Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden
dc.contributor.author | Allphin, Alex J | |
dc.contributor.author | Mowery, Yvonne M | |
dc.contributor.author | Lafata, Kyle J | |
dc.contributor.author | Clark, Darin P | |
dc.contributor.author | Bassil, Alex M | |
dc.contributor.author | Castillo, Rico | |
dc.contributor.author | Odhiambo, Diana | |
dc.contributor.author | Holbrook, Matthew D | |
dc.contributor.author | Ghaghada, Ketan B | |
dc.contributor.author | Badea, Cristian T | |
dc.date.accessioned | 2022-03-16T17:50:50Z | |
dc.date.available | 2022-03-16T17:50:50Z | |
dc.date.updated | 2022-03-16T17:50:49Z | |
dc.description.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> | |
dc.identifier.issn | 2379-139X | |
dc.identifier.uri | ||
dc.language | en | |
dc.publisher | MDPI AG | |
dc.relation.ispartof | Tomography | |
dc.relation.isversionof | 10.3390/tomography8020061 | |
dc.title | Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden | |
dc.type | Journal article | |
duke.contributor.orcid | Mowery, Yvonne M|0000-0002-9839-2414 | |
duke.contributor.orcid | Badea, Cristian T|0000-0002-1850-2522 | |
pubs.begin-page | 740 | |
pubs.end-page | 753 | |
pubs.issue | 2 | |
pubs.organisational-group | Duke | |
pubs.organisational-group | Pratt School of Engineering | |
pubs.organisational-group | School of Medicine | |
pubs.organisational-group | Clinical Science Departments | |
pubs.organisational-group | Institutes and Centers | |
pubs.organisational-group | Biomedical Engineering | |
pubs.organisational-group | Radiology | |
pubs.organisational-group | Duke Cancer Institute | |
pubs.publication-status | Published online | |
pubs.volume | 8 |
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