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Peer-Reviewed Publication
Radiother Oncol2025;212111110.November 1, 2025Journal Article

Predicting radiation pneumonitis in lung cancer patients using robust 4DCT-ventilation and perfusion imaging.

Taindra Neupane1, Edward Castillo2, Yingxuan Chen3, Soroush Heidari Pahlavian4, Richard Castillo5, Yevgeniy Vinogradskiy6, Wookjin Choi7
1Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States. Electronic address: taindra.neupane@jefferson.edu.
2Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States. Electronic address: edward.castillo@utexas.edu.
3Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States. Electronic address: yingxuan.chen@jefferson.edu.
4MIM Software Inc., Beachwood, OH, United States. Electronic address: spahlavian@mimsoftware.com.
5Department of Radiation Oncology, Emory University, Atlanta, GA, United States. Electronic address: edward.castillo@utexas.edu.
6Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States. Electronic address: yevgeniy.vinogradskiy@jefferson.edu.
7Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States. Electronic address: wookjin.choi@jefferson.edu.
PubMed DOI

Abstract

PURPOSE: Methods have been developed that apply image processing to 4-Dimension computed tomography (4DCT) to generate lung ventilation (4DCT-ventilation). Traditional methods for 4DCT-ventilation rely on density-change methods and lack reproducibility and do not provide 4DCT-perfusion data. Novel 4DCT-ventilation/perfusion methods have been developed that are robust and provide 4DCT-perfusion inf…

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