Right ventricular scalloping index as cardiac magnetic resonance-derived marker for diagnosis of arrhythmogenic right ventricular cardiomyopathy.

BACKGROUND: The cardiac magnetic resonance (CMR) evaluation of right ventricular (RV) morphologic abnormalities in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) is subjective. Here, we aimed to use a quantitative index, the right ventricular scalloping index (RVSI), to standardize the measurement of RV free wall scalloping and aid in the imaging diagnosis. METHODS: We retrospectively included 15 patients with definite ARVC and 45 age- and sex-matched patients with idiopathic right ventricular outflow tract ventricular arrhythmia (RVOT-VA) as controls. The RVSI was measured from cine images on four-chamber view to evaluate its ability to distinguish between ARVC and RVOT-VA patients. Other cardiac functional parameters including strain analysis were also performed. RESULTS: The RVSI was significantly higher in the ARVC than RVOT-VA group (1.56 ± 0.23 vs 1.30 ± 0.08, p < 0.001). The diagnostic performance of the RVSI was superior to the RV global longitudinal, circumferential, and radial strains, RV ejection fraction, and RV end-diastolic volume index. The RVSI demonstrated high intraobserver and interobserver reliability (intraclass correlation coefficient, 0.94 and 0.96, respectively). RVSI was a strong discriminator between ARVC and RVOT-VA patients (area under curve [AUC], 0.91; 95% CI, 0.82-0.99). A cutoff value of RVSI ≥1.49 provided an accuracy of 90.0%, specificity of 97.8%, sensitivity of 66.7%, positive predictive value (PPV) of 90.9%, and a negative predictive value (NPV) of 89.8%. In a multivariable analysis, a family history of ARVC or sudden cardiac death (odds ratio, 38.71; 95% CI, 1.48-1011.05; p = 0.028) and an RVSI ≥1.49 (odds ratio, 64.72; 95% CI, 4.58-914.63; p = 0.002) remained predictive of definite ARVC. CONCLUSION: RVSI is a quantitative method with good performance for the diagnosis of definite ARVC.

Use of artificial intelligence and I-Score for prediction of recurrence before catheter ablation of atrial fibrillation.

BACKGROUND: Based solely on pre-ablation characteristics, previous risk scores have demonstrated variable predictive performance. This study aimed to predict the recurrence of AF after catheter ablation by using artificial intelligence (AI)-enabled pre-ablation computed tomography (PVCT) images and pre-ablation clinical data. METHODS: A total of 638 drug-refractory paroxysmal atrial fibrillation (AF) patients undergone ablation were recruited. For model training, we used left atria (LA) acquired from pre-ablation PVCT slices (126,288 images). A total of 29 clinical variables were collected before ablation, including baseline characteristics, medical histories, laboratory results, transthoracic echocardiographic parameters, and 3D reconstructed LA volumes. The I-Score was applied to select variables for model training. For the prediction of one-year AF recurrence, PVCT deep-learning and clinical variable machine-learning models were developed. We then applied machine learning to ensemble the PVCT and clinical variable models. RESULTS: The PVCT model achieved an AUC of 0.63 in the test set. Various combinations of clinical variables selected by I-Score can yield an AUC of 0.72, which is significantly better than all variables or features selected by nonparametric statistics (AUCs of 0.66 to 0.69). The ensemble model (PVCT images and clinical variables) significantly improved predictive performance up to an AUC of 0.76 (sensitivity of 86.7% and specificity of 51.0%). CONCLUSIONS: Before ablation, AI-enabled PVCT combined with I-Score features was applicable in predicting recurrence in paroxysmal AF patients. Based on all possible predictors, the I-Score is capable of identifying the most influential combination.

Risk factors for pneumothorax and pulmonary hemorrhage following computed tomography-guided transthoracic core-needle biopsy of subpleural lung lesions.

