Computers in biology and medicine · 2017
Multivariate modeling using quantitative CT metrics may improve accuracy of diagnosis of bronchiolitis obliterans syndrome after lung transplantation.
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Barbosa E Mortani, Simpson S, Lee J C, Tustison N, Gee J, Shou H
Аннотация
BACKGROUND: To assess how quantitative CT (qCT) metrics compare to pulmonary function testing (PFT) and semi-quantitative image scores (SQS) to diagnose bronchiolitis obliterans syndrome (BOS), manifestation of chronic lung allograft dysfunction after lung transplantation (LTx), according to the type of LTx (unilateral or bilateral).
METHODS: Paired inspiratory-expiratory CT scans and PFTs of 176 LTx patients were analyzed retrospectively, and separated into BOS (78) and non-BOS (98) cohorts. SQS were assessed by 2 radiologists and graded (0-3) for features including mosaic attenuation and bronchiectasis. qCT metrics included lung volumes and air trapping volumes. Multivariate logistic regression (MVLR) and support vector machines (SVM) were used for the classification task.
RESULTS: MVLR and SVM models using PFT metrics demonstrated highest accuracy for bilateral LTx (max AUC 0.771), whereas models using qCT metrics-only outperformed models using SQS or PFTs in unilateral LTx (max AUC 0.817), to diagnose BOS. Adding PC (principal components) from qCT on top of PFT improved model diagnostic accuracy for all transplant types.
CONCLUSIONS: Combinations of qCT metrics augment the diagnostic performance of PFTs, are superior to SQS to predict BOS status, and outperform PFTs in the unilateral LTx group. This suggests that latent information on paired volumetric CT may allow early diagnosis of BOS in LTx patients, particularly in unilateral LTx.
METHODS: Paired inspiratory-expiratory CT scans and PFTs of 176 LTx patients were analyzed retrospectively, and separated into BOS (78) and non-BOS (98) cohorts. SQS were assessed by 2 radiologists and graded (0-3) for features including mosaic attenuation and bronchiectasis. qCT metrics included lung volumes and air trapping volumes. Multivariate logistic regression (MVLR) and support vector machines (SVM) were used for the classification task.
RESULTS: MVLR and SVM models using PFT metrics demonstrated highest accuracy for bilateral LTx (max AUC 0.771), whereas models using qCT metrics-only outperformed models using SQS or PFTs in unilateral LTx (max AUC 0.817), to diagnose BOS. Adding PC (principal components) from qCT on top of PFT improved model diagnostic accuracy for all transplant types.
CONCLUSIONS: Combinations of qCT metrics augment the diagnostic performance of PFTs, are superior to SQS to predict BOS status, and outperform PFTs in the unilateral LTx group. This suggests that latent information on paired volumetric CT may allow early diagnosis of BOS in LTx patients, particularly in unilateral LTx.