Radiomics assisted machine learning model for predication of prostate specific antigen levels

We proposed a machine learning model to predict prostate specific antigen (PSA) levels for intermediate or high-risk prostate cancer patients undergoing definitive treatment course.

For this purpose, a data set consists of 100 localized prostate cancer patients treated with a combination of radiation therapy and Androgen Deprivation Therapy (ADT). Pretreatment imaging data including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET/CT), and Prostate Specific Antigen (PSMA) scans were used to extract the radiomic features with reference to follow-up PSA levels.

Patient's demographics, tumor stage, Gleason score, histopathology, radiation therapy dose, immunotherapy dose, pre and post treatment PSA levels along with significant radiomic features are used in training of machine learning model.

We expect this model will help clinicians in predicting early prognosis along with treatment intensification options (if required) for localized prostate cancer patients.