Pulmonary function tests (PFTs) play an important role in screening and following-up pulmonary involvement in systemic sclerosis (SSc). However, some patients are not able to perform PFTs due to... Show morePulmonary function tests (PFTs) play an important role in screening and following-up pulmonary involvement in systemic sclerosis (SSc). However, some patients are not able to perform PFTs due to contraindications. In addition, it is unclear how lung function is affected by changes in lung structure in SSc. Therefore, this study aims to explore the potential of automatically estimating PFT results from chest CT scans of SSc patients and how different regions influence the estimation of PFTs. Deep regression networks were developed with transfer learning to estimate PFTs from 316 SSc patients. Segmented lungs and vessels were used to mask the CT images to train the network with different inputs: from entire CT scan, lungs-only to vessels-only. The network trained on entire CT scans with transfer learning achieved an ICC of 0.71, 0.76, 0.80, and 0.81 for the estimation of DLCO, FEV1, FVC and TLC, respectively. The performance of the networks gradually decreased when trained on data from lungs-only and vessels-only. Regression attention maps showed that regions close to large vessels were highlighted more than other regions, and occasionally regions outside the lungs were highlighted. These experiments show that apart from the lungs and large vessels, other regions contribute to PFT estimation. In addition, adding manually designed biomarkers increased the correlation (R) from 0.75, 0.74, 0.82, and 0.83 to 0.81, 0.83, 0.88, and 0.90, respectively. This suggests that that manually designed imaging biomarkers can still contribute to explaining the relation between lung function and structure. Show less
Venlet, J.; Tao, Q.; Graaf, M.A. de; Glashan, C.A.; Silva, M.D.; Geest, R.J. van der; ... ; Zeppenfeld, K. 2020
OBJECTIVES This study sought to evaluate whether right ventricular (RV) tissue heterogeneity on computed tomography (CT): 1) is associated with conduction delay in arrhythmogenic right ventricular... Show moreOBJECTIVES This study sought to evaluate whether right ventricular (RV) tissue heterogeneity on computed tomography (CT): 1) is associated with conduction delay in arrhythmogenic right ventricular cardiomyopathy (ARVC); and 2) distinguishes patients with ARVC from those with exercise-induced arrhythmogenic remodeling (EIAR) and control individuals.BACKGROUND ARVC is characterized by fibrofatty replacement, related to conduction delay and ventricular tachycardiac. Distinguishing ARVC from acquired, EIAR is challenging.METHODS Patients with ARVC or EIAR and combined endocardial-epicardiat electroanatomic voltage mapping for VT ablation with CT integration were enrolled. Patients without structural heart disease served as control individuals. Tissue heterogeneity on CT (CT heterogeneity) was automatically quantified within the 2-mm subepicardium of the entire RV free wall at normal sites and tow voltage sites harboring late potentials (LP+) in ARVC/EIAR.RESULTS Seventeen patients with ARVC (15 mates; age: 50 17 years), 9 patients with EIAR (7 males; age: 45 14 years) and 17 control individuals (14 males; age: 50 +/- 15 years) were enrolled. Of 5,215 ARVC mapping points, 560 (11%) showed LP+ . CT heterogeneity was higher at sites with LP-i compared to normal sites (median: 31 HU/mm; IQR: 23 to 46 HU/mm vs. median: 16 HU/mm; IQR: 13 to 21 HU/mm; p < 0.001). The optimal CT heterogeneity cutoff for detection of LP+ was 25 HU/mm (area under the curve [AUG 0.80; sensitivity: 72%; specificity: 78%). Overall CT heterogeneity allowed highly accurate differentiation between patients with ARVC and control individuals (AUC: 0.97; sensitivity: 100%; specificity: 82%) and between ARVC and EIAR (AUC: 0.78; sensitivity: 65%; spedficity: 89%).CONCLUSIONS In patients with ARVC, tissue heterogeneity on CT can be used to identify LP+ as a surrogate for ventricular tachycardia substrate. The overall tissue heterogeneity on CT allows the distinguishing of patients with ARVC from those with EIAR and control individuals. (C) 2020 by the American College of Cardiology Foundation. Show less
