Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden.
Alex J AllphinYvonne M MoweryKyle J LafataDarin P ClarkAlex M BassilRico CastilloDiana OdhiamboMatthew D HolbrookKetan B GhaghadaCristian T BadeaPublished in: Tomography (Ann Arbor, Mich.) (2022)
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.
Keyphrases
- dual energy
- image quality
- computed tomography
- contrast enhanced
- radiation therapy
- optical coherence tomography
- cancer therapy
- high resolution
- magnetic resonance
- magnetic resonance imaging
- positron emission tomography
- risk factors
- type diabetes
- drug delivery
- machine learning
- peripheral blood
- adipose tissue
- skeletal muscle
- insulin resistance
- liquid chromatography
- fluorescence imaging