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

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 2 groups. The TAA + H 2 group showed a lower model-derived burden of the two selected lesion categories. The framework does not assess the full spectrum of TAA-associated liver pathology and should not be interpreted as an independently validated diagnostic classifier. Independent animal-level validation is required before diagnostic or therapeutic claims can be made.