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Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden

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Authors
Allphin, Alex J
Mowery, Yvonne M
Lafata, Kyle J
Clark, Darin P
Bassil, Alex M
Castillo, Rico
Odhiambo, Diana
Holbrook, Matthew D
Ghaghada, Ketan B
Badea, Cristian T
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Abstract
<jats:p>The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/− and Rag2−/− mice to model varying lymphocyte burden. Mice received radiation therapy (20 Gy) to the tumor-bearing hind limb and were injected with a liposomal iodinated contrast agent. Five days later, animals underwent conventional micro-CT imaging using an energy integrating detector (EID) and spectral micro-CT imaging using a photon-counting detector (PCD). Tumor volumes and iodine uptakes were measured. The radiomic features (RF) were grouped into feature-spaces corresponding to EID, PCD, and spectral decomposition images. The RFs were ranked to reduce redundancy and increase relevance based on TL burden. A stratified repeated cross validation strategy was used to assess separation using a logistic regression classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2+/− (TLs present) and Rag2−/− (TL-deficient) tumors. The RFs further enabled differentiation between Rag2+/− and Rag2−/− tumors. The PCD-derived RFs provided the highest accuracy (0.68) followed by decomposition-derived RFs (0.60) and the EID-derived RFs (0.58). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies.</jats:p>
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Journal article
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https://hdl.handle.net/10161/24543
Published Version (Please cite this version)
10.3390/tomography8020061
Publication Info
Allphin, Alex J; Mowery, Yvonne M; Lafata, Kyle J; Clark, Darin P; Bassil, Alex M; Castillo, Rico; ... Badea, Cristian T (n.d.). Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden. Tomography, 8(2). pp. 740-753. 10.3390/tomography8020061. Retrieved from https://hdl.handle.net/10161/24543.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Badea

Cristian Tudorel Badea

Professor in Radiology
Our lab's research focus lies primarily in developing novel quantitative imaging systems, reconstruction algorithms and analysis methods.  My major expertise is in preclinical CT. Currently, we are particularly interested in developing novel strategies for spectral CT imaging using nanoparticle-based contrast agents for theranostics (i.e. therapy and diagnostics). We are also engaged in developin
Clark

Darin Clark

Assistant Professor in Radiology
Lafata

Kyle Jon Lafata

Thaddeus V. Samulski Assistant Professor of Radiation Oncology
Kyle Lafata is the Thaddeus V. Samulski Assistant Professor at Duke University in the Departments of Radiation Oncology, Radiology, Medical Physics, and Electrical & Computer Engineering. After earning his PhD in Medical Physics in 2018, he completed postdoctoral training at the U.S. Department of Veterans Affairs in the Big Data Scientist Training Enhancement Program. Prof. Lafata has broad expertise in imaging science, digital pathology, computer vision, biophysics, and
Mowery

Yvonne Marie Mowery

Butler Harris Assistant Professor in Radiation Oncology
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