Dr. Cristina Magi-Galluzzi Profile

Dr. Cristina Magi-Galluzzi

at Cleveland Clinic

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Publications (1)

Proceedings Article | 13 March 2019 Paper

Radiomic features derived from pre-operative multi-parametric MRI of prostate cancer are associated with Decipher risk score

Lin Li, Rakesh Shiradkar, Ahmad Algohary, Patrick Leo, Cristina Magi-Galluzzi, Eric Klein, Andrei Purysko, Anant Madabhushi

Proc. SPIE. 10950, Medical Imaging 2019: Computer-Aided Diagnosis

KEYWORDS: Feature extraction, Prostate cancer, Cancer, Magnetic resonance imaging, Pathology, Prostate, Tissues, Diffusion, Image registration

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Decipher, a genomic test, is used to predict the likelihood of metastasis and prostate cancer (PCa) specific mortality based on expression patterns of 22 RNA markers from radical prostatectomy (RP) specimens. It has been shown to be strongly correlated with metastasis-free prognosis and has been integrated with the National Comprehensive Cancer Network (NCCN) guidelines. However, Decipher is expensive and tissue destructive. Radiomic features refer to the high-throughput computational texture or shape features extracted from radiographic scans. Radiomic features derived from multi-parametric magnetic resonance imaging (mpMRI) of prostate cancer have been shown to be associated with clinically significant PCa. In this study, we sought to evaluate whether radiomic features derived from T2-weighted MRI (T2WI) and apparent diffusion coefficient (ADC) maps of the prostate could distinguish different Decipher risk groups (low, intermediate and high). We also explored correlations between Decipher risk associated radiomic features and features relating to gland morphology on corresponding digitized surgical specimens. A retrospectively acquired, de-identified cohort of 70 PCa patients (N = 74 lesions) who underwent 3T mpMRI prior to RP and Decipher tests after RP were used in this study. The Decipher risk score, ranging from 0 to 1, was used to categorize patients into low/intermediate (D₁) and high (D₂) risk groups. A multivariate logistic regression model was trained (N = 37 lesions) using radiomic features selected via elastic-net regularization to predict the Decipher risk groups. The model was evaluated on a hold-out test set (N = 37 lesions) and resulted in an area under the receiver operating characteristic curve (AUC) = 0:80. Our model outperformed the prediction using PIRADS v2 (AUC = 0:67), but showed comparable performance with Gleason Grade Group (GGG) (AUC = 0:80). We observed that the best discriminating radiomic features were correlated with gland morphology and gland packing on corresponding histopathology (R = 0.43, p < 0.05).

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