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SignificanceExtremity injury represents the leading cause of trauma hospitalizations among adults under the age of 65 years, and long-term impairments are often substantial. Restoring function depends, in large part, on bone and soft tissue healing. Thus, decisions around treatment strategy are based on assessment of the healing potential of injured bone and/or soft tissue. However, at the present, this assessment is based on subjective clinical clues and/or cadaveric studies without any objective measure. Optical imaging is an ideal method to solve several of these issues.AimThe aim is to highlight the current challenges in assessing bone and tissue perfusion/viability and the potentially high impact applications for optical imaging in orthopaedic surgery.ApproachThe prospective will review the current challenges faced by the orthopaedic surgeon and briefly discuss optical imaging tools that have been published. With this in mind, it will suggest key research areas that could be evolved to help make surgical assessments more objective and quantitative.ResultsOrthopaedic surgical procedures should benefit from incorporation of methods to measure functional blood perfusion or tissue metabolism. The types of measurements though can vary in the depth of tissue sampled, with some being quite superficial and others sensing several millimeters into the tissue. Most of these intrasurgical imaging tools represent an ideal way to improve surgical treatment of orthopaedic injuries due to their inherent point-of-care use and their compatibility with real-time management.ConclusionWhile there are several optical measurements to directly measure bone function, the choice of tools can determine also the signal strength and depth of sampling. For orthopaedic surgery, real-time data regarding bone and tissue perfusion should lead to more effective patient-specific management of common orthopaedic conditions, requiring deeper penetrance commonly seen with indocyanine green imaging. This will lower morbidity and result in decreased variability associated with how these conditions are managed.

Ideally, neoadjuvant chemotherapy (NAC) assessment should predict pathologic complete response (pCR), a surrogate clinical endpoint for 5-year survival, as early as possible during typical 3- to 6-month breast cancer treatments. We introduce and demonstrate an approach for predicting pCR within 10 days of initiating NAC. The method uses a bedside diffuse optical spectroscopic imaging (DOSI) technology and logistic regression modeling. Tumor and normal tissue physiological properties were measured longitudinally throughout the course of NAC in 33 patients enrolled in the American College of Radiology Imaging Network multicenter breast cancer DOSI trial (ACRIN-6691). An image analysis scheme, employing z-score normalization to healthy tissue, produced models with robust predictions. Notably, logistic regression based on z-score normalization using only tissue oxygen saturation (StO2) measured within 10 days of the initial therapy dose was found to be a significant predictor of pCR (AUC  =  0.92; 95% CI: 0.82 to 1). This observation suggests that patients who show rapid convergence of tumor tissue StO2 to surrounding tissue StO2 are more likely to achieve pCR. This early predictor of pCR occurs prior to reductions in tumor size and could enable dynamic feedback for optimization of chemotherapy strategies in breast cancer.

We present a framework for characterizing the performance of an experimental imaging technology, diffuse optical spectroscopic imaging (DOSI), in a 2-year multicenter American College of Radiology Imaging Network (ACRIN) breast cancer study (ACRIN-6691). DOSI instruments combine broadband frequency-domain photon migration with time-independent near-infrared (650 to 1000 nm) spectroscopy to measure tissue absorption and reduced scattering spectra and tissue hemoglobin, water, and lipid composition. The goal of ACRIN-6691 was to test the effectiveness of optically derived imaging endpoints in predicting the final pathologic response of neoadjuvant chemotherapy (NAC). Sixty patients were enrolled over a 2-year period at participating sites and received multiple DOSI scans prior to and during 3- to 6-month NAC. The impact of three sources of error on accuracy and precision, including different operators, instruments, and calibration standards, was evaluated using a broadband reflectance standard and two different solid tissue-simulating optical phantoms. Instruments showed

Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <2% residual error margin. Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification. [ABSTRACT FROM AUTHOR]

The feasibility of spatial frequency domain imaging (SFDI) for breast surgical margin assessment was evaluated in tissue-simulating phantoms and in fully intact lumpectomy specimens at the time of surgery. Phantom data was evaluated according to contrast-detail resolution, quantitative accuracy and model-data goodness of fit, where optical parameters were estimated by minimizing the residual sum of squares between the measured modulation amplitude and its solutions, modeled according to diffusion and scaled-Monte Carlo simulations. In contrast-detail phantoms, a 1.25-mm-diameter surface inclusion was detectable for scattering contrast >28%; a fraction of this scattering contrast (7%) was detectable for a 10 mm surface inclusion and at least 33% scattering contrast was detected up to 1.5 mm below the phantom surface, a probing depth relevant to breast surgical margin assessment. Recovered hemoglobin concentrations were insensitive to changes in scattering, except for overestimation at visible wavelengths for total hemoglobin concentrations

The goal of our study was to determine the susceptibility of different pancreatic cell lines to clinically applicable photodynamic therapy (PDT). The efficacy of PDT of two different commercially available photosensitizers, verteporfin and sodium porfimer, was compared using a panel of four different pancreatic cancer cell lines, PANC-1, BxPC-3, CAPAN-2, and MIA PaCa-2, and an immortalized non-neoplastic pancreatic ductal epithelium cell line, HPNE. The minimum effective concentrations and dose-dependent curves of verteporfin and sodium porfimer on PANC-1 were determined. Since pancreatic cancer is known to have significant stromal components, the effect of PDT on stromal cells was also assessed. To mimic tumor-stroma interaction, a coculture of primary human fibroblasts or human pancreatic stellate cell (HPSCs) line with PANC-1 was used to test verteporfin-PDT-mediated cell death of PANC-1. Two cytokines (TNF-α and IL-1β) were used for stimulation of primary fibroblasts (derived from human esophageal biopsies) or HPSCs. The increased expression of smooth muscle actin (α-SMA) confirmed the activation of fibroblasts or HPSC upon treatment with TNF-α and IL-1β. Cell death assays showed that both sodium porfimer- and verteporfin-mediated PDT-induced cell death in a dose-dependent manner. However, verteporfin-PDT treatment had a greater efficiency with 60× lower concentration than sodium porfimer-PDT in the PANC-1 incubated with stimulated fibroblasts or HPSC. Moreover, activation of stromal cells did not affect the treatment of the pancreatic cancer cell lines, suggesting that the effects of PDT are independent of the inflammatory microenvironment found in this two-dimensional culture model of cancers. [ABSTRACT FROM AUTHOR]