An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence

Josh Sanyal; Imon Banerjee; Lewis Hahn; Daniel Rubin

An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence

AMIA Jt Summits Transl Sci Proc. 2020 May 30:2020:552-560. eCollection 2020.

Authors

Josh Sanyal¹, Imon Banerjee^{1

2

3

4}, Lewis Hahn³, Daniel Rubin^{1

3}

Affiliations

¹ Department of Biomedical Data Science, Stanford University, Stanford, CA.
² Department of Biomedical Informatics, Emory University, Atlanta, GA.
³ Department of Radiology, Stanford University, Stanford, CA.
⁴ Department of Radiology, Emory University, Atlanta, GA.

PMID: 32477677
PMCID: PMC7233091

Abstract

A substantial percentage of prostate cancer cases are overdiagnosed and overtreated due to the challenge in deter- mining aggressiveness. Multi-parametric MR is a powerful imaging technique to capture distinct characteristics of prostate lesions that are informative for aggressiveness assessment. However, manual interpretation requires a high level of expertise, is time-consuming, and significant inter-observer variation exists for radiologists. We propose a completely automated approach to assessing pixel-level aggressiveness of prostate cancer in multi-parametric MRI. Our model efficiently combines traditional computer vision and deep learning algorithms, to remove reliance on manual features, prostate segmentation, and prior lesion detection and identified optimal combinations of MR pulse sequences for assessment. Using ADC and DWI, our proposed model achieves ROC-AUC of 0.86 and ROC-AUC of 0.88 for the diagnosis of aggressive and non-aggressive prostate lesions, respectively. In performing pixel-level clas- sification, our model's classifications are easily interpretable and allow clinicians to infer localized analyses of the lesion.