Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.

ABSTRACT:

Purpose

To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2).

Methods

T2w, DWI (12 b values, 0-2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair

SUBMITTER: Toivonen J 

PROVIDER: S-EPMC6613688 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature