Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma

Hyung Min Kim, Seok Soo Byun, Jung Kwon Kim, Chang Wook Jeong, Cheol Kwak, Eu Chang Hwang, Seok Ho Kang, Jinsoo Chung, Yong June Kim, Yun Sok Ha, Sung Hoo Hong

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Renal cell carcinoma is characterized by a late recurrence that occurs 5 years after surgery; hence, continuous monitoring and follow-up is necessary. Prognosis of late recurrence of renal cell carcinoma can only be improved if it is detected early and treated appropriately. Therefore, tools for rapid and accurate renal cell carcinoma prediction are essential. Methods: This study aimed to develop a prediction model for late recurrence after surgery in patients with renal cell carcinoma that can be used as a clinical decision support system for the early detection of late recurrence. We used the KOrean Renal Cell Carcinoma database that contains large-scale cohort data of patients with renal cell carcinoma in Korea. From the collected data, we constructed a dataset of 2956 patients for the analysis. Late recurrence and non-recurrence were classified by applying eight machine learning models, and model performance was evaluated using the area under the receiver operating characteristic curve. Results: Of the eight models, the AdaBoost model showed the highest performance. The developed algorithm showed a sensitivity of 0.673, specificity of 0.807, accuracy of 0.799, area under the receiver operating characteristic curve of 0.740, and F1-score of 0.609. Conclusions: To the best of our knowledge, we developed the first algorithm to predict the probability of a late recurrence 5 years after surgery. This algorithm may be used by clinicians to identify patients at high risk of late recurrence that require long-term follow-up and to establish patient-specific treatment strategies.

Original languageEnglish
Article number241
JournalBMC Medical Informatics and Decision Making
Volume22
Issue number1
DOIs
StatePublished - Dec 2022
Externally publishedYes

Keywords

  • Late recurrence
  • Machine learning
  • ROC curve: KOrean Renal Cell Carcinoma
  • Renal cell carcinoma

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