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).

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 out
cross validation was used to discriminate tumors with 3+3 vs >3+3 GS.

In
total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3
and >3+3, respectively. The best model performance was obtained by
selecting the top 1% features of T₂w, ADC_m and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T₂
mapping provided little added value. The most useful texture features
were based on the gray-level co-occurrence matrix, Gabor transform, and
Zernike moments.

Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T₂w
demonstrated good classification performance for GS of PCa. In
multisequence setting, the optimal radiomics based texture extraction
methods and parameters differed between different image types.