{"doi":"10.3390/tomography8020061","title":"Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden","abstract":"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.","journal":"Tomography","year":2022,"id":260718,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9619,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":411779,"name":"Yvonne M. Mowery","orcid":"0000-0002-9839-2414","position":1,"is_corresponding":false},{"id":237303,"name":"Kyle J. Lafata","orcid":"0000-0002-4513-6249","position":2,"is_corresponding":false},{"id":914465,"name":"Darin P. Clark","orcid":"0000-0002-8496-4565","position":3,"is_corresponding":false},{"id":907112,"name":"Alex M. Bassil","orcid":"0000-0001-9066-879X","position":4,"is_corresponding":false},{"id":914903,"name":"Rico Castillo","orcid":null,"position":5,"is_corresponding":false},{"id":476970,"name":"Diana Odhiambo","orcid":"0000-0002-6899-561X","position":6,"is_corresponding":false},{"id":411778,"name":"Matt Holbrook","orcid":"0000-0002-5832-8725","position":7,"is_corresponding":false},{"id":370854,"name":"Ketan B. Ghaghada","orcid":"0000-0003-1012-8668","position":8,"is_corresponding":false},{"id":411781,"name":"Cristian T. Badea","orcid":"0000-0002-1850-2522","position":9,"is_corresponding":false},{"id":914464,"name":"Alex J. Allphin","orcid":"0000-0003-2789-705X","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-07-19T00:26:07.666421Z","pmid":"35314638","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}