BACKGROUND: Identifying the risk factors for complications may alert the physicians and help them adjust their plans before performing computed tomography-guided lung biopsies. Reportedly, a pleura-nodule distance longer than 2.0 cm is a strong predictor for pneumothorax and pulmonary hemorrhage. However, the rate and risk factors of biopsy-associated complications in subpleural lesions have not been assessed. This study aimed to identify the risk factors for pneumothorax and pulmonary hemorrhage in subpleural lesions ≤2.0 cm in depth. METHODS: Altogether, 196 patients (196 subpleural lesions, lesion depth: 0.1-2.0 cm) who underwent computed tomography-guided transthoracic core-needle biopsies between March 2017 and November 2017 were retrospectively analyzed. Univariate analysis of risk factors including patient-related, lesion-related, and procedure-related characteristics was performed for pneumothorax ≥1 cm and pulmonary hemorrhage ≥2 cm after the biopsy. Multivariate logistic regression analysis was performed to identify the independent risk factors. RESULTS: Pneumothorax ≥1 cm and pulmonary hemorrhage ≥2 cm were identified in 35 (17.9%) and 32 (16.3%) cases, respectively. In the multivariate analysis, a longer needle path (odds ratio [OR], 1.976; 95% confidence interval [CI], 1.113-3.506; p = 0.020) and low attenuation along the biopsy tract (OR, 3.080; 95% CI, 1.038-9.139; p = 0.043) were predictors of pneumothorax ≥ 1 cm. Ground-glass lesions (OR, 2.360; 95% CI, 1.009-5.521; p = 0.048) and smaller needle-pleura angle (OR, 0.325; 95% CI, 0.145-0.728; p = 0.006) were associated with pulmonary hemorrhage ≥2 cm. CONCLUSION: For subpleural lesions ≤2.0 cm in depth, a puncture route having a shorter needle path and passing through the lung parenchyma with higher attenuation may reduce the risk of biopsy-associated pneumothorax ≥1 cm. A higher needle-pleura angle may reduce the risk of pulmonary hemorrhage ≥2 cm in the short axis.

Intrathoracic myolipoma from parietal pleura with oestrogen and progesterone receptor.

Myolipoma is a rare benign lipomatous soft tissue neoplasm, occurring most frequently in adults in the abdomen, pelvis or retroperitoneum. We presented a case of a 39-year-old female with an epipleural lesion at the left paraspinal region identified using computed tomography. Magnetic resonance imaging revealed a fat-containing lesion attached to the pleura over the left paraspinal region. Surgical resection was performed, and histopathological analysis revealed a tumour composed of interlacing bundles of neoplastic smooth muscle cells with low cellularity and rare mitotic figures intermixing with mature adipocytes. The presence of oestrogen and progesterone receptors in smooth muscle cells was noted. The mass was reported as myolipoma. To the best of our knowledge, this case is the first description of an intrathoracic myolipoma.

Active surveillance or early resection for ground-glass nodules that need preoperative localization.

INTRODUCTION: Few studies have investigated the impact of active surveillance on pathological outcome ground-glass nodules (GGNs). We focused on GGNs that needed preoperative localization before resection and compared the pathological results between GGNs that underwent early resection or active surveillance. METHODS: We retrospectively reviewed data of resected GGNs between January 2017 and December 2018. GGNs were classified by early resection (Group A) and active surveillance (Group B). Group B was subclassified as no (Group B1) and with (Group B2) growth, and intergroup comparison of pathological results was undertaken. RESULTS: In total, 509 GGNs (124, 275, and 110 in Groups A, B1, and B2, respectively) were included. Malignancy (primary lung cancer) ratios were 68% and 72% in Groups A and B (p = .312) and 65% and 92% in Groups B1 and B2, respectively (p < .001). The ratios of invasive carcinoma were 21.4%, 9.6%, and 35.6% in Groups A, B1, and B2, respectively. Predictors for invasive carcinoma included history of lung cancer, GGN size ≥ 10 mm, solid size ≥ 6 mm, and GGN growth. CONCLUSIONS: The pathological findings were similar for GGNs in the early resection and active surveillance groups. However, rates of malignancy and invasive carcinoma increased in the group that manifested growth during active surveillance.

