Frontiers in Immunology (Sep 2023)

Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept

  • Muhammad Aminu,
  • Naval Daver,
  • Myrna C. B. Godoy,
  • Girish Shroff,
  • Carol Wu,
  • Luis F. Torre-Sada,
  • Alberto Goizueta,
  • Vickie R. Shannon,
  • Saadia A. Faiz,
  • Mehmet Altan,
  • Guillermo Garcia-Manero,
  • Hagop Kantarjian,
  • Farhad Ravandi-Kashani,
  • Tapan Kadia,
  • Marina Konopleva,
  • Courtney DiNardo,
  • Sherry Pierce,
  • Aung Naing,
  • Sang T. Kim,
  • Dimitrios P. Kontoyiannis,
  • Fareed Khawaja,
  • Caroline Chung,
  • Jia Wu,
  • Ajay Sheshadri

DOI
https://doi.org/10.3389/fimmu.2023.1249511
Journal volume & issue
Vol. 14

Abstract

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BackgroundImmune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers.Materials and methodsWe studied CT images from 97 patients with pneumonia and 29 patients with pneumonitis from acute myeloid leukemia treated with ICIs. We developed a CT-derived signature using a habitat imaging algorithm, whereby infected lungs are segregated into clusters (“habitats”). We validated the model and compared it with a clinical-blood model to determine whether imaging can add diagnostic value.ResultsHabitat imaging revealed intrinsic lung inflammation patterns by identifying 5 distinct subregions, correlating to lung parenchyma, consolidation, heterogenous ground-glass opacity (GGO), and GGO-consolidation transition. Consequently, our proposed habitat model (accuracy of 79%, sensitivity of 48%, and specificity of 88%) outperformed the clinical-blood model (accuracy of 68%, sensitivity of 14%, and specificity of 85%) for classifying pneumonia versus pneumonitis. Integrating imaging and blood achieved the optimal performance (accuracy of 81%, sensitivity of 52% and specificity of 90%). Using this imaging-blood composite model, the post-test probability for detecting pneumonitis increased from 23% to 61%, significantly (p = 1.5E − 9) higher than the clinical and blood model (post-test probability of 22%).ConclusionHabitat imaging represents a step forward in the image-based detection of pneumonia and pneumonitis, which can complement known blood biomarkers. Further work is needed to validate and fine tune this imaging-blood composite model and further improve its sensitivity to detect pneumonitis.

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