Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis
IntroductionMetabolic dysfunction-associated steatohepatitis (MASH) is a significant liver disease that can lead to cirrhosis and liver cancer. Accurate assessment of liver fibrosis is crucial for diagnosis, prognosis, and informed treatment decision-making. Staging of liver fibrosis in MASH is base...
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Frontiers Media S.A.
2025-07-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1629036/full |
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| author | Soo Min Ko Jae-ik Shin Jae-ik Shin Yiyu Hong Hyunji Kim Insuk Sohn Ji-Young Lee Hyo-Jeong Han Da Som Jeong Yerin Lee Woo-Chan Son |
| author_facet | Soo Min Ko Jae-ik Shin Jae-ik Shin Yiyu Hong Hyunji Kim Insuk Sohn Ji-Young Lee Hyo-Jeong Han Da Som Jeong Yerin Lee Woo-Chan Son |
| author_sort | Soo Min Ko |
| collection | DOAJ |
| description | IntroductionMetabolic dysfunction-associated steatohepatitis (MASH) is a significant liver disease that can lead to cirrhosis and liver cancer. Accurate assessment of liver fibrosis is crucial for diagnosis, prognosis, and informed treatment decision-making. Staging of liver fibrosis in MASH is based on Kleiner’s score, which categorizes fibrosis based on its location within the liver as observed microscopically. This scoring system is part of a standard clinical research network and relies heavily on the expertise of pathologists.MethodsThis study utilized Sirius Red-stained whole slide images of liver tissue obtained from various MASH animal models to develop deep learning (DL) models for scoring liver fibrosis, with a focus on the criteria outlined in Kleiner’s score. We created a trainable and testable dataset of whole-slide images of the liver, consisting of 999,711 patch images derived from 914 whole-slide images. The performance of the multi-class classification model was evaluated using the kappa statistic, area under the precision-recall curve (AUPRC), area under the receiver operating characteristic curve (AUROC), and Matthews correlation coefficient (MCC).ResultsTo address challenges in clinical subclassification, a 5-class classification model was initially applied; the model achieved moderate agreement. A more refined 7-class model was subsequently developed, which outperformed the 5-class classification model. The enhanced subclassification significantly improved classification performance, as evidenced by the superior AUROC and AUPRC values of the 7-class model.DiscussionThis study highlights that DL models for scoring liver fibrosis can support expert pathologists in staging liver fibrosis in preclinical animal studies. |
| format | Article |
| id | doaj-art-0dea2645a68049a98ab04669a3541d5b |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-0dea2645a68049a98ab04669a3541d5b2025-08-20T03:29:10ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.16290361629036Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitisSoo Min Ko0Jae-ik Shin1Jae-ik Shin2Yiyu Hong3Hyunji Kim4Insuk Sohn5Ji-Young Lee6Hyo-Jeong Han7Da Som Jeong8Yerin Lee9Woo-Chan Son10Department of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of KoreaDepartment of R&D Center, Arontier Co., Ltd., Seoul, Republic of KoreaDepartment of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of KoreaDepartment of R&D Center, Arontier Co., Ltd., Seoul, Republic of KoreaDepartment of R&D Center, Arontier Co., Ltd., Seoul, Republic of KoreaDepartment of R&D Center, Arontier Co., Ltd., Seoul, Republic of KoreaDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of KoreaDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of KoreaDepartment of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of KoreaDepartment of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of KoreaDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of KoreaIntroductionMetabolic dysfunction-associated steatohepatitis (MASH) is a significant liver disease that can lead to cirrhosis and liver cancer. Accurate assessment of liver fibrosis is crucial for diagnosis, prognosis, and informed treatment decision-making. Staging of liver fibrosis in MASH is based on Kleiner’s score, which categorizes fibrosis based on its location within the liver as observed microscopically. This scoring system is part of a standard clinical research network and relies heavily on the expertise of pathologists.MethodsThis study utilized Sirius Red-stained whole slide images of liver tissue obtained from various MASH animal models to develop deep learning (DL) models for scoring liver fibrosis, with a focus on the criteria outlined in Kleiner’s score. We created a trainable and testable dataset of whole-slide images of the liver, consisting of 999,711 patch images derived from 914 whole-slide images. The performance of the multi-class classification model was evaluated using the kappa statistic, area under the precision-recall curve (AUPRC), area under the receiver operating characteristic curve (AUROC), and Matthews correlation coefficient (MCC).ResultsTo address challenges in clinical subclassification, a 5-class classification model was initially applied; the model achieved moderate agreement. A more refined 7-class model was subsequently developed, which outperformed the 5-class classification model. The enhanced subclassification significantly improved classification performance, as evidenced by the superior AUROC and AUPRC values of the 7-class model.DiscussionThis study highlights that DL models for scoring liver fibrosis can support expert pathologists in staging liver fibrosis in preclinical animal studies.https://www.frontiersin.org/articles/10.3389/fmed.2025.1629036/fullartificial intelligencedeep learningmetabolic dysfunction-associated steatohepatitisliver fibrosishistopathology |
| spellingShingle | Soo Min Ko Jae-ik Shin Jae-ik Shin Yiyu Hong Hyunji Kim Insuk Sohn Ji-Young Lee Hyo-Jeong Han Da Som Jeong Yerin Lee Woo-Chan Son Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis Frontiers in Medicine artificial intelligence deep learning metabolic dysfunction-associated steatohepatitis liver fibrosis histopathology |
| title | Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis |
| title_full | Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis |
| title_fullStr | Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis |
| title_full_unstemmed | Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis |
| title_short | Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis |
| title_sort | deep learning based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction associated steatohepatitis |
| topic | artificial intelligence deep learning metabolic dysfunction-associated steatohepatitis liver fibrosis histopathology |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1629036/full |
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