皮肤鳞状细胞癌（CSCC）的风险分层对于患者管理至关重要。人工智能和机器学习可能通过使用不仅仅是临床和组织病理学因素来进行CSCC患者的风险分层。检索了104例切除带清晰边缘的CSCC的回顾性队列。评估了临床和组织病理学的风险因素。对已染上血红蛋白和伊红染的幻灯片进行扫描和分析，使用基于堆叠预测稀疏分解技术的算法。通过机器学习鉴定细胞形态计量标志物（CMBs），并用它们来派生一个细胞形态风险评分（CMRS），将CSCC分类为不同预后的集群。进行协调性分析，计算其敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和准确性，并与Brigham and Women's Hospital（BWH）分期系统的结果进行比较。还分析了BWH分期系统和CMBs的组合的性能。在临床和组织病理学风险因素和T分期分配方面，CMRS组之间没有差异，但在预后方面存在显著差异。将CMRS与BWH分期系统结合使用可以增加差异性并改善预后性能。当结合这两种方法时，局部复发的C索引为0.92，淋巴结转移的C索引为0.91。当采用组合方法时，阴性预测值为94.41%和96.00%，阳性预测值为36.36%和41.67%，准确率为86.75%和89.16%。CMRS有助于CSCC的风险分层，超越了经典的临床和组织病理学风险因素。将CMRS和BWH分期系统的信息结合在一起，可以为高风险CSCC患者提供出色的预后性能。© 作者 2023。由牛津大学出版社代表英国皮肤病学协会发表。版权所有。如需授权，请发送电子邮件至：journals.permissions@oup.com。

Risk stratification of cutaneous squamous cell carcinoma (CSCC) is essential for managing patients. Artificial intelligence and machine learning might help stratify patients with CSCC by risk using more than solely clinical and histopathological factors.A retrospective cohort of 104 CSCCs excised with clear margins was retrieved. Clinical and histopathological risk factors were evaluated. Hematoxylin and eosin-stained slides were scanned and analyzed by an algorithm based on the stacked predictive sparse decomposition technique. Cellular morphometric biomarkers (CMBs) were identified via machine learning and used to derive a cellular morphometric risk score (CMRS) that classified CSCC into clusters of differential prognosis. Concordance analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated and compared with results obtained with the Brigham and Women's Hospital (BWH) staging system. The performance of the combination of the BWH staging system and the CMBs was also analyzed.There were no differences among CMRS groups in terms of clinical and histopathological risk factors and T-stage assignment, but there were significant differences in prognosis. Combining the CMRS with BWH staging systems increased distinctiveness and improved prognostic performance. C-indices were 0.92 for local recurrence and 0.91 for nodal metastasis when combining the two approaches. The NPV was 94.41% and 96.00%, the PPV was 36.36% and 41.67%, and accuracy reached 86.75% and 89.16% with the combined approach.CMRS is helpful for CSCC risk stratification beyond classic clinical and histopathological risk features. Combining the information from the CMRS and the BWH staging system offers outstanding prognostic performance for high-risk CSCC patients.© The Author(s) 2023. Published by Oxford University Press on behalf of British Association of Dermatologists. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.