Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI

Title

Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI

Creator

Chirra Prathyush; Leo Patrick; Yim Michael; Bloch B Nicolas; Rastinehad Ardeshir R; Purysko Andrei; Rosen Mark; Madabhushi Anant; Viswanath Satish E

Publisher

Journal of Medical Imaging (Bellingham, Wash.)

Date

2019
2019-04

Description

Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈ 0.8 ). By contrast, a majority of Laws features are highly variable across sites (reproducible in < 75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies ( < 0.6 ), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.

Subject

discriminability; feature analysis; magnetic resonance imaging; multisite; prostate; radiomics; reproducibility; stability

Search for Full-text

Users with a NEOMED Library login can search for full-text journal articles at the following url: https://libraryguides.neomed.edu/home

Rights

Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).

Pages

024502-024502

Issue

2

Volume

6

Citation

Chirra Prathyush; Leo Patrick; Yim Michael; Bloch B Nicolas; Rastinehad Ardeshir R; Purysko Andrei; Rosen Mark; Madabhushi Anant; Viswanath Satish E, “Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI,” NEOMED Bibliography Database, accessed September 28, 2021, https://neomed.omeka.net/items/show/6515.

Social Bookmarking