Our Duke U24 group contributed to these 3 CIRP network papers focused on preclinical imaging:

1. 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.; Manning, H.C.; Roudi, R.; Daldrup-Link, H.E.; Lewis, M.T.; Rubin, D.L.; Yankeelov, T.E.; Shoghi, K.I. Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging. Tomography 2023, 9, 995-1009. https://doi.org/10.3390/tomography9030081
2. Gammon, S.T.; Cohen, A.S.; Lehnert, A.L.; Sullivan, D.C.; Malyarenko, D.; Manning, H.C.; Hormuth, D.A.; Daldrup-Link, H.E.; An, H.; Quirk, J.D.; Shoghi, K.; Pagel, M.D.; Kinahan, P.E.; Miyaoka, R.S.; Houghton, A.M.; Lewis, M.T.; Larson, P.; Sriram, R.; Blocker, S.J.; Pickup, S.; Badea, A.; Badea, C.T.; Yankeelov, T.E.; Chenevert, T.L. An Online Repository for Pre-Clinical Imaging Protocols (PIPs). Tomography 2023, 9, 750-758. https://doi.org/10.3390/tomography9020060
3. Peehl, D.M.; Badea, C.T.; Chenevert, T.L.; Daldrup-Link, H.E.; Ding, L.; Dobrolecki, L.E.; Houghton, A.M.; Kinahan, P.E.; Kurhanewicz, J.; Lewis, M.T.; Li, S.; Luker, G.D.; Ma, C.X.; Manning, H.C.; Mowery, Y.M.; O’Dwyer, P.J.; Pautler, R.G.; Rosen, M.A.; Roudi, R.; Ross, B.D.; Shoghi, K.I.; Sriram, R.; Talpaz, M.; Wahl, R.L.; Zhou, R. Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials. Tomography 2023, 9, 657-680. https://doi.org/10.3390/tomography9020053

https://doi.org/10.1158/1535-7163.MCT-21-0991 

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 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 used for training and analysis can be found at https://github.com/mdholbrook/lung-tumor-detection.

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

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 (MCA) into the gastrocnemius muscle of wild-type 129/SvJ mice. This is describe in:

Primary Sarcoma Model Protocols – Ad-Cre or Ad-sgp53-Cas9 Intramuscular Injection

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/

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

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. D. Holbrook,Y. M. Mowery,D. C. Sullivan,C. T. Badea, The impact of respiratory gating on improving volume measurement of murine lung tumors in micro-CT imaging, https://doi.org/10.1371/journal.pone.0225019