Organ-at-risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT.

OBJECTIVE: To quantify the clinical performance of a machine learning (ML) algorithm for organ-at-risk (OAR) dose prediction for lung stereotactic body radiation therapy (SBRT) and estimate the treatment planning benefit from having upfront access to these dose predictions. METHODS: ML models were trained using multi-center data consisting of 209 patients previously treated with lung SBRT. Two prescription levels were investigated, 50 Gy in five fractions and 54 Gy in three fractions. Models were generated using a gradient-boosted regression tree algorithm using grid searching with fivefold cross-validation. Twenty patients not included in the training set were used to test OAR dose prediction performance, ten for each prescription. We also performed blinded re-planning based on OAR dose predictions but without access to clinically delivered plans. Differences between predicted and delivered doses were assessed by root-mean square deviation (RMSD), and statistical differences between predicted, delivered, and re-planned doses were evaluated with one-way analysis of variance (ANOVA) tests. RESULTS: ANOVA tests showed no significant differences between predicted, delivered, and replanned OAR doses (all p ≥ 0.36). The RMSD was 2.9, 3.9, 4.3, and 1.7Gy for max dose to the spinal cord, great vessels, heart, and trachea, respectively, for 50 Gy in five fractions. Average improvements of 1.0, 1.4, and 2.0 Gy were seen for spinal cord, esophagus, and trachea max doses in blinded replans compared to clinically delivered plans with 54 Gy in three fractions, and 1.8, 0.7, and 1.5 Gy, respectively, for the esophagus, heart and bronchus max doses with 50 Gy in five fractions. Target coverage was similar with an average PTV V100% of 94.7% for delivered plans compared to 97.3% for blinded re-plans for 50 Gy in five fractions, and respectively 98.4% versus 99.2% for 54 Gy in three fractions. CONCLUSION: This study validated ML-based OAR dose prediction for lung SBRT, showing potential for improved OAR dose sparing and more consistent plan quality using dose predictions for patient-specific planning guidance.

Spatial genetic structure in American black bears (Ursus americanus): female philopatry is variable and related to population history.

Previously, American black bears (Ursus americanus) were thought to follow the pattern of female philopatry and male-biased dispersal. However, recent studies have identified deviations from this pattern. Such flexibility in dispersal patterns can allow individuals greater ability to acclimate to changing environments. We explored dispersal and spatial genetic relatedness patterns across ten black bear populations-including long established (historic), with known reproduction >50 years ago, and newly established (recent) populations, with reproduction recorded <50 years ago-in the Interior Highlands and Southern Appalachian Mountains, United States. We used spatially explicit, individual-based genetic simulations to model gene flow under scenarios with varying levels of population density, genetic diversity, and female philopatry. Using measures of genetic distance and spatial autocorrelation, we compared metrics between sexes, between population types (historic and recent), and among simulated scenarios which varied in density, genetic diversity, and sex-biased philopatry. In empirical populations, females in recent populations exhibited stronger patterns of isolation-by-distance (IBD) than females and males in historic populations. In simulated populations, low-density populations had a stronger indication of IBD than medium- to high-density populations; however, this effect varied in empirical populations. Condition-dependent dispersal strategies may permit species to cope with novel conditions and rapidly expand populations. Pattern-process modeling can provide qualitative and quantitative means to explore variable dispersal patterns, and could be employed in other species, particularly to anticipate range shifts in response to changing climate and habitat conditions.

Genetic architecture and evolution of color variation in American black bears.

Color variation is a frequent evolutionary substrate for camouflage in small mammals, but the underlying genetics and evolutionary forces that drive color variation in natural populations of large mammals are mostly unexplained. The American black bear, Ursus americanus (U. americanus), exhibits a range of colors including the cinnamon morph, which has a similar color to the brown bear, U. arctos, and is found at high frequency in the American southwest. Reflectance and chemical melanin measurements showed little distinction between U. arctos and cinnamon U. americanus individuals. We used a genome-wide association for hair color as a quantitative trait in 151 U. americanus individuals and identified a single major locus (p < 10-13). Additional genomic and functional studies identified a missense alteration (R153C) in Tyrosinase-related protein 1 (TYRP1) that likely affects binding of the zinc cofactor, impairs protein localization, and results in decreased pigment production. Population genetic analyses and demographic modeling indicated that the R153C variant arose 9.36 kya in a southwestern population where it likely provided a selective advantage, spreading both northwards and eastwards by gene flow. A different TYRP1 allele, R114C, contributes to the characteristic brown color of U. arctos but is not fixed across the range.

