Longitudinal assessment of lung cancer progression in the mouse using in vivo micro-CT imaging

Multiphase micro-computed tomography reconstructions provide dynamic respiratory function in a mouse lung fibrosis model

Micro-computed tomography derived functional biomarkers used in lung disease research can significantly complement end-stage histomorphometric measures while also allowing for longitudinal studies. However, no approach for visualizing lung dynamics across a full respiratory cycle has yet been described. Using bleomycin-induced lung fibrosis and the antifibrotic drug nintedanib as a test model, we implemented a four-dimensional (4D) micro-CT imaging approach consisting of 30 reconstructed volumes per respiratory cycle, coupled with deep-learning-assisted segmentation of lung volumes. 4D micro-CT provided an accurate description of inhalatory and exhalatory lung dynamics under resting conditions and revealed an inflammation-related obstructive pattern at day 7, followed by a restrictive pattern associated with fibrosis development at day 21. A milder restriction and fibrotic pathology resulted from nintedanib treatment. The similarity of 4D micro-CT data with those produced by diagnostic measurements, also points to its great potential as an exploratory tool for the discovery of clinically relevant therapeutic compounds.

Measurement of mouse head and neck tumors by automated analysis of CBCT images

Animal experiments are often used to determine effects of drugs and other biological conditions on cancer progression, but poor accuracy and reproducibility of established tumor measurement methods make results unreliable. In orthotopic mouse models of head and neck cancer, tumor volumes approximated from caliper measurements are conventionally used to compare groups, but geometrical challenges make the procedure imprecise. To address this, we developed software to better measure these tumors by automated analysis of cone-beam computed tomography (CBCT) scans. This allows for analyses of tumor shape and growth dynamics that would otherwise be too inaccurate to provide biological insight. Monitoring tumor growth by calipers and imaging in parallel, we find that caliper measurements of small tumors are weakly correlated with actual tumor volume and highly susceptible to experimenter bias. The method presented provides a unique window to sources of error in a foundational aspect of preclinical head and neck cancer research and a valuable tool to mitigate them.

Micro-CT-derived ventilation biomarkers for the longitudinal assessment of pathology and response to therapy in a mouse model of lung fibrosis

Experimental in-vivo animal models are key tools to investigate the pathogenesis of lung disease and to discover new therapeutics. Histopathological and biochemical investigations of explanted lung tissue are currently considered the gold standard, but they provide space-localized information and are not amenable to longitudinal studies in individual animals. Here, we present an imaging procedure that uses micro-CT to extract morpho-functional indicators of lung pathology in a murine model of lung fibrosis. We quantified the decrease of lung ventilation and measured the antifibrotic effect of Nintedanib. A robust structure-function relationship was revealed by cumulative data correlating micro-CT with histomorphometric endpoints. The results highlight the potential of in-vivo micro-CT biomarkers as novel tools to monitor the progression of inflammatory and fibrotic lung disease and to shed light on the mechanism of action of candidate drugs. Our platform is also expected to streamline translation from preclinical studies to human patients.

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning

We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76-0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.

Monitoring treatment effects in lung cancer-bearing mice: clinical CT and clinical MRI compared to micro-CT

Background

Compared to histology-based methods, imaging can reduce animal usage in preclinical studies. However, availability of dedicated scanners is limited. We evaluated clinical computed tomography (CT) and magnetic resonance imaging (MRI) in comparison to dedicated CT (micro-CT) for assessing therapy effects in lung cancer-bearing mice.

Methods

Animals received cisplatin (n = 10), sham (n = 12), or no treatment (n = 9). All were examined via micro-CT, CT, and MRI before and after treatment. Semiautomated tumour burden (TB) calculation was performed. The Bland-Altman, receiver operating characteristic (ROC), and Spearman statistics were used.

Results

All modalities always allowed localising and measuring TB. At all modalities, mice treated with cisplatin showed a TB reduction (p ≤ 0.012) while sham-treated and untreated individuals presented tumour growth (p < 0.001). Mean relative difference (limits of agreement) between TB on micro-CT and clinical scanners was 24.7% (21.7–27.7%) for CT and 2.9% (−4.0–9.8%) for MRI. Relative TB changes before/after treatment were not different between micro-CT and CT (p = 0.074) or MRI (p = 0.241). Mice with cisplatin treatment were discriminated from those with sham or no treatment at all modalities (p ≤ 0.001). Using micro-CT as reference standard, ROC areas under the curves were 0.988–1.000 for CT and 0.946–0.957 for MRI. TB changes were highly correlated across modalities (r ≥ 0.900, p < 0.001).

Conclusions

Clinical CT and MRI are suitable for treatment response evaluation in lung cancer-bearing mice. When dedicated scanners are unavailable, they should be preferred to improve animal welfare.

Optimizing Energy Transfer in Nanostructures Enables In Vivo Cancer Lesion Tracking via Near-Infrared Excited Hypoxia Imaging

To explore highly sensitive and low-toxicity techniques for tracking and evaluation of non-small-cell lung cancer (NSCLC), one of the most mortal tumors in the world, it is utterly imperative for doctors to select the appropriate treatment strategies. Herein, developing near-infrared (NIR) excited nanosensors, in which the donor and acceptor pairs within a biological metal-organic framework (bio-MOF) matrix are precisely controlled to rationalize upconversion Förster resonance energy transfer (FRET), is suggested for detecting the O₂ concentration inside tumors with reduced signal disturbance and health detriment. Under NIR excitation, as-fabricated core/satellite nanosensors exhibit much improved FRET efficiency and reversible hypoxic response with high sensitivity, which are effective both in vitro and in vivo (zebrafish) for cycling normoxia-hypoxia imaging. Significantly, combined with a reliable preclinical genetically engineered murine model, such nanosensors successfully realize tracking of in vivo NSCLC lesions upon clear and gradient hypoxia signals without apparent long-term biotoxicity, illustrating their exciting potential for efficient NSCLC evaluation and prognosis.

A Novel Method for Quantifying Total Thoracic Tumor Burden in Mice

Mouse models are powerful tools to study lung cancer initiation and progression in vivo and have contributed significantly to recent advances in therapy. Using micro-computed tomography to monitor and study parenchymal and extra-parenchymal metastases in existing murine models of lung cancer is challenging owing to a lack of radiographic contrast and difficulty in achieving respiratory gating. To facilitate the analysis of these in vivo imaging studies and study of tumor progression in murine models we developed a novel, rapid, semi-automated method of calculating thoracic tumor burden from computed tomography images. This method, in which commercially available software is used to calculate the mass of the thoracic cavity (MTC), takes into account the aggregate tumor burden in the thoracic cavity. The present study showed that in tumor-free mice, the MTC does not change over time and is not affected by breathing, whereas in tumor-bearing mice, the increase in the MTC is a measure of tumor mass that correlates well with tumor burden measured by lung weight. Tumor burden calculated with our MTC method correlated with that measured by lung weight as well as or better than that calculated using four established methods. To test this method, we assessed metastatic tumor development and response to a pharmacologic PLK1 inhibitor in an orthotopic xenograft mouse model. PLK1 inhibition significantly inhibited tumor growth. Our results demonstrate that the MTC method can be used to study dynamic changes in tumor growth and response to therapeutics in genetically engineered mouse models and orthotopic xenograft mouse models of lung cancer.

Optical model of the murine lung to optimize pulmonary illumination

We describe a Monte Carlo model of the mouse torso to optimize illumination of the mouse lung for fluorescence detection of low levels of pulmonary pathogens, specifically Mycobacterium tuberculosis. After validation of the simulation with an internally illuminated optical phantom, the entire mouse torso was simulated to compare external and internal illumination techniques. Measured optical properties of deflated mouse lungs were scaled to mimic the diffusive properties of inflated lungs in vivo. Using the full-torso model, a 2  ×   to 3  ×   improvement in average fluence rate in the lung was seen for dorsal compared with ventral positioning of the mouse with external illumination. The enhancement in average fluence rate in the lung using internal excitation was 40  ×   to 60  ×   over external illumination in the dorsal position. Parameters of the internal fiber optic source were manipulated in the model to guide optimization of the physical system and experimental protocol for internal illumination and whole-body detection of fluorescent mycobacteria in a mouse model of infection.

