Publications [#363901] of Kyle J. Lafata

Papers Published

1. Allphin, AJ; Mowery, Y; Lafata, KJ; Clark, DP; Basil, A; Castillo, R; Holbrook, MD; Ghaghada, KB; Badea, CT, Spectral micro-CT and nanoradiomic analysis for classification of tumors based on lymphocytic burden in cancer therapy studies, Progress in Biomedical Optics and Imaging Proceedings of SPIE, vol. 12036 (January, 2022), ISBN 9781510649477 [doi]
   (last updated on 2026/01/15)

   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 tumor-infiltrating lymphocyte (TIL) burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/- and Rag2-/- mice to model varying TIL 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. RFs were ranked to reduce redundancy and increase relevance based on TIL burden. A leave one out strategy was used to assess separation using a neural network classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2+/- (TILs present) and Rag2-/- (TIL-deficient) tumors. RFs further enabled differentiation between Rag2+/- and Rag2-/- tumors. The PCD-derived RFs provided the highest accuracy (0.84) followed by decomposition-derived RFs (0.78) and the EID-derived RFs (0.65). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies.