Whole-slide deep-learning quantification of fibrosis and pathologist-defined proliferative/neoplastic regions in a thioacetamide-induced rat liver model.

Feng Liu, Yating Pan, Xinyi Liao, Yating Deng, Deqiang Cheng, Guanzhen Yu, Ying Chen, Yu Cheng

Abstract read

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Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Feng Liu *School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.

Yating Pan *Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Xinyi Liao *Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Yating Deng *Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Deqiang ChengSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.

Guanzhen YuDepartment of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Ying ChenDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.

Yu ChengAllergy and Cancer Research Center, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automated analysis of whole-slide images may support objective quantification of selected lesions in experimental liver pathology. Using H&E-stained whole-slide images from rats exposed to thioacetamide (TAA), we developed a deep-learning framework for whole-slide segmentation and quantification of two prespecified targets: fibrosis and a single pooled class of pathologist-defined proliferative/neoplastic regions. Benign proliferation, dysplasia/preneoplasia, and malignant neoplasia were not annotated or trained as separate subclasses. The available project archive did not contain an auditable whole-slide- or animal-level training/validation manifest; the available training code implements a patch-level random split. The framework generated fibrosis proportion, proliferative/neoplastic-region proportion, and the count of spatially separated positive regions for each whole-slide image. These model-derived measurements were used to describe changes across experimental time points and to explore differences between the TAA and TAA + H

Indexed as

deep learningfibrosishydrogen interventionliver pathologyproliferative/neoplastic lesionwhole-slide image (WSI)

Identifiers

PMID42857165

DOI10.3389/frai.2026.1884918

PMCPMC13649968

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.