Computed tomography-based visual assessment of chronic obstructive pulmonary disease: comparison with pulmonary function test and quantitative computed tomography

BackgroundChronic obstructive pulmonary disease (COPD) has variable subtypes involving mixture of large airway inflammation, small airway disease, and emphysema. This study evaluated the relationship between visually assessed computed tomography (CT) subtypes and clinical/imaging characteristics.MethodsIn total, 452 participants were enrolled in this study between 2012 and 2017. Seven subtypes were defined by visual evaluation of CT images using Fleischner Society classification: normal, paraseptal emphysema (PSE), bronchial disease, and centrilobular emphysema (trace, mild, moderate and confluent/advanced destructive). The differences in several variables, including clinical, laboratory, spirometric, and quantitative CT features among CT-based visual subtypes, were compared using the chi-square tests and one-way analysis of variance.ResultsSubjects who had PSE had better forced expiratory volume in 1 second (FEV1) (P=0.03) percentage and higher lung density (P<0.05) than those with moderate to confluent/advanced destructive centrilobular emphysema. As the visual grade of centrilobular emphysema worsened, pulmonary function declined and modified Medical Research Council, COPD assessment test (CAT) score, and quantitative assessment (emphysema index and air trapping) increased. The bronchial subtype was associated with higher body mass index (BMI), better lung function and higher lung density. Participants with trace emphysema showed a rapid increase in functional small airway diseaseConclusionsClassifying subtypes using visual CT imaging features can reflect heterogeneity and pathological processes of COPD.     

Prediction of pancreatic fistula after pancreatectomy by virtual touch tissue imaging and quantification (VTIQ) technology

ObjectivesThe aim of this study was to quantitatively evaluate the stiffness of pancreatic parenchyma and solid focal pancreatic lesions (FPLs) by virtual touch tissue imaging and quantification (VTIQ) technique and to investigate the potential usefulness of VTIQ method in the prediction of post-operative pancreatic fistula (POPF) after pancreatectomy.MethodsIn this prospective study, patients who scheduled to undergo pancreatectomy were initially enrolled and received VTIQ assessment within one week before surgery. VTIQ elastography (Siemens ACUSON Sequoia, 5C-1 transducer) was used to measure the shear wave velocity (SWV) value of FPLs and the body part pancreatic parenchyma. The palpation stiffness of pancreas was qualitatively evaluated during operation by surgeons. POPF was finally diagnosed and graded through a three-weeks post-operative follow-up according to international study group of pancreatic fistula (ISGPF). SWV values were compared between POPF positive and negative group. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficacy of SWV value in predicting POPF.ResultsFrom December 2020 to June 2021, 44 patients were finally enrolled in this study, among which, 26 patients were identified to develop POPF after pancreatectomy. The SWV value of pancreatic parenchyma in POPF positive group was significantly lower than that in POPF negative group (P = 0.001). However, there was no significant difference in palpation stiffness between the two groups (P = 0.124). Besides, neither the SWV value of FPL nor the SWV ratio between FPL to surrounding pancreatic parenchyma differ significantly between POPF positive and negative group (P > 0.05). Taking SWV value of pancreatic parenchyma >1.10 m/s as a cut-off value for predicting POPF, area under the receiver operating characteristic curve (AUROC) was 0.864 with 72.2% sensitivity, 92.3% specificity, 86.7% positive predictive value (PPV) and 82.8% negative predictive value (NPV), respectively.ConclusionsVTIQ technique might be a potential non-invasive imaging method to predict POPF before pancreatectomy in future clinical practice.     

声触诊组织成像定量技术评价正常成人睾丸生精功能

目的 采用声触诊组织成像定量（VTIQ）技术测量正常成人睾丸剪切波速度（SWV）,分析影响睾丸弹性硬度的主要因素,探讨其评价男性睾丸生精功能的价值。方法 招募200名正常成年男性志愿者行睾丸常规超声检查,测量双侧睾丸体积。以VTIQ技术测量双侧睾丸上极、中份、下极的SWV,比较双侧睾丸、不同年龄组睾丸SWV差异,并与年龄、睾丸体积和精子活动率和精子密度进行相关性分析。结果 200名正常成年男性左右侧睾丸SWV均为（1.23±0.18）m/s,左右侧差异无统计学意义（t=-0.376,P=0.710）。21~30岁组、31~40岁组、41~50岁与51~60岁组间双侧睾丸SWV值总体差异均有统计学意义（P均<0.001）。睾丸SWV与年龄呈正相关（r=0.454,P<0.001）,与睾丸体积、精子活动率和精子密度分别呈负相关（r=-0.649、-0.668、-0.675,P均<0.001）。结论 VTIQ技术可定量测量睾丸组织硬度,有望用于初步评估睾丸生精功能。     

