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.

Low-dose CT screening among never-smokers with or without a family history of lung cancer in Taiwan: a prospective cohort study.

BACKGROUND: In Taiwan, lung cancers occur predominantly in never-smokers, of whom nearly 60% have stage IV disease at diagnosis. We aimed to assess the efficacy of low-dose CT (LDCT) screening among never-smokers, who had other risk factors for lung cancer. METHODS: The Taiwan Lung Cancer Screening in Never-Smoker Trial (TALENT) was a nationwide, multicentre, prospective cohort study done at 17 tertiary medical centres in Taiwan. Eligible individuals had negative chest radiography, were aged 55-75 years, had never smoked or had smoked fewer than 10 pack-years and stopped smoking for more than 15 years (self-report), and had one of the following risk factors: a family history of lung cancer; passive smoke exposure; a history of pulmonary tuberculosis or chronic obstructive pulmonary disorders; a cooking index of 110 or higher; or cooking without using ventilation. Eligible participants underwent LDCT at baseline, then annually for 2 years, and then every 2 years up to 6 years thereafter, with follow-up assessments at each LDCT scan (ie, total follow-up of 8 years). A positive scan was defined as a solid or part-solid nodule larger than 6 mm in mean diameter or a pure ground-glass nodule larger than 5 mm in mean diameter. Lung cancer was diagnosed through invasive procedures, such as image-guided aspiration or biopsy or surgery. Here, we report the results of 1-year follow-up after LDCT screening at baseline. The primary outcome was lung cancer detection rate. The p value for detection rates was estimated by the χ2 test. Univariate and multivariable logistic regression analyses were used to assess the association between lung cancer incidence and each risk factor. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of LDCT screening were also assessed. This study is registered with ClinicalTrials.gov, NCT02611570, and is ongoing. FINDINGS: Between Dec 1, 2015, and July 31, 2019, 12 011 participants (8868 females) were enrolled, of whom 6009 had a family history of lung cancer. Among 12 011 LDCT scans done at baseline, 2094 (17·4%) were positive. Lung cancer was diagnosed in 318 (2·6%) of 12 011 participants (257 [2·1%] participants had invasive lung cancer and 61 [0·5%] had adenocarcinomas in situ). 317 of 318 participants had adenocarcinoma and 246 (77·4%) of 318 had stage I disease. The prevalence of invasive lung cancer was higher among participants with a family history of lung cancer (161 [2·7%] of 6009 participants) than in those without (96 [1·6%] of 6002 participants). In participants with a family history of lung cancer, the detection rate of invasive lung cancer increased significantly with age, whereas the detection rate of adenocarcinoma in situ remained stable. In multivariable analysis, female sex, a family history of lung cancer, and age older than 60 years were associated with an increased risk of lung cancer and invasive lung cancer; passive smoke exposure, cumulative exposure to cooking, cooking without ventilation, and a previous history of chronic lung diseases were not associated with lung cancer, even after stratification by family history of lung cancer. In participants with a family history of lung cancer, the higher the number of first-degree relatives affected, the higher the risk of lung cancer; participants whose mother or sibling had lung cancer were also at an increased risk. A positive LDCT scan had 92·1% sensitivity, 84·6% specificity, a PPV of 14·0%, and a NPV of 99·7% for lung cancer diagnosis. INTERPRETATION: TALENT had a high invasive lung cancer detection rate at 1 year after baseline LDCT scan. Overdiagnosis could have occurred, especially in participants diagnosed with adenocarcinoma in situ. In individuals who do not smoke, our findings suggest that a family history of lung cancer among first-degree relatives significantly increases the risk of lung cancer as well as the rate of invasive lung cancer with increasing age. Further research on risk factors for lung cancer in this population is needed, particularly for those without a family history of lung cancer. FUNDING: Ministry of Health and Welfare of Taiwan.

Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images.

Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3-523) days, longer than that for radiologists (8 (0-263) days). The AI model can assist radiologists in the early detection of lung nodules.

RN-Net: A Deep Learning Approach to 0-2 Hour Rainfall Nowcasting Based on Radar and Automatic Weather Station Data.

Precipitation has an important impact on people's daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Furthermore, existing rainfall nowcasting methods based on machine learning and deep learning cannot provide large-area rainfall nowcasting with high spatiotemporal resolution. This paper proposes a dual-input dual-encoder recurrent neural network, namely Rainfall Nowcasting Network (RN-Net), to solve this problem. It takes the past grid rainfall data interpolated by automatic weather stations and doppler radar mosaic data as input data, and then forecasts the grid rainfall data for the next 2 h. We conduct experiments on the Southeastern China dataset. With a threshold of 0.25 mm, the RN-Net's rainfall nowcasting threat scores have reached 0.523, 0.503, and 0.435 within 0.5 h, 1 h, and 2 h. Compared with the Weather Research and Forecasting model rainfall nowcasting, the threat scores have been increased by nearly four times, three times, and three times, respectively.

Long-term imaging follow-up to evaluate restenosis in patients with carotid stenosis after angioplasty and stenting.

BACKGROUND: Stent patency after carotid angioplasty and stenting (CAS) correlates not only with stroke prevention but also with improvements in cognition and quality of life by positively influencing cerebral perfusion. The long-term outcomes of CAS after more than 5 years have still not been well described. This retrospective study was designed to evaluate the stent patency and significant restenosis (SR) after CAS with more than 5 years of follow-up. METHODS: Between 2006 and 2012, 118 patients with carotid stenosis who underwent 131 CAS procedures with regular annual imaging follow-up for more than 5 years were enrolled. We evaluated their demographic characteristics and the risk factors related to stent restenosis. Patients with SR (restenosis ≥ 50%) were compared with those with no significant restenosis (NSR, patency or restenosis < 50%) to identify the restenosis predictors and restenosis-free survival. RESULTS: Of the 131 CAS procedures, 16.0% (21/131) had SR. A history of head and neck radiotherapy (HNRT) was a predictor for SR (HR, 6.352; 95% CI, 2.504-16.112; p < 0.001) and was associated with shorter restenosis-free survival (log-rank test p value < 0.001, median time of restenosis-free survival was 38 months). Left-sided stenting was an associated factor for SR (HR, 3.007; 95% CI, 1.068-8.467; p = 0.037) with a trend of less restenosis-free survival (log-rank test p value 0.067). CONCLUSION: Both HNRT and left-sided carotid stenosis were predictors of SR after CAS in more than 5 years of long-term follow-up. Restenosis-free survival was significantly shorter in patients with prior HNRT than in patients without previous irradiation treatment. We suggest close follow-up and aggressive medical treatment for patients with prior HNRT and left-sided carotid stenosis undergoing CAS.

Computed tomography-based differentiation of primary pulmonary lymphoepithelioma-like carcinoma and small-cell lung cancer.

BACKGROUND: Primary pulmonary lymphoepithelioma-like carcinoma (LELC) is a rare subtype of lung cancer. Both small-cell lung cancer (SCLC) and LELC often manifest as a centrally located tumor with lymphadenopathy. This retrospective study investigated and compared the initial computed tomography (CT) features and subsequent survival outcomes of LELC and SCLC. METHODS: A total of 50 patients with a confirmed diagnosis of LELC were enrolled and matched at a ratio of 1:1 with patients with SCLC according to the tumor stage. Utilizing a consensus approach, two radiologists reviewed pretreatment CT images. Survival outcomes were analyzed. RESULTS: Well-defined tumors were significantly more common in the LELC group (LELC: 42% vs SCLC: 24%, p = 0.005). Based on the comparisons of the primary tumor with the muscles, LELC tumors exhibited a significantly higher percentage of attenuation on contrast-enhanced CT scans (21.6% ± 29% vs -14.2% ± 37%, p < 0.001). The prevalence of vascular or bronchial encasement (18% vs 40%, p = 0.028), background emphysematous changes (10% vs 60%, p < 0.001), and tumors located in upper lobes (18% vs 64%, p < 0.001) was significantly lower in the LELC group. Female gender (70% vs 12%, p < 0.001), younger age (57.6 ± 12.0 years vs 68.0 ± 11.0 years, p < 0.001), and without a history of smoking (16% vs 88%, p < 0.001) were factors more commonly found in the LELC group. The patients with LELC had a better prognosis with significantly longer median survival than did the patients with SCLC (23.4 months vs 17.3 months, p = 0.01). CONCLUSION: Because SCLC demonstrated a more aggressive disease progression, differentiating LELC from SCLC is crucial. In Epstein-Barr virus-endemic areas, the diagnosis of LELC should be considered when approaching a patient with the above-mentioned CT and clinical features.

