arXiv cs.AI· Yesung Cho, Dongmyung Shin, Sujeong Hong, Jooyeon Lee, Seongmin Park, Geongyu Lee, Won Young Park, Bongjin Park, Jongbae Park, Hong Koo Ha·· 4 小时前AI 评分27
计算高效的多切片全切片图像分析：捕捉前列腺癌多灶性以预测生化复发
A Computationally Efficient Multi-Section Whole Slide Image Analysis for Capturing Prostate Cancer Multifocality in Biochemical Recurrence Prediction
AI 导读
一种计算高效的多切片全切片图像分析框架被提出，用于捕捉前列腺癌全腺体肿瘤分布、预测根治性前列腺切除术后生化复发（BCR）。研究基于 789 名患者共 23,451 张切片，通过优化 patch 与切片子采样大幅降低训练和推理成本，在 1 年和 2 年 BCR 预测上达到或超过既有临床基准；AI 风险评分在多变量 Cox 分析中仍是独立预后因素，独立队列外部验证初步显示了可迁移性。
正文 · 原文
Abstract:Prostate cancer is among the most frequently diagnosed malignancies in men worldwide. However, precise prediction of biochemical recurrence (BCR) after radical prostatectomy remains challenging. Here, we propose a computationally efficient multi section whole slide image analysis framework designed to capture the whole gland tumor landscape. We curated a large scale dataset comprising 23,451 slides from 789 patients and systematically optimized patch and slide sub sampling to substantially reduce the computational cost of training and inference. Under this efficient configuration, the framework achieved strong predictive performance for 1- and 2-year BCR, with discrimination and clinical net benefit comparable to or exceeding those of established clinical benchmarks. In multivariable Cox proportional hazards analysis, the AI derived risk score remained an independent prognostic factor among the covariates examined, and its integration with clinical variables further improved prognostic performance. External validation in an independent cohort provided preliminary evidence of transferability. Finally, attention-dispersion analysis showed that the model drew prognostic information from multiple sections, consistent with the multifocal nature of prostate cancer. Collectively, these findings support the potential of multi section slide analysis as a scalable approach for postoperative BCR risk assessment in prostate cancer.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.20273 [cs.CV] |
| (or arXiv:2603.20273v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.20273 arXiv-issued DOI via DataCite |
Submission history
From: Geongyu Lee [view email]
[v1]
Tue, 17 Mar 2026 07:58:02 UTC (1,328 KB)
[v2]
Tue, 27 Oct 2026 06:32:34 UTC (6,013 KB)
来源：arXiv cs.AI · arxiv.org