Radioluminescent nanophosphors enable multiplexed small-animal imaging.

We demonstrate the ability to image multiple nanoparticle-based contrast agents simultaneously using a nanophosphor platform excited by either radiopharmaceutical or X-ray irradiation. These radioluminescent nanoparticles emit optical light at unique wavelengths depending on their lanthanide dopant, enabling multiplexed imaging. This study demonstrates the separation of two distinct nanophosphor contrast agents in gelatin phantoms with a recovered phosphor separation correlation of -0.98. The ability to distinguish the two nanophosphors and a Cerenkov component is then demonstrated in a small animal phantom. Combined with the high-resolution potential of low-scattering X-ray excitation, this imaging technique may be a promising method to probe molecular processes in living organisms.

Artificial Intelligence-Guided Prediction of Dental Doses Before Planning of Radiation Therapy for Oropharyngeal Cancer: Technical Development and Initial Feasibility of Implementation.

Purpose: The aim was to develop a novel artificial intelligence (AI)-guided clinical decision support system, to predict radiation doses to subsites of the mandible using diagnostic computed tomography scans acquired before any planning of head and neck radiation therapy (RT). Methods and Materials: A dose classifier was trained using RT plans from 86 patients with oropharyngeal cancer; the test set consisted of an additional 20 plans. The classifier was trained to predict whether mandible subsites would receive a mean dose >50 Gy. The AI predictions were prospectively evaluated and compared with those of a specialist head and neck radiation oncologist for 9 patients. Positive predictive value (PPV), negative predictive value (NPV), Pearson correlation coefficient, and Lin concordance correlation coefficient were calculated to compare the AI predictions to those of the physician. Results: In the test data set, the AI predictions had a PPV of 0.95 and NPV of 0.88. For 9 patients evaluated prospectively, there was a strong correlation between the predictions of the AI algorithm and physician (P = .72, P < .001). Comparing the AI algorithm versus the physician, the PPVs were 0.82 versus 0.25, and the NPVs were 0.94 versus 1.0, respectively. Concordance between physician estimates and final planned doses was 0.62; this was 0.71 between AI-based estimates and final planned doses. Conclusion: AI-guided decision support increased precision and accuracy of pre-RT dental dose estimates.

Dosimetric Planning Tradeoffs to Reduce Heart Dose Using Machine Learning-Guided Decision Support Software in Patients with Lung Cancer.

PURPOSE: Cardiac toxicity is a well-recognized risk after radiation therapy (RT) in patients with non-small cell lung cancer (NSCLC). However, the extent to which treatment planning optimization can reduce mean heart dose (MHD) without untoward increases in lung dose is unknown. METHODS AND MATERIALS: Retrospective analysis of RT plans from 353 consecutive patients with locally advanced NSCLC treated with intensity modulated RT (IMRT) or 3-dimensional conformal RT. Commercially available machine learning-guided clinical decision support software was used to match RT plans. A leave-one-out predictive model was used to examine lung dosimetric tradeoffs necessary to achieve a MHD reduction. RESULTS: Of all 232 patients, 91 patients (39%) had RT plan matches showing potential MHD reductions of >4 to 8 Gy without violating the upper limit of lung dose constraints (lung volume [V] receiving 20 Gy (V20 Gy) <37%, V5 Gy <70%, and mean lung dose [MLD] <20 Gy). When switching to IMRT, 75 of 103 patients (72.8%) had plan matches demonstrating improved MHD (average 2.0 Gy reduction, P < .0001) without violating lung constraints. Examining specific lung dose tradeoffs, a mean ≥3.7 Gy MHD reduction was achieved with corresponding absolute increases in lung V20 Gy, V5 Gy, and MLD of 3.3%, 5.0%, and 1.0 Gy, respectively. CONCLUSIONS: Nearly 40% of RT plans overall, and 73% when switched to IMRT, were predicted to have reductions in MHD >4 Gy with potentially clinically acceptable tradeoffs in lung dose. These observations demonstrate that decision support software for optimizing heart-lung dosimetric tradeoffs is feasible and may identify patients who might benefit most from more advanced RT technologies.

