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

Abstract

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.

MRI Radiomics in Prostate Cancer: A Reliability Study

“…Similar to our results, in this study, the differences originating from MRI acquisition were notably described as the most impactful on reliability ( 33 ), and only a few features showed good to excellent repeatability and reproducibility coefficients, although such identification is still insufficient for a reliable radiomics study ( 32 ). The observed reliability is poor also in other studies, in accordance with our results ( 34 , 35 ).…”

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

“…Articles that studied radiomics in PCa are just briefly reminded and not analyzed in detail, and we had them incorporated in a table, along with their clinical outcomes and results. In PCa, the use of radiomics aids prostate volume selection and segmentation [ 30 , 40 , 46 , 73 , 74 , 75 , 76 ], PCa screening [ 28 , 77 , 78 ], detection and classification [ 29 , 77 , 79 , 80 , 81 ], in addition to its role in risk stratification [ 61 , 76 , 82 , 83 ], treatment [ 59 , 75 , 78 , 84 , 85 , 86 ], and prognosis. One of the first studies that analyzed the imaging features for PCA was performed by Khalvati et al [ 87 ], with the goal of creating a radiomics-based auto detection method utilizing an mpMRI feature model that combined computed high b-value DWI (diffusion-weighted imaging) and correlated diffusion imaging, which was then evaluated through a support vector machine (SVM) classifier.…”

Magnetic resonance imaging-derived radiomic signature predicts locoregional failure after organ preservation therapy in patients with hypopharyngeal squamous cell carcinoma

“…Further study is warranted to assess whether DWI-driven radiomic signatures can predict 1-year LRF in patients with advanced HPSCC who want to receive OPT. Taken together, these findings suggest that a combination of image features through the analysis of CT images, MRI images (included DWI), and 18 F-FDG PET might provide enormous information about the tumor phenotypic features [40][41][42][43][44], and the aforementioned approach should be confirmed in future studies for patients with HPSCC who received CCRT for organ preservation.…”

“…Recently, Leithner et al reported that radiomic analysis derived from DWI imaging can provide high accuracy in subclassifying the molecular subtypes of breast cancer (luminals A and B, and HER2 enriched) [40]. Dulhanty et al also demonstrated that radiomics-driven DWI can be used as a forcefully sensing strategy to help physicians precisely screen zone-level prostate cancers for further targeted biopsy treatment [41]. Further study is warranted to assess whether DWI-driven radiomic signatures can predict 1-year LRF in patients with advanced HPSCC who want to receive OPT.…”