Management of sagittal craniosynostosis: morphological comparison of eight surgical techniques

The aim of this study was to carry out a retrospective multicentre study comparing the morphological outcome of 8 techniques used for the management of sagittal synostosis versus a large cohort of control patients. Computed tomographic (CT) images were obtained from children CT-scanned for non-craniosynostosis related events (n = 241) and SS patients at preoperative and postoperative follow-up stages (n = 101). No significant difference in morphological outcomes was observed between the techniques considered in this study. However, the majority of techniques showed a tendency for relapse. Further, the more invasive procedures at older ages seem to lead to larger intracranial volume compared to less invasive techniques at younger ages. This study can be a first step towards future multicentre studies, comparing surgical results and offering a possibility for objective benchmarking of outcomes between methods and centres.     

3D-CRT 低剂量区 V5体积的大小对非小细胞肺癌患者肺功能的影响

目的：探讨3D-CRT 低剂量区 V5体积的大小对非小细胞肺癌患者肺功能的影响。方法：收集我院肿瘤科2014年4月～2015年10月收治的46例首次行三维适形放射治疗且顺利完成治疗的非小细胞肺癌患者，对每例患者分别在治疗前以及治疗后的1、3个月进行肺功能检测，分析3D-CRT 低剂量区 V5体积的大小与肺功能变化之间的关系。结果：放疗后1个月的 FEV1％、FVC％值明显高于放疗前且差异均具有显著性。放疗后1个月、3个月的 DLCO％值明显低于放疗前，且放疗前的与放疗后3个月的差异具有显著性；V5≦45％、45％55％三组放疗前后的 FEV1％值和 FVC％值比较差异均不具有显著性。V5≦45％、45％55％三组放疗前后的 DLCO％值均呈逐渐下降趋势，且 V5>55％组的放疗前后 DLCO％值比较差异具有显著性。而 V5≦45％组和45％55％时，DLCO％值降低的趋势更加显著。     

Resistin is elevated in cystic fibrosis sputum and correlates negatively with lung function

Background

Resistin is an immunometabolic mediator that is elevated in several inflammatory disorders. A ligand for Toll-like receptor 4, resistin modulates the recruitment and activation of myeloid cells, notably neutrophils. Neutrophils are major drivers of cystic fibrosis (CF) lung disease, in part due to the release of human neutrophil elastase- and myeloperoxidase-rich primary granules, leading to tissue damage. Here we assessed the relationship of resistin to CF lung disease.

弥散张量成像在癫痫中的研究进展

癫痫作为多种病因引起的神经系统慢性、发作性疾病,严重影响着患者的生活质量,因此对其及时诊断和早期治疗极为重要。目前已有多种神经影像技术用于癫痫的定位、定侧和病理生理研究。弥散张量成像是利用水分子在组织中弥散的各向异性成像的磁共振技术,是目前唯一能在活体中无创性地显示脑白质纤维束的方法,它能敏感地显示脑部细微结构,并能揭示各个结构间的功能联系,有助于癫痫的研究。本文主要从癫痫的病因诊断、癫痫手术的辅助指导、癫痫的结构网络及其与癫病功能障碍的相关性研究等方面对DTI应用于癫痫的最新研究进展进行综述。     

Morphological,Functional, and Tissue Characterization of Silent Myocardial Involvement in Patients With Primary Biliary Cholangitis

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De novo p.G696S mutation in COL4A1 causes intracranial calcification and late-onset cerebral hemorrhage: A case report and review of the literature

BackgroundThe collagen type IV alpha 1 chain (COL4A1) is an essential component of the basement membrane in small vessels. Pathogenic variants in COL4A1 cause perinatal cerebral hemorrhages in an autosomal-dominant fashion. However, little is known about the long-term outcomes of patients with mildly affecting COL4A1 mutations.Case reportWe report a 17-year-old boy, who presented with recurrent intracranial hemorrhages in the periventricular white matter. He had been followed-up as a child with cerebral palsy bearing intracranial calcifications, developmental delay and epilepsy. Screening tests in infancy provided negative results for intrauterine infections. Severe motor and cognitive deficits persisted after admission. Carbazochrome was introduced on day 19 of admission, which appeared to prevent extension and reactivation of cerebral hemorrhages for over 6 months after discharge.ResultsTargeted sequencing of NOTCH3 and TREX1 excluded causal mutations in these genes. The whole-exome sequencing revealed that he carried a de novo mutation in COL4A1 (p.Gly696Ser). An overview of the literature for 345 cases with COL4A1 mutations supported evidence that p.Gly696Ser is associated with the unique phenotype of late-onset hemorrhage among patients with COL4A1-associated cerebral angiopathy.ConclusionsThis case first demonstrates that infants with COL4A1-associated leukoencephalopathy and calcifications have a risk for developing the rupture of small vessels in the cerebral white matter after 10 years of age.     

Prostatic Artery Embolization—Anatomic Predictors of Technical Outcomes

PurposeTo determine if cone-beam CT and digital subtraction angiography analysis of pelvic arterial anatomy has predictive value for radiation exposure and technical success of prostatic artery embolization (PAE).Materials and MethodsThis prospective, nonrandomized, single-center study included 104 consecutive patients with lower urinary tract symptoms secondary to benign prostatic hyperplasia. Cone-beam CT was performed in 160/208 (76.9%) hemipelves to determine prostatic artery (PA) origin. Classification of pelvic arterial tortuosity was possible in 73/104 (70.2%) patients. Learning curves of 2 interventionalists who performed 86.5% of PAEs were analyzed.ResultsTortuosity of pelvic arteries was classified as mild in 25 (34.2%) patients median age 64 years, moderate in 40 (54.8%) patients median age 69 years, and severe in 8 (11.0%) patients median age 70 years (mild vs moderate, P = .002; mild vs severe, P = .019); median fluoroscopy times were 24, 36, and 46 minutes (P = .008, P = .023); median contrast volumes were 105, 122.5, and 142 mL (P = .029, P = .064); and bilateral PAE rates were 84.0%, 77.5%, and 62.5% (P = .437), respectively. PA origin from superior vesical artery was most frequent (27.5%) and showed higher dose area product (median 402.4 vs 218 Gy ∙ cm², P = .033) and fluoroscopy time (median 42.5 vs 27 min, P = .01) compared with PA origin from obturator artery, which was least frequent. Interventionalist experience revealed significant impact on procedure times (median 159 vs 130 min, P = .006).ConclusionsTortuosity of pelvic arteries was more frequent in older patients and predicted worse technical outcomes of PAE. PA origin from obturator artery was associated with lower dose area product and fluoroscopy time, especially compared with PA origin from superior vesical artery. Interventionalist experience showed significant influence on technical outcome.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.