Prospective study of artificial intelligence-based decision support to improve head and neck radiotherapy plan quality.

BACKGROUND AND PURPOSE: Volumetric modulated arc therapy (VMAT) planning for head and neck cancer is a complex process. While the lowest achievable dose for each individual organ-at-risk (OAR) is unknown a priori, artificial intelligence (AI) holds promise as a tool to accurately estimate the expected dose distribution for OARs. We prospectively investigated the benefits of incorporating an AI-based decision support tool (DST) into the clinical workflow to improve OAR sparing. MATERIALS AND METHODS: The DST dose prediction model was based on 276 institutional VMAT plans. Under an IRB-approved prospective trial, the physician first generated a custom OAR directive for 50 consecutive patients (physician directive, PD). The DST then estimated OAR doses (AI directive, AD). For each OAR, the treating physician used the lower directive to form a hybrid directive (HD). The final plan metrics were compared to each directive. A dose difference of 3 Gray (Gy) was considered clinically significant. RESULTS: Compared to the AD and PD, the HD reduced OAR dose objectives by more than 3 Gy in 22% to 75% of cases, depending on OAR. The resulting clinical plan typically met these lower constraints and achieved mean dose reductions between 4.3 and 16 Gy over the PD, and 5.6 to 9.1 Gy over the AD alone. Dose metrics achieved using the HD were significantly better than institutional historical plans for most OARs and NRG constraints for all OARs. CONCLUSIONS: The DST facilitated a significantly improved treatment directive across all OARs for this generalized H&N patient cohort, with neither the AD nor PD alone sufficient to optimally direct planning.

Tomographic molecular imaging of x-ray-excitable nanoparticles.

X-ray luminescence computed tomography (XLCT) is proposed as a new dual molecular/anatomical imaging modality. XLCT is based on the selective excitation and optical detection of x-ray-excitable nanoparticles. As a proof of concept, we built a prototype XLCT system and imaged near-IR-emitting Gd(2)O(2)S:Eu phosphors in various phantoms. Imaging in an optically diffusive medium shows that imaging performance is not affected by optical scatter; furthermore, the linear response of the reconstructed images suggests that XLCT is capable of quantitative imaging.

Evaluation of breast tumor response to neoadjuvant chemotherapy with tomographic diffuse optical spectroscopy: case studies of tumor region-of-interest changes.

PURPOSE: To evaluate two methods of summarizing tomographic diffuse optical spectroscopic (DOS) data through region-of-interest (ROI) analysis to differentiate complete from incomplete responses in patients with locally advanced breast cancer undergoing neoadjuvant treatment and to estimate the standard deviations of these methods for power analysis of larger study designs in the future. MATERIALS AND METHODS: Subjects participating in the HIPAA-compliant imaging study, approved by the institutional review board, provided written informed consent and were compensated for their examination participation. Seven of 16 cases in women with complete study data were analyzed by using both fixed- and variable-size (full-width-at-half-maximum) ROI measures of the DOS total hemoglobin concentration (Hb(T)), blood oxygen saturation, water fraction, optical scattering amplitude, and scattering power in the ipsilateral and contralateral breasts. Postsurgical histopathologic analysis was used to categorize patients as having a complete or incomplete treatment response. RESULTS: Average normalized change in Hb(T) was the only DOS parameter to show significant differences (P < or = .05) in the pathologic complete response (pCR) and pathologic incomplete response (pIR) outcomes in seven patients. Mean values of the changes for fixed-size ROIs were -64.2% +/- 50.8 (standard deviation) and 16.9% +/- 38.2 for the pCR and pIR groups, respectively, and those for variable-size ROIs were -96.7% +/- 91.8, and 14.1% +/- 26.7 for the pCR and pIR groups, respectively. CONCLUSION: Tomographic DOS may provide findings predictive of therapeutic response, which could lead to superior individualized patient treatment. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/2522081202/DC1.