The Clinical Application of the Deep Learning Technique for Predicting Trigger Origins in Patients With Paroxysmal Atrial Fibrillation With Catheter Ablation.

BACKGROUND: Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post-atrial fibrillation ablation. Elimination of NPV triggers can reduce the recurrence of postablation atrial fibrillation. Deep learning was applied to preablation pulmonary vein computed tomography geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation. METHODS: We retrospectively analyzed 521 patients with paroxysmal atrial fibrillation who underwent catheter ablation of paroxysmal atrial fibrillation. Among them, pulmonary vein computed tomography geometric slices from 358 patients with nonrecurrent atrial fibrillation (1-3 mm interspace per slice, 20-200 slices for each patient, ranging from the upper border of the left atrium to the bottom of the heart, for a total of 23 683 images of slices) were used in the deep learning process, the ResNet34 of the neural network, to create the prediction model of the NPV trigger. There were 298 (83.2%) patients with only pulmonary vein triggers and 60 (16.8%) patients with NPV triggers±pulmonary vein triggers. The patients were randomly assigned to either training, validation, or test groups, and their data were allocated according to those sets. The image datasets were split into training (n=17 340), validation (n=3491), and testing (n=2852) groups, which had completely independent sets of patients. RESULTS: The accuracy of prediction in each pulmonary vein computed tomography image for NPV trigger was up to 82.4±2.0%. The sensitivity and specificity were 64.3±5.4% and 88.4±1.9%, respectively. For each patient, the accuracy of prediction for a NPV trigger was 88.6±2.3%. The sensitivity and specificity were 75.0±5.8% and 95.7±1.8%, respectively. The area under the curve for each image and patient were 0.82±0.01 and 0.88±0.07, respectively. CONCLUSIONS: The deep learning model using preablation pulmonary vein computed tomography can be applied to predict the trigger origins in patients with paroxysmal atrial fibrillation receiving catheter ablation. The application of this model may identify patients with a high risk of NPV trigger before ablation.

Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique.

BACKGROUND: Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create three-dimensional (3D) geometries. METHODS: Five hundred eighteen patients who underwent procedures for circumferential isolation of four pulmonary veins were enrolled. Cardiac CT images (from 97 patients) were used to construct the LA detection and segmentation models. These images were reviewed by the cardiologists such that images containing the LA were identified/segmented as the ground truth for model training. Two DCNNs which incorporated transfer learning with the architectures of ResNet50/U-Net were trained for image-based LA classification/segmentation. The LA geometry created by the deep learning model was correlated to the outcomes of AF ablation. RESULTS: The LA detection model achieved an overall 99.0% prediction accuracy, as well as a sensitivity of 99.3% and a specificity of 98.7%. Moreover, the LA segmentation model achieved an intersection over union of 91.42%. The estimated mean LA volume of all the 518 patients studied herein with the deep learning model was 123.3 ± 40.4 ml. The greatest area under the curve with a LA volume of 139 ml yielded a positive predictive value of 85.5% without detectable AF episodes over a period of one year following ablation. CONCLUSIONS: The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry.

Elongation of the Aorta after Thoracic Endovascular Aortic Repair: A longitudinal study.

Aortic morphology is associated with age, with the diameter being larger in older people. Thoracic endovascular aortic repair (TEVAR) is a treatment for aortic diseases, such as aortic dissection. When evaluating patients, aortic elongation could interfere with the classification of TEVAR complications. The longitudinal change in aortic length has not been studied in detail. In patients receiving thoracic endovascular aortic repair between 2007 and 2013, we determined the aortic length between the sinotubular junction, left common carotid artery, subclavian artery, and celiac artery on their first five annual follow-up computed tomography (CT) exams. Using the immediate post-TEVAR follow-up CT as the comparison reference and a lengthening of the aortic segment by 10 mm or more as the definition of elongation, 16 of 41 (39%) showed elongation between the innominate artery and celiac artery. When compared with the immediate follow-up CT, a higher proportion of patients showed elongation at the fifth year's follow-up CT than the first year's follow-up CT (p < 0.01), and the average lengthening per year was 1.7 mm. There was progressive lengthening of the aorta after TEVAR.

