A Deep Learning-Enabled Computational Pathology Pipeline for Comparative Assessment of Intratumoral and Peritumoral Budding in Colorectal Cancer

Yihan Xu

doi:10.47297/taposatWSP2633-456924.20260703

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A Deep Learning-Enabled Computational Pathology Pipeline for Comparative Assessment of Intratumoral and Peritumoral Budding in Colorectal Cancer

7卷第3期

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A Deep Learning-Enabled Computational Pathology Pipeline for Comparative Assessment of Intratumoral and Peritumoral Budding in Colorectal Cancer
A Deep Learning-Enabled Computational Pathology Pipeline for Comparative Assessment of Intratumoral and Peritumoral Budding in Colorectal Cancer
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
2026年7卷第3期页码：119-122
纸质出版：2026-03-30
DOI：10.47297/taposatWSP2633-456924.20260703
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Yihan Xu. (2026). A Deep Learning-Enabled Computational Pathology Pipeline for Comparative Assessment of Intratumoral and Peritumoral Budding in Colorectal Cancer. 科技理论与实践（英文版）, 7(3), 119-122.

Xu Yihan.A Deep Learning-Enabled Computational Pathology Pipeline for Comparative Assessment of Intratumoral and Peritumoral Budding in Colorectal Cancer[J].Theory and Practice of Science and Technology,2026,07(03):119-122.
Yihan Xu. (2026). A Deep Learning-Enabled Computational Pathology Pipeline for Comparative Assessment of Intratumoral and Peritumoral Budding in Colorectal Cancer. 科技理论与实践（英文版）, 7(3), 119-122. DOI： 10.47297/taposatWSP2633-456924.20260703.

Xu Yihan.A Deep Learning-Enabled Computational Pathology Pipeline for Comparative Assessment of Intratumoral and Peritumoral Budding in Colorectal Cancer[J].Theory and Practice of Science and Technology,2026,07(03):119-122. DOI： 10.47297/taposatWSP2633-456924.20260703.

摘要

Abstract

Tumor budding is a well-established adverse prognostic factor in colorectal cancer

but current guideline-based assessment mainly emphasizes peritumoral budding (PTB) at the invasive margin

whereas the significance of intratumoral budding (ITB) remains less clear. Manual evaluation is also labor-intensive

subjective

and difficult to standardize. In this retrospective study

245 colorectal cancer cases were analyzed to investigate the clinicopathological relevance of both ITB and PTB and to develop an AI-assisted computational pathology workflow for objective budding assessment. The workflow integrated multimodal whole-slide image registration

deep learning-based tissue segmentation with a Wide ResNet-101 backbone

and morphology-guided tumor budding detection on CAM5.2-stained whole-slide images. A total of 2

045 budding events were identified

including 1

310 PTB and 735 ITB. High-grade ITB was significantly associated with adverse pathological features

including poor differentiation

lymph node metastasis

and vascular invasion. The automated workflow showed strong concordance with expert assessment in both budding quantification and risk stratification. These findings suggest that ITB is closely associated with PTB and may serve as a complementary indicator of tumor aggressiveness. More importantly

the proposed workflow provides a robust and standardized framework for automated tumor budding assessment in computational pathology.

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references

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