Mr. Alex Bassil Profile

Alex Bassil

at Duke Univ Medical Ctr

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Proceedings Article | 4 April 2022 Presentation + Paper

Spectral micro-CT and radiomic analysis for classification of tumors based on lymphocytic burden in cancer therapy studies

A. Allphin, Y. Mowery, K. Lafata, D. Clark, A. Basil, Rico Castillo, M. Holbrook, K. Ghaghada, C. Badea

Proceedings Volume 12036, 120361H (2022) https://doi.org/10.1117/12.2611519

KEYWORDS: Tumors, Iodine, Computed tomography, Statistical analysis, Cancer, In vivo imaging, Image segmentation, Nanoparticles

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The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrastenhancement 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.

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