Volumetric and linear measurements of lung tumor burden from non-gated micro-CT imaging correlate with histological analysis in a genetically engineered mouse model of non-small cell lung cancer

In vivo micro-computed tomography (CT) imaging allows longitudinal studies of pulmonary neoplasms in genetically engineered mouse models. Respiratory gating increases the accuracy of lung tumor measurements but lengthens anesthesia time in animals that may be at increased risk for complications. We hypothesized that semiautomated, volumetric, and linear tumor measurements performed in micro-CT images from non-gated scans would have correlation with histological findings. Primary lung tumors were induced in eight FVB mice with two transgenes (FVB/N-Tg(tetO-Kras2)12Hev/J; FVB.Cg-Tg(Scgb1a1-rtTA)1Jaw/J). Non-gated micro-CT scans were performed and the lungs were subsequently harvested. In the acquired micro-CT scans, measurements of all identified tumors were determined using the following methods: semiautomated three-dimensional (3D) volume, ellipsoid volume, Response Evaluation Criteria in Solid Tumors (RECIST; sum of largest axial (i.e., transverse) diameter from five tumors), sum of largest axial diameters from all tumors (modified RECIST), and average axial diameter. For histological analysis, all five lung lobes were analyzed and the tumor area was summed from measurements made on five histological sections that were 300 µm apart from each other (covering a total depth of 1200 µm). All micro-CT measurement methods had very strong correlation with histological tumor burden (Pearson's correlation coefficient, 0.87 ( p = 0.0053) -0.98 ( p < 0.0001)). The only methods found to have different correlations were the semiautomated 3D method and the RECIST method (Williams' test for dependent overlapping correlations, p = 0.013). Our results suggest quantification of lung tumor burden from non-gated micro-CT imaging will reflect histological differences between mice and can therefore be used for between-group comparisons or when concerns about systemic health of research animals may limit lengthy anesthetic procedures.

A respiratory-gated micro-CT comparison of respiratory patterns in free-breathing and mechanically ventilated rats

In this study, we aim to quantify the differences in lung metrics measured in free-breathing and mechanically ventilated rodents using respiratory-gated micro-computed tomography. Healthy male Sprague-Dawley rats were anesthetized with ketamine/xylazine and scanned with a retrospective respiratory gating protocol on a GE Locus Ultra micro-CT scanner. Each animal was scanned while free-breathing, then intubated and mechanically ventilated (MV) and rescanned with a standard ventilation protocol (56 bpm, 8 mL/kg and PEEP of 5 cm H₂O) and again with a ventilation protocol that approximates the free-breathing parameters (88 bpm, 2.14 mL/kg and PEEP of 2.5 cm H₂O). Images were reconstructed representing inspiration and end expiration with 0.15 mm voxel spacing. Image-based measurements of the lung lengths, airway diameters, lung volume, and air content were compared and used to calculate the functional residual capacity (FRC) and tidal volume. Images acquired during MV appeared darker in the airspaces and the airways appeared larger. Image-based measurements showed an increase in lung volume and air content during standard MV, for both respiratory phases, compared with matched MV and free-breathing. Comparisons of the functional metrics showed an increase in FRC for mechanically ventilated rats, but only the standard MV exhibited a significantly higher tidal volume than free-breathing or matched MV Although standard mechanical ventilation protocols may be useful in promoting consistent respiratory patterns, the amount of air in the lungs is higher than in free-breathing animals. Matching the respiratory patterns with the free-breathing case allowed similar lung morphology and physiology measurements while reducing the variability in the measurements.

Time-to-Progression of NSCLC from Early to Advanced Stages: An Analysis of data from SEER Registry and a Single Institute

The average time required for cancers to progress through stages can be reflected in the average age of the patients diagnosed at each stage of disease. To estimate the time it takes for non-small-cell lung cancer (NSCLC) to progress through different tumor, node and metastasis (TNM) stages and sizes, we compared the mean adjusted age of 45904 NSCLC patients with different stages and tumor sizes from Surveillance, Epidemiology and End Results (SEER) cancer registry database and our institute. Multiple-linear-regression models for age were generated adjusting for various factors. Caucasian, African-American and Asian patients with stage IA cancers were on average 0.8, 1.0 and 1.38 adjusted years younger, respectively, than those with stage IIIB cancers (p < 0.001). And these with T1a cancers were on average 0.84, 0.92 and 1.21 adjusted years younger, respectively, than patients with T3 cancers (p < 0.001). Patients with tumors measuring larger than 8 cm in diameter were on average 0.85 adjusted years older than these with tumors smaller than 1 cm (p < 0.001), with Caucasian demonstrating the shortest age span (0.79 years, P < 0.001). In conclusion, the time-to-progression of NSCLC from early to advanced stages varied among ethnicities, Caucasian patients demonstrating a more rapid progression nature of tumor than their African-American and Asian counterparts.

Computer-aided pulmonary image analysis in small animal models

PURPOSE

To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models.

METHODS

The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors' system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next. The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters.

RESULTS

133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT'09 data set.

CONCLUSIONS

The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.

In vivo small animal micro-CT using nanoparticle contrast agents

Computed tomography (CT) is one of the most valuable modalities for in vivo imaging because it is fast, high-resolution, cost-effective, and non-invasive. Moreover, CT is heavily used not only in the clinic (for both diagnostics and treatment planning) but also in preclinical research as micro-CT. Although CT is inherently effective for lung and bone imaging, soft tissue imaging requires the use of contrast agents. For small animal micro-CT, nanoparticle contrast agents are used in order to avoid rapid renal clearance. A variety of nanoparticles have been used for micro-CT imaging, but the majority of research has focused on the use of iodine-containing nanoparticles and gold nanoparticles. Both nanoparticle types can act as highly effective blood pool contrast agents or can be targeted using a wide variety of targeting mechanisms. CT imaging can be further enhanced by adding spectral capabilities to separate multiple co-injected nanoparticles in vivo. Spectral CT, using both energy-integrating and energy-resolving detectors, has been used with multiple contrast agents to enable functional and molecular imaging. This review focuses on new developments for in vivo small animal micro-CT using novel nanoparticle probes applied in preclinical research.

Overview of KRAS-Driven Genetically Engineered Mouse Models of Non-Small Cell Lung Cancer

KRAS, the most frequently mutated oncogene in non-small cell lung cancer, has been utilized extensively to model human lung adenocarcinomas. The results from such studies have enhanced considerably an understanding of the relationship between KRAS and the development of lung cancer. Detailed in this overview are the features of various KRAS-driven genetically engineered mouse models (GEMMs) of non-small cell lung cancer, their utilization, and the potential of these models for the study of lung cancer biology.

Optical coherence tomography in respiratory science and medicine: from airways to alveoli

Optical coherence tomography is a rapidly maturing optical imaging technology, enabling study of the in vivo structure of lung tissue at a scale of tens of micrometers. It has been used to assess the layered structure of airway walls, quantify both airway lumen caliber and compliance, and image individual alveoli. This article provides an overview of the technology and reviews its capability to provide new insights into respiratory disease.

Preclinical mouse models of osteosarcoma

Osteosarcoma is the most common form of primary bone tumors with high prevalence in children. Survival rates of osteosarcoma are low, especially in the case of metastases. Mouse models of this disease have been very valuable in investigation of mechanisms of tumorigenesis, metastasis, as well as testing possible therapeutic options. In this chapter, we summarize currently available mouse models for osteosarcoma and provide detailed methodology for the isolation of cell lines from genetically engineered mouse models (GEMMs), gene modification and tumor cell injection methods, as well as imaging techniques.

Quantification of Tumor Burden in a Genetically Engineered Mouse Model of Lung Cancer by Micro-CT and Automated Analysis

