AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection
Background & Aims: Tumor-infiltrating lymphocytes (TILs), particularly CD8+ TILs, are key prognostic markers in many cancers. However, their prognostic value in hepatocellular carcinoma (HCC) remains controversial, with different evidence. Given the heterogeneous outcomes in patients with HC...
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Elsevier
2025-02-01
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| Series: | JHEP Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S258955592400274X |
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| author | Zhiyang Chen Tingting Xie Shuting Chen Zhenhui Li Su Yao Xuanjun Lu Wenfeng He Chao Tang Dacheng Yang Shaohua Li Feng Shi Huan Lin Zipei Li Anant Madabhushi Xiangtian Zhao Zaiyi Liu Cheng Lu |
| author_facet | Zhiyang Chen Tingting Xie Shuting Chen Zhenhui Li Su Yao Xuanjun Lu Wenfeng He Chao Tang Dacheng Yang Shaohua Li Feng Shi Huan Lin Zipei Li Anant Madabhushi Xiangtian Zhao Zaiyi Liu Cheng Lu |
| author_sort | Zhiyang Chen |
| collection | DOAJ |
| description | Background & Aims: Tumor-infiltrating lymphocytes (TILs), particularly CD8+ TILs, are key prognostic markers in many cancers. However, their prognostic value in hepatocellular carcinoma (HCC) remains controversial, with different evidence. Given the heterogeneous outcomes in patients with HCC undergoing liver resection, this study aims to develop an AI-based system to quantify CD8+ TILs and assess their prognostic value for patients with HCC. Methods: We conducted a retrospective multicenter study on patients undergoing liver resection across three cohorts (N = 514). We trained a deep neural network and a random forest model to segment tumor regions and locate CD8+ TILs in H&E and CD8-stained whole-slide images. We quantified CD8+ TIL density and established an Automated CD8+ Tumor-infiltrating Lymphocyte Scoring (ATLS-8) system to assess its prognostic value. Results: In the discovery cohort, the 5-year overall survival (OS) rates were 34.05% for ATLS-8 low-score and 65.03% for ATLS-8 high-score groups (hazard ratio [HR] 2.40; 95% CI, 1.37–4.19; p = 0.015). These findings were confirmed in validation cohort 1, which had 5-year OS rates of 28.57% and 68.73% (HR 3.38; 95% CI, 1.27–9.02; p = 0.0098), and validation cohort 2, which had 59.26% and 81.48% (HR 2.74; 95% CI, 1.05–7.15; p = 0.031). ATLS-8 improved the prognostic model based on clinical variables (C-index 0.770 vs. 0.757; 0.769 vs. 0.727; 0.712 vs. 0.642 in three cohorts). Conclusions: We developed an automated system using CD8-stained whole-slide images to assess immune infiltration (ATLS-8). In patients with HCC undergoing resection, higher CD8+ TIL density correlates with better OS, as per ATLS-8 assessment. This system is a promising tool for advancing clinical immune microenvironment assessment and outcome prediction. Impact and implications:: CD8+ tumor-infiltrating lymphocytes (TILs) have been identified as a prognostic factor associated with many cancers. In this study, CD8+ TILs were identified as an independent prognostic factor for overall survival in patients with hepatocellular carcinoma who undergoing liver resection. Therefore, ATLS-8, a novel digital biomarker based on whole-slide image-level CD8+ TILs, could play an important role in the prognostic assessment of patients with HCC and could be integrated into clinicopathological models to participate in the decision-making and prognostic assessment of patients. The scoring system combined with artificial intelligence is essential for automated, quantitative, whole-slide image-level assessment of TILs, which can be widely applied to quantify the immune profile of multi-cancer disease types with the discussion of subsequent immunotherapy. |
| format | Article |
| id | doaj-art-5906390f28be4c939ac70abf574352b8 |
| institution | Kabale University |
| issn | 2589-5559 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JHEP Reports |
| spelling | doaj-art-5906390f28be4c939ac70abf574352b82025-02-07T04:48:09ZengElsevierJHEP Reports2589-55592025-02-0172101270AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resectionZhiyang Chen0Tingting Xie1Shuting Chen2Zhenhui Li3Su Yao4Xuanjun Lu5Wenfeng He6Chao Tang7Dacheng Yang8Shaohua Li9Feng Shi10Huan Lin11Zipei Li12Anant Madabhushi13Xiangtian Zhao14Zaiyi Liu15Cheng Lu16Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Clinical Dentistry, University of Sheffield, Sheffield, UKMedical Imaging Center, Peking University Shenzhen Hospital, Shenzhen, Guangdong, ChinaDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, ChinaDepartment of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan, ChinaDepartment of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, ChinaSchool of Electronics Engineering, Xi’an Shiyou University, Xi’an, ChinaDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, ChinaDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, ChinaMedical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou ChinaDepartment of Liver Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, ChinaDepartment of Interventional Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, ChinaDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, ChinaSchool of Computer Science, University of St Andrews, Fife, UKWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Atlanta Veterans Administration Medical Center, Atlanta, GA, USADepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Corresponding authors: Addresses: Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China (X. Zhao, Z. Liu, C. Lu).Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Corresponding authors: Addresses: Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China (X. Zhao, Z. Liu, C. Lu).Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou China; Corresponding authors: Addresses: Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China (X. Zhao, Z. Liu, C. Lu).Background & Aims: Tumor-infiltrating lymphocytes (TILs), particularly CD8+ TILs, are key prognostic markers in many cancers. However, their prognostic value in hepatocellular carcinoma (HCC) remains controversial, with different evidence. Given the heterogeneous outcomes in patients with HCC undergoing liver resection, this study aims to develop an AI-based system to quantify CD8+ TILs and assess their prognostic value for patients with HCC. Methods: We conducted a retrospective multicenter study on patients undergoing liver resection across three cohorts (N = 514). We trained a deep neural network and a random forest model to segment tumor regions and locate CD8+ TILs in H&E and CD8-stained whole-slide images. We quantified CD8+ TIL density and established an Automated CD8+ Tumor-infiltrating Lymphocyte Scoring (ATLS-8) system to assess its prognostic value. Results: In the discovery cohort, the 5-year overall survival (OS) rates were 34.05% for ATLS-8 low-score and 65.03% for ATLS-8 high-score groups (hazard ratio [HR] 2.40; 95% CI, 1.37–4.19; p = 0.015). These findings were confirmed in validation cohort 1, which had 5-year OS rates of 28.57% and 68.73% (HR 3.38; 95% CI, 1.27–9.02; p = 0.0098), and validation cohort 2, which had 59.26% and 81.48% (HR 2.74; 95% CI, 1.05–7.15; p = 0.031). ATLS-8 improved the prognostic model based on clinical variables (C-index 0.770 vs. 0.757; 0.769 vs. 0.727; 0.712 vs. 0.642 in three cohorts). Conclusions: We developed an automated system using CD8-stained whole-slide images to assess immune infiltration (ATLS-8). In patients with HCC undergoing resection, higher CD8+ TIL density correlates with better OS, as per ATLS-8 assessment. This system is a promising tool for advancing clinical immune microenvironment assessment and outcome prediction. Impact and implications:: CD8+ tumor-infiltrating lymphocytes (TILs) have been identified as a prognostic factor associated with many cancers. In this study, CD8+ TILs were identified as an independent prognostic factor for overall survival in patients with hepatocellular carcinoma who undergoing liver resection. Therefore, ATLS-8, a novel digital biomarker based on whole-slide image-level CD8+ TILs, could play an important role in the prognostic assessment of patients with HCC and could be integrated into clinicopathological models to participate in the decision-making and prognostic assessment of patients. The scoring system combined with artificial intelligence is essential for automated, quantitative, whole-slide image-level assessment of TILs, which can be widely applied to quantify the immune profile of multi-cancer disease types with the discussion of subsequent immunotherapy.http://www.sciencedirect.com/science/article/pii/S258955592400274XHepatocellular carcinomaCytotoxic T lymphocyteTumor-infiltrating lymphocyteArtificial intelligence |
| spellingShingle | Zhiyang Chen Tingting Xie Shuting Chen Zhenhui Li Su Yao Xuanjun Lu Wenfeng He Chao Tang Dacheng Yang Shaohua Li Feng Shi Huan Lin Zipei Li Anant Madabhushi Xiangtian Zhao Zaiyi Liu Cheng Lu AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection JHEP Reports Hepatocellular carcinoma Cytotoxic T lymphocyte Tumor-infiltrating lymphocyte Artificial intelligence |
| title | AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection |
| title_full | AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection |
| title_fullStr | AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection |
| title_full_unstemmed | AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection |
| title_short | AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection |
| title_sort | ai based tumor infiltrating lymphocyte scoring system for assessing hcc prognosis in patients undergoing liver resection |
| topic | Hepatocellular carcinoma Cytotoxic T lymphocyte Tumor-infiltrating lymphocyte Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S258955592400274X |
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