Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden


<jats:p>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.</jats:p>






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Publication Info

Allphin, Alex J, Yvonne M Mowery, Kyle J Lafata, Darin P Clark, Alex M Bassil, Rico Castillo, Diana Odhiambo, Matthew D Holbrook, et al. (n.d.). Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden. Tomography, 8(2). pp. 740–753. 10.3390/tomography8020061 Retrieved from

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Yvonne Marie Mowery

Adjunct Assistant Professor in the Department of Radiation Oncology

Kyle Jon Lafata

Thaddeus V. Samulski Associate Professor of Radiation Oncology

Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University in the Departments of Radiation Oncology, Radiology, Medical Physics and Electrical & Computer Engineering. After earning his PhD in Medical Physics in 2018, he completed postdoctoral training at the U.S. Department of Veterans Affairs in the Big Data Scientist Training Enhancement Program. Prof. Lafata has broad expertise in imaging science, digital pathology, computer vision, biophysics, and applied mathematics. His dissertation work focused on the applied analysis of stochastic differential equations and high-dimensional radiomic phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems). 

Prof. Lafata has worked in various areas of computational medicine and biology, resulting in 39 peer-reviewed journal publications, 15 invited talks, and more than 50 national conference presentations. At Duke, the Lafata Lab focuses on the theory, development, and application of multiscale computational biomarkers. Using computational and mathematical methods, they study the appearance and behavior of disease across different physical length-scales (i.e., radiomics ~10−3 m, pathomics ~10−6 m, and genomics ~10−9 m) and time-scales (e.g., the natural history of disease, response to treatment). The overarching goal of the lab is to develop and apply new technology that transforms imaging into basic science findings and computational biomarker discovery.


Darin Clark

Assistant Professor in Radiology

Cristian Tudorel Badea

Professor in Radiology

  • Our lab's research focus lies primarily in developing novel quantitative imaging systems, reconstruction algorithms and analysis methods.  My major expertise is in preclinical CT.
  • Currently, we are particularly interested in developing novel strategies for spectral CT imaging using nanoparticle-based contrast agents for theranostics (i.e. therapy and diagnostics).
  • We are also engaged in developing new approaches for multidimensional CT image reconstruction suitable to address difficult undersampling cases in cardiac and spectral CT (dual energy and photon counting) using compressed sensing and/or deep learning.

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