Serial H&E Pathology Reveals Progression‐Associated Nuclear Size Drift as a Longitudinal Digital Readout of Bladder Cancer Evolution

Serial pathological specimens are routinely generated during bladder cancer surveillance, but their temporal information remains underused in computational pathology. This study develops a longitudinal digital pathology framework to quantify direction-preserving nuclear morphological change within the same patient. Paired hematoxylin and eosin whole-slide images are organized as T0, the earlier specimen, and T1, the later specimen. Signed Δ features, defined as T1-T0, are extracted across nuclear size, shape regularity, local cellularity, and spatial organization. After quality control, the discovery cohort includes 86 pairs from 86 patients, and the independent confirmation cohort includes 96 pairs from 96 patients. A separate four-center resource of 1977 whole-slide images from 1660 patients provides a static contextual baseline. Several nuclear features showed the same direction of association with progression across the two cohorts. Nuclear size-related change was most prominent in the independent confirmation cohort, particularly for equivalent nuclear diameter and nuclear area. Spatial analysis shows a broader upward distributional shift in nuclear area in progression pairs. Static foundation-model representations show variable locked cross-center transferability, whereas Ki-67 immunohistochemistry and TCGA-BLCA transcriptomic analyses provide complementary tissue-level and molecular context. Routine serial hematoxylin and eosin specimens may therefore provide interpretable, direction-preserving digital readouts of bladder cancer evolution.

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Publication Details

Journal
Advanced Science
Published
2026-10-14
DOI
https://doi.org/10.1002/advs.78299
Primary Topic
AI in cancer detection
Type
article
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article

Serial H&E Pathology Reveals Progression‐Associated Nuclear Size Drift as a Longitudinal Digital Readout of Bladder Cancer Evolution

Han Han, Zhigang Jia, Xing Liu, Conghui Han et al.
Advanced Science
AI in cancer detection
article

Serial H&E Pathology Reveals Progression‐Associated Nuclear Size Drift as a Longitudinal Digital Readout of Bladder Cancer Evolution

Han Han, Zhigang Jia, Xing Liu, Conghui Han, Yu Zhang, Shichao Zhuo, Yibing Shi, Xuan Lin, Jianming Wang, Fan‐Lai Meng, Zhen‐Duo Shi, Yi Gong, Gong‐Cheng Wang, Ying Liu, Di Wu
article en

Abstract

Serial pathological specimens are routinely generated during bladder cancer surveillance, but their temporal information remains underused in computational pathology. This study develops a longitudinal digital pathology framework to quantify direction-preserving nuclear morphological change within the same patient. Paired hematoxylin and eosin whole-slide images are organized as T0, the earlier specimen, and T1, the later specimen. Signed Δ features, defined as T1-T0, are extracted across nuclear size, shape regularity, local cellularity, and spatial organization. After quality control, the discovery cohort includes 86 pairs from 86 patients, and the independent confirmation cohort includes 96 pairs from 96 patients. A separate four-center resource of 1977 whole-slide images from 1660 patients provides a static contextual baseline. Several nuclear features showed the same direction of association with progression across the two cohorts. Nuclear size-related change was most prominent in the independent confirmation cohort, particularly for equivalent nuclear diameter and nuclear area. Spatial analysis shows a broader upward distributional shift in nuclear area in progression pairs. Static foundation-model representations show variable locked cross-center transferability, whereas Ki-67 immunohistochemistry and TCGA-BLCA transcriptomic analyses provide complementary tissue-level and molecular context. Routine serial hematoxylin and eosin specimens may therefore provide interpretable, direction-preserving digital readouts of bladder cancer evolution.

Advanced Science
Jiangsu University (CN), Jiangsu Normal University (CN), Xuzhou Medical College (CN), Huaian First People’s Hospital (CN), Linyi People's Hospital (CN), Xuzhou Cancer Hospital (CN), Second People’s Hospital of Huai’an (CN), Xuzhou Central Hospital (CN), State Key Laboratory of Digital Medical Engineering (CN), Southeast University (CN), Nanjing Medical University (CN)
Openalex Percentile: Top 13%
AI in cancer detection
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