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CIRP Papers
Our Duke U24 group contributed to these 3 CIRP network papers focused on preclinical imaging: Moore, S.M.; Quirk, J.D.; Lassiter, A.W.; Laforest, R.; Ayers, G.D.; Badea, C.T.; Fedorov, A.Y.; Kinahan, P.E.; Holbrook, M.; Larson, P.E.Z.; Sriram, R.; Chenevert, T.L.; Malyarenko, D.; Kurhanewicz, J.; Houghton, A.M.; Ross, B.D.; Pickup, S.; Gee, J.C.; Zhou, R.; Gammon, S.T.; […]
Preclinical implementation of a clinical trial
https://doi.org/10.1158/1535-7163.MCT-21-0991
Photon counting CT can differentiate tumors based on lymphocyte burden
Allphin, A. J., Y. M. Mowery, K. J. Lafata, D. P. Clark, A. M. Bassil, R. Castillo, D. Odhiambo, M. D. Holbrook, K. B. Ghaghada and C. T. Badea (2022). “Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden.” Tomography 8(2): 740-753. https://www.mdpi.com/1535918
Detection of Lung Nodules via Deep Learning in Micro-CT
Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning Matthew D. Holbrook; Darin P. Clark; Rutulkumar Patel; Yi Qi; Alex M. Bassil; Yvonne M. Mowery; Cristian T. Badea Tomography 2021, Volume 7, Issue 3, 358-372 Data and Code Availability: The data presented in this work are available by request at https://civmvoxport.vm.duke.edu, and the code […]
Tumor Mapping
Our tumor mapping paper is now published and our images made the cover: Blocker SJ et al. Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas. Radiol Imaging Cancer 2021 May;3(3):e200103. doi: 10.1148/rycan.2021200103
Protocol on the Primary Sarcoma Model
Our p53/MCA High Mutational Load Model of Soft Tissue Sarcoma Dr. Yvonne Mowery To recapitulate human soft tissue sarcoma (STS) in the preclinical setting of our co-clinical trial, we generate a primary mouse model of STS by intramascular injection of adenovirus containing Cas9 gene and a guide RNA targeting p53 gene (Adeno-Cas9-sgRNAp53) and carcinogen 3-methylcholanthrene […]
Towards deep learning detection of lung nodules using micro-CT data
M. Holbrook, et al. Towards deep learning segmentation of lung nodules using micro-CT data Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116000I (2021) https://doi.org/10.1117/12.2581120 Event: SPIE Medical Imaging, 2021 Presentation: Holbrook_SPIE_LungTumors_2021
New CIRP network publication!
Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine. Shoghi KI, Badea CT, Blocker SJ, Chenevert TL, Laforest R, Lewis MT, Luker GD, Manning HC, Marcus DS, Mowery YM, Pickup S, Richmond A, Ross BD, Vilgelm AE, Yankeelov TE, Zhou R. Tomography. 2020 Sep;6(3):273-287. doi: 10.18383/j.tom.2020.00023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442091/
MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma
We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies. Link to our new paper
The impact of respiratory gating
As part of our co-clinical trial studying immunotherapy and radiotherapy in sarcomas, we are using micro-CT of the lungs to detect and measure metastases as a metric of disease progression. In this study, we have addressed the impact of respiratory gating during micro-CT acquisition on improving lung tumor detection and volume quantitation. S. J. Blocker,M. […]
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