A Machine Learning Approach to Predict the Probability of Brain Metastasis in Renal Cell Carcinoma Patients
Source abstract
Abstract
Patients with brain metastasis (BM) have a better prognosis when it is detected early. However, current guidelines recommend brain imaging only when there are central nervous system symptoms or abnormal experimental values. Therefore, metastases are discovered later in asymptomatic patients. As a result, there is a need for an algorithm that predicts the possibility of BM using clinical data and machine learning (ML). Data from 3153 patients with renal cell carcinoma (RCC) were collected from the 11-institution Korean Renal Cancer Study group (KRoCS) database. To predict BM, clinical information of 1282 patients was extracted from the database and used to compare the performance of six ML algorithms. The final model selection was based on the area under the receiver operating characteristic (AUROC) curve. After optimizing the hyperparameters for each model, the adaptive boosting (AdaBoost) model outperformed the others, with an AUROC of 0.716. We developed an algorithm to predict the probability of BM in patients with RCC. Using the developed predictive model, it is possible to avoid detection delays by performing computed tomography scans on potentially asymptomatic patients.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Metastases and Treatment · Renal cell carcinoma treatment · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
Department of Urology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul 03080, Korea
Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
Department of Urology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea
Department of Urology, National Cancer Center, Goyang 10408, Korea
Department of Urology, Chonnam National University Medical School, Gwangju 61469, Korea
Department of Urology, Korea University College of Medicine, Korea University Ansan Hospital, Ansan 15355, Korea
Department of Urology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
Funding
Korea Medical Device Development Fund of the Korean government · National Research Foundation of Korea (NRF) grant funded by the Korea government
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