A Novel Machine Learning Algorithm Combined With Multivariate Analysis for the Prognosis of Renal Collecting Duct Carcinoma

Frontiers in Oncology

Oncology

Yongdi Huang¹  Lihong Cui¹  Guangyi Huang²  Zheng Chen²  Jinhua Li²  Liwei Wei²  Xiaoping Qin²  Yumin Zhuo² 

[1] College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing, China;Department of Urology, The First Affiliated Hospital of Jinan University, Guangzhou, China;

关键词: renal cell carcinoma;    collecting duct carcinoma;    machine learning;    predictive model;    SEER database;   

DOI  :  10.3389/fonc.2021.777735

 received in 2021-09-15, accepted in 2021-12-21,  发布年份 2022

来源: Frontiers

【 摘 要 】

ObjectivesTo investigate the clinical and non-clinical characteristics that may affect the prognosis of patients with renal collecting duct carcinoma (CDC) and to develop an accurate prognostic model for this disease.MethodsThe characteristics of 215 CDC patients were obtained from the U.S. National Cancer Institute’s surveillance, epidemiology and end results database from 2004 to 2016. Univariate Cox proportional hazard model and Kaplan-Meier analysis were used to compare the impact of different factors on overall survival (OS). 10 variables were included to establish a machine learning (ML) model. Model performance was evaluated by the receiver operating characteristic curves (ROC) and calibration plots for predictive accuracy and decision curve analysis (DCA) were obtained to estimate its clinical benefits.ResultsThe median follow-up and survival time was 16 months during which 164 (76.3%) patients died. 4.2, 32.1, 50.7 and 13.0% of patients were histological grade I, II, III, and IV, respectively. At diagnosis up to 61.9% of patients presented with a pT3 stage or higher tumor, and 36.7% of CDC patients had metastatic disease. 10 most clinical and non-clinical factors including M stage, tumor size, T stage, histological grade, N stage, radiotherapy, chemotherapy, age at diagnosis, surgery and the geographical region where the care delivered was either purchased or referred and these were allocated 95, 82, 78, 72, 49, 38, 36, 35, 28 and 21 points, respectively. The points were calculated by the XGBoost according to their importance. The XGBoost models showed the best predictive performance compared with other algorithms. DCA showed our models could be used to support clinical decisions in 1-3-year OS models.ConclusionsOur ML models had the highest predictive accuracy and net benefits, which may potentially help clinicians to make clinical decisions and follow-up strategies for patients with CDC. Larger studies are needed to better understand this aggressive tumor.

【 授权许可】

Unknown   
Copyright © 2022 Wei, Huang, Chen, Li, Huang, Qin, Cui and Zhuo