Electromagnetic navigation-guided versus computed tomography-guided percutaneous localization of small lung nodules before uniportal video-assisted thoracoscopic surgery: a propensity score-matched analysis.

OBJECTIVES: An optimal method for preoperative localization of small lung nodules is yet to be established, and there are few comparative studies in the literature. In the present study, we aimed to compare electromagnetic navigation-guided and computed tomography (CT)-guided methods of percutaneous transthoracic localization. METHODS: The clinical, radiographic, surgical and pathological data of patients who underwent electromagnetic navigation-guided localization (EMNGL) and CT-guided localization (CTGL) before uniportal video-assisted thoracic surgery (VATS) were reviewed. Propensity score matching analysis was performed to compare the localization and surgical results. RESULTS: After matching, 25 EMNGL and 50 CTGL patients were included in the analysis. In the CTGL group, pulmonary haemorrhage and pneumothorax were noted in 56% and 34% of patients, respectively, on postprocedural CT scans. Successful localization was achieved in 96% and 100% of patients in the EMNGL and CTGL groups, respectively (P = 0.333). The median time in the operation room was significantly shorter in the CTGL group {142.5 [interquartile range (IQR) 123.8-175.0] vs 205.0 [IQR 177.5-290.0] min, P < 0.001}. In contrast, EMNGL significantly decreased the total time [205.0 (IQR 177.5-290.0) vs 324.0 (IQR 228.3-374.0) min, P = 0.002]. The median duration of chest drainage was 1 day shorter in the EMNGL group [2.0 (IQR 1.5-2.5) vs 3.0 (IQR 2.0-3.0), P = 0.002]; the surgical complication rates were comparable between the 2 groups. CONCLUSIONS: The localization and surgical results were similar between the EMNGL and CTGL groups. EMNGL is comparable to conventional CTGL with respect to preoperative localization of small lung nodules before uniportal VATS.

Association Between Targeted Aortic Segment Tortuosity and Stent-Graft-Induced New Entry After Thoracic Endovascular Aortic Repair for Aortic Dissection or Intramural Hematoma.

OBJECTIVE. The aim of this study was to investigate the association between the tortuosity of the targeted aortic segment (TAS) for stent-graft implantation and distal stent-graft-induced new entry (SINE) after thoracic endovascular aortic repair (TEVAR) for aortic dissection or intramural hematoma. MATERIALS AND METHODS. We retrospectively analyzed data from 70 patients who underwent TEVAR using a single stent-graft between 2006 and 2016, and the tortuosity index of the TAS was measured. The patients were divided into high and low TAS tortuosity groups according to the median value of the tortuosity index. The incidence of distal SINE was compared between the two groups. RESULTS. The cumulative incidence of distal SINE at 2 years after TEVAR was 39% in patients in the high TAS tortuosity group and 7% in patients in the low TAS tortuosity group. The incidence of distal SINE was higher in patients in the high TAS tortuosity group than in those in the low TAS tortuosity group (p < 0.01, log-rank test). Multivariate Cox regression showed a higher risk of distal SINE in the high TAS tortuosity group (adjusted hazard ratio, 4.56 [95% CI, 1.40-14.86]; p = 0.01). CONCLUSION. Patients with high TAS tortuosity have a higher incidence of distal SINE after TEVAR. More caution must be exercised during follow-up of patients with high TAS tortuosity after TEVAR.

Interobserver and intraobserver variability in measuring the tortuosity of the thoracic aorta on computed tomography.

