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Proceedings Paper

A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer
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Paper Abstract

Bi-parametric MRI (bpMRI: T2W MRI and Apparent Diffusion Coefficient maps (ADC) derived from diffusion weighted imaging) is increasingly being used to characterize prostate cancer (PCa). However, inter- and intrareader variability hinders interpretation of MRI. Deep learning networks may aid in PCa characterization and may allow for non-invasively distinguishing clinically significant (csPCa: GGG<1) and insignificant (ciPCa: GGG=1) PCa. Recent studies have shown that signatures from peri-tumoral (PT) region on imaging add significant value to those from intra-tumoral (IT) region for disease detection and characterization. In this work, we present a multi-sequence multi-instance learning convolutional neural network trained using 2D patches extracted from PCa regions of interest (ROIs) on prostate bpMRI to distinguish csPCa and ciPCa. The trained classifier is used to extract pooled features from both the IT and PT ROIs, which are then used to train a random forest classifier to distinguish csPCa and ciPCa. We train and test our models using patient studies from two different institutions (n=298) with GGG obtained either from post-surgical specimens or biopsies. Model built using IT (DIT) and PT (DPT) deep features alone resulted in an area under the curve (AUC) of 0.83 and 0.73 respectively, while models computed from IT (RIT) and PT (RPT) radiomic features resulted in an AUC of 0.77 and 0.75 respectively. The models DIP and RIP trained on combination of IT and PT deep features and radiomic features resulted in an AUC of 0.86 and 0.80 respectively. In both cases, we observe that combining IT and PT features helps in improving the overall classifier performance in distinguishing csPCa and ciPCa.

Paper Details

Date Published: 16 March 2020
PDF: 10 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140M (16 March 2020);
Show Author Affiliations
Amogh Hiremath, Case Western Reserve Univ. (United States)
Rakesh Shiradkar, Case Western Reserve Univ. (United States)
Nathaniel Braman, Case Western Reserve Univ. (United States)
Prateek Prasanna, Case Western Reserve Univ. (United States)
Art Rastinehad, Icahn School of Medicine at Mount Sinai (United States)
Andrei Purysko, Cleveland Clinic (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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