Integrating zonal priors and pathomic MRI biomarkers for improved aggressive prostate cancer detection on MRI

Indrani Bhattacharya; Wei Shao; Simon J. C. Soerensen; Richard E. Fan; Jeffrey B. Wang; Christian Kunder; Pejman Ghanouni; Geoffrey A. Sonn; Mirabela Rusu

doi:10.1117/12.2612433

4 April 2022 Integrating zonal priors and pathomic MRI biomarkers for improved aggressive prostate cancer detection on MRI

Indrani Bhattacharya, Wei Shao, Simon J. C. Soerensen, Richard E. Fan, Jeffrey B. Wang, Christian Kunder, Pejman Ghanouni, Geoffrey A. Sonn, Mirabela Rusu

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120330P (2022) https://doi.org/10.1117/12.2612433
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Automated detection of aggressive prostate cancer on Magnetic Resonance Imaging (MRI) can help guide targeted biopsies and reduce unnecessary invasive biopsies. However, automated methods of prostate cancer detection often have a sensitivity-specificity trade-off (high sensitivity with low specificity or vice-versa), making them unsuitable for clinical use. Here, we study the utility of integrating prior information about the zonal distribution of prostate cancers with a radiology-pathology fusion model in reliably identifying aggressive and indolent prostate cancers on MRI. Our approach has two steps: 1) training a radiology-pathology fusion model that learns pathomic MRI biomarkers (MRI features correlated with pathology features) and uses them to selectively identify aggressive and indolent cancers, and 2) post-processing the predictions using zonal priors in a novel optimized Bayes’ decision framework. We compare this approach with other approaches that incorporate zonal priors during training. We use a cohort of 74 radical prostatectomy patients as our training set, and two cohorts of 30 radical prostatectomy patients and 53 biopsy patients as our test sets. Our rad-path-zonal fusion-approach achieves cancer lesion-level sensitivities of 0.77±0.29 and 0.79±0.38, and specificities of 0.79±0.23 and 0.62±0.27 on the two test sets respectively, compared to baseline sensitivities of 0.91±0.27 and 0.94±0.21 and specificities of 0.39±0.33 and 0.14±0.19, verifying its utility in achieving balance between sensitivity and specificity of lesion detection.

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Indrani Bhattacharya, Wei Shao, Simon J. C. Soerensen, Richard E. Fan, Jeffrey B. Wang, Christian Kunder, Pejman Ghanouni, Geoffrey A. Sonn, and Mirabela Rusu "Integrating zonal priors and pathomic MRI biomarkers for improved aggressive prostate cancer detection on MRI", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120330P (4 April 2022); https://doi.org/10.1117/12.2612433

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