ArticleInternational journal of general medicine2021
Risk Assessment of Pulmonary Metastasis for Cervical Cancer Patients by Ensemble Learning Models: A Large Population Based Real-World Study.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePulmonary metastasis (PM) is an independent risk factor affecting the prognosis of cervical patients, but it still lacks a prediction. This study aimed to develop machine learning-based predictive models for PM.
methodsA total of 22,766 patients diagnosed with or without PM from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled in this study. The cohort was randomly split into a train set (70%) and a validation set (30%). In addition, 884 Chinese patients from two tertiary medical centers were included as an external validation set. Duplicated and useless candidate variables were excluded, and sixteen variables were included for the machine learning algorithm. We developed five predictive models, including the generalized linear model (GLM), random forest model (RFM), naive Bayesian model (NBM), artificial neural networks model (ANNM), and decision tree model (DTM). The predictive performance of these models was evaluated by the receiver operating characteristic (ROC) curve and calibration curve. The Cox proportional hazard model (CPHM) and competing risk model (CRM) were also included for survival outcome prediction.
resultsOf the patients included in the analysis, 2456 (4.38%) patients were diagnosed with PM. Age, organ-site metastasis (liver, bone, brain), distant lymph metastasis, tumor size, and pathology were the important predictors of PM. The RFM with 9 variables introduced was identified as the best predictive model for PM (AUC = 0.972, 95% CI: 0.958-0.986). The C-index for the CPHM and CRM was 0.626 (95% CI: 0.604-0.648) and 0.611 (95% CI: 0.586-0.636), respectively.
conclusionThe prediction algorithm derived by machine-learning-based methods shows a robust ability to predict PM. This result suggests that machine learning techniques have the potential to improve the development and validation of predictive modeling in cervical patients with PM.
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