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.

Preoperative biopsy and tumor recurrence of stage I adenocarcinoma of the lung.

PURPOSE: To evaluate whether preoperative biopsy affects the outcomes of patients undergoing at least lobectomy for stage I lung adenocarcinoma. METHODS: We reviewed the medical records of patients who underwent surgery for stage I lung adenocarcinoma between 2006 and 2013. Tumor recurrence and survival were compared between patients who underwent preoperative biopsy, including computed tomographic-guided needle biopsy and transbronchial biopsy, and those who underwent intraoperative frozen section. RESULTS: Among 509 patients, 229 patients (44.9%) underwent preoperative biopsy and 280 patients had lung adenocarcinoma diagnosed by intraoperative frozen section (reference group). Recurrence developed in 65 (12.8%) patients within a median follow-up period of 54.4 months. Multivariate analysis demonstrated that preoperative biopsy (OR 1.97, p = 0.045), radiological solid appearance (OR 5.43, p < 0.001), and angiolymphatic invasion (OR 2.48, p = 0.010) were independent predictors of recurrence. In the overall cohort, preoperative biopsy appeared to worsen 5-year disease-free and overall survival significantly (76.6% vs. 93.0%, p < 0.001; and 83.8% vs. 94.5%, p = 0.002, respectively) compared with the reference group. After propensity matching, multivariable logistic regression still identified preoperative biopsy as an independent predictor of overall recurrence (OR 2.21, p = 0.048) after adjusting for tumor characteristics. CONCLUSION: Preoperative biopsy might be considered a prognosticator of recurrence of stage I adenocarcinoma of the lungs in patients who undergo at least anatomic lobectomy without postoperative adjuvant chemotherapy.

Pulmonary tuberculous cavities in diabetic patients: Glycemic control is still the dominant factor despite the emerging role of metformin.

BACKGROUND: Previous studies have reported an increased risk of cavities in diabetic patients with pulmonary tuberculosis (PTB), which may be associated with poor glycemic control. Cavities have a negative impact on PTB treatment outcomes; however, the possible interaction of other potentially confounding diabetes-related variables regarding pulmonary cavities have not been fully evaluated. METHODS: We conducted a retrospective cohort study of diabetic patients with culture-proven PTB. The patients' chest X-rays (CXRs) and computed tomography (CT) scans were reviewed to assess the effects of clinical factors, glycosylated hemoglobin (HbA1c) levels, and antidiabetic agents on cavitary lesions. RESULTS: Among 128 diabetic PTB patients, those with pulmonary cavities on CXRs and CT scans presented younger ages, lack of metformin treatment, and significantly higher HbA1c levels than those without cavities. Multivariate logistic regression analysis revealed significantly higher HbA1c levels in patients with cavities than in those without cavities on CXRs (odds ratio [OR], 1.34; 95% confidence interval [CI], 1.12-1.61) and CT scans (OR, 1.36; 95% CI, 1.13-1.64). Patients with multiple cavities had significantly higher HbA1c levels than those with a single cavity on CT scans (p = 0.002). No significant differences in other variables, including metformin treatment, were noted between the groups. CONCLUSION: This study suggests that despite multiple potential confounding variables, including metformin use, poor glycemic control is still the dominant risk factor for cavitary lesions in diabetic patients with PTB. Efforts to improve glycemic control in diabetic PTB patients may be of considerable value in facilitating antimycobacterial treatment.

