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Significance: The study has confirmed the feasibility of using ultraviolet (UV) excitation to visualize and quantify desmoplasia in fresh tumor tissue of pancreatic adenocarcinoma (PDAC) in an orthotopic xenograft mouse model, which provides a useful imaging platform to evaluate acute therapeutic responses. Aim: Stromal network of collagen prominent in PDAC tumors is examined by imaging fresh tissue samples stained with histological dyes. Fluorescence signals are color-transferred to mimic Masson’s trichrome staining. Approach: Murine tumor samples were stained with Hoechst, eosin, and rhodamine B and excited at 275-nm. Fluorescence signals in the visible spectrum were captured by a CMOS color camera with high contrast and resolution at whole-tumor slice field of view. Results: Fluorescence imaging using UV excitation is capable of visualizing collagen deposition in PDAC tumors. Both fluorescence and histology data showed collagen content of up to 30%. The collagen modulation effect due to photodynamic priming treatment was observed showing 13% of collagen reduction. Necrosis area is visible and perfusion imaging using Texas Red dextran is feasible. Conclusions: The study demonstrates collagen visualization in fresh PDAC tumor samples using UV excitation. This imaging platform also provides quantitative stromal information from fiber analysis and visibility of necrosis and perfusion, suitable for therapeutic response assessment of photodynamic therapy.

Significance: A key risk faced by oncological surgeons continues to be complete removal of tumor. Currently, there is no intraoperative imaging device to detect kidney tumors during excision. Aim: We are evaluating molecular chemical imaging (MCI) as a technology for real-time tumor detection and margin assessment during tumor removal surgeries. Approach: In exploratory studies, we evaluate visible near infrared (Vis-NIR) MCI for differentiating tumor from adjacent tissue in ex vivo human kidney specimens, and in anaesthetized mice with breast or lung tumor xenografts. Differentiation of tumor from nontumor tissues is made possible with diffuse reflectance spectroscopic signatures and hyperspectral imaging technology. Tumor detection is achieved by score image generation to localize the tumor, followed by application of computer vision algorithms to define tumor border. Results: Performance of a partial least squares discriminant analysis (PLS-DA) model for kidney tumor in a 22-patient study is 0.96 for area under the receiver operating characteristic curve. A PLS-DA model for in vivo breast and lung tumor xenografts performs with 100% sensitivity, 83% specificity, and 89% accuracy. Conclusion: Detection of cancer in surgically resected human kidney tissues is demonstrated ex vivo with Vis-NIR MCI, and in vivo on mice with breast or lung xenografts.

pColorectal cancer is the second most common malignancy diagnosed globally. Critical gaps exist in diagnostic and surveillance imaging modalities for colorectal neoplasia. Although prior studies have demonstrated the capability of photoacoustic imaging techniques to differentiate normal from neoplastic tissue in the gastrointestinal tract, evaluation of deep tissue with a fast speed and a large field of view remains limited. To investigate the ability of photoacoustic technology to image deeper tissue, we conducted a pilot study using a real-time co-registered photoacoustic tomography (PAT) and ultrasound (US) system. A total of 23italicex vivo/italichuman colorectal tissue samples were imaged immediately after surgical resection. Co-registered photoacoustic images of malignancies showed significantly increased PAT signal compared to normal regions of the same sample. The quantitative relative total hemoglobin (rHbT) concentration computed from four optical wavelengths, the spectral features, such as the mean spectral slope, and 0.5-MHz intercept extracted from PAT and US spectral data, and image features, such as the first- and second-order statistics along with the standard deviation of the mean radon transform of PAT images, have shown statistical significance between untreated colorectal tumors and the normal tissue. Using either a logistic regression model or a support vector machine, the best set of parameters of rHbT and PAT intercept has achieved area-under-the-curve (AUC) values of 0.97 and 0.95 for both training and testing data sets, respectively, for prediction of histologically confirmed invasive carcinoma./p.