ArticleChest2020
Machine Learning Algorithms to Differentiate Among Pulmonary Complications After Hematopoietic Cell Transplant.
Article in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).Sensors (Basel, Switzerland) · 2020Pooled it
- Quantitative lung tissue functional analysis for pulmonary adverse event risk assessment prior to thoracic radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Monitoring for paediatric pulmonary graftERJ open research · 2025Article
- Addressing Knowledge Gaps in the Early Detection of Bronchiolitis Obliterans Syndrome after Hematopoietic Cell Transplantation: An Official American Thoracic Society Research Statement.American journal of respiratory and critical care medicine · 2025Article
- Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes.Diagnostics (Basel, Switzerland) · 2025Article
- CT strain metrics allow for earlier diagnosis of bronchiolitis obliterans syndrome after hematopoietic cell transplant.Blood advances · 2024Article
- Clinical impact of airflow obstruction after allogeneic hematopoietic stem cell transplantation.International journal of hematology · 2024Article
- How I diagnose and treat organizing pneumonia in hematopoietic cell transplant recipients.Blood · 2024Review
- Early and rapid development of bronchiolitis obliterans syndrome after allogeneic hematopoietic cell transplantation.Respiratory medicine case reports · 2024Article
- Quantitative CT Scan Imaging of the Airways for Diagnosis and Management of Lung Disease.Chest · 2023Review
- Imaging of pediatric hematopoietic stem cell transplant recipients: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper.Pediatric blood & cancer · 2023Article
- Bronchiolitis obliterans syndrome after lung or haematopoietic stem cell transplantation: current management and future directions.ERJ open research · 2022Review
- Article
- CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT.Radiology. Artificial intelligence · 2022Article
- Noninfectious complications of hematopoietic cell transplantation.Hematology. American Society of Hematology. Education Program · 2021Article
- National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: IV. The 2020 Highly morbid forms report.Transplantation and cellular therapy · 2021Article
- Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment.Advances in radiation oncologyArticle
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10 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPulmonary complications, including infections, are highly prevalent in patients after hematopoietic cell transplantation with chronic graft-vs-host disease. These comorbid diseases can make the diagnosis of early lung graft-vs-host disease (bronchiolitis obliterans syndrome) challenging. A quantitative method to differentiate among these pulmonary diseases can address diagnostic challenges and facilitate earlier and more targeted therapy. STUDY DESIGN AND
methodsWe conducted a single-center study of 66 patients with CT chest scans analyzed with a quantitative imaging tool known as parametric response mapping. Parametric response mapping results were correlated with pulmonary function tests and clinical characteristics. Five parametric response mapping metrics were applied to K-means clustering and support vector machine models to distinguish among posttransplantation lung complications solely from quantitative output.
resultsCompared with parametric response mapping, spirometry showed a moderate correlation with radiographic air trapping, and total lung capacity and residual volume showed a strong correlation with radiographic lung volumes. K-means clustering analysis distinguished four unique clusters. Clusters 2 and 3 represented obstructive physiology (encompassing 81% of patients with bronchiolitis obliterans syndrome) in increasing severity (percentage air trapping 15.6% and 43.0%, respectively). Cluster 1 was dominated by normal lung, and cluster 4 was characterized by patients with parenchymal opacities. A support vector machine algorithm differentiated bronchiolitis obliterans syndrome with a specificity of 88%, sensitivity of 83%, accuracy of 86%, and an area under the receiver operating characteristic curve of 0.85.
interpretationOur machine learning models offer a quantitative approach for the identification of bronchiolitis obliterans syndrome vs other lung diseases, including late pulmonary complications after hematopoietic cell transplantation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.