Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

Rakesh Shiradkar; Soumya Ghose; Ivan Jambor; Pekka Taimen; Otto Ettala; Andrei S Purysko; Anant Madabhushi

doi:10.1002/jmri.26178

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

J Magn Reson Imaging. 2018 Dec;48(6):1626-1636. doi: 10.1002/jmri.26178. Epub 2018 May 7.

Authors

Rakesh Shiradkar¹, Soumya Ghose¹, Ivan Jambor^{2

3}, Pekka Taimen^{4

5}, Otto Ettala⁶, Andrei S Purysko⁷, Anant Madabhushi¹

Affiliations

¹ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
² Department of Diagnostic Radiology, University of Turku, Finland.
³ Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁴ Institute of Biomedicine, University of Turku, Turku, Finland.
⁵ Department of Pathology, Turku University Hospital, Turku, Finland.
⁶ Department of Urology, Turku University Hospital, Turku, Finland.
⁷ Section of Abdominal Imaging and Nuclear Radiology Department, Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA.

Abstract

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, I₁ and I₂ , partitioned into training set D₁ (N = 70) from I₁ and independent validation set D₂ (N = 50) from I₂ . All patients were followed for ≥3 years.

Sequence: 3T, T₂ -weighted (T₂ WI) and apparent diffusion coefficient (ADC) maps derived from diffusion-weighted sequences.

Assessment: PCa regions of interest (ROIs) on T₂ WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T₂ 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 T₂ WI and ADC were associated with BCR (P < 0.05) on D₁ . C_BCR predictions resulted in a mean AUC = 0.84 on D₁ and AUC = 0.73 on D₂ . A significant difference in BCR-free survival between the predicted classes (BCR + and BCR-) was observed (P = 0.02) on D₂ 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.

Keywords: computer-assisted image processing; magnetic resonance imaging; projections and predictions; prostate cancer; recurrence.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Aged
Aged, 80 and over
Algorithms
Diagnosis, Computer-Assisted*
Diffusion Magnetic Resonance Imaging
Humans
Image Interpretation, Computer-Assisted / methods
Image Processing, Computer-Assisted / methods
Machine Learning
Magnetic Resonance Imaging*
Male
Middle Aged
Neoplasm Grading
Neoplasm Recurrence, Local
Pattern Recognition, Automated
Prostatic Neoplasms / diagnostic imaging*
ROC Curve
Radiometry
Retrospective Studies

Abstract

Publication types

MeSH terms

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