocellular Carcinoma: a Brief Review from Pathologist Standpoint. J Gastrointest Cancer 2021; 51:1176-1186. [PMID: 32844348 DOI: 10.1007/s12029-020-00499-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Hepatocellular carcinoma is one of the most common cancers and an important health problem all over the world. Its prognosis is poor. For better patient care, early diagnosis is essential. Although new imaging techniques have a big impact on hepatocellular carcinoma diagnosis, histopathological examination is still the gold standard for precise diagnosis. Histopathological evaluation gives exact diagnosis in the meaning of tumor size, histological subtypes, grading, and differential diagnosis from metastasis and other tumors. Immunohistochemistry as a part of diagnostic histopathological technique plays an important role in routine practice. Immunohistochemistry is useful for confirming of hepatocytic origin, supporting hepatocellular malignancy, and differential diagnosis. It also gives prognostic information. There are growing attempts to classify tumors by their molecular genetic signatures. This is also actual for hepatocellular carcinoma. This mini review focuses on the histopathology of hepatocellular carcinoma including subtypes; differential diagnosis and immunohistochemistry as an ancillary diagnostic tool, updated or added entities, i.e., combined hepatocellular-cholangiocarcinoma; small hepatocellular carcinoma; correlation with molecular studies; and future perspectives.
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Affiliation(s)
- Nese Karadag Soylu
- Department of Pathology, Faculty of Medicine, Inonu University, Elazig Yolu 10. Km, 44280, Malatya, Turkey.
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18
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Shi JY, Wang X, Ding GY, Dong Z, Han J, Guan Z, Ma LJ, Zheng Y, Zhang L, Yu GZ, Wang XY, Ding ZB, Ke AW, Yang H, Wang L, Ai L, Cao Y, Zhou J, Fan J, Liu X, Gao Q. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning. Gut 2021; 70:951-961. [PMID: 32998878 DOI: 10.1136/gutjnl-2020-320930] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 07/02/2020] [Accepted: 07/20/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Tumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging. DESIGN An interpretable, weakly supervised deep learning framework incorporating prior knowledge was proposed to analyse hepatocellular carcinoma (HCC) and explore new prognostic phenotypes on pathological whole-slide images (WSIs) from the Zhongshan cohort of 1125 HCC patients (2451 WSIs) and TCGA cohort of 320 HCC patients (320 WSIs). A 'tumour risk score (TRS)' was established to evaluate patient outcomes, and then risk activation mapping (RAM) was applied to visualise the pathological phenotypes of TRS. The multi-omics data of The Cancer Genome Atlas(TCGA) HCC were used to assess the potential pathogenesis underlying TRS. RESULTS Survival analysis revealed that TRS was an independent prognosticator in both the Zhongshan cohort (p<0.0001) and TCGA cohort (p=0.0003). The predictive ability of TRS was superior to and independent of clinical staging systems, and TRS could evenly stratify patients into up to five groups with significantly different prognoses. Notably, sinusoidal capillarisation, prominent nucleoli and karyotheca, the nucleus/cytoplasm ratio and infiltrating inflammatory cells were identified as the main underlying features of TRS. The multi-omics data of TCGA HCC hint at the relevance of TRS to tumour immune infiltration and genetic alterations such as the FAT3 and RYR2 mutations. CONCLUSION Our deep learning framework is an effective and labour-saving method for decoding pathological images, providing a valuable means for HCC risk stratification and precise patient treatment.
