Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Dec 11, 2020
Date Accepted: Jan 29, 2021
Machine Learning Approach to Predict the Probability of Recurrence after Surgery for Renal Cell Carcinoma: Prediction Model Development Study
ABSTRACT
Background:
Renal cell carcinoma (RCC) has a high recurrence rate of 20–30 % after nephrectomy for clinically localized disease, and more than 40 % of patients eventually die of the disease, making regular monitoring and constant management of utmost importance.
Objective:
The objective of this study was to develop an algorithm that predicts the probability of recurrence within 5 and 10 years of RCC.
Methods:
Data from 6,849 Korean RCC patients were collected from 8 tertiary care hospitals listed in the KOrean Renal Cell Carcinoma (KORCC) web-based database (DB). To predict RCC recurrence, 2,814 analytical data were extracted from the DB. Eight machine learning algorithms were used to predict the probability of RCC recurrence, and the results were compared.
Results:
Within five years of surgery, the highest area under the receiver operating characteristic curve (AUROC) was obtained from the naive Bayes (NB) model, with a value of 0.836. Within 10 years of surgery, the highest AUROC was obtained from the NB model, with a value of 0.784.
Conclusions:
An algorithm was developed that predicts the probability of RCC recurrence within 5 and 10 years using the KORCC DB, a large-scale RCC cohort in Korea. It is expected that the developed algorithm will help clinicians manage prognosis and establish customized treatment strategies for patients with RCC after surgery.
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