Hybrid Intelligence for Trustable Diagnosis And Patient Management of Prostate Cancer (HIT-PIRADS)

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Project Summary/Abstract Prostate Cancer (PCa) is among the most common cancers in men worldwide, with an estimated 1.6M cases and 366K deaths annually [1]. In the US, 11% of men are diagnosed with PCa over their lifetime, with incidence generally rising with age [2]. The Prostate Imaging Reporting and Data System (PI-RADS) has become a standard tool for diagnosing PCa using multi-parametric MR images (mp-MRI). PI-RADS aims to standardize the way to classify the cancer grades. However, PI-RADS does not use clinical and demographic patient information, and MR images are assessed qualitatively or at most semi-quantitatively causing under-detection of dangerous cancer and over-detection of insignificant cancer. This proposal is to develop artificial intelligence (AI) algorithms to improve the detection accuracy by reducing assessment variations and providing trustable predictions. Our algorithms will use diverse population data and eventually a far better evaluation system. This new system will input mp-MRI, clinical (digital rectal exam, PCa family history), demographic (age, race), and laboratory (serum PSA) data to provide risk scores for intraprostatic lesions, and improve patient management for diverse populations. The smart system we will develop is called Hybrid Intelligence and Trustable (HIT)-PIRADS and specific aims of this proposal are three-fold: First, we will develop a new pre-processing framework for enhancing mp-MRI data and minimizing data biases. MRI quality varies significantly, which makes standardization very difficult. To normalize MRI, we will correct artifacts, remove inhomogeneity and noise as the pre-processing step. Next, dataset bias, such as over/under-representation of race will be dealt with as biases cause skewed and inaccurate outcomes. We will examine imbalances and quantify uncertainties in data representation to develop a visual bias-estimation tool (ViBeT) to identify potential biases in the data. Second, we will develop joint segmentation, detection, and classification algorithms for PCa using mp-MRI. Quantification of prostate and PCa is essential for lesion identification, risk stratification, biopsy guidance, and lesion targeting for surgery/focal therapies. We will use our innovative capsule-based neural networks algorithms and extend its strength to analyze mp-MRI and nonimaging data. This step will improve generalization of our algorithms to all risk groups, races, and ages. There will be also an explanation module in the HIT-PIRADAS: we will embed both radiographical interpretations and visual explanations into the baseline HIT-PIRADS. Third, we will evaluate and validate the efficacy of the HIT-PIRADS both retrospectively and prospectively. We will prove the effectiveness of HIT-PIRADS in over 7000 patients’ data (3846 retrospective, 3200 prospective). We will rigorously evaluate sources of variations and standardize HIT-PIRADS for adoption in the clinics. The outcome of this project will be a first-of-its-kind and easy-to-use recommendation system for PCa detection and patient management (HIT-PIRADS) to provide more accurate, unbiased, reproducible results to reduce PCa related morbidity and mortality. In the long term, we expect HIT-PIRADS to be widely adopted in clinics and trigger other treatment & prevention strategies to be developed based on HIT-PIRADS.