Genetically engineered mouse models (GEMMs) of lung cancer closely recapitulate the human disease but suffer from the difficulty of evaluating tumor growth by conventional methods. Herein, a novel automated image analysis method for estimating the lung tumor burden from in vivo micro-computed tomography (micro-CT) data is described. The proposed tumor burden metric is the segmented soft tissue volume contained within a chest space region of interest, excluding an estimate of the heart volume. The method was validated by comparison with previously published manual analysis methods and applied in two therapeutic studies in a mutant K-ras GEMM of non-small cell lung carcinoma. Mice were imaged by micro-CT pre-treatment and stratified into four treatment groups: an antibody inhibiting vascular endothelial growth factor (anti-VEGF), chemotherapy, combination of anti-VEGF and chemotherapy, or control antibody. In the first study, post-treatment imaging was performed 4 weeks later. In the second study, mice were scanned serially on a high-throughput scanner every 2 weeks for 8 weeks during treatment. In both studies, the automated tumor burden estimates were well correlated with manual metrics (r value range: 0.83-0.93, P < .0001) and showed a similar, significant reduction in tumor growth in mice treated with anti-VEGF alone or in combination with chemotherapy. Given the fully automated nature of this technique, the proposed analysis method can provide a valuable tool in preclinical drug research for screening and randomizing animals into treatment groups and evaluating treatment efficacy in mouse models of lung cancer in a highly robust and efficient manner.

Preclinical anatomical, molecular, and functional imaging of the lung with multiple modalities

In vivo imaging is an important tool for preclinical studies of lung function and disease. The widespread availability of multimodal animal imaging systems and the rapid rate of diagnostic contrast agent development have empowered researchers to noninvasively study lung function and pulmonary disorders. Investigators can identify, track, and quantify biological processes over time. In this review, we highlight the fundamental principles of bioluminescence, fluorescence, planar X-ray, X-ray computed tomography, magnetic resonance imaging, and nuclear imaging modalities (such as positron emission tomography and single photon emission computed tomography) that have been successfully employed for the study of lung function and pulmonary disorders in a preclinical setting. The major principles, benefits, and applications of each imaging modality and technology are reviewed. Limitations and the future prospective of multimodal imaging in pulmonary physiology are also discussed. In vivo imaging bridges molecular biological studies, drug design and discovery, and the imaging field with modern medical practice, and, as such, will continue to be a mainstay in biomedical research.

Growth pattern analysis of murine lung neoplasms by advanced semi-automated quantification of micro-CT images

Computed tomography (CT) is a non-invasive imaging modality used to monitor human lung cancers. Typically, tumor volumes are calculated using manual or semi-automated methods that require substantial user input, and an exponential growth model is used to predict tumor growth. However, these measurement methodologies are time-consuming and can lack consistency. In addition, the availability of datasets with sequential images of the same tumor that are needed to characterize in vivo growth patterns for human lung cancers is limited due to treatment interventions and radiation exposure associated with multiple scans. In this paper, we performed micro-CT imaging of mouse lung cancers induced by overexpression of ribonucleotide reductase, a key enzyme in nucleotide biosynthesis, and developed an advanced semi-automated algorithm for efficient and accurate tumor volume measurement. Tumor volumes determined by the algorithm were first validated by comparison with results from manual methods for volume determination as well as direct physical measurements. A longitudinal study was then performed to investigate in vivo murine lung tumor growth patterns. Individual mice were imaged at least three times, with at least three weeks between scans. The tumors analyzed exhibited an exponential growth pattern, with an average doubling time of 57.08 days. The accuracy of the algorithm in the longitudinal study was also confirmed by comparing its output with manual measurements. These results suggest an exponential growth model for lung neoplasms and establish a new advanced semi-automated algorithm to measure lung tumor volume in mice that can aid efforts to improve lung cancer diagnosis and the evaluation of therapeutic responses.

Individual nodule tracking in micro-CT images of a longitudinal lung cancer mouse model

We present and evaluate an automatic and quantitative method for the complex task of characterizing individual nodule volumetric progression in a longitudinal mouse model of lung cancer. Fourteen A/J mice received an intraperitoneal injection of urethane. Respiratory-gated micro-CT images of the lungs were acquired at 8, 22, and 37 weeks after injection. A radiologist identified a total of 196, 585 and 636 nodules, respectively. The three micro-CT image volumes from every animal were then registered and the nodules automatically matched with an average accuracy of 99.5%. All nodules detected at week 8 were tracked all the way to week 37, and volumetrically segmented to measure their growth and doubling rates. 92.5% of all nodules were correctly segmented, ranging from the earliest stage to advanced stage, where nodule segmentation becomes more challenging due to complex anatomy and nodule overlap. Volume segmentation was validated using a foam lung phantom with embedded polyethylene microspheres. We also correlated growth rates with nodule phenotypes based on histology, to conclude that the growth rate of malignant tumors is significantly higher than that of benign lesions. In conclusion, we present a turnkey solution that combines longitudinal imaging with nodule matching and volumetric nodule segmentation resulting in a powerful tool for preclinical research.

