Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer.

ABSTRACT:

Purpose

To evaluate repeatability of prostate DWI-derived radiomics and machine learning methods for prostate cancer (PCa) characterization.

Methods

A total of 112 patients with diagnosed PCa underwent 2 prostate MRI examinations (Scan1 and Scan2) performed on the same day. DWI was performed using 12 b-values (0-2000 s/mm² ), post-processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance mi

SUBMITTER: Merisaari H 

PROVIDER: S-EPMC7047644 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature