A Framework for Prostate Cancer Diagnosis: Can AI improve Clinical Workflows?

Prostate cancer (PCa) is the second most common cancer in men worldwide, presenting significant challenges in both diagnosis and treatment. To determine the most effective therapy for each patient, accurate staging and grading of the cancer are essential, but often difficult due to the complexities involved in both staging and grading. Staging prostate cancer involves assessing its extent within the prostate and its spread to other parts of the body. This requires precise imaging and interpretation of scans, such as magnetic resonance imaging (MRI), and can be challenging due to the prostate lesions’ small size, often resulting in missed lesions. Grading evaluates how much cancer cells differ from normal cells, typically using the Gleason scoring system, where pathologists examine tissue samples under a microscope. This process is challenging because it heavily depends on the pathologist’s expertise, leading to variability and inconsistent results. To address these challenges, we employ recent advances in deep learning, specifically self-supervised learning (SSL), and transformer-architectures to develop an open-source AI framework aimed at enhancing the staging and grading process. Our framework is also equipped with a module interface that allows for integrating individual image analysis use cases. To demonstrate the potential of our framework, we simulate actual clinical trials to test the AI system under realistic conditions. Our AI system has demonstrated the capability to accurately identify cancerous lesions in the majority of MRI and histopathology cases. Moreover, it can grade a substantial number of cases, including identifying various subtypes of prostate cancer, and assists in routine tasks. The results of our studies indicate that AI may indeed enhance the accuracy of staging and grading in prostate cancer diagnosis and has the potential to make diagnostic practice more efficient and reproducible.