Postablation assessment of hepatocellular carcinoma using dual-energy CT: Comparison of half versus standard iodine contrast medium.

This retrospective study was aimed to evaluate the reduced iodine load on image quality and diagnostic performance in multiphasic hepatic CT using a novel monoenergetic reconstruction algorithm (nMERA) in assessment of local tumor progression after radiofrequency ablation (RFA) of hepatocellular carcinoma (HCC). Ninety patients who underwent CT 1 month after RFA of HCC. Forty-five patients had multiphasic hepatic dual-energy CT with a half-reduced contrast medium (HRCM) of 277.5 mg I/kg. The nMERA (40-70-keV) images were reconstructed in each phase. Another 45 patients received a standard contrast medium (SCM) of 555 mg I/kg, and the images were reconstructed as a simulated 120-kVp images. Primary outcome was accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in assessment of local tumor progression. Additional advanced assessments included the image noise, attenuation value, contrast-to-noise ratio (CNR), and subjective image quality between the groups. The accuracy, sensitivity and specificity of nMERA HRCM images were 95.7%, 100% and 93.9% for 40 keV, 95.7%, 85.7% and 100% for 50 keV, 83.0%, 42.8% and 100% for 60 keV, and 83.0%, 42.9% and 100% for 70 keV. The AUROC was 0.99, 0.99, 0.94, and 0.93 for 40-70 keV nMERA HRCM images, respectively. Compared with simulated 120-kVp SCM images, nMERA HRCM images demonstrated comparable noise at 70-keV (P < 0.05), and comparable CNR at 40- and 50-keV (P < 0.05). nMERA DECT enables the contrast medium to be reduced to up to 50% in multiphasic hepatic CT while preserving diagnostic accuracy.

ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation.

This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number of background pixels in SSS image, the imbalance classification remains an issue. What is more, the SSS images contain undesirable speckle noise and intensity inhomogeneity. We define and detail a network and training strategy that tackle these three important issues for SSS images segmentation. Our proposed method performs image-to-image prediction by leveraging fully convolutional neural networks and deeply-supervised nets. The architecture consists of an encoder network to capture context, a corresponding decoder network to restore full input-size resolution feature maps from low-resolution ones for pixel-wise classification and a single stream deep neural network with multiple side-outputs to optimize edge segmentation. We performed prediction time of our network on our dataset, implemented on a NVIDIA Jetson AGX Xavier, and compared it to other similar semantic segmentation networks. The experimental results show that the presented method for SSS image segmentation brings obvious advantages, and is applicable for real-time processing tasks.

RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images.

Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.

Epstein-Barr Virus-Associated Pulmonary Carcinoma: Proposing an Alternative Term and Expanding the Histologic Spectrum of Lymphoepithelioma-like Carcinoma of the Lung.

Lymphoepithelioma-like carcinoma (LELC) of the lung is a rare Epstein-Barr virus (EBV)-associated carcinoma. It is histologically characterized by a syncytial growth pattern with marked lymphocytic infiltration that is indistinguishable from the histology observed in undifferentiated nasopharyngeal carcinomas. However, it has been noted that LELC can display nonclassic morphology and lack significant lymphocytic infiltration. In this study, we conducted a comprehensive clinicopathologic analysis of 61 patients with pulmonary LELC and performed automatic quantification of the lymphocytic infiltrate using the IHC Profiler software. We demonstrated that pulmonary LELCs have a morphologically continuous spectrum, ranging from classic poorly differentiated tumors with intense lymphocytic infiltration to nonclassic morphology with little lymphocytic infiltration. These EBV-associated tumors represent a distinct entity and usually occur in female and nonsmoking patients. Tumors with low lymphocytic infiltration can closely resemble nonkeratinizing squamous cell carcinoma and tend to be larger in size, have higher maximum standardized uptake values on radiography, and exhibit shorter times to recurrence than those with high lymphocytic infiltration. Through detailed pathologic examination, we observed several distinct morphologic features in pulmonary LELCs, including granulomatous inflammation, focal keratinization, spread through alveolar spaces, and lepidic spreading pattern. We also found that patients with tumors exhibiting granulomatous inflammation have favorable outcomes; however, spread through alveolar spaces did not significantly correlate with prognosis. As many of these "LELCs" do not resemble undifferentiated nasopharyngeal carcinoma or lymphoepithelioma, we propose using an alternative term, EBV-associated pulmonary carcinoma, to encompass the entire morphologic spectrum of this distinct disease entity.