Methodology development for three-dimensional MR-guided near infrared spectroscopy of breast tumors.

Combined Magnetic Resonance (MR) and Near Infrared Spectroscopy (NIRS) has been proposed as a unique method to quantify hemodynamics, water content, and cellular size and packing density of breast tumors, as these tissue constituents can be quantified with increased resolution and overlaid on the structural features identified by the MR. However, the choices in how to reconstruct and visualize this information can have a dramatic impact on the feasibility of implementing this modality in the clinic. This is especially true in 3 dimensions, as there is often limited optical sampling of the breast tissue, and methods need to accurately reflect the tissue composition. In this paper, the implementation and display of fully 3D MR image-guided NIRS is outlined and demonstrated using in vivo data from three healthy women and a volunteer undergoing neoadjuvant chemotherapy. Additionally, a display feature presented here scales the transparency of the optical images to the sensitivity of the measurements, providing a logical way to incorporate partial volume sets of optical images onto the MR volume. These concepts are demonstrated with 3D data sets using Volview software online.

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

Developments in quantitative oxygen-saturation imaging of breast tissue in vivo using multispectral near-infrared tomography.

Imaging of oxygen saturation provides a spatial map of the tissue metabolic activity and has potential in diagnosis and treatment monitoring of breast cancer. Oxygen-saturation imaging is possible through near-infrared (NIR) tomography, but has low signal-to-noise ratio (SNR). This can be augmented by using NIR tomography as an add-on to MRI. Presented are results from a free-standing NIR system and a hybrid MR-guided system for breast imaging. In results from imaging 60 healthy volunteers in the initial NIR system, oxygen saturation was a significant discriminator between the BIRADS classifications of adipose tissue, heterogeneously dense, and extremely dense tissue. By using the MR-guided NIR system, more accurate tissue-specific data were obtained on adipose and fibroglandular volumes, with 11 healthy volunteers. In these data, oxygen saturation in the adipose tissue correlated with percentage of adipose tissue. In two case studies of infiltrating ductal carcinomas, oxygen saturation was reduced at the site of the tumor, as compared with the surrounding healthy tissue, agreeing with conventional thought that hypoxia exists in larger solid tumors. The MRI-guided NIR images of oxygen saturation provide higher resolution and superior SNR and will likely be used in the future to study and characterize specific tissue volumes.

Structural information within regularization matrices improves near infrared diffuse optical tomography.

Near-Infrared (NIR) tomographic image reconstruction is a non-linear, ill-posed and ill-conditioned problem, and so in this study, different ways of penalizing the objective function with structural information were investigated. A simple framework to incorporate structural priors is presented, using simple weight matrices that have either Laplacian or Helmholtz-type structures. Using both MRI-derived breast geometry and phantom data, a systematic and quantitative comparison was performed with and without spatial priors. The Helmholtz-type structure can be seen as a more generalized approach for incorporating spatial priors into the reconstruction scheme. Moreover, parameter reduction (i.e. hard prior information) in the imaging field through the enforcement of spatially explicit regions may lead to erroneous results with imperfect spatial priors.

A boundary element approach for image-guided near-infrared absorption and scatter estimation.

Multimodality NIR spectroscopy systems offer the possibility of region-based vascular and molecular characterization of tissue in vivo. However, computationally efficient 3D image reconstruction algorithms specific to these image-guided systems currently do not exist. Image reconstruction is often based on finite-element methods (FEMs), which require volume discretization. Here, a boundary element method (BEM) is presented using only surface discretization to recover the optical properties in an image-guided setting. The reconstruction of optical properties using BEM was evaluated in a domain containing a 30 mm inclusion embedded in two layer media with different noise levels and initial estimates. For 5% noise in measurements, and background starting values for reconstruction, the optical properties were recovered to within a mean error of 6.8%. When compared with FEM for this case, BEM showed a 28% improvement in computational time. BEM was also applied to experimental data collected from a gelatin phantom with a 25 mm inclusion and could recover the true absorption to within 6% of expected values using less time for computation compared with FEM. When applied to a patient-specific breast mesh generated using MRI, with a 2 cm ductal carcinoma, BEM showed successful recovery of optical properties with less than 5% error in absorption and 1% error in scattering, using measurements with 1% noise. With simpler and faster meshing schemes required for surface grids as compared with volume grids, BEM offers a powerful and potentially more feasible alternative for high-resolution 3D image-guided NIR spectroscopy.

Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.

BACKGROUND AND PURPOSE: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify which previously approved historical treatment plans are achievable for a new patient to aid in selection of therapy. MATERIAL AND METHODS: Treatment data were collected for early-stage lung and postoperative oropharyngeal cancers treated using photon (lung and head and neck) and proton (head and neck) radiotherapy. Machine-learning classifiers were constructed using patient-specific feature-sets and a library of historical plans. Model accuracy was analyzed using learning curves, and historical treatment plan matching was investigated. RESULTS: Learning curves demonstrate that for these datasets, approximately 45, 60, and 30 patients are needed for a sufficiently accurate classification model for radiotherapy for early-stage lung, postoperative oropharyngeal photon, and postoperative oropharyngeal proton, respectively. The resulting classification model provides a database of previously approved treatment plans that are achievable for a new patient. An exemplary case, highlighting tradeoffs between the heart and chest wall dose while holding target dose constant in two historical plans is provided. CONCLUSIONS: We report on the first artificial-intelligence based clinical decision support system that connects patients to past discrete treatment plans in radiation oncology and demonstrate for the first time how this tool can enable clinicians to use past decisions to help inform current assessments. Clinicians can be informed of dose tradeoffs between critical structures early in the treatment process, enabling more time spent on finding the optimal course of treatment for individual patients.

Flexible radioluminescence imaging for FDG-guided surgery.

PURPOSE: Flexible radioluminescence imaging (Flex-RLI) is an optical method for imaging 18F-fluorodeoxyglucose (FDG)-avid tumors. The authors hypothesize that a gadolinium oxysulfide: terbium (GOS:Tb) flexible scintillator, which loosely conforms to the body contour, can enhance tumor signal-to-background ratio (SBR) compared with RLI, which utilizes a flat scintillator. The purpose of this paper is to characterize flex-RLI with respect to alternative modalities including RLI, beta-RLI (RLI with gamma rejection), and Cerenkov luminescence imaging (CLI). METHODS: The photon sensitivity, spatial resolution, and signal linearity of flex-RLI were characterized with in vitro phantoms. In vivo experiments utilizing 13 nude mice inoculated with the head and neck (UMSCC1-Luc) cell line were then conducted in accordance with the institutional Administrative Panel on Laboratory Animal Care. After intravenous injection of 18F-FDG, the tumor SBR values for flex-RLI were compared to those for RLI, beta-RLI, and CLI using the Wilcoxon signed rank test. RESULTS: With respect to photon sensitivity, RLI, beta-RLI, and flex-RLI produced 1216.2, 407.0, and 98.6 times more radiance per second than CLI. Respective full-width half maximum values across a 0.5 mm capillary tube were 6.9, 6.4, 2.2, and 1.5 mm, respectively. Flex-RLI demonstrated a near perfect correlation with 18F activity (r = 0.99). Signal uniformity for flex-RLI improved after more aggressive homogenization of the GOS powder with the silicone elastomer during formulation. In vivo, the SBR value for flex-RLI (median 1.29; interquartile range 1.18-1.36) was statistically greater than that for RLI (1.08; 1.02-1.14; p < 0.01) by 26%. However, there was no statistically significant difference in SBR values between flex-RLI and beta-RLI (p = 0.92). Furthermore, there was no statistically significant difference in SBR values between flex-RLI and CLI (p = 0.11) in a more limited dataset. CONCLUSIONS: Flex-RLI provides high quality images with SBRs comparable to those from CLI and beta-RLI in a single 10 s acquisition.

Scintillating balloon-enabled fiber-optic system for radionuclide imaging of atherosclerotic plaques.

