Journal Article

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

Merisaari, Harri, Jambor, Ivan, Toivonen, Jussi, Kiviniemi, Aida, Pahikkala, Tapio, Boström, Peter J., Taimen, Pekka, Pohjankukka, Jonne, Movahedi, Parisa, Pesola, Marko, Aronen, Hannu J., Montoya Perez, Ileana

2019

Overview

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, ADCm 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Aged / Artificial intelligence / Biology and Life Sciences / Cancer surgery / Datasets / Diagnosis / Driving while intoxicated / Feature extraction / Gabor transformation / Hospitals / Humans / Image acquisition / Image Interpretation, Computer-Assisted / Kurtosis / Learning algorithms / Lesions / Machine Learning / Magnetic resonance imaging / Male / Mapping / Medical imaging / Medicine and Health Sciences / Methods / Middle Aged / Mortality / Multiparametric Magnetic Resonance Imaging / NMR / Nuclear magnetic resonance / Pattern recognition / Prostate cancer / Prostatic Neoplasms - diagnostic imaging / Radiomics / Regression analysis / Research and Analysis Methods / Statistical analysis / Technology application / Texture / Tumors / Urological surgery / Urology