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.

Pleuroparenchymal fibroelastosis presenting with pneumothorax.

A 47-year-old woman presented with spontaneous right side pneumothorax. Image studies showed consolidations and reticular opacities involving the pleural and subpleural regions of bilateral lungs. Wedge biopsy specimens of right upper, middle and lower lobes showed fibrosis of the visceral pleura and subpleural area in all three lobes, more significant in the upper lobe. Elastic Van Gieson stain showed a pattern of alveolar septal elastosis with intra-alveolar fibrosis. The clinical presentation and pathological findings are compatible with pleuroparenchymal fibroelastosis, a rare and distinct type of interstitial lung disease. This entity is different from usual interstitial pneumonia by its relationship to pleura, upper lobe predominance and temporal homogeneity. It is different from non-specific interstitial pneumonia by its pleural involvement and scanty inflammatory cell infiltration. Pleuroparenchymal fibroelastosis is a slowly progressive disease; about half of the patients die in 10 years. No curative treatment is available at present time.

Attenuation values of renal parenchyma in virtual noncontrast images acquired from multiphase renal dual-energy CT: Comparison with standard noncontrast CT.

OBJECTIVES: To compare the renal parenchyma attenuation of virtual noncontrast (VNC) images derived from multiphase renal dual-energy computed tomography (DECT) with standard noncontrast (SNC) images, and to determine the optimum phase for VNC images. MATERIALS AND METHODS: Twenty-nine men and 16 women (mean age, 61 ±â€¯13 years; range, 37-89 years) underwent dynamic renal DECT (100/Sn140 kVp) were included in this institutional review board-approved retrospective study. There were four phases of the scan, which included noncontrast, corticomedullary (CMP), nephrographic (NP), and excretory phases (EP). The VNC images was generated from CMP, NP and EP. CT numbers of SNC images and VNC images of each phases were measured in the renal cortex and medulla. Mean standard deviation of subcutaneous fat was measured as image noise on SNC and VNC images. Radiation dose was recorded and potential radiation dose reduction was estimated. Results were tested for statistical significance using the unpaired t-test and agreement using Bland-Altman plot analysis. RESULTS: The difference in mean attenuation between SNC and each phase of VNC images were ≤4 HU. The mean attenuation of renal cortex and medulla was 33.2 ±â€¯4.4 HU, and 34.2 ±â€¯4.8 HU in SNC, 33.6 ±â€¯7.6 HU and 31.1 ±â€¯8.3 HU in VNC of CMP, 34.8 ±â€¯8.6 HU and 35.6 ±â€¯8.5 HU in VNC of NP, 31.5 ±â€¯7.6 HU and 32.4 ±â€¯7.5 HU in VNC of EP. In VNC of CMP, the attenuation of the cortex was higher than the medulla (p < 0.05), and the attenuation of medulla was significant lower than that of SNC (p < 0.01). In VNC of NP, the attenuation of renal cortex was higher than SNC (p < 0.05). In VNC of EP, the attenuation of cortex and medulla were lower than SNC (p < 0.05), and inadequate iodine subtraction in collecting system was noted. Image noise was significantly greater in SNC (p < 0.001). Mean radiation dose reduction achievable by removing the SNC was 12.3% ±â€¯0.9%. CONCLUSIONS: VNC images from multiphase renal DECT were similar to SNC images. Using the nephrographic phase can gives more comparable VNC images to SNC images in renal parenchyma than other phases.

