Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

Simple Summary The integration of artificial intelligence (AI) into radiomic models has become increasingly popular due to advances in computer-aided diagnosis tools. These tools utilize statistical and machine learning methods to evaluate various medical image analysis modalities. In the case of prostate cancer, there are multiple areas in the radiomics pipeline that can be improved. This article explores the latest developments in mpMRI for PCa and examines the radiomic flowchart, as well as the fusion of traditional medical imaging with AI to overcome challenges and limitations in clinical applications. Furthermore, it addresses challenges related to radiomics, radiogenomics, and multi-omics in prostate cancer and suggests the necessary critical steps for clinical validation. The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.