一个模型搞定多种癌症的肿瘤分割，效果不输专用模型。
Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
- 用超2万张病理全切片图像训练通用分割模型。
- 跨6种癌症验证，平均Dice系数超80%且无性能下降。
- 适合需要统一病理分析工具的研究者和临床团队。
深度学习有望通过自动化肿瘤分割辅助病理科医生。本研究旨在开发一个适用于多种癌症类型的通用肿瘤分割模型，并评估其在不同癌种中的表现。模型基于超过4000名患者的结直肠、子宫内膜、肺及前列腺癌全切片图像（共逾20,000张）进行训练。在预设的外部队列中，对包含超过3000名患者的六种癌症类型进行验证，探索性分析还涵盖来自癌症基因组图谱（TCGA）的1500多名患者数据。所有验证队列中，采用整块切除样本的平均Dice系数均超过80%。与针对单一癌种的专用模型相比，通用模型未出现性能下降。结论表明，通过大规模严谨评估，单一模型可在多种癌症类型、患者群体、样本制备方式及扫描仪条件下实现通用肿瘤分割。
原文摘要 · Abstract (English)
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.
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