Combined micro-PET/micro-CT imaging of lung tumours in SPC-raf and SPC-myc transgenic mice

Introduction

SPC-raf and SPC-myc transgenic mice develop disseminated and circumscribed lung adenocarcinoma respectively, allowing for assessment of carcinogenesis and treatment strategies. The purpose of this study was to investigate the technical feasibility, the correlation of initial findings to histology and the administered radiation dose of combined micro-PET/micro-CT in these animal models.

Material and Methods

14 C57BL/6 mice (4 nontransgenic, 4 SPC-raf transgenic, 6 SPC-myc transgenic) were examined using micro-CT and ¹⁸F-Fluoro-deoxyglucose micro-PET in-vivo. Micro-PET data was corrected for random events and scatter prior to reconstruction with a 3D-FORE/2D-OSEM iterative algorithm. Rigid micro-PET/micro-CT registration was performed. Tumour-to-non-tumour ratios were calculated for different lung regions and focal lesions. Diffuse tumour growth was quantified using a semiautomated micro-CT segmentation routine reported earlier. Regional histologic tumour load was assessed using a 4-point rating scale. Gamma radiation dose was determined using thermoluminescence dosimeters.

Results

Micro-CT allowed visualisation of diffuse and circumscribed tumours in SPC-raf and SPC-myc transgenic animals along with morphology, while micro-PET provided information on metabolism, but lacked morphologic detail. Mean tumour-to-non-tumour ratio was 2.47 for circumscribed lesions. No significant correlation could be shown between histological tumour load and tumour-to-nontumour ratio for diffuse tumours in SPC-raf transgenic animals. Calculation of the expected dose based on gamma dosimetry yielded approximately 140 mGy/micro-PET examination additional to approximately 200 mGy due to micro-CT.

Conclusions

Combined micro-PET/micro-CT imaging allows for in-vivo assessment of lung tumours in SPC-raf and SPC-myc transgenic mice. The technique has potential for the evaluation of carcinogenesis and treatment strategies in circumscribed lung tumours.

Animal models of extracranial pediatric solid tumors

Animal models, including xenografts, models of metastatic invasion, syngeneic models and transgenic models, are important tools for basic research in solid pediatric tumors, while humanized animal models are useful for novel immunotherapeutical approaches. Optical and molecular imaging techniques are used for in vivo imaging and may be used in conjunction with alternative treatment approaches, including photodynamic therapy. The aim of this review is to highlight the various animal models that may be used for basic research in pediatric solid tumors.

Lung tumour growth kinetics in SPC-c-Raf-1-BB transgenic mice assessed by longitudinal in-vivo micro-CT quantification

Background

SPC-c-Raf-1-BxB transgenic mice develop genetically induced disseminated lung adenocarcinoma allowing examination of carcinogenesis and evaluation of novel treatment strategies. We report on assessment of lung tumour growth kinetics using a semiautomated region growing segmentation algorithm.

Methods

156 non contrast-enhanced respiratory gated micro-CT of the lungs were obtained in 12 SPC-raf transgenic (n = 9) and normal (n = 3) mice at different time points. Region-growing segmentation of the aerated lung areas was obtained as an inverse surrogate for tumour burden. Time course of segmentation volumes was assessed to demonstrate the potential of the method for follow-up studies.

Results

Micro-CT allowed assessment of tumour growth kinetics and semiautomated region growing enabled quantitative analysis. Significant changes of the segmented lung volumes over time could be shown (p = 0.009). Significant group differences could be detected between transgenic and normal animals for time points 8 to 13 months (p = 0.043), when marked tumour progression occurred.

Conclusion

The presented region-growing segmentation algorithm allows in-vivo quantification of multifocal lung adenocarcinoma in SPC-raf transgenic mice. This enables the assessment of tumour load and progress for the study of carcinogenesis and the evaluation of novel treatment strategies.

