Browsing by Author "Ghaghada, Ketan B"

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    Enhancing in vivo preclinical studies with VivoVist™ and photon-counting micro-CT imaging
    (Medical Imaging 2024: Clinical and Biomedical Imaging, 2024-04-02) Badea, Cristian T; Rickard, Ashlyn; Allphin, Alex; Clark, Darin P; Ghaghada, Ketan B; Ridwan, S; Smilowitz, Henry M; Hainfeld, James; Mowery, Yvonne M
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    Micro-CT imaging of breast tumors in rodents using a liposomal, nanoparticle contrast agent.
    (Int J Nanomedicine, 2009) Samei, Ehsan; Saunders, Robert S; Badea, Cristian T; Ghaghada, Ketan B; Hedlund, Laurence W; Qi, Yi; Yuan, Hong; Bentley, Rex C; Mukundan, Srinivasan
    A long circulating liposomal, nanoscale blood pool agent encapsulating traditional iodinated contrast agent (65 mg I/mL) was used for micro-computed tomography (CT) imaging of rats implanted with R3230AC mammary carcinoma. Three-dimensional vascular architecture of tumors was imaged at 100-micron isotropic resolution. The image data showed good qualitative correlation with pathologic findings. The approach holds promise for studying tumor angiogenesis and for evaluating anti-angiogenesis therapies.
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    Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden
    (Tomography) Allphin, Alex J; Mowery, Yvonne M; Lafata, Kyle J; Clark, Darin P; Bassil, Alex M; Castillo, Rico; Odhiambo, Diana; Holbrook, Matthew D; Ghaghada, Ketan B; Badea, Cristian T
    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.