KEYWORDS: Computed tomography, Lung, Emphysema, Image segmentation, Education and training, Chest, Voxels, Radio over Fiber, Deep learning, Data modeling
We propose to develop a prediction model that uses the quantitative lung fissure integrity score from chest CT scans that can identify emphysema patients that successfully respond to endobronchial valve (EBV) treatment. It is hypothesized that patients with high fissure integrity are more likely to respond to EBV treatment and achieve volume reduction of the emphysematous lobe. This study retrospectively collected 89 anonymized pre-treatment chest CT exams from patients with moderate to severe emphysema and who underwent EBV treatment. Previous work used a deep learning approach that segmented lung fissure and quantified a fissure integrity score (FIS) for the right horizontal fissure (RHF), right oblique fissure (ROF), and left oblique fissure (LOF). A FIS is defined as the percentage of total fissure voxels present along the interlobar region. Fissures were categorized as complete with a FIS of ≥90%; otherwise, it was considered incomplete. The response to EBV treatment was recorded as the amount of targeted lobe volume reduction (TLVR) compared to target lobe volume prior to treatment. EBV placement was considered successful with a TLVR of ≥350 ml. Statistical analyses were performed separately for each targeted lobe and a logistic regression model was trained using the extracted FIS. From the test set, 8 subjects achieved TLVR with a mean(±SD) FIS of 0.943(±0.052). 23 targeted lobes did not achieve the desired TLVR, with a mean(±SD) FIS of 0.751(±0.201). The EBV prediction model using the FIS achieved an AUC of 0.842. 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.
Complete pulmonary fissures are assessed on computed tomography (CT) and are required for emphysema patients to be successfully treated with endobronchial valve (EBV) therapy. We propose a deep learning (DL) pipeline that uses a patch-based approach to quantitatively assess fissure completeness on CT and evaluate it in a clinical trial cohort. From the EBV for emphysema palliation trial (VENT), 130 CT scans were used in this study. The DL model utilizes nnU-Net as a backbone for the automatic pre- and post-processing of CT images and configuration of a 3D U-Net to segment patches of fissure and non-fissure. Five-fold cross validation is applied for training and inferences are obtained using a sliding window approach. Average symmetric surface distance (ASSD) and surface dice coefficient (SDC) at a threshold of 2mm evaluates segmentation performance. A fissure integrity score (FIS) is calculated as the percentage of complete fissure voxels along the surface of the assumed interlobar region using pulmonary lobar segmentations. A predicted-FIS (p-FIS) is quantified from the CNN output and is compared to the reference-FIS (r-FIS) as complete (FIS≥90%), partial (10%≤ FIS< 90%) or absent (FIS< 10%). A mean(±SD) SDC of 0.95(±0.037) is achieved for the left oblique fissure (LOF); 0.84(±0.144) for the right horizontal fissure (RHF), and 0.94(±0.098) for the right oblique fissure (ROF). Concordance rate of p-FIS and r-FIS is 86.4%, 88.6%, and 86.4% for the LOF, RHF, and ROF, respectively. A DL pipeline using a patch-based approach has potential to segment interlobar fissures from CT to quantitatively assess fissure completeness.
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