Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings.

PLoS ONE. 2018;13(8):e0200730 DOI 10.1371/journal.pone.0200730

 

Journal Homepage

Journal Title: PLoS ONE

ISSN: 1932-6203 (Online)

Publisher: Public Library of Science (PLoS)

LCC Subject Category: Medicine | Science

Country of publisher: United States

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS

Gregory Penzias
Asha Singanamalli
Robin Elliott
Jay Gollamudi
Natalie Shih
Michael Feldman
Phillip D Stricker
Warick Delprado
Sarita Tiwari
Maret Böhm
Anne-Maree Haynes
Lee Ponsky
Pingfu Fu
Pallavi Tiwari
Satish Viswanath
Anant Madabhushi

EDITORIAL INFORMATION

Peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 24 weeks

 

Abstract | Full Text

Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level.