Dataset Information

Bioluminescent imaging of HPV-positive oral tumor growth and its response to image-guided radiotherapy.

ABSTRACT: The treatment paradigms for head and neck squamous cell cancer (HNSCC) are changing due to the emergence of human papillomavirus (HPV)-associated tumors possessing distinct molecular profiles and responses to therapy. Although patients with HNSCCs are often treated with radiotherapy, preclinical models are limited by the ability to deliver precise radiation to orthotopic tumors and to monitor treatment responses accordingly. To better model this clinical scenario, we developed a novel autochthonous HPV-positive oral tumor model to track responses to small molecules and image-guided radiation. We used a tamoxifen-regulated Cre recombinase system to conditionally express the HPV oncogenes E6 and E7 as well as a luciferase reporter (iHPV-Luc) in the epithelial cells of transgenic mice. In the presence of activated Cre recombinase, luciferase activity, and by proxy, HPV oncogenes were induced to 11-fold higher levels. In triple transgenic mice containing the iHPV-Luc, K14-CreER(tam), and LSL-Kras transgenes, tamoxifen treatment resulted in oral tumor development with increased bioluminescent activity within 6 days that reached a maximum of 74.8-fold higher bioluminescence compared with uninduced mice. Oral tumors expressed p16 and MCM7, two biomarkers associated with HPV-positive tumors. After treatment with rapamycin or image-guided radiotherapy, tumors regressed and possessed decreased bioluminescence. Thus, this novel system enables us to rapidly visualize HPV-positive tumor growth to model existing and new interventions using clinically relevant drugs and radiotherapy techniques.

SUBMITTER: Zhong R

PROVIDER: S-EPMC4662542 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Bioluminescent imaging of HPV-positive oral tumor growth and its response to image-guided radiotherapy.

Zhong Rong R Pytynia Matt M Pelizzari Charles C Spiotto Michael M

Cancer research 20140213 7

The treatment paradigms for head and neck squamous cell cancer (HNSCC) are changing due to the emergence of human papillomavirus (HPV)-associated tumors possessing distinct molecular profiles and responses to therapy. Although patients with HNSCCs are often treated with radiotherapy, preclinical models are limited by the ability to deliver precise radiation to orthotopic tumors and to monitor treatment responses accordingly. To better model this clinical scenario, we developed a novel autochthon ...[more]

PMID: 24525739

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Project description:BackgroundThe imaging dose of cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) poses adverse effects on patient health. To improve the quality of sparse-view low-dose CBCT images, a projection synthesis convolutional neural network (SynCNN) model is proposed.MethodsIncluded in this retrospective, single-center study were 223 patients diagnosed with brain tumours from Beijing Cancer Hospital. The proposed SynCNN model estimated two pairs of orthogonally direction-separable spatial kernels to synthesize the missing projection in between the input neighboring sparse-view projections via local convolution operations. The SynCNN model was trained on 150 real patients to learn patterns for inter-view projection synthesis. CBCT data from 30 real patients were used to validate the SynCNN, while data from a phantom and 43 real patients were used to test the SynCNN externally. Sparse-view projection datasets with 1/2, 1/4, and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored using the SynCNN model. The tomographic images were then reconstructed with the Feldkamp-Davis-Kress algorithm. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) metrics were measured in both the projection and image domains. Five experts were invited to grade the image quality blindly for 40 randomly selected evaluation groups with a four-level rubric, where a score greater than or equal to 2 was considered acceptable image quality. The running time of the SynCNN model was recorded. The SynCNN model was directly compared with the three other methods on 1/4 sparse-view reconstructions.ResultsThe phantom and patient studies showed that the missing projections were accurately synthesized. In the image domain, for the phantom study, compared with images reconstructed from sparse-view projections, images with SynCNN synthesis exhibited significantly improved qualities with decreased values in RMSE and increased values in PSNR and SSIM. For the patient study, between the results with and without the SynCNN synthesis, the averaged RMSE decreased by 3.4×10-4, 10.3×10-4, and 21.7×10-4, the averaged PSNR increased by 3.4, 6.6, and 9.4 dB, and the averaged SSIM increased by 5.2×10-2, 18.9×10-2 and 33.9×10-2, for the 1/2, 1/4, and 1/8 sparse-view reconstructions, respectively. In expert subjective evaluation, both the median scores and acceptance rates of the images with SynCNN synthesis were higher than those reconstructed from sparse-view projections. It took the model less than 0.01 s to synthesize an inter-view projection. Compared with the three other methods, the SynCNN model obtained the best scores in terms of the three metrics in both domains.ConclusionsThe proposed SynCNN model effectively improves the quality of sparse-view CBCT images at a low time cost. With the SynCNN model, the CBCT imaging dose in IGRT could be reduced potentially.

Dataset Information

Bioluminescent imaging of HPV-positive oral tumor growth and its response to image-guided radiotherapy.

Publications

Bioluminescent imaging of HPV-positive oral tumor growth and its response to image-guided radiotherapy.

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