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Standard clinical management of extremity soft tissue sarcomas includes surgery with radiation therapy. Wound complications (WCs) arising from treatment may occur due to bacterial infection and tissue breakdown. The ability to detect changes in these parameters during treatment may lead to earlier interventions that mitigate WCs. We describe the use of a new system composed of an autofluorescence imaging device and an optical three-dimensional tracking system to detect and coregister the presence of bacteria with radiation doses. The imaging device visualized erythema using white light and detected bacterial autofluorescence using 405-nm excitation light. Its position was tracked relative to the patient using IR reflective spheres and registration to the computed tomography coordinates. Image coregistration software was developed to spatially overlay radiation treatment plans and dose distributions on the white light and autofluorescence images of the surgical site. We describe the technology, its use in the operating room, and standard operating procedures, as well as demonstrate technical feasibility and safety intraoperatively. This new clinical tool may help identify patients at greater risk of developing WCs and investigate correlations between radiation dose, skin response, and changes in bacterial load as biomarkers associated with WCs.

Transillumination breast spectroscopy (TiBS) uses nonionizing optical radiation to gain information about breast tissue morphological and structural properties. TiBS spectra are obtained from 232 women and compared to mammographic density (MD) quantified using Cumulus. The ability of TiBS to estimate MD is assessed using partial least-squares (PLS) regression methods, which requires TiBS spectra as input (X) and Cumulus MD as target (Y) data. Multiple PLS models are considered to determine the optimal processing technique(s) for the input (X) and target (Y) data. For each model, the association between TiBS estimated MD (Y) and Cumulus MD (Y) is established using Spearman's rank correlation and linear regression analysis. The model that best estimates MD has the fewest assumptions regarding target (Y) and spectral (X) processing. The Spearman's correlation coefficient between predicted MD and Cumulus MD for this model is 0.88, with a regression slope (beta) of 0.93 (95% CI 0.83-1.02) and an R(2) of 0.78. The approximation of individual MD was within 10% of Cumulus MD for the majority of women (80%), without stratification on age, body mass index (BMI), and menopausal status. TiBS provides an alternative to mammography assessed MD enabling frequent and earlier use of MD as a risk marker in preventive oncology.