Quantitative monitoring of mouse lung tumors by magnetic resonance imaging

Primary lung cancer remains the leading cause of cancer-related death in the Western world, and the lung is a common site for recurrence of extrathoracic malignancies. Small-animal (rodent) models of cancer can have a very valuable role in the development of improved therapeutic strategies. However, detection of mouse pulmonary tumors and their subsequent response to therapy in situ is challenging. We have recently described MRI as a reliable, reproducible and nondestructive modality for the detection and serial monitoring of pulmonary tumors. By combining respiratory-gated data acquisition methods with manual and automated segmentation algorithms described by our laboratory, pulmonary tumor burden can be quantitatively measured in approximately 1 h (data acquisition plus analysis) per mouse. Quantitative, analytical methods are described for measuring tumor burden in both primary (discrete tumors) and metastatic (diffuse tumors) disease. Thus, small-animal MRI represents a novel and unique research tool for preclinical investigation of therapeutic strategies for treatment of pulmonary malignancies, and it may be valuable in evaluating new compounds targeting lung cancer in vivo.

Dynamic contrast-enhanced micro-CT on mice with mammary carcinoma for the assessment of antiangiogenic therapy response

OBJECTIVE

To evaluate the potential of in vivo dynamic contrast-enhanced micro-computed tomography (DCE micro-CT) for the assessment of antiangiogenic drug therapy response of mice with mammary carcinoma.

METHODS

20 female mice with implanted MCF7 tumours were split into control group and therapy group treated with a known effective antiangiogenic drug. All mice underwent DCE micro-CT for the 3D analysis of functional parameters (relative blood volume [rBV], vascular permeability [K], area under the time-enhancement curve [AUC]) and morphology. All parameters were determined for total, peripheral and central tumour volumes of interest (VOIs). Immunohistochemistry was performed to characterise tumour vascularisation. 3D dose distributions were determined.

RESULTS

The mean AUCs were significantly lower in therapy with P values of 0.012, 0.007 and 0.023 for total, peripheral and central tumour VOIs. K and rBV showed significant differences for the peripheral (P(per)(K) = 0.032, P(per) (rBV) = 0.029), but not for the total and central tumour VOIs (P(total)(K) = 0.108, P(central)(K) = 0.246, P(total) (rBV) = 0.093, P(central) (rBV) = 0.136). Mean tumour volume was significantly smaller in therapy (P (in vivo) = 0.001, P (ex vivo) = 0.005). Histology revealed greater vascularisation in the controls and central tumour necrosis. Doses ranged from 150 to 300 mGy.

CONCLUSIONS

This study indicates the great potential of DCE micro-CT for early in vivo assessment of antiangiogenic drug therapy response.

KEY POINTS

Dynamic contrast enhanced micro-CT (computed tomography) is a new experimental laboratory technique. DCE micro-CT allows early in vivo assessment of antiangiogenic drug therapy response. Pharmaceutical drugs can be tested before translation to clinical practice. Both morphological and functional parameters can be obtained using DCE micro-CT. Antiangiogenic effects can be visualised with DCE micro-CT.

Optimization of microCT imaging and blood vessel diameter quantitation of preclinical specimen vasculature with radiopaque polymer injection medium

Vascular networks within a living organism are complex, multi-dimensional, and challenging to image capture. Radio-angiographic studies in live animals require a high level of infrastructure and technical investment in order to administer costly perfusion mediums whose signals metabolize and degrade relatively rapidly, diminishing within a few hours or days. Additionally, live animal specimens must not be subject to long duration scans, which can cause high levels of radiation exposure to the specimen, limiting the quality of images that can be captured. Lastly, despite technological advances in live-animal specimen imaging, it is quite difficult to minimize or prevent movement of a live animal, which can cause motion artifacts in the final data output. It is demonstrated here that through the use of postmortem perfusion protocols of radiopaque silicone polymer mediums and ex-vivo organ harvest, it is possible to acquire a high level of vascular signal in preclinical specimens through the use of micro-computed tomographic (microCT) imaging. Additionally, utilizing high-order rendering algorithms, it is possible to further derive vessel morphometrics for qualitative and quantitative analysis.