UNLABELLED: Atherosclerosis underlies coronary artery disease, the leading cause of death in the United States and worldwide. Detection of coronary plaque inflammation remains challenging. In this study, we developed a scintillating balloon-enabled fiber-optic radionuclide imaging (SBRI) system to improve the sensitivity and resolution of plaque imaging using (18)F-FDG, a marker of vascular inflammation, and tested it in a murine model. METHODS: The fiber-optic system uses a Complementary Metal-Oxide Silicon (CMOS) camera with a distal ferrule terminated with a wide-angle lens. The novelty of this system is a scintillating balloon in the front of the wide-angle lens to image light from the decay of (18)F-FDG emission signal. To identify the optimal scintillating materials with respect to resolution, we calculated the modulation transfer function of yttrium-aluminum-garnet doped with cerium, anthracene, and calcium fluoride doped with europium (CaF2:Eu) phosphors using an edge pattern and a thin-line optical phantom. The scintillating balloon was then fabricated from 10 mL of silicone RTV catalyst mixed with 1 mL of base and 50 mg of CaF2:Eu per mL. The addition of a lutetium oxyorthosilicate scintillating crystal (500 µm thick) to the balloon was also investigated. The SBRI system was tested in a murine atherosclerosis model: carotid-ligated mice (n = 5) were injected with (18)F-FDG, followed by ex vivo imaging of the macrophage-rich carotid plaques and nonligated controls. Confirmatory imaging of carotid plaques and controls was also performed by an external optical imaging system and autoradiography. RESULTS: Analyses of the different phosphors showed that CaF2:Eu enabled the best resolution of 1.2 µm. The SBRI system detected almost a 4-fold-higher radioluminescence signal from the ligated left carotid artery than the nonligated right carotid: 1.63 × 10(2) ± 4.01 × 10(1) vs. 4.21 × 10(1) ± 2.09 × 10(0) (photon counts), P = 0.006. We found no significant benefit to adding a scintillating crystal to the balloon: 1.65 × 10(2) ± 4.07 × 10(1) vs. 4.44 × 10(1) ± 2.17 × 10(0) (photon counts), P = 0.005. Both external optical imaging and autoradiography confirmed the high signal from the (18)F-FDG in carotid plaques versus controls. CONCLUSION: This SBRI system provides high-resolution and sensitive detection of (18)F-FDG uptake by murine atherosclerotic plaques.

ß-Radioluminescence Imaging: A Comparative Evaluation with Cerenkov Luminescence Imaging.

UNLABELLED: Cerenkov luminescence imaging (CLI) can provide high-resolution images of (18)F-FDG-avid tumors but requires prolonged acquisition times because of low photon sensitivity. In this study, we proposed a new modality, termed ß-radioluminescence imaging (ß-RLI), which incorporates a scintillator with a γ-rejection strategy for imaging ß particles. We performed a comparative evaluation of ß-RLI with CLI in both in vitro and in vivo systems. METHODS: Using in vitro phantoms, we characterized the photon sensitivity and resolution of CLI and ß-RLI. We also conducted a series of in vivo experiments with xenograft mouse models using both amelanotic (A375, UMSCC1-Luc) and melanotic (B16F10-Luc) cell lines. The B16F10 and UMSCC1 cell lines were transfected with the luciferase gene (Luc). CLI was acquired over 300 s, and ß-RLI was acquired using two 10-s acquisitions. We correlated (18)F -: FDG activities, as assessed by PET, with tumor radiances for both ß-RLI and CLI. We also compared tumor signal-to-background ratios (SBRs) between these modalities for amelanotic and melanotic tumors. RESULTS: For in vitro experiments, the photon sensitivity for ß-RLI was 560-fold greater than that for CLI. However, the spatial resolution for ß-RLI (4.4 mm) was inferior to that of CLI (1.0 mm). For in vivo experiments, correlations between (18)F-FDG activity and tumor radiance were 0.52 (P < 0.01) for ß-RLI, 0.81 (P = 0.01) for amelanotic lesions with CLI, and -0.08 (negative contrast; P = 0.80) for melanotic lesions with CLI. Nine of 13 melanotic lesions had an SBR less than 1 for CLI, despite an SBR greater than 1 among all lesions for ß-RLI. CONCLUSION: ß-RLI can produce functional images of both amelanotic and melanotic tumors in a shorter time frame than CLI. Further engineering developments are needed to realize the full clinical potential of this modality.

