Enhancing Trust in Clinically Significant Prostate Cancer Prediction with Multiple Magnetic Resonance Imaging Modalities

TL;DR

This paper proposes a deep learning approach that combines multiple MRI modalities to improve the trustworthiness and accuracy of predicting clinically significant prostate cancer, addressing a gap in current single-modality models.

Contribution

It introduces a novel training pipeline that integrates multiple MRI modalities, enhancing model trust and performance in prostate cancer prediction.

Findings

Improved prediction accuracy with multi-modality data

Enhanced trust in deep learning models among clinical scientists

Effective training pipeline for multi-modal MRI integration

Abstract

In the United States, prostate cancer is the second leading cause of deaths in males with a predicted 35,250 deaths in 2024. However, most diagnoses are non-lethal and deemed clinically insignificant which means that the patient will likely not be impacted by the cancer over their lifetime. As a result, numerous research studies have explored the accuracy of predicting clinical significance of prostate cancer based on magnetic resonance imaging (MRI) modalities and deep neural networks. Despite their high performance, these models are not trusted by most clinical scientists as they are trained solely on a single modality whereas clinical scientists often use multiple magnetic resonance imaging modalities during their diagnosis. In this paper, we investigate combining multiple MRI modalities to train a deep learning model to enhance trust in the models for clinically significant prostate cancer prediction. The promising performance and proposed training pipeline showcase the benefits of incorporating multiple MRI modalities for enhanced trust and accuracy.