Abstract
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.
Highlights
Renal Cell Carcinoma (RCC) is the most encountered type of renal cancer and represents about 3.7 % of new cancer occurrences
Another study with RCC stage-4 patients reported that this gene could have potential as a PREDICTING THE POLYBROMO-1 (PBRM1) MUTATION OF A CLEAR CELL RENAL CELL CARCINOMA USING COMPUTED TOMOGRAPHY IMAGES AND KNN CLASSIFICATION WITH RANDOM SUBSPACE
Our results showed that using classification learner, KNN with Random Subspace model can correctly predict PBRM1 and NON-PBRM1 data with 83.8 %
Summary
Renal Cell Carcinoma (RCC) is the most encountered type of renal cancer and represents about 3.7 % of new cancer occurrences. Another study with RCC stage-4 patients reported that this gene could have potential as a PREDICTING THE POLYBROMO-1 (PBRM1) MUTATION OF A CLEAR CELL RENAL CELL CARCINOMA USING COMPUTED TOMOGRAPHY IMAGES AND KNN CLASSIFICATION WITH RANDOM SUBSPACE. The goal was to address these issues and develop a method for predicting the PBRM1 gene mutation non-invasively using CT images and the KNN classification method with Random Subspaces for the first time. Random Subspace Method for kNN Classification was shown to improve accuracy [14] This superiority was demonstrated to be preserved with even smaller number of training samples, a condition often encountered with radiogenomic data. The main novel contributions of this work are: – The KNN method in Random Subspaces has been applied to CCRCC Radiogenomics for the first time, – A relatively large number of patient data has been used (259). Results from classification will be discussed in view of the literature and necessary future work will be indicated
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