AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives

TL;DR

This study introduces a deep learning approach that enhances prostate cancer detection on biparametric MRI by classifying radiologist-identified cases, improving diagnostic accuracy and clinical utility.

Contribution

The paper presents a novel voxel-level classification model trained specifically on radiologist-positives, which improves accuracy over traditional all-patient models.

Findings

Improved specificity from 36.3% to 44.1% at 80% sensitivity.

Enhanced diagnostic accuracy by augmenting radiologist interpretation.

Validated on two large clinical datasets with over 1300 patients.

Abstract

Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to develop deep learning models that improve the overall cancer diagnostic accuracy, by classifying radiologist-identified patients or lesions (i.e. radiologist-positives), as opposed to the existing models that are trained to discriminate over all patients. We develop a single voxel-level classification model, with a simple percentage threshold to determine positive cases, at levels of lesions, Barzell-zones and patients. Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging. Among varying definition of clinical significance, the proposed strategy, for example, achieved a specificity of 44.1% (with AI assistance) from 36.3% (by radiologists alone), at a controlled sensitivity of 80.0% on the publicly available UCLA data set. This provides measurable clinical values in a range of applications such as reducing unnecessary biopsies, lowering cost in cancer screening and quantifying risk in therapies.