声触诊组织量化成像评价注射A型肉毒毒素治疗脑卒中后前臂旋前肌痉挛肌肉硬度改变

目的 探讨声触诊组织量化成像（VTIQ）评价注射A型肉毒毒素治疗脑卒中后前臂旋前肌痉挛肌肉硬度改变的价值。方法 收集31例脑卒中后前臂旋前肌痉挛患者,在其松弛状态下,于超声引导注射A型肉毒毒素,并于治疗前和治疗后1个月对双侧旋前圆肌、旋前方肌行VTIQ检查,测量剪切波速度（SWV）,对比治疗前后以及患侧与健侧SWV差异。于治疗前后对患侧上肢肌力进行改良Ashworth量表（MAS）评分并比较其差异,评价其与SWV的相关性。结果 治疗前患侧旋前圆肌SWV值高于健侧（P=0.005）;治疗后1个月,患侧旋前圆肌、旋前方肌SWV值与健侧比较差异均无统计学意义（P均>0.05）。治疗后患侧旋前圆肌、旋前方肌SWV值均小于治疗前（P均<0.05）,治疗前后患侧上肢MAS评分差异有统计学意义（Z=-2.252,P=0.024）。治疗前后患侧上肢肌张力MAS评分与治疗前后患侧旋前圆肌的SWV值呈正相关（r_s=0.629、0.653,P均<0.001）。结论 VTIQ可用于动态观察脑卒中后前臂旋前肌痉挛患者旋前圆肌、旋前方肌的肌肉硬度,由此定量评估注射A型肉毒毒素治疗脑卒中后前臂旋前肌痉挛的疗效。     

Identification and Quantification of Cardiovascular Structures From CCTA: An End-to-End,Rapid, Pixel-Wise,Deep-Learning Method

ObjectivesThis study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification.BackgroundSegmentation of cardiac structures from coronary computed tomography angiography (CCTA) images is laborious. We designed an end-to-end deep-learning solution.MethodsScans were obtained from multicenter registries of 166 patients who underwent clinically indicated CCTA. Left ventricular volume (LVV) and right ventricular volume (RVV), left atrial volume (LAV) and right atrial volume (RAV), and left ventricular myocardial mass (LVM) were manually annotated as ground truth. A U-Net−inspired, deep-learning model was trained, validated, and tested in a 70:20:10 split.ResultsMean age was 61.1 ± 8.4 years, and 49% were women. A combined overall median Dice score of 0.9246 (interquartile range: 0.8870 to 0.9475) was achieved. The median Dice scores for LVV, RVV, LAV, RAV, and LVM were 0.938 (interquartile range: 0.887 to 0.958), 0.927 (interquartile range: 0.916 to 0.946), 0.934 (interquartile range: 0.899 to 0.950), 0.915 (interquartile range: 0.890 to 0.920), and 0.920 (interquartile range: 0.811 to 0.944), respectively. Model prediction correlated and agreed well with manual annotation for LVV (r = 0.98), RVV (r = 0.97), LAV (r = 0.78), RAV (r = 0.97), and LVM (r = 0.94) (p < 0.05 for all). Mean difference and limits of agreement for LVV, RVV, LAV, RAV, and LVM were 1.20 ml (95% CI: −7.12 to 9.51), −0.78 ml (95% CI: −10.08 to 8.52), −3.75 ml (95% CI: −21.53 to 14.03), 0.97 ml (95% CI: −6.14 to 8.09), and 6.41 g (95% CI: −8.71 to 21.52), respectively.ConclusionsA deep-learning model rapidly segmented and quantified cardiac structures. This was done with high accuracy on a pixel level, with good agreement with manual annotation, facilitating its expansion into areas of research and clinical import.