Fiber-optic system for dual-modality imaging of glucose probes 18F-FDG and 6-NBDG in atherosclerotic plaques.

BACKGROUND: Atherosclerosis is a progressive inflammatory condition that underlies coronary artery disease (CAD)-the leading cause of death in the United States. Thus, the ultimate goal of this research is to advance our understanding of human CAD by improving the characterization of metabolically active vulnerable plaques within the coronary arteries using a novel catheter-based imaging system. The aims of this study include (1) developing a novel fiber-optic imaging system with a scintillator to detect both 18F and fluorescent glucose probes, and (2) validating the system on ex vivo murine plaques. METHODS: A novel design implements a flexible fiber-optic catheter consisting of both a radio-luminescence and a fluorescence imaging system to detect radionuclide 18F-fluorodeoxyglucose (18F-FDG) and the fluorescent analog 6-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-6-Deoxyglucose (6-NBDG), respectively. Murine macrophage-rich atherosclerotic carotid plaques were imaged ex vivo after intravenous delivery of 18F-FDG or 6-NBDG. Confirmatory optical imaging by IVIS-200 and autoradiography were also performed. RESULTS: Our fiber-optic imaging system successfully visualized both 18F-FDG and 6-NBDG probes in atherosclerotic plaques. For 18F-FDG, the ligated left carotid arteries (LCs) exhibited 4.9-fold higher radioluminescence signal intensity compared to the non-ligated right carotid arteries (RCs) (2.6 × 10(4) ± 1.4 × 10(3) vs. 5.4 × 10(3) ± 1.3 × 10(3) A.U., P = 0.008). Similarly, for 6-NBDG, the ligated LCs emitted 4.3-fold brighter fluorescent signals than the control RCs (1.6 × 10(2) ± 2.7 × 10(1) vs. 3.8 × 10(1) ± 5.9 A.U., P = 0.002). The higher uptake of both 18F-FDG and 6-NBDG in ligated LCs were confirmed with the IVIS-200 system. Autoradiography further verified the higher uptake of 18F-FDG by the LCs. CONCLUSIONS: This novel fiber-optic imaging system was sensitive to both radionuclide and fluorescent glucose probes taken up by murine atherosclerotic plaques. In addition, 6-NBDG is a promising novel fluorescent probe for detecting macrophage-rich atherosclerotic plaques.

Cerenkov luminescence endoscopy: improved molecular sensitivity with ß--emitting radiotracers.

UNLABELLED: Cerenkov luminescence endoscopy (CLE) is an optical technique that captures the Cerenkov photons emitted from highly energetic moving charged particles (ß(+) or ß(-)) and can be used to monitor the distribution of many clinically available radioactive probes. A main limitation of CLE is its limited sensitivity to small concentrations of radiotracer, especially when used with a light guide. We investigated the improvement in the sensitivity of CLE brought about by using a ß(-) radiotracer that improved Cerenkov signal due to both higher ß-particle energy and lower γ noise in the imaging optics because of the lack of positron annihilation. METHODS: The signal-to-noise ratio (SNR) of (90)Y was compared with that of (18)F in both phantoms and small-animal tumor models. Sensitivity and noise characteristics were demonstrated using vials of activity both at the surface and beneath 1 cm of tissue. Rodent U87MG glioma xenograft models were imaged with radiotracers bound to arginine-glycine-aspartate (RGD) peptides to determine the SNR. RESULTS: γ noise from (18)F was demonstrated by both an observed blurring across the field of view and a more pronounced fall-off with distance. A decreased γ background and increased energy of the ß particles resulted in a 207-fold improvement in the sensitivity of (90)Y compared with (18)F in phantoms. (90)Y-bound RGD peptide produced a higher tumor-to-background SNR than (18)F in a mouse model. CONCLUSION: The use of (90)Y for Cerenkov endoscopic imaging enabled superior results compared with an (18)F radiotracer.