Radiomics and Machine Learning Application in Prostate Cancer: A Review

Radiomics is a rapidly growing field within medical imaging that focuses on extracting and analyzing quantitative features from various types of medical images. These features can include texture, shape, intensity, and spatial relationships within the images. Radiomics represents a promising field in cancer research. By utilizing automated methods and automatic algorithms, radiomics aims to capture patterns and features within the images that may be challenging for human observers to detect and analyze. As medical image techniques such as Magnetic Resonance Imaging (MRI) that can produce detailed quality images become more prevalent, there is growing interest in leveraging machine learning and artificial intelligence methodologies to facilitate the extraction and analysis of features from prostate images. Radiomics analysis utilizes these computational techniques, to derive insights from these quantitative image features. This would undoubtedly facilitate the detection, diagnosis, and evaluation of the aggressiveness of prostate cancer lesions, as well as facilitate clinical decision-making and management of prostate cancer patients. This study aims to review the radiomics workflow for prostate cancer detection and diagnosis and the current trends in machine-learning methods for the prediction, detection, and diagnosis of prostate cancer.