Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings

• Main Authors: Abdelhalim, I., Alksas, A., Balaha, H. M., Badawy, M., El-Ghar, M. Abou, Alghamdi, N. S., Ghazal, M., Contractor, S., Bogaert, E. V., Gondim, D., Silva, S. R., Khalifa, F., El-Baz, A.
• Format: Conference Proceeding
• Language: English
• Subjects: Clinical Markers; Data mining; Deep Learning; Demographic Markers; Feature extraction; Hormonal Therapy; Magnetic resonance imaging; Medical treatment; Pathology Markers; Prediction algorithms; Prostate Cancer; Prostate-Specific Antigen; Sensitivity; Soft sensors; Transfer Learning
• ISSN: 1945-8452
• DOI: 10.1109/ISBI56570.2024.10635491

Personalized prediction of hormonal therapy response in Prostate Cancer (PC) is crucial for planning effective treatment. In this paper, we propose a novel framework to combine MRI imaging, pathology, clinical, and demographic markers, aiming to develop a robust prediction system. The process involves sequential steps: preprocessing, prostate/tumor localization, feature extraction, and classification. Using the Multibranch Multimodality MRI Feature Extractor (M3FE), a deep learning technique, we extract salient information from MRI images. The final step employs a weighted sum fusion algorithm to combine MRI features with other markers. Testing on a dataset of 39 patients demonstrates that the framework effectively predicts hormonal therapy effects on PC with 97.5% sensitivity and 100% specificity. This highlights the potential of using radiomics, which involves the analysis of image features, along with other data sources for the precise prediction of hormonal therapy responses in PC.