(Sunday, 7/14/2019) 5:00 PM - 6:00 PM
Room: 221AB
Purpose: Currently, no clinical standard exists to measure pulmonary dysfunction in non-small cell lung cancer (NSCLC) patients prior to radiation treatment (RT). Parametric response mapping (PRM) of inhale/exhale lung CT is a promising solution that offers the ability to quantify interstitial lung disease (ILD), emphysema, and functional small airway disease in a patient’s lungs. The purpose of this study was to establish the expected PRM estimates in NSCLC patients undergoing RT and to compare PRM estimates of ILD with perfusion SPECT-based metrics for low-function.
Methods: Deep inspiration and expiration high-resolution CT scans were obtained as part of two IRB-approved clinical trials from 23 NSCLC patients at pre-treatment, 11 patients at mid-treatment, and 3 patients at post-treatment. Global PRM estimates were established within the total lungs structure and regional PRM estimates were calculated within lung sextants, all excluding the tumor volume. Percent SPECT-based low-function was directly compared to PRM-based ILD estimates for 6 patients in the cohort, which included two cases of Grade 2+ radiation pneumonitis (RP).
Results: NSCLC patients presented with an average of 63% of normal lung and 24% of lung classified as ILD, based on PRM analysis. The average percent of normal lung at mid- and post-treatment was found to be 74% and 65%, respectively. Of the six patients with SPECT data, mean lung volumes classified as ILD by PRM and low-function (LF) by SPECT were substantially larger in the two patients that incurred RP [avg. ILD volume = 2001 cubic cm; avg. %LF = 46%] compared to the cohort average (avg. ILD volume = 389 cubic cm; avg. %LF = 6%).
Conclusion: PRM offers the ability to quantify patient-specific estimates of pulmonary dysfunction. In a small subsample of patients with PRM- and SPECT-based functional metrics, large volumes of dysfunction were estimated in both patients that incurred RP.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by P30-CA046592 (Lawrence) and P01-CA059872 (Ten Haken/Lawrence).
Keywords
Radiation Risk, Radiosensitivity, Quantitative Imaging
Taxonomy
IM- CT: Quantitative imaging/analysis
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