TY  - JOUR
AU  - Kim, HyungMin
AU  - Lee, Sun Jung
AU  - Park, So Jin
AU  - Choi, In Young
AU  - Hong, Sung-Hoo
PY  - 2021
DA  - 2021/3/1
TI  - Machine Learning Approach to Predict the Probability of Recurrence of Renal Cell Carcinoma After Surgery: Prediction Model Development Study
JO  - JMIR Med Inform
SP  - e25635
VL  - 9
IS  - 3
KW  - renal cell carcinoma
KW  - recurrence
KW  - machine learning
KW  - naïve Bayes
KW  - algorithm
KW  - cancer
KW  - surgery
KW  - web-based
KW  - database
KW  - prediction
KW  - probability
KW  - carcinoma
KW  - kidney
KW  - model
KW  - development
AB  - Background: Renal cell carcinoma (RCC) has a high recurrence rate of 20% to 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 of RCC within 5 and 10 years of surgery. Methods: Data from 6849 Korean patients with RCC were collected from eight tertiary care hospitals listed in the KOrean Renal Cell Carcinoma (KORCC) web-based database. To predict RCC recurrence, analytical data from 2814 patients were extracted from the database. Eight machine learning algorithms were used to predict the probability of RCC recurrence, and the results were compared. Results: Within 5 years of surgery, the highest area under the receiver operating characteristic curve (AUROC) was obtained from the naïve 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 database, 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2021/3/e25635
UR  - https://doi.org/10.2196/25635
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33646127
DO  - 10.2196/25635
ID  - info:doi/10.2196/25635
ER  -