使用深度学习预测皮肤恶性黑色素瘤患者的生存情况：一项回顾性队列研究。
Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study.
机构信息
Dermatology Department, General Hospital of Western Theater Command PLA, No. 270, Rongdu Avenue, Chengdu, 610083, Sichuan, China.
Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, No. 9 Beiguan Street, Tongzhou District, Beijing, 101149, China.
出版信息
J Cancer Res Clin Oncol. 2023 Dec;149(19):17103-17113. doi: 10.1007/s00432-023-05421-7. Epub 2023 Sep 27.
BACKGROUND
Cutaneous malignant melanoma (CMM) has the worst prognosis among skin cancers, especially metastatic CMM. Predicting its prognosis accurately could direct clinical decisions.
METHODS
The Surveillance, Epidemiology, and End Results database was screened to collect CMM patients' data. According to diagnosed time, patients were subdivided into three cohorts, train cohort (diagnosed between 2010 and 2013), validation cohort (diagnosed in 2014), and test cohort (diagnosed in 2015). Train cohort was used to train deep learning survival model for cutaneous malignant melanoma (DeepCMM). DeepCMM was then evaluated in train cohort and validation cohort internally, and validated in test cohort externally.
RESULTS
DeepCMM showed 0.8270 (95% CI, confidence interval, CI 0.8260-0.8280) as area under the receiver operating characteristic curve (AUC) in train cohort, 0.8274 (95% CI 0.8286-0.8298) AUC in validation cohort, and 0.8303 (95% CI 0.8289-0.8316) AUC in test cohort. Then DeepCMM was packaged into a Windows 64-bit software for doctors to use.
CONCLUSION
Deep learning survival model for cutaneous malignant melanoma (DeepCMM) can offer a reliable prediction on cutaneous malignant melanoma patients' overall survival.
背景
皮肤恶性黑色素瘤（CMM）是皮肤癌中预后最差的一种，尤其是转移性 CMM。准确预测其预后可以指导临床决策。
方法
筛选监测、流行病学和最终结果数据库，以收集 CMM 患者的数据。根据诊断时间，患者被分为三个队列：训练队列（诊断于 2010 年至 2013 年之间）、验证队列（诊断于 2014 年）和测试队列（诊断于 2015 年）。训练队列用于训练皮肤恶性黑色素瘤的深度学习生存模型（DeepCMM）。然后在训练队列和验证队列内部对 DeepCMM 进行内部评估，并在外部的测试队列中进行验证。
结果
DeepCMM 在训练队列中的接收器工作特征曲线（AUC）下面积为 0.8270（95%置信区间，CI 0.8260-0.8280），在验证队列中的 AUC 为 0.8274（95% CI 0.8286-0.8298），在测试队列中的 AUC 为 0.8303（95% CI 0.8289-0.8316）。然后，将 DeepCMM 打包成一个适用于 Windows 64 位系统的软件，供医生使用。
结论
皮肤恶性黑色素瘤深度学习生存模型（DeepCMM）可以为皮肤恶性黑色素瘤患者的总生存提供可靠的预测。
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