Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden
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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https://hdl.handle.net/10161/24543Published Version (Please cite this version)
10.3390/tomography8020061Publication 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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Show full item recordScholars@Duke
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
Darin Clark
Assistant Professor in Radiology
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
Yvonne Marie Mowery
Butler Harris Assistant Professor in Radiation Oncology
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