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Affiliation(s)
- Jie-Yi Shi
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China
| | - Xiaodong Wang
- School of Computer Science and Technology, Xidian University, Xi'an, P. R. China
| | - Guang-Yu Ding
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China
| | - Zhou Dong
- School of Computer Science, Northwestern Polytechnical University, Xi'an, P. R. China
| | - Jing Han
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, P. R. China
| | - Zehui Guan
- School of Computer Science, Northwestern Polytechnical University, Xi'an, P. R. China
| | - Li-Jie Ma
- Department of General Surgery, Zhongshan Hospital (South), Public Health Clinical Centre, Fudan University, Shanghai, P. R. China
| | - Yuxuan Zheng
- School of Computer Science and Technology, Xidian University, Xi'an, P. R. China
| | - Lei Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, P. R. China
| | - Guan-Zhen Yu
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, P. R. China
| | - Xiao-Ying Wang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China
| | - Zhen-Bin Ding
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China
| | - Ai-Wu Ke
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China
| | - Haoqing Yang
- School of Computer Science and Technology, Xidian University, Xi'an, P. R. China
| | - Liming Wang
- School of Computer Science and Technology, Xidian University, Xi'an, P. R. China
| | - Lirong Ai
- School of Computer Science, Northwestern Polytechnical University, Xi'an, P. R. China
| | - Ya Cao
- Cancer Research Institute, Xiangya School of Medicine, Central South University, Hunan, P. R. China
| | - Jian Zhou
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China
- Institute of Biomedical Sciences, Fudan University, Shanghai, P. R. China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China
- Institute of Biomedical Sciences, Fudan University, Shanghai, P. R. China
| | - Xiyang Liu
- School of Computer Science and Technology, Xidian University, Xi'an, P. R. China
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, P. R. China
- Institute of Biomedical Sciences, Fudan University, Shanghai, P. R. China
- State Key Laboratory of Genetic Engineering at Fudan University, Shanghai, P. R. China
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Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2:42-55. [DOI: 10.35712/aig.v2.i2.42] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/25/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most commonly diagnosed type of liver cancer and the fourth leading cause of cancer-related mortality worldwide. The early identification of HCC and effective treatments for it have been challenging. Due to the sufficient compensatory ability of early patients and its nonspecific symptoms, HCC is more likely to escape diagnosis in the incipient stage, during which patients can achieve a more satisfying overall survival if they undergo resection or liver transplantation. Patients at advanced stages can profit from radical therapies in a limited way. In order to improve the unfavorable prognosis of HCC, diagnostic ability and treatment efficiency must be improved. The past decade has seen rapid advancements in artificial intelligence, underlying its unique usefulness in almost every field, including that of medicine. Herein, we sought and reviewed studies that put emphasis on artificial intelligence and HCC.
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Affiliation(s)
- Peng-Sheng Yi
- Department of Hepato-Biliary-Pancreas II, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chen-Jun Hu
- Department of Hepato-Biliary-Pancreas II, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chen-Hui Li
- Department of Obstetrics and Gynecology, Nanchong Traditional Chinese Medicine Hospital, Nanchong 637000, Sichuan Province, China
| | - Fei Yu
- Department of Radiology, Yingshan County People’s Hospital, Nanchong 610041, Sichuan Province, China
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20
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Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021; 12:31. [PMID: 33675433 PMCID: PMC7936998 DOI: 10.1186/s13244-021-00977-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/15/2021] [Indexed: 12/24/2022] Open
Abstract
With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.
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Affiliation(s)
- Zhi-Min Zou
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China
| | - De-Hua Chang
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Hui Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China
| | - Yu-Dong Xiao
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China.
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21
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Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci Rep 2021; 11:2047. [PMID: 33479370 PMCID: PMC7820423 DOI: 10.1038/s41598-021-81506-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 01/05/2021] [Indexed: 12/22/2022] Open
Abstract
Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system. The model’s risk score stratified patients into low- and high-risk subgroups with statistically significant differences in their survival distributions, and was an independent risk factor for post-surgical recurrence in both test cohorts. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods and help refine the clinical management of patients undergoing primary surgical resection for HCC.
