Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

RATIONALE AND
OBJECTIVES: Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV1) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS.
MATERIALS AND METHODS: Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points. The BOS cohort experienced a reduction in FEV1 of >10% compared to baseline FEV1 post LTx. Multifactor analysis correlated declining FEV1 with qCT features linked to acute inflammation or BOS onset. Student t test and ML were applied on baseline qCT features to identify lung transplant patients at baseline that eventually developed BOS.
RESULTS: The FEV1 decline in the BOS cohort correlated with an increase in the lung volume (P = .027) and in the central airway volume at functional residual capacity (P = .018), not observed in non-BOS patients, whereas the non-BOS cohort experienced a decrease in the central airway volume at total lung capacity with declining FEV1 (P = .039). Twenty-three baseline qCT parameters could significantly distinguish between non-BOS patients and eventual BOS developers (P < .05), whereas no pulmonary function testing parameters could. Using ML methods (support vector machine), we could identify BOS developers at baseline with an accuracy of 85%, using only three qCT parameters.
CONCLUSIONS: ML utilizing qCT could discern distinct mechanisms driving FEV1 decline in BOS and non-BOS LTx patients and predict eventual onset of BOS. This approach may become useful to optimize management of LTx patients.