TY - JOUR
T1 - Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia
T2 - proof of concept
AU - Aminu, Muhammad
AU - Daver, Naval
AU - Godoy, Myrna C.B.
AU - Shroff, Girish
AU - Wu, Carol
AU - Torre-Sada, Luis F.
AU - Goizueta, Alberto
AU - Shannon, Vickie R.
AU - Faiz, Saadia A.
AU - Altan, Mehmet
AU - Garcia-Manero, Guillermo
AU - Kantarjian, Hagop
AU - Ravandi-Kashani, Farhad
AU - Kadia, Tapan
AU - Konopleva, Marina
AU - DiNardo, Courtney
AU - Pierce, Sherry
AU - Naing, Aung
AU - Kim, Sang T.
AU - Kontoyiannis, Dimitrios P.
AU - Khawaja, Fareed
AU - Chung, Caroline
AU - Wu, Jia
AU - Sheshadri, Ajay
N1 - Publisher Copyright:
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.
PY - 2023
Y1 - 2023
N2 - 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 = 1.5E − 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.
AB - 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 = 1.5E − 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.
KW - acute myeloid leukemia
KW - habitat analysis
KW - immune checkpoint inhibitor
KW - non-small cell lung cancer
KW - pneumonitis
UR - http://www.scopus.com/inward/record.url?scp=85174140096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174140096&partnerID=8YFLogxK
U2 - 10.3389/fimmu.2023.1249511
DO - 10.3389/fimmu.2023.1249511
M3 - Article
C2 - 37841255
AN - SCOPUS:85174140096
SN - 1664-3224
VL - 14
JO - Frontiers in immunology
JF - Frontiers in immunology
M1 - 1249511
ER -