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22
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Wang H, Jiang Y, Li B, Cui Y, Li D, Li R. Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes. Cancers (Basel) 2020; 12:E3562. [PMID: 33260561 PMCID: PMC7761227 DOI: 10.3390/cancers12123562] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/17/2020] [Accepted: 11/26/2020] [Indexed: 12/22/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a heterogeneous disease with diverse characteristics and outcomes. Here, we aim to develop a histological classification for HCC by integrating computational imaging features of the tumor and its microenvironment. We first trained a multitask deep-learning neural network for automated single-cell segmentation and classification on hematoxylin- and eosin-stained tissue sections. After confirming the accuracy in a testing set, we applied the model to whole-slide images of 304 tumors in the Cancer Genome Atlas. Given the single-cell map, we calculated 246 quantitative image features to characterize individual nuclei as well as spatial relations between tumor cells and infiltrating lymphocytes. Unsupervised consensus clustering revealed three reproducible histological subtypes, which exhibit distinct nuclear features as well as spatial distribution and relation between tumor cells and lymphocytes. These histological subtypes were associated with somatic genomic alterations (i.e., aneuploidy) and specific molecular pathways, including cell cycle progression and oxidative phosphorylation. Importantly, these histological subtypes complement established molecular classification and demonstrate independent prognostic value beyond conventional clinicopathologic factors. Our study represents a step forward in quantifying the spatial distribution and complex interaction between tumor and immune microenvironment. The clinical relevance of the imaging subtypes for predicting prognosis and therapy response warrants further validation.
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Affiliation(s)
- Haiyue Wang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Shandong 250358, Jinan, China;
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA 94304, USA; (Y.J.); (B.L.); (Y.C.)
| | - Yuming Jiang
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA 94304, USA; (Y.J.); (B.L.); (Y.C.)
| | - Bailiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA 94304, USA; (Y.J.); (B.L.); (Y.C.)
| | - Yi Cui
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA 94304, USA; (Y.J.); (B.L.); (Y.C.)
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Shandong 250358, Jinan, China;
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA 94304, USA; (Y.J.); (B.L.); (Y.C.)
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23
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Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26:5617-5628. [PMID: 33088156 PMCID: PMC7545389 DOI: 10.3748/wjg.v26.i37.5617] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/01/2020] [Accepted: 09/17/2020] [Indexed: 02/06/2023] Open
Abstract
Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.
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Affiliation(s)
- Miguel Jiménez Pérez
- UGC de Aparato Digestivo, Unidad de Hepatología-Trasplante Hepático, Hospital Regional Universitario de Málaga, Málaga 29010, Spain
| | - Rocío González Grande
- UGC de Aparato Digestivo, Unidad de Hepatología-Trasplante Hepático, Hospital Regional Universitario de Málaga, Málaga 29010, Spain
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24
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Ma B, Guo Y, Hu W, Yuan F, Zhu Z, Yu Y, Zou H. Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach. Front Pharmacol 2020; 11:572372. [PMID: 33132910 PMCID: PMC7562716 DOI: 10.3389/fphar.2020.572372] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 09/08/2020] [Indexed: 12/23/2022] Open
Abstract
Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.
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Affiliation(s)
- Bowei Ma
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Yucheng Guo
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Weian Hu
- Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhenggang Zhu
- Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingyan Yu
- Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Zou
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
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25
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Liao H, Long Y, Han R, Wang W, Xu L, Liao M, Zhang Z, Wu Z, Shang X, Li X, Peng J, Yuan K, Zeng Y. Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma. Clin Transl Med 2020; 10:e102. [PMID: 32536036 PMCID: PMC7403820 DOI: 10.1002/ctm2.102] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 05/27/2020] [Accepted: 05/27/2020] [Indexed: 02/05/2023] Open
Affiliation(s)
- Haotian Liao
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Yuxi Long
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Ruijiang Han
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Wei Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Lin Xu
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Mingheng Liao
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Zhen Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhenru Wu
- Laboratory of Pathology, Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, China
| | - Xuefeng Li
- School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China.,Shenzhen Luohu People's Hospital, The Third Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, China
| | - Kefei Yuan
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Yong Zeng
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
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