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Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation

BACKGROUND AND PURPOSE: Tumor recurrence after liver transplantation (LT) impedes the curative chance for hepatocellular carcinoma (HCC) patients. This study aimed to develop a deep pathomics score (DPS) for predicting tumor recurrence after liver transplantation using deep learning. PATIENTS AND ME...

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Autores principales: Qu, Wei-Feng, Tian, Meng-Xin, Lu, Hong-Wei, Zhou, Yu-Fu, Liu, Wei-Ren, Tang, Zheng, Yao, Zhao, Huang, Run, Zhu, Gui-Qi, Jiang, Xi-Fei, Tao, Chen-Yang, Fang, Yuan, Gao, Jun, Wu, Xiao-Ling, Chen, Jia-Feng, Zhao, Qian-Fu, Yang, Rui, Chu, Tian-Hao, Zhou, Jian, Fan, Jia, Yu, Jin-Hua, Shi, Ying-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386986/
https://www.ncbi.nlm.nih.gov/pubmed/37031334
http://dx.doi.org/10.1007/s12072-023-10511-2
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author Qu, Wei-Feng
Tian, Meng-Xin
Lu, Hong-Wei
Zhou, Yu-Fu
Liu, Wei-Ren
Tang, Zheng
Yao, Zhao
Huang, Run
Zhu, Gui-Qi
Jiang, Xi-Fei
Tao, Chen-Yang
Fang, Yuan
Gao, Jun
Wu, Xiao-Ling
Chen, Jia-Feng
Zhao, Qian-Fu
Yang, Rui
Chu, Tian-Hao
Zhou, Jian
Fan, Jia
Yu, Jin-Hua
Shi, Ying-Hong
author_facet Qu, Wei-Feng
Tian, Meng-Xin
Lu, Hong-Wei
Zhou, Yu-Fu
Liu, Wei-Ren
Tang, Zheng
Yao, Zhao
Huang, Run
Zhu, Gui-Qi
Jiang, Xi-Fei
Tao, Chen-Yang
Fang, Yuan
Gao, Jun
Wu, Xiao-Ling
Chen, Jia-Feng
Zhao, Qian-Fu
Yang, Rui
Chu, Tian-Hao
Zhou, Jian
Fan, Jia
Yu, Jin-Hua
Shi, Ying-Hong
author_sort Qu, Wei-Feng
collection PubMed
description BACKGROUND AND PURPOSE: Tumor recurrence after liver transplantation (LT) impedes the curative chance for hepatocellular carcinoma (HCC) patients. This study aimed to develop a deep pathomics score (DPS) for predicting tumor recurrence after liver transplantation using deep learning. PATIENTS AND METHODS: Two datasets of 380 HCC patients who underwent LT were enrolled. Residual convolutional neural networks were used to identify six histological structures of HCC. The individual risk score of each structure and DPS were derived by a modified DeepSurv network. Cox regression analysis and Concordance index were used to evaluate the prognostic significance. The cellular exploration of prognostic immune biomarkers was performed by quantitative and spatial proximity analysis according to three panels of 7-color immunofluorescence. RESULTS: The overall classification accuracy of HCC tissue was 97%. At the structural level, immune cells were the most significant tissue category for predicting post-LT recurrence (HR 1.907, 95% CI 1.490–2.440). The C-indices of DPS achieved 0.827 and 0.794 in the training and validation cohorts, respectively. Multivariate analysis for recurrence-free survival (RFS) showed that DPS (HR 4.795, 95% CI 3.017–7.619) was an independent risk factor. Patients in the high-risk subgroup had a shorter RFS, larger tumor diameter and a lower proportion of clear tumor borders. At the cellular level, a higher infiltration of intratumoral NK cells was negatively correlated with recurrence risk. CONCLUSIONS: This study established an effective DPS. Immune cells were the most significant histological structure related to HCC recurrence. DPS performed well in post-LT recurrence prediction and the identification of clinicopathological features. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12072-023-10511-2.
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spelling pubmed-103869862023-07-31 Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation Qu, Wei-Feng Tian, Meng-Xin Lu, Hong-Wei Zhou, Yu-Fu Liu, Wei-Ren Tang, Zheng Yao, Zhao Huang, Run Zhu, Gui-Qi Jiang, Xi-Fei Tao, Chen-Yang Fang, Yuan Gao, Jun Wu, Xiao-Ling Chen, Jia-Feng Zhao, Qian-Fu Yang, Rui Chu, Tian-Hao Zhou, Jian Fan, Jia Yu, Jin-Hua Shi, Ying-Hong Hepatol Int Original Article BACKGROUND AND PURPOSE: Tumor recurrence after liver transplantation (LT) impedes the curative chance for hepatocellular carcinoma (HCC) patients. This study aimed to develop a deep pathomics score (DPS) for predicting tumor recurrence after liver transplantation using deep learning. PATIENTS AND METHODS: Two datasets of 380 HCC patients who underwent LT were enrolled. Residual convolutional neural networks were used to identify six histological structures of HCC. The individual risk score of each structure and DPS were derived by a modified DeepSurv network. Cox regression analysis and Concordance index were used to evaluate the prognostic significance. The cellular exploration of prognostic immune biomarkers was performed by quantitative and spatial proximity analysis according to three panels of 7-color immunofluorescence. RESULTS: The overall classification accuracy of HCC tissue was 97%. At the structural level, immune cells were the most significant tissue category for predicting post-LT recurrence (HR 1.907, 95% CI 1.490–2.440). The C-indices of DPS achieved 0.827 and 0.794 in the training and validation cohorts, respectively. Multivariate analysis for recurrence-free survival (RFS) showed that DPS (HR 4.795, 95% CI 3.017–7.619) was an independent risk factor. Patients in the high-risk subgroup had a shorter RFS, larger tumor diameter and a lower proportion of clear tumor borders. At the cellular level, a higher infiltration of intratumoral NK cells was negatively correlated with recurrence risk. CONCLUSIONS: This study established an effective DPS. Immune cells were the most significant histological structure related to HCC recurrence. DPS performed well in post-LT recurrence prediction and the identification of clinicopathological features. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12072-023-10511-2. Springer India 2023-04-08 /pmc/articles/PMC10386986/ /pubmed/37031334 http://dx.doi.org/10.1007/s12072-023-10511-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Qu, Wei-Feng
Tian, Meng-Xin
Lu, Hong-Wei
Zhou, Yu-Fu
Liu, Wei-Ren
Tang, Zheng
Yao, Zhao
Huang, Run
Zhu, Gui-Qi
Jiang, Xi-Fei
Tao, Chen-Yang
Fang, Yuan
Gao, Jun
Wu, Xiao-Ling
Chen, Jia-Feng
Zhao, Qian-Fu
Yang, Rui
Chu, Tian-Hao
Zhou, Jian
Fan, Jia
Yu, Jin-Hua
Shi, Ying-Hong
Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation
title Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation
title_full Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation
title_fullStr Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation
title_full_unstemmed Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation
title_short Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation
title_sort development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386986/
https://www.ncbi.nlm.nih.gov/pubmed/37031334
http://dx.doi.org/10.1007/s12072-023-10511-2
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