Using principal component analysis for the prediction of tumor response to transarterial chemoembolization.

PURPOSE: To quantitate the tumor blush of hepatocellular carcinoma (HCC) at the time of transarterial chemoembolization (TACE) using principal component analysis (PCA), and to correlate the quantitated tumor blush to response to therapy. MATERIALS AND METHODS: In this proof-of-concept study, 27 primary HCC tumors in 25 patients (18 men, 7 women; mean age 66 years ± 9) were analyzed. We conducted a retrospective analysis of TACE procedures that were performed during March through July of 2017. Digital subtraction angiography (DSA) was combined with PCA to condense spatial and temporal information into a single image. The tumor and liver contrast enhancements were calculated, and the ratio was used to determine the relative vascular enhancement of the tumor. Tumor response to therapy was determined at 1-month post procedure. RESULTS: Using PCA-generated fluoroscopic imaging (PCA-FI), we quantitated the tumor blush and assigned a vascular enhancement value (VEV) to each tumor. Tumors that responded to treatment (N = 12) had statistically higher VEVs compared with the nonresponders (N = 15), with a mean value of 0.96 ± 0.455 vs. 0.57 ± 0.309, (p = 0.013). CONCLUSIONS: We developed a method for quantitating tumor blush using routine angiographic images. The VEVs calculated using these images may allow for the prediction of tumor response to therapy. This pilot study suggests that there is a correlation between tumor blush intensity and tumor response.

Noninvasive depth estimation using tissue optical properties and a dual-wavelength fluorescent molecular probe in vivo.

Translation of fluorescence imaging using molecularly targeted imaging agents for real-time assessment of surgical margins in the operating room requires a fast and reliable method to predict tumor depth from planar optical imaging. Here, we developed a dual-wavelength fluorescent molecular probe with distinct visible and near-infrared excitation and emission spectra for depth estimation in mice and a method to predict the optical properties of the imaging medium such that the technique is applicable to a range of medium types. Imaging was conducted at two wavelengths in a simulated blood vessel and an in vivo tumor model. Although the depth estimation method was insensitive to changes in the molecular probe concentration, it was responsive to the optical parameters of the medium. Results of the intra-tumor fluorescent probe injection showed that the average measured tumor sub-surface depths were 1.31 ± 0.442 mm, 1.07 ± 0.187 mm, and 1.42 ± 0.182 mm, and the average estimated sub-surface depths were 0.97 ± 0.308 mm, 1.11 ± 0.428 mm, 1.21 ± 0.492 mm, respectively. Intravenous injection of the molecular probe allowed for selective tumor accumulation, with measured tumor sub-surface depths of 1.28 ± 0.168 mm, and 1.50 ± 0.394 mm, and the estimated depths were 1.46 ± 0.314 mm, and 1.60 ± 0.409 mm, respectively. Expansion of our technique by using material optical properties and mouse skin optical parameters to estimate the sub-surface depth of a tumor demonstrated an agreement between measured and estimated depth within 0.38 mm and 0.63 mm for intra-tumor and intravenous dye injections, respectively. Our results demonstrate the feasibility of dual-wavelength imaging for determining the depth of blood vessels and characterizing the sub-surface depth of tumors in vivo.

Multimodal fluorescence molecular imaging for in vivo characterization of skin cancer using endogenous and exogenous fluorophores.

Similarity of skin cancer with many benign skin pathologies requires reliable methods to detect and differentiate the different types of these lesions. Previous studies have explored the use of disparate optical techniques to identify and estimate the invasive nature of melanoma and basal cell carcinoma with varying outcomes. Here, we used a concerted approach that provides complementary information for rapid screening and characterization of tumors, focusing on squamous cell carcinoma (SCC) of the skin. Assessment of in vivo autofluorescence lifetime (FLT) imaging of endogenous fluorophores that are excitable at longer wavelengths (480 nm) than conventional NADH and FAD revealed a decrease in the short FLT component for SCC compared to normal skin, with mean values of 0.57 ± 0.026 ?? ns and 0.61 ± 0.021 ?? ns , respectively ( p = 0.004 ). Subsequent systemic administration of a near-infrared fluorescent molecular probe in SCC bearing mice, followed by the implementation of image processing methods on data acquired from two-dimensional and three-dimensional fluorescence molecular imaging, allowed us to estimate the tumor volume and depth, as well as quantify the fluorescent probe in the tumor. The result suggests the involvement of lipofuscin-like lipopigments and riboflavin in SCC metabolism and serves as a model for staging SCC.

Enhancing in vivo tumor boundary delineation with structured illumination fluorescence molecular imaging and spatial gradient mapping.

Fluorescence imaging, in combination with tumor-avid near-infrared (NIR) fluorescent molecular probes, provides high specificity and sensitivity for cancer detection in preclinical animal models, and more recently, assistance during oncologic surgery. However, conventional camera-based fluorescence imaging techniques are heavily surface-weighted such that surface reflection from skin or other nontumor tissue and nonspecific fluorescence signals dominate, obscuring true cancer-specific signals and blurring tumor boundaries. To address this challenge, we applied structured illumination fluorescence molecular imaging (SIFMI) in live animals for automated subtraction of nonspecific surface signals to better delineate accumulation of an NIR fluorescent probe targeting α4ß1 integrin in mice bearing subcutaneous plasma cell xenografts. SIFMI demonstrated a fivefold improvement in tumor-to-background contrast when compared with other full-field fluorescence imaging methods and required significantly reduced scanning time compared with diffuse optical spectroscopy imaging. Furthermore, the spatial gradient mapping enhanced highlighting of tumor boundaries. Through the relatively simple hardware and software modifications described, SIFMI can be integrated with clinical fluorescence imaging systems, enhancing intraoperative tumor boundary delineation from the uninvolved tissue.

Gradient-Based Algorithm for Determining Tumor Volumes in Small Animals Using Planar Fluorescence Imaging Platform.

Planar fluorescence imaging is widely used in biological research because of its simplicity, use of non-ionizing radiation, and high-throughput data acquisition. In cancer research, where small animal models are used to study the in vivo effects of cancer therapeutics, the output of interest is often the tumor volume. Unfortunately, inaccuracies in determining tumor volume from surface-weighted projection fluorescence images undermine the data, and alternative physical or conventional tomographic approaches are prone to error or are tedious for most laboratories. Here, we report a method that uses a priori knowledge of a tumor xenograft model, a tumor-targeting near infrared probe, and a custom-developed image analysis planar view tumor volume algorithm (PV-TVA) to estimate tumor volume from planar fluorescence images. Our algorithm processes images obtained using near infrared light for improving imaging depth in tissue in comparison with light in the visible spectrum. We benchmarked our results against the actual tumor volume obtained from a standard water volume displacement method. Compared with a caliper-based method that has an average deviation from an actual volume of 18% (204.34 ± 115.35 mm3), our PV-TVA average deviation from the actual volume was 9% (97.24 ± 70.45 mm3; P < .001). Using a normalization-based analysis, we found that bioluminescence imaging and PV-TVA average deviations from actual volume were 36% and 10%, respectively. The improved accuracy of tumor volume assessment from planar fluorescence images, rapid data analysis, and the ease of archiving images for subsequent retrieval and analysis potentially lend our PV-TVA method to diverse cancer imaging applications.