Machine learning and deep learning to improve overall survival prediction in cervical cancer patients.

IF 1.7 4区医学 Q4 ONCOLOGY

Translational cancer research Pub Date : 2025-05-30 Epub Date: 2025-05-26 DOI:10.21037/tcr-2024-2304

Nan Jiang, Xing Xiong, Xue Chen, Mengmeng Feng, Yan Guo, Chunhong Hu

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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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机器学习和深度学习提高宫颈癌患者总体生存预测。

背景：宫颈癌是最常见的妇科恶性肿瘤之一。既往研究表明，影响CC预后的因素很多。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）。方法：从监测、流行病学和最终结果（SEER）数据库中收集2007 - 2016年CC患者的数据，随机分为训练集（1743例）和测试集（747例）。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患者分为三个不同的阶层。采用最小绝对收缩和选择算子（LASSO）和多变量Cox回归来确定OS的独立预后因素，并进一步构建Cox boost、RandomForest、SuperPC XGBoost和DeepSurv生存模型来预测1年、3年和5年OS。结果：年龄、婚姻状况、肿瘤分级、肿瘤大小、手术、放疗、种族、美国癌症联合委员会（AJCC）分期、AJCC_T、AJCC_M等参数与生存相关，并进一步纳入5种模型。一致性指数（C-index）值分别为0.858、0.848、0.849、0.840、0.869，受试者工作特征（ROC）曲线具有较好的预测效果。在五个模型中，DeepSurv是性能最好的模型。ROC曲线验证了1年、3年和5年OS的曲线下面积（AUC）值，分别为0.936、0.915和0.900。结论：DeepSurv算法建立的预后模型及独立预后因素可用于CC患者的个性化治疗方案制定及预后评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational cancer research ONCOLOGY-

CiteScore

2.10

自引率

0.00%

发文量

252

期刊介绍： Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.