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Updated: May 22, 2025
Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
Published on: August 9, 2024
Using a fully automated, quantitative fissure integrity score extracted from chest CT scans of emphysema patients to
Dallas K Tada1, Grace H Kim1, Jonathan G Goldin1
1The University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.
Purpose:
We aim to develop and validate a prediction model using a previously developed fully automated quantitative fissure integrity score (FIS) extracted from pre-treatment CT images to identify suitable candidates for endobronchial valve (EBV) treatment.
Approach:
We retrospectively collected 96 anonymized pre- and post-treatment chest computed tomography (CT) exams from patients with moderate to severe emphysema and who underwent EBV treatment. We used a previously developed fully automated, deep learning-based approach to quantitatively assess the completeness of each fissure by obtaining the FIS for each fissure from each patient's pre-treatment CT exam. The response to EBV treatment was recorded as the amount of targeted lobe volume reduction (TLVR) compared with target lobe volume prior to treatment as assessed on the pre- and post-treatment CT scans. EBV placement was considered successful with a TLVR of . The dataset was split into a training set ( ) and a test set ( ) to train and validate a logistic regression model using fivefold cross-validation; the extracted FIS of each patient's targeted treatment lobe was the primary CT predictor. Using the training set, a receiver operating characteristic (ROC) curve analysis and predictive values were quantified over a range of FIS thresholds to determine an optimal cutoff value that would distinguish complete and incomplete fissures, which was used to evaluate predictive values of the test set cases.
Results:
ROC analysis of the training set provided an AUC of 0.83, and the determined FIS threshold was 89.5%. Using this threshold on the test set achieved an accuracy of 81.6%, specificity (Sp) of 90.9%, sensitivity (Sn) of 77.8%, positive predictive value (PPV) of 62.5%, and negative predictive value of 95.5%.
Conclusions:
A model using the quantified FIS shows potential as a predictive biomarker for whether a targeted lobe will achieve successful volume reduction from EBV treatment.
