Quantifying supraspinatus tendon responses to exposures emulative of human physiological levels in an animal model.

Rotator cuff pathology typically originates in the supraspinatus tendon, but uncertainty exists on how combinations of glenohumeral elevation angle and load intensity influence responses of the intact, functional supraspinatus unit. This study exposed the supraspinatus tendon to mechanical loading scenarios emulative of derived muscle force and postural conditions measured in vivo to document its responses. Right shoulders from 48 Sprague-Dawley rats were placed into one of eight testing groups combining glenohumeral elevation angles (0/30/60/75°) and a high or low load intensity for 1500 cycles at 0.25 Hz using a custom mounting apparatus attached to a tensile testing system. Load intensities were derived from in vivo human partitional muscular activation levels collected previously and scaled to the animal model. Mechanical response variables examined included tangent stiffness and hysteresis, in addition to localized surface stretch ratios calculated via virtual tracking points. A significant three-way interaction (p = 0.0009) between elevation angle, load magnitude and cycle number occurred for tangent stiffness, with increasing angles, loads and cycles increasing stiffness by up to 49%. Longitudinal stretch ratios had significant interactions (p = 0.0396) with increasing elevation angles, load intensities and cycle numbers, and differences existed between the articular and bursal sides of the tendon. Complex interactions between angle, load and cycle number suggest higher abduction angles, increased load magnitude and higher loading cycles increase tangent stiffness, stretch ratios and hysteresis within the tendon.

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