CT strain metrics allow for earlier diagnosis of bronchiolitis obliterans syndrome after hematopoietic cell transplant

Bronchiolitis obliterans syndrome (BOS) after hematopoietic cell transplantation (HCT) is associated with substantial morbidity and mortality. Quantitative computed tomography (qCT) can help diagnose advanced BOS meeting National Institutes of Health (NIH) criteria (NIH-BOS) but has not been used to diagnose early, often asymptomatic BOS (early BOS), limiting the potential for early intervention and improved outcomes. Using pulmonary function tests (PFTs) to define NIH-BOS, early BOS, and mixed BOS (NIH-BOS with restrictive lung disease) in patients from 2 large cancer centers, we applied qCT to identify early BOS and distinguish between types of BOS. Patients with transient impairment or healthy lungs were included for comparison. PFTs were done at month 0, 6, and 12. Analysis was performed with association statistics, principal component analysis, conditional inference trees (CITs), and machine learning (ML) classifier models. Our cohort included 84 allogeneic HCT recipients, 66 with BOS (NIH-defined, early, or mixed) and 18 without BOS. All qCT metrics had moderate correlation with forced expiratory volume in 1 second, and each qCT metric differentiated BOS from those without BOS (non-BOS; P < .0001). CITs distinguished 94% of participants with BOS vs non-BOS, 85% of early BOS vs non-BOS, 92% of early BOS vs NIH-BOS. ML models diagnosed BOS with area under the curve (AUC) of 0.84 (95% confidence interval [CI], 0.74-0.94) and early BOS with AUC of 0.84 (95% CI, 0.69-0.97). qCT metrics can identify individuals with early BOS, paving the way for closer monitoring and earlier treatment in this vulnerable population.

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Publication Details

Journal
Blood Advances
Published
2024-09-27
DOI
https://doi.org/10.1182/bloodadvances.2024013748
Citations
5
Primary Topic
Transplantation: Methods and Outcomes
Type
article
Field-Weighted Citation Impact
2.86

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article

CT strain metrics allow for earlier diagnosis of bronchiolitis obliterans syndrome after hematopoietic cell transplant

Eric A. Hoffman, Keshav Datta, Gabriela Rondón, Christopher Bertini et al.
5 citations
Blood Advances
Transplantation: Methods and Outcomes
2.86
article

CT strain metrics allow for earlier diagnosis of bronchiolitis obliterans syndrome after hematopoietic cell transplant

Eric A. Hoffman, Keshav Datta, Gabriela Rondón, Christopher Bertini, Mario Castro, Mark R. Nicolls, Carlos Hernandez Borges, Lara Bashoura, Husham Sharifi, Mansour Alkhunaizi, Richard E. Champlin, Elizabeth J. Shpall, Ying Lü, Burton F. Dickey, Joseph M. Reinhardt, S. Peterson, Ajay Sheshadri, Craig J. Galbán, H. Henry Guo, Gregory A. Yanik, Joe L. Hsu, Ihsan Turk, Myrna C.B. Godoy, Zayan Musa, Maria Paulina Hernandez, Amin Alousi, Guang‐Shing Cheng
article en
5 citations

Abstract

Bronchiolitis obliterans syndrome (BOS) after hematopoietic cell transplantation (HCT) is associated with substantial morbidity and mortality. Quantitative computed tomography (qCT) can help diagnose advanced BOS meeting National Institutes of Health (NIH) criteria (NIH-BOS) but has not been used to diagnose early, often asymptomatic BOS (early BOS), limiting the potential for early intervention and improved outcomes. Using pulmonary function tests (PFTs) to define NIH-BOS, early BOS, and mixed BOS (NIH-BOS with restrictive lung disease) in patients from 2 large cancer centers, we applied qCT to identify early BOS and distinguish between types of BOS. Patients with transient impairment or healthy lungs were included for comparison. PFTs were done at month 0, 6, and 12. Analysis was performed with association statistics, principal component analysis, conditional inference trees (CITs), and machine learning (ML) classifier models. Our cohort included 84 allogeneic HCT recipients, 66 with BOS (NIH-defined, early, or mixed) and 18 without BOS. All qCT metrics had moderate correlation with forced expiratory volume in 1 second, and each qCT metric differentiated BOS from those without BOS (non-BOS; P < .0001). CITs distinguished 94% of participants with BOS vs non-BOS, 85% of early BOS vs non-BOS, 92% of early BOS vs NIH-BOS. ML models diagnosed BOS with area under the curve (AUC) of 0.84 (95% confidence interval [CI], 0.74-0.94) and early BOS with AUC of 0.84 (95% CI, 0.69-0.97). qCT metrics can identify individuals with early BOS, paving the way for closer monitoring and earlier treatment in this vulnerable population.

Blood AdvancesVol. 8(19)
Northwestern University (US), University of Iowa (US), The University of Texas MD Anderson Cancer Center (US), Palo Alto University (US), University of Michigan (US), University Medical Center (US), Northwestern Memorial Hospital (US), Fred Hutch Cancer Center (US), Michigan United (US), Ann Arbor VA Medical Center (US), University of Kansas Medical Center (US), Vida Diagnostics (United States) (US), The University of Texas Health Science Center at Houston (US), Stanford University (US)
National Institutes of Health, University of Texas MD Anderson Cancer Center, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Allergy and Infectious Diseases
Good health and well-being
Openalex Percentile: Top 9%
Transplantation: Methods and Outcomes
2.86
DOIOpen Access PDFOpenAlex
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