Machine Learning-Based Grading of ccRCC Using Convolutional Neural Networks on CT Scans

With approximately 431,000 cases, kidney cancer ranks 14th in terms of diagnostic frequency and accounts for 2.2% of all new cancer cases worldwide.12. For malignant clear cell renal cell carcinoma (ccRCC), which necessitates WHO/ISUP grading based on tissue microscopic analysis, early diagnosis is critical for targeted treatment. With the use of a convolutional neural network with three blocks, this study presents a machine learning-based system to grade kidney tumors using CT scans. The system divides tumours into low-grade (grades 1 and 2) and high-grade (grades 3 and 4), which corresponds to the tumour’s aggressiveness and helps determine the prognosis. The challenge kits21 database’s 237 preoperative CT scans were used to train the model, which yielded an accuracy of 93.67% and an f-score of 91.80%. For reliable performance validation, the approach consists of data preprocessing, augmentation, and a binary classifier with a majority voting ensemble model. The objective of this non-invasive method is to improve diagnostic accuracy in clinical settings by decreasing inter-observer variability. The dataset will be enlarged in future work to enable wider application. Although there is hope for the system’s clinical support, more validation is necessary before generalization can occur.