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Zhang X, Yu X, Liang W, Zhang Z, Zhang S, Xu L, Zhang H, Feng Z, Song M, Zhang J, Feng S. Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images. Cancer Med 2024; 13:e7104. [PMID: 38488408 PMCID: PMC10941532 DOI: 10.1002/cam4.7104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/13/2023] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis. MATERIALS AND METHODS We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI. CONCLUSIONS We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.
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Affiliation(s)
- Xiuming Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Xiaotian Yu
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Wenjie Liang
- Department of Radiology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Zhongliang Zhang
- School of ManagementHangzhou Dianzi UniversityHangzhouP. R. China
| | - Shengxuming Zhang
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Linjie Xu
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Han Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Zunlei Feng
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Mingli Song
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Jing Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Shi Feng
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
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Cao L, Wang Q, Hong J, Han Y, Zhang W, Zhong X, Che Y, Ma Y, Du K, Wu D, Pang T, Wu J, Liang K. MVI-TR: A Transformer-Based Deep Learning Model with Contrast-Enhanced CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:cancers15051538. [PMID: 36900327 PMCID: PMC10001339 DOI: 10.3390/cancers15051538] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 03/05/2023] Open
Abstract
In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients with early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types of DL models based only on venous phase (VP) of contrast-enhanced computed tomography (CECT) were constructed and validated. From our hospital (First Affiliated Hospital of Zhejiang University, Zhejiang, P.R. China), 559 patients, who had histopathological confirmed MVI status, participated in this study. All preoperative CECT were collected, and the patients were randomly divided into training and validation cohorts at a ratio of 4:1. We proposed a novel transformer-based end-to-end DL model, named MVI-TR, which is a supervised learning method. MVI-TR can capture features automatically from radiomics and perform MVI preoperative assessments. In addition, a popular self-supervised learning method, the contrastive learning model, and the widely used residual networks (ResNets family) were constructed for fair comparisons. With an accuracy of 99.1%, a precision of 99.3%, an area under the curve (AUC) of 0.98, a recalling rate of 98.8%, and an F1-score of 99.1% in the training cohort, MVI-TR achieved superior outcomes. Additionally, the validation cohort's MVI status prediction had the best accuracy (97.2%), precision (97.3%), AUC (0.935), recalling rate (93.1%), and F1-score (95.2%). MVI-TR outperformed other models for predicting MVI status, and showed great preoperative predictive value for early-stage HCC patients.
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Affiliation(s)
- Linping Cao
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
- Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou 310003, China
| | - Qing Wang
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiawei Hong
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
- Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou 310003, China
| | - Yuzhe Han
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Weichen Zhang
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
- Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou 310003, China
| | - Xun Zhong
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
- Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou 310003, China
| | - Yongqian Che
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yaqi Ma
- Department of Pathology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Keyi Du
- Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou 310003, China
| | - Dongyan Wu
- Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou 310003, China
| | - Tianxiao Pang
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jian Wu
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
- Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou 310003, China
- Correspondence: (J.W.); (K.L.)
| | - Kewei Liang
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
- Correspondence: (J.W.); (K.L.)
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