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.

Procedural Characteristics and Outcomes of Transcatheter Aortic Valve Implantation: A Single-Center Experience of the First 100 Inoperable or High Surgical Risk Patients with Severe Aortic Stenosis.

BACKGROUND: This is the first study of a Taiwanese population reporting transcatheter aortic valve implantation (TAVI) outcomes of the first 100 cases from a single center offering two different transcatheter heart valve technologies via six types of approaches. We herein report the 30-day and one-year outcomes in our first 100 TAVI patients at Taipei Veterans General Hospital. METHODS: From May 2010 to April 2016, 100 consecutive patients with severe aortic stenosis (AS) who were considered unsuitable or at high risk for surgical aortic valve replacement underwent TAVI. Patient outcomes were classified according to the Valve Academic Research Consortium-2 (VARC-2) definitions. The device performance was assessed using transthoracic echocardiography by independent investigators. RESULTS: The mean patient age was 81.1 years, where 54% were female, and the mean Logistic EuroSCORE was 21.5%. The Medtronic CoreValve was used in 84 patients and the Edwards Sapien or Sapien XT valve (ESV) in 16. The transfemoral approach was the most frequently used route (83%), followed by transapical (9%) access. Overall, there was no procedural death. The VARC-2 outcomes were as follows: device success, 95%; stroke, 1%; major vascular complication, 3%; a need for pacemaker implantation, 5.1%. At discharge, the incidence of postoperative mild, moderate or severe paravalvular leak was 30%, 3% and 0%, respectively. At one year, the all-cause mortality rate was 14.0%, and no valve-related dysfunction was observed. Through multivariable analysis, non-transfemoral access [hazard ratios (HR) 4.81; 95% confidence interval (CI) 1.66-14.09; p = 0.004] and advanced chronic kidney disease (stages 4-5), (HR 3.13; 95% CI 1.07-9.09; p = 0.036) were independently associated with an increased risk of one-year mortality. CONCLUSIONS: We demonstrated that TAVI shows good early and mid-term outcomes in terms of survival, technical success, valve-related adverse events and haemodynamic performance in high-risk patients with severe AS.

Radiation recall pneumonitis induced by epidermal growth factor receptor-tyrosine kinase inhibitor in patients with advanced nonsmall-cell lung cancer.

BACKGROUND: Radiation recall pneumonitis (RRP) is a special form of radiation pneumonitis precipitated by certain pharmacological agents. Epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) is an effective treatment for advanced nonsmall-cell lung cancer (NSCLC) and has been reported as a potent radiation sensitizer. The incidence and general characteristics of EGFR-TKI-related RRP in patients with NSCLC remain unclear. METHODS: Clinical records and serial chest images of consecutive patients with advanced NSCLC who had received thoracic radiotherapy (TRT) and EGFR-TKI treatment were retrospectively reviewed. EGFR-TKI-related RRP was diagnosed according to history, clinical manifestations, and radiographic characteristics. Potential risk factors were analyzed. RESULTS: In total, 160 patients with NSCLC who received EGFR-TKI after TRT were identified. Of these patients, seven (4.4%) developed EGFR-TKI-related RRP. The median time interval between the end of radiotherapy and RRP was 124 days (range, 80-635 days) and that between the initiation of EGFR-TKI and RRP was 43 days (range, 18-65 days). No risk factor for the development of RRP was identified except that patients in whom EGFR-TKI was initiated within 90 days after the completion of radiotherapy had significantly higher rates of RRP than those of patients who began receiving EGFR-TKI treatment after 90 days (21% vs. 2.1%, p = 0.005). CONCLUSION: In patients with NSCLC who have a history of TRT, treatment with EGFR-TKI may induce not only interstitial lung disease but also RRP. Physicians should be aware of both unexpected adverse events when using EGFR-TKI.