Jiang Nan, Xiong Xing, Chen Xue, Feng Mengmeng, Guo Yan, Hu Chunhong
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China.
Transl Cancer Res. 2025 May 30;14(5):3057-3068. doi: 10.21037/tcr-2024-2304. Epub 2025 May 26.
Cervical cancer (CC) is one of the most common gynecological malignancies. Previous studies have shown that the prognosis of CC is affected by many factors. Our study aimed to identify key prognostic factors and use machine learning and deep learning algorithms to construct models to predict the overall survival (OS) of CC patients.
Data of CC patients collected between 2007 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and were randomly divided into the training set (1,743 patients) and test set (747 patients). Moreover, in order to enhance the practical application of the model, we conducted an X-tile analysis to categorize the patients into three distinct strata based on their age and tumor size. Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to identify the independent prognostic factors for OS, which were further used to construct CoxBoost, RandomForest, SuperPC XGBoost, and DeepSurv survival models to predict 1-, 3-, and 5-year OS.
The parameters, including age, marital status, grade, tumor size, surgery, radiation, race, the American Joint Committee on Cancer (AJCC)_stage, AJCC_T, and AJCC_M, were associated with survival and were further incorporated into the five models. The concordance index (C-index) value was 0.858, 0.848, 0.849, 0.840, and 0.869, respectively, and the receiver operating characteristic (ROC) curves showed exceptional predictive performance. Among the five models, DeepSurv was the model with best performance. The ROC curve validated the area under the curve (AUC) values for 1-year OS, 3-year OS, and 5-year OS, which were 0.936, 0.915, and 0.900, respectively.
The prognostic model conducted by DeepSurv algorithm and the independent prognostic factors can potentially be applied in making personalized treatment plans and evaluating the prognosis of CC patients.
宫颈癌（CC）是最常见的妇科恶性肿瘤之一。既往研究表明，CC的预后受多种因素影响。我们的研究旨在确定关键预后因素，并使用机器学习和深度学习算法构建模型来预测CC患者的总生存期（OS）。
收集2007年至2016年期间从监测、流行病学和最终结果（SEER）数据库中获取的CC患者数据，并随机分为训练集（1743例患者）和测试集（747例患者）。此外，为了增强模型的实际应用，我们进行了X-tile分析，根据患者的年龄和肿瘤大小将其分为三个不同的分层。采用最小绝对收缩和选择算子（LASSO）及多变量Cox回归来确定OS的独立预后因素，并进一步用于构建CoxBoost、随机森林、SuperPC XGBoost和DeepSurv生存模型，以预测1年、3年和5年OS。
包括年龄、婚姻状况、分级、肿瘤大小、手术、放疗、种族、美国癌症联合委员会（AJCC）分期、AJCC_T和AJCC_M等参数与生存相关，并进一步纳入五个模型。一致性指数（C-index）值分别为0.858、0.848、0.849、0.840和0.869，受试者工作特征（ROC）曲线显示出卓越的预测性能。在这五个模型中，DeepSurv是性能最佳的模型。ROC曲线验证了1年OS、3年OS和5年OS的曲线下面积（AUC）值，分别为0.936、0.915和0.900。
由DeepSurv算法得出的预后模型及独立预后因素可能有助于制定个性化治疗方案并评估CC患者的预后。
