Andrew Tom W, Alrawi Mogdad, Plummer Ruth, Reynolds Nick, Sondak Vern, Brownell Isaac, Lovat Penny E, Rose Aidan, Shalhout Sophia Z
Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Department of Plastic and Reconstructive Surgery, Royal Victoria Infirmary, Newcastle Upon Tyne Hospital NHS Foundation Trust (NuTH), Newcastle upon Tyne, UK.
NPJ Digit Med. 2025 Jan 8;8(1):15. doi: 10.1038/s41746-024-01329-9.
Accurate prognostication guides optimal clinical management in skin cancer. Merkel cell carcinoma (MCC) is the most aggressive form of skin cancer that often presents in advanced stages and is associated with poor survival rates. There are no personalized prognostic tools in use in MCC. We employed explainability analysis to reveal new insights into mortality risk factors for this highly aggressive cancer. We then combined deep learning feature selection with a modified XGBoost framework, to develop a web-based prognostic tool for MCC termed 'DeepMerkel'. DeepMerkel can make accurate personalised, time-dependent survival predictions for MCC from readily available clinical information. It demonstrated generalizability through high predictive performance in an international clinical cohort, out-performing current population-based prognostic staging systems. MCC and DeepMerkel provide the exemplar model of personalised machine learning prognostic tools in aggressive skin cancers.
准确的预后判断可为皮肤癌的最佳临床管理提供指导。默克尔细胞癌（MCC）是最具侵袭性的皮肤癌形式，通常在晚期出现，且生存率较低。目前MCC中尚无个性化的预后工具。我们采用可解释性分析来揭示这种高度侵袭性癌症的死亡风险因素的新见解。然后，我们将深度学习特征选择与改进的XGBoost框架相结合，开发了一种用于MCC的基于网络的预后工具，称为“DeepMerkel”。DeepMerkel可以根据 readily available 临床信息对MCC做出准确的个性化、时间依赖性生存预测。它通过在国际临床队列中的高预测性能证明了其通用性，优于当前基于人群的预后分期系统。MCC和DeepMerkel为侵袭性皮肤癌中的个性化机器学习预后工具提供了范例模型。
