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Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings
Author(s) -
Shiradkar Rakesh,
Ghose Soumya,
Jambor Ivan,
Taimen Pekka,
Ettala Otto,
Purysko Andrei S.,
Madabhushi Anant
Publication year - 2018
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26178
Subject(s) - prostate cancer , artificial intelligence , biochemical recurrence , medicine , wilcoxon signed rank test , breakpoint cluster region , nuclear medicine , radiomics , context (archaeology) , effective diffusion coefficient , magnetic resonance imaging , mathematics , pattern recognition (psychology) , computer science , cancer , radiology , prostatectomy , biology , paleontology , receptor , mann–whitney u test
Background Radiomics or computer‐extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa. Purpose To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR. Study Type Retrospective. Subjects In all, 120 PCa patients from two institutions, I 1 and I 2 , partitioned into training set D 1 ( N = 70) from I 1 and independent validation set D 2 ( N = 50) from I 2 . All patients were followed for ≥3 years. Sequence 3T, T 2 ‐weighted (T 2 WI) and apparent diffusion coefficient (ADC) maps derived from diffusion‐weighted sequences. Assessment PCa regions of interest (ROIs) on T 2 WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T 2 WI and ADC maps) were extracted from the ROIs. A machine‐learning classifier ( C BCR ) was trained with the best discriminating set of radiomic features to predict BCR ( p BCR ). Statistical Tests Wilcoxon rank‐sum tests with P < 0.05 were considered statistically significant. Differences in BCR‐free survival at 3 years using p BCR was assessed using the Kaplan–Meier method and compared with Gleason Score (GS), PSA, and PIRADS‐v2. Results Distribution statistics of co‐occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T 2 WI and ADC were associated with BCR ( P < 0.05) on D 1 . C BCR predictions resulted in a mean AUC = 0.84 on D 1 and AUC = 0.73 on D 2 . A significant difference in BCR‐free survival between the predicted classes (BCR + and BCR–) was observed ( P = 0.02) on D 2 compared to those obtained from GS ( P = 0.8), PSA ( P = 0.93) and PIRADS‐v2 ( P = 0.23). Data Conclusion Radiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy. Level of Evidence: 4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626–1636