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Cited 3 time in webofscience Cited 4 time in scopus
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Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinomaopen access

Authors
Kim, Hyung MinByun, Seok-SooKim, Jung KwonJeong, Chang WookKwak, CheolHwang, Eu ChangKang, Seok HoChung, JinsooKim, Yong-JuneHa, Yun-SokHong, Sung-Hoo
Issue Date
Sep-2022
Publisher
BioMed Central
Keywords
Renal cell carcinoma; Machine learning; ROC curve; KOrean Renal Cell Carcinoma; Late recurrence
Citation
BMC Medical Informatics and Decision Making, v.22, no.1
Indexed
SCIE
SCOPUS
Journal Title
BMC Medical Informatics and Decision Making
Volume
22
Number
1
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/61516
DOI
10.1186/s12911-022-01964-w
ISSN
1472-6947
1472-6947
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.
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Kang, Seok Ho
Anam Hospital (Department of Urology, Anam Hospital)
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