OBJECTIVE: The variability in measuring the tortuosity of the thoracic aorta has not been previously studied. This study evaluated the interobserver and intraobserver variability of major methods used for measuring the tortuosity of the thoracic aorta in patients with aortic arch or descending thoracic aortic aneurysm. METHODS: This retrospective study enrolled 66 patients with aortic arch or descending thoracic aortic aneurysm who had undergone thoracic endovascular aortic repair. Two radiologists used preoperative computed tomography images to measure the tortuosity of the thoracic aorta at multiple segments by using the fitting circle diameter, tortuosity index, and centerline angle methods; these measurements were repeated after an interval of >28 days. The variability of the methods was analyzed for interobserver and intraobserver reliability and agreement. The estimated intraclass correlation coefficient (ICC) was used to analyze the reliability. The Bland-Altman plot was used to analyze the interobserver and intraobserver agreement. The association between aortic characteristics, including calcification, luminal irregularity, shape, and diameter, and the variability of the measurements was also analyzed. RESULTS: The interobserver ICC estimates for the tortuosity index at multiple aortic segments, centerline angle methods at the supra-aortic branch orifices, and fitting circle diameter on the greater and lesser curvature sides were 0.97 to 0.98, 0.39 to 0.75, and 0.82 to 0.84, respectively. The corresponding intraobserver ICC estimates were 0.98 to 1.00, 0.44 to 0.75, and 0.82 to 0.85, respectively. In the agreement analysis, the 95% limits of agreement for the tortuosity index, centerline angle, and fitting circle diameter were -5.5% to 5.6%, -10.9% to 10.9%, and -18.0% to 24.0%, respectively. The tortuosity index had the highest ICC estimate and narrowest 99.5% limits of agreement of the three methods. Aortic characteristics, including calcification, grade of atheroma, aneurysm shape, and diameter, were not associated with the variability of the tortuosity index method in the thoracic aorta. CONCLUSIONS: The tortuosity index method has low interobserver and intraobserver variability in measuring the tortuosity of the thoracic aorta in patients with thoracic aortic aneurysm. The characteristics of the aorta and aneurysm are not associated with the interobserver or intraobserver variability of the tortuosity index.

Diagnostic feasibility and safety of CT-guided core biopsy for lung nodules less than or equal to 8 mm: A single-institution experience.

OBJECTIVES: This retrospective study evaluated the diagnostic yield and safety of CT-guided core biopsy of pulmonary nodules ≤8 mm. METHODS: We determined the diagnostic yield and safety profile of CT-guided lung biopsies for 125 pulmonary nodules ≤8 mm. Pathological diagnoses were made by a combination of histopathological examination and imprint cytology. Results were compared with biopsy results for 134 pulmonary nodules >8 and ≤10 mm. RESULTS: Final diagnoses were established in 94 nodules ≤8 mm. The sensitivity, specificity and diagnostic accuracy of CT-guided core biopsy for nodules ≤8 mm were 87.1 % (61/70 nodules), 100 % (24/24) and 90.4 % (85/94), respectively. Diagnostic failure rates were comparable for nodules ≤8 mm and nodules >8 mm and ≤10 mm (9/94, 9.6 % and 7/111, 6.3 %, respectively, P=0.385). The rate of tube thoracostomy for nodules ≤8 mm was comparable to that for nodules >8 and ≤10 mm (1.6 % vs. 0.7 %, P=0.611). Nodules ≤6 mm had a higher non-diagnostic result rate of 15.4 % (6/39) than did nodules >8 and ≤10 mm (3.7 %, 5/134, P=0.017). CONCLUSIONS: CT-guided pulmonary biopsy is feasible for lung nodules ≤8 mm, especially those >6 mm, and has an acceptable diagnostic yield and safety profile. KEY POINTS: • CT-guided biopsy of lung nodules ≤8 mm has high diagnostic accuracy. • Safety profiles are similar between nodules ≤8 mm and 8-10 mm. • Nodules ≤6 mm have higher rates of non-diagnostic results in biopsy. • Non-subpleural nodules and old age are risk factors for higher grade haemorrhage. • Biopsy is feasible for diagnosing nodules >6 and ≤8 mm.