A1 Journal article – refereed
Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study




List of Authors: Ginsburg SB, Algohary A, Pahwa S, Gulani V, Ponsky L, Aronen HJ, Boström PJ, Böhm M, Haynes AM, Brenner P, Delprado W, Thompson J, Pulbrock M, Taimen P, Villani R, Stricker P, Rastinehad AR, Jambor I, Madabhushi A
Publication year: 2017
Volume number: 46
Issue number: 1
Number of pages: 10
eISSN: 1522-2586

Abstract

PURPOSE:

To
evaluate in a multi-institutional study whether radiomic features
useful for prostate cancer (PCa) detection from 3 Tesla (T)
multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from
those in the peripheral zone (PZ).

MATERIALS AND METHODS:

3T
mpMRI, including T2-weighted (T2w), apparent diffusion coefficient
(ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were
retrospectively obtained from 80 patients at three institutions. This
study was approved by the institutional review board of each
participating institution. First-order statistical, co-occurrence, and
wavelet features were extracted from T2w MRI and ADC maps, and contrast
kinetic features were extracted from DCE-MRI. Feature selection was
performed to identify 10 features for PCa detection in the TZ and PZ,
respectively. Two logistic regression classifiers used these features to
detect PCa and were evaluated by area under the receiver-operating
characteristic curve (AUC). Classifier performance was compared with a
zone-ignorant classifier.

RESULTS:

Radiomic
features that were identified as useful for PCa detection differed
between TZ and PZ. When classification was performed on a per-voxel
basis, a PZ-specific classifier detected PZ tumors on an independent
test set with significantly higher accuracy (AUC = 0.61-0.71) than a
zone-ignorant classifier trained to detect cancer throughout the entire
prostate (P < 0.05). When classifiers were evaluated on MRI data from
multiple institutions, statistically similar AUC values (P > 0.14)
were obtained for all institutions.

CONCLUSION:

A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ.

LEVEL OF EVIDENCE:

3 J. Magn. Reson. Imaging 2016.



Last updated on 2019-20-07 at 15:07