Optimal treatment selection for localized renal tumors is challenging due to their variable biological behavior and limited ability to pre-operatively assess their aggressiveness. We investigated hyperpolarized (HP) ¹³C pyruvate MRI to noninvasively assess tumor lactate production and compartmentalization, which are strongly associated with renal tumor aggressiveness. Orthotopic tumors were created in mice using human renal cell carcinoma (RCC) lines (A498, 786-O, UOK262) with varying expression of lactate dehydrogenase A (LDHA) which catalyzes the pyruvate-to-lactate conversion, and varying expression of monocarboxylate transporter 4 (MCT4) which mediates lactate export out of the cells. Dynamic HP ¹³C pyruvate MRI showed that the A498 tumors had significantly higher ¹³C pyruvate-to-lactate conversion than the UOK262 and 786-O tumors, corresponding to higher A498 tumor LDHA expression. Additionally, diffusion-weighted HP ¹³C pyruvate MRI showed that the A498 tumors had significantly higher ¹³C lactate apparent diffusion coefficients compared to 786-O tumors, with corresponding higher MCT4 expression, which likely reflects more rapid lactate export in the A498 tumors. Our data demonstrate the feasibility of HP ¹³C pyruvate MRI to inform on tumor lactate production and compartmentalization, and provide the scientific premise for future clinical investigation into the utility of this technique to noninvasively interrogate renal tumor aggressiveness and to guide treatment selection.

Non-invasive assessment of the biological aggressiveness of prostate cancer (PCa) is needed for men with localized disease. Hyperpolarized (HP) ¹³C magnetic resonance (MR) spectroscopy is a powerful approach to image metabolism, specifically the conversion of HP [1-¹³C]pyruvate to [1-¹³C]lactate, catalyzed by lactate dehydrogenase (LDH). Significant increase in tumor lactate was measured in high-grade PCa relative to benign and low-grade cancer, suggesting that HP ¹³C MR could distinguish low-risk (Gleason score ≤3 + 4) from high-risk (Gleason score ≥4 + 3) PCa. To test this and the ability of HP ¹³C MR to detect these metabolic changes, we cultured prostate tissues in an MR-compatible bioreactor under continuous perfusion. ³¹P spectra demonstrated good viability and dynamic HP ¹³C-pyruvate MR demonstrated that high-grade PCa had significantly increased lactate efflux compared to low-grade PCa and benign prostate tissue. These metabolic differences are attributed to significantly increased LDHA expression and LDH activity, as well as significantly increased monocarboxylate transporter 4 (MCT4) expression in high- versus low- grade PCa. Moreover, lactate efflux, LDH activity, and MCT4 expression were not different between low-grade PCa and benign prostate tissues, indicating that these metabolic alterations are specific for high-grade disease. These distinctive metabolic alterations can be used to differentiate high-grade PCa from low-grade PCa and benign prostate tissues using clinically translatable HP [1-¹³C]pyruvate MR.

Despite the utility of musculoskeletal dynamics modeling, there exists no safe, noninvasive method of measuring in vivo muscle output force in real time — limiting both biomechanical insight into dexterous motion and intuitive control of assistive devices. In this paper, we demonstrate that muscle deformation constitutes a promising, yet unexplored signal from which to 1) infer such forces and 2) build novel device control schemes. Through a case study of the elbow joint on a preliminary cohort of 10 subjects, we show that muscle deformation (specifically, thickness change of the brachioradialis, as measured via ultrasound and tracked via optical flow) correlates well with elbow output force to an extent comparable with standard surface electromyography (sEMG) activation during varied isometric elbow contraction. We then show that, given real-time visual feedback, subjects can readily perform a trajectory tracking task using this deformation signal, and that they largely prefer this method to a comparable sEMG-based control scheme and perform the tracking task with similar accuracy. Together, these contributions illustrate muscle deformation’s potential utility for both biomechanical study of individual muscle dynamics and device control, in a manner that — thanks to, unlike sEMG, the localized nature of the signal and its tight mechanistic coupling to output force — is readily extensible to multiple muscles and device degrees of freedom. To enable such future extensions, all modeling, tracking, and visualization software described in this paper, as well as all raw and processed data, have been made available on SimTK as part of the Open-Arm project ( https://simtk.org/projects/openarm ) for general research use.