ArticleBMC medical informatics and decision making2022
Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Late recurrence after radical nephrectomy for renal cell carcinoma in a patient with horseshoe kidney: a case report.International journal of surgery case reports · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm.Abdominal radiology (New York) · 2025Article
- Navigating advanced renal cell carcinoma in the era of artificial intelligence.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Review
- Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma.BMC medical informatics and decision making · 2024Article
- Evaluation of prognostic factors for late recurrence in clear cell renal carcinoma: an institutional study.Frontiers in oncology · 2024Article
- Prediction of Wilms' Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System.Diagnostics (Basel, Switzerland) · 2023Article
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11 authors.
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Abstract
backgroundRenal 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.
methodsThis 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.
resultsOf 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.
conclusionsTo 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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