학술논문
Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept.
Document Type
Academic Journal
Author
Aminu M; Departments of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Daver N; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Godoy MCB; Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Shroff G; Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Wu C; Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Torre-Sada LF; Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Goizueta A; Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Shannon VR; Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Faiz SA; Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Altan M; Departments of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Garcia-Manero G; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Kantarjian H; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Ravandi-Kashani F; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Kadia T; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Konopleva M; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; DiNardo C; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Pierce S; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Naing A; Departments of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Kim ST; Departments of Rheumatology and Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Kontoyiannis DP; Departments of Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Khawaja F; Departments of Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Chung C; Departments of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Wu J; Departments of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States.; Sheshadri A; Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Source
Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101560960 Publication Model: eCollection Cited Medium: Internet ISSN: 1664-3224 (Electronic) Linking ISSN: 16643224 NLM ISO Abbreviation: Front Immunol Subsets: MEDLINE
Subject
Language
English
Abstract
Background: Immune 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 Methods: We 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.
Results: Habitat 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 = 1.5 E - 9) higher than the clinical and blood model (post-test probability of 22%).
Conclusion: Habitat 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.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023 Aminu, Daver, Godoy, Shroff, Wu, Torre-Sada, Goizueta, Shannon, Faiz, Altan, Garcia-Manero, Kantarjian, Ravandi-Kashani, Kadia, Konopleva, DiNardo, Pierce, Naing, Kim, Kontoyiannis, Khawaja, Chung, Wu and Sheshadri.)
Materials and Methods: We 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.
Results: Habitat 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 = 1.5 E - 9) higher than the clinical and blood model (post-test probability of 22%).
Conclusion: Habitat 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.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023 Aminu, Daver, Godoy, Shroff, Wu, Torre-Sada, Goizueta, Shannon, Faiz, Altan, Garcia-Manero, Kantarjian, Ravandi-Kashani, Kadia, Konopleva, DiNardo, Pierce, Naing, Kim, Kontoyiannis, Khawaja, Chung, Wu and Sheshadri.)