Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data

Abstract

Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not differ even among different tumor types poses a significant diagnostic challenge for radiologists. In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. Methods: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. The proposed model performs a more comprehensive analysis by combining the T2 phase obtained before the administration of contrast material with the arterial (A) and venous (V) phases recorded after the injection of contrast material. Results: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. A total of 1275 MRI images from different phases were classified with SVM, and 90% accuracy was achieved. Conclusions: The findings reveal that the integration of multiphase MRI data and deep learning-based models can provide a significant improvement in RCC subtype classification and contribute to clinical decision support processes.