本文旨在开发一个通用的肿瘤分割模型，并在多种癌症类型中验证其性能。研究使用了超过20,000张病理切片图像，涵盖4000多例患者的结直肠癌、子宫内膜癌、肺癌和前列腺癌。模型在所有验证队列中均表现出色，平均Dice系数超过80%，证明了通用肿瘤分割模型在不同癌症类型、患者群体、样本制备和切片扫描器上的可行性。

arXiv:2510.11182v1 Announce Type: cross Abstract: Deep learning is expected to aid pathologists by automating tasks such as tumour segmentation. We aimed to develop one universal tumour segmentation model for histopathological images and examine its performance in different cancer types. The model was developed using over 20 000 whole-slide images from over 4 000 patients with colorectal, endometrial, lung, or prostate carcinoma. Performance was validated in pre-planned analyses on external cohorts with over 3 000 patients across six cancer types. Exploratory analyses included over 1 500 additional patients from The Cancer Genome Atlas. Average Dice coefficient was over 80% in all validation cohorts with en bloc resection specimens and in The Cancer Genome Atlas cohorts. No loss of performance was observed when comparing the universal model with models specialised on single cancer types. In conclusion, extensive and rigorous evaluations demonstrate that generic tumour segmentation by a single model is possible across cancer types, patient populations, sample preparations, and slide scanners.