The role of Radiomics Analysis of PET imaging in predicting Treatment Response and Prognosis of Metastatic Prostate Cancer

Metastatic prostate cancer is often characterized as a cancer from which there are low chances of recovery. Treatment of this disease is further made difficult by the way it spreads throughout the body and how the variation occurs in a specific area over time. It may also spread to bones. The uneven distribution of this cancer in the body makes it difficult to differentiate it with solid biopsies. With just a simple assessment, it is impossible to characterize the heterogeneity. Advanced image analytics along with radiomics makes it possible. Early detection of mPCa is complimented by radiomics that produces useful quantitative imaging biomarkers. It also helps in predicting the progression, assess response, and selecting the choice of the treatment procedure. Using domain knowledge and expertise, many hand-crafted features are extracted from medical images. This is the typical procedure of traditional radiomics. Whereas the new AI techniques make human intervention minimal as it uses automated feature extraction. Radiomics models have promising potential for the field of oncology, but its clinical integration is hindered by its limited reproducibility. This review explains the radiomic techniques the factors that influence the reproducibility feature and how AI techniques will help reduce the human workload. The current state of publications that use radiomics methodology in the treatment of mPCa will be studied, surveyed, and reviewed. Data from imaging techniques such as computed tomography (CT), bone scans, and positron emission tomography (PET) scans are used in several studies using different radioactive tracers and multiparametric magnetic resonance imaging (MRI). Clinical factors (covariates) and these models of imaging are combined in various research cohorts. Their usage predicts the outcomes relating to the progression of mPCa and survival chances. The Radiomics quality score is used to measure the methodological quality of each study. Identification of a few weaknesses and shortcomings was made during the analysis. The application of the findings in clinical practice was hindered by the absence of prospective studies and the lack of external validation, specifically. Based on these findings, several suggestions are made by the researchers for future research directions in the field. These recommendations are aimed at identifying deficits and improving the quality and applicability of future studies. This review aims to: a. Elucidate a methodological overview of the radiomics workflow and how the artificial intelligence techniques complement the process. b. Provide an explanation as to how the current literature helps us to understand how the radiomics models are being or can be utilized in the treatment of metastatic prostate cancer. Whereas, how in the form of RQS quality assessment can be displayed c. Introduce the limitations of the field and suggestions for future research.