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

• Type of Material: Article
• Authors: Josh Sanyal, Stanford University; Imon Banerjee, Emory University; Lewis Hahn, Stanford University; Daniel Rubin, Stanford University
• Language: English
• Date: 2020
• Publisher: American Medical Informatics Association
• Publication Version: Final Published Version
• Copyright Statement: ©2020 AMIA - All rights reserved.
• Title of Journal or Parent Work: Proceedings - AMIA Joint Summits Translational Science
• Volume: 2020
• Start Page: 552
• End Page: 560
• Keywords: Radiology; Biomedical Social Sciences; Radiology, Nuclear Medicine & Medical Imaging; Bioinformatics; Oncology; Data science
• Research Categories: Biology, Bioinformatics; Health Sciences, Radiology; Health Sciences, Oncology; Engineering, Biomedical

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.