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Open AccessArticle

Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

1
Vision and Image Processing Research Group, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
3
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
4
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada
5
Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON N2L 3G1, Canada
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(5), 1539; https://doi.org/10.3390/s20051539
Received: 9 February 2020 / Revised: 2 March 2020 / Accepted: 6 March 2020 / Published: 10 March 2020
(This article belongs to the Special Issue Biomedical Imaging and Sensing)
Prostate cancer is the most commonly diagnosed cancer in North American men; however, prognosis is relatively good given early diagnosis. This motivates the need for fast and reliable prostate cancer sensing. Diffusion weighted imaging (DWI) has gained traction in recent years as a fast non-invasive approach to cancer sensing. The most commonly used DWI sensing modality currently is apparent diffusion coefficient (ADC) imaging, with the recently introduced computed high-b value diffusion weighted imaging (CHB-DWI) showing considerable promise for cancer sensing. In this study, we investigate the efficacy of ADC and CHB-DWI sensing modalities when applied to zone-level prostate cancer sensing by introducing several radiomics driven zone-level prostate cancer sensing strategies geared around hand-engineered radiomic sequences from DWI sensing (which we term as Zone-X sensing strategies). Furthermore, we also propose Zone-DR, a discovery radiomics approach based on zone-level deep radiomic sequencer discovery that discover radiomic sequences directly for radiomics driven sensing. Experimental results using 12,466 pathology-verified zones obtained through the different DWI sensing modalities of 101 patients showed that: (i) the introduced Zone-X and Zone-DR radiomics driven sensing strategies significantly outperformed the traditional clinical heuristics driven strategy in terms of AUC, (ii) the introduced Zone-DR and Zone-SVM strategies achieved the highest sensitivity and specificity, respectively for ADC amongst the tested radiomics driven strategies, (iii) the introduced Zone-DR and Zone-LR strategies achieved the highest sensitivities for CHB-DWI amongst the tested radiomics driven strategies, and (iv) the introduced Zone-DR, Zone-LR, and Zone-SVM strategies achieved the highest specificities for CHB-DWI amongst the tested radiomics driven strategies. Furthermore, the results showed that the trade-off between sensitivity and specificity can be optimized based on the particular clinical scenario we wish to employ radiomic driven DWI prostate cancer sensing strategies for, such as clinical screening versus surgical planning. Finally, we investigate the critical regions within sensing data that led to a given radiomic sequence generated by a Zone-DR sequencer using an explainability method to get a deeper understanding on the biomarkers important for zone-level cancer sensing. View Full-Text
Keywords: prostate cancer sensing; zone-level sensing; radiomics; discovery radiomics prostate cancer sensing; zone-level sensing; radiomics; discovery radiomics
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MDPI and ACS Style

Dulhanty, C.; Wang, L.; Cheng, M.; Gunraj, H.; Khalvati, F.; Haider, M.A.; Wong, A. Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing. Sensors 2020, 20, 1539. https://doi.org/10.3390/s20051539

AMA Style

Dulhanty C, Wang L, Cheng M, Gunraj H, Khalvati F, Haider MA, Wong A. Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing. Sensors. 2020; 20(5):1539. https://doi.org/10.3390/s20051539

Chicago/Turabian Style

Dulhanty, Chris; Wang, Linda; Cheng, Maria; Gunraj, Hayden; Khalvati, Farzad; Haider, Masoom A.; Wong, Alexander. 2020. "Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing" Sensors 20, no. 5: 1539. https://doi.org/10.3390/s20051539

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