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Purpose: Molecular genetic knowledge of clear-cell renal-cell carcinoma (CCRCC) plays an important role in predicting the prognosis and may be used as a guide in treatment decisions and the conception of clinical trials. It would then be desirable to predict these mutations non-invasively from CT images which are already available for CCRCC patients. Methods: TCGAKIRC data were obtained from the National Cancer Institute’s (NCI) image dataset. We used 191 patient data of which 63 were associated with PBRM1 mutations. The tumors were delineated by a radiologist with over 10 years of experience, on slices that displayed the largest diameter of the tumor. Features were extracted and normalized. After feature selection, the KNN classification with Random Subspace method was used as it is known to have advantages over the simple k-nearest-neighbor method. Results: Prediction accuracy for PBRM1 was found 83.8 %. Conclusions: A single slice of the CT scan image of CCRCC can be used for predicting PBRM1 mutations using KNN classification in Random Subspaces with an acceptable accuracy.

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Predicting the polybromo-1 (PBRM1) mutation of a clear cell renal cell carcinoma using computed tomography images and KNN classification with random subspace