Peleva Emilia, Chen Yue, Finke Bernhard, Rizvi Hasan, Healy Eugene, Lai Chester, Craig Paul, Rickaby William, Schoenherr Christina, Nourse Craig, Proby Charlotte, Inman Gareth J, Leigh Irene M, Harwood Catherine A, Wang Jun
Centre for Cancer Evolution, Barts Cancer Institute, Queen Mary University of London, London, UK.
Dermatology, The Royal London Hospital, Barts Health NHS Trust, London, UK.
NPJ Precis Oncol. 2025 Sep 2;9(1):308. doi: 10.1038/s41698-025-01065-7.
Cutaneous squamous cell carcinoma (cSCC) is the most common skin cancer with metastatic potential and development of metastases carries a poor prognosis. To address the need for reliable risk stratification, we developed cSCCNet, a deep learning model using digital pathology of primary cSCC to predict metastatic risk. A retrospective cohort of 227 primary cSCC from four centres is used for model development. cSCCNet automatically selects the tumour area in standard histopathological slides and then stratifies primary cSCC into high- vs. low-risk categories, with heatmaps indicating most predictive tiles contributing to explainability. On a 20% hold-out testing cohort, cSCCNet achieves an area under the curve (AUC) of 0.95 and 95% accuracy in predicting risk of metastasis, outperforming gene expression-based tools and clinicopathologic classifications. Multivariate analysis including common clinicopathologic classifications confirms cSCCNet as an independent predictor for metastasis, implying it identifies predictive features beyond known clinicopathologic risk factors. Histopathological analysis including multiplex immunohistochemistry suggests that tumour differentiation, acantholysis, desmoplasia, and the spatial localisation of lymphocytes relative to tumour tissue may be important in predicting risk of developing metastasis. Although further validation including prospective evaluation is required, cSCCNet has potential as a reliable and accurate tool for metastatic risk prediction that could be easily integrated into existing histopathology workflows.
皮肤鳞状细胞癌（cSCC）是最常见的具有转移潜能的皮肤癌，发生转移时预后较差。为满足可靠风险分层的需求，我们开发了cSCCNet，这是一种利用原发性cSCC的数字病理学来预测转移风险的深度学习模型。来自四个中心的227例原发性cSCC的回顾性队列用于模型开发。cSCCNet会自动在标准组织病理学切片中选择肿瘤区域，然后将原发性cSCC分为高风险和低风险类别，热图显示了有助于解释的最具预测性的切片。在20%的留出测试队列中，cSCCNet在预测转移风险方面的曲线下面积（AUC）为0.95，准确率为95%，优于基于基因表达的工具和临床病理分类。包括常见临床病理分类在内的多变量分析证实cSCCNet是转移的独立预测因子，这意味着它识别出了已知临床病理风险因素之外的预测特征。包括多重免疫组化在内的组织病理学分析表明，肿瘤分化、棘层松解、促纤维增生以及淋巴细胞相对于肿瘤组织的空间定位可能在预测转移风险方面具有重要意义。尽管需要进一步验证，包括前瞻性评估，但cSCCNet有潜力成为一种可靠且准确的转移风险预测工具，可轻松整合到现有的组织病理学工作流程中。
