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%.     

Current status and challenges of Additive manufacturing in orthopaedics: An overview

Additive manufacturing is a rapidly emerging technology which is being successfully implemented in the various field of medicine as well as in orthopaedics, where it has applications in reducing cartilage defects and treatments of bones. The technology helps through systematic collection of information about the shape of the "defects" and precise fabrication of complex 3D constructs such as cartilage, heart valve, trachea, myocardial bone tissue and blood vessels. In this paper, a large number of the relevant research papers on the additive manufacturing and its application in medical specifically orthopaedics are identified through Scopus had been studied using Bibliometric analysis and application analysis is undertaken. The bibliometric analysis shows that there is an increasing trend in the research reports on additive manufacturing applications in the field of orthopaedics. Discussions are on using technological advancement like scanning techniques and various challenges of the orthopaedic being met by additive manufacturing technology. For patient-specific orthopaedic applications, these techniques incorporate clinical practice and use for effective planning. 3D printed models printed by this technology are accepted for orthopaedic surgery such as revision of lumbar discectomy, pelvic surgery and large scapular osteochondroma. The applications of additive manufacturing in orthopaedics will experience a rapid translation in future. An orthopaedic surgeon can convert need/idea into a reality by using computer-aided design (CAD) software, analysis software to facilitate the manufacturing. Thus, AM provides a comprehensive opportunity to manufacture orthopaedic implantable medical devices.     

正常小肠螺旋CT三期扫描技术和方法研究

目的探索正常小肠螺旋CT三期扫描技术和方法的最佳合理配伍关系。方法 80例正常小肠患者 ,随机分为两组 ,按两种配对参数各自进行螺旋CT三期扫描 ,通过双盲法分别选择确定两组显示良好影像质量小肠CT片 ,其中甲组 36例 ,乙组2 5例 ,两组经X2 检验 ,其P CT三期扫描技术和方法的最佳合理配伍是良好显示小肠位置 ,形态 ,周围结构的关键 ,同时也是认识异常小肠的基础     

重症颅脑损伤患者CT检查的护理

我院自 1997～ 2 0 0 1年以来 ,CT扫描诊断检查重症颅脑外伤 (GCS评分 3～ 8分 ) 16 4例 ,其中 16 1例平稳度过扫描期 ,3例因伤势严重在扫描过程中呼吸心跳停止 ,经抢救无效死亡。1　临床资料　　 16 4例重症颅脑损伤中 ,单纯颅内血肿 6 3例占 38% ;血肿伴脑挫裂伤 38例占 2 3%     

儿童颈椎间盘钙化症的影像诊断

目的 探讨儿童颈椎间盘钙化症的影像表现及诊断价值，并提高对本病的认识。方法 回顾分析16例儿童颈椎钙化性椎间盘病的影像表现。结果 16例患均行颈椎平片检查，5例行CT扫描并作了三维重建，8例行MRI检查。16例平片均显示了椎间盘钙化灶，均为单发，CT平扫加三维重建显示4例钙化呈卵圆形，1例钙化为不规则形。MRI检查不仅显示了椎间盘内改变还清晰显示了病变相邻椎体边缘信号改变，在T1、T2加权像上呈低信号。结论 平片检查可以显示椎间盘钙化；CT平扫加三维重建能清晰显示了钙化灶的大小、形态；MRI检查在显示受累椎体及周围组织的改变方面明显优于CT和平片。     

基底动脉尖综合征6例

基底动脉尖综合征(atrum Basilar ArterySyndrom RBAS)是指以基底动脉顶端为中心的5条血管交叉部,由于各种原因引起该区域的循环障碍。病因主要为栓塞和血栓形成,本病以老年人发病为多,平均年龄为65岁,以眩晕、吐和视物模糊为主诉,常有神经眼征。     

小脑出血的临床、CT及预后评估

目的探讨小脑出血的原因、临床特点、CT表现、治疗及预后情况。方法回顾分析32例患者的临床及CT影像资料。结果本病多见于60岁以上,以高血压动脉硬化为主要原因,以突发性眩晕、呕吐为发病的主要症状,头颅CT检查可确诊。预后与患者年龄、出血量、出血部位、意识状态等多种因素密切相关。结论CT扫描可及时准确诊断,避免误诊漏诊,临床上根据患者的综合情况采取不同的救治策略有助于提高疗效,改善预后。     

颈椎病与中医辨证关系的探讨(附100例分析)

目的:探讨颈椎病与中医分型的辨证关系。方法:对100例该病的影像表现与中医分型进行系统分析。结果:主要临床表现颈部活动受限,颈、肩、背部疼痛,常伴头痛、头晕、视物模糊、耳鸣等症状。风寒型以软组织所致颈曲改变为主。肝肾亏虚型、痰湿交阻型以骨质增生、椎间隙及椎间孔狭窄为主。结论:X线、CT是本病首选检查方法,能与中医辨证有机结合,对中西医治疗有重要意义。     

晚发性维生素K缺乏致颅内出血1例

1病历简介患儿,男,45天,呕吐抽搐5天伴发热2天。体检:体温38.5℃。前囟稍隆起,面色苍白,嘴角向左侧歪斜,两肺呼吸音粗未闻及罗音。实验室检查:凝血酶原时间30s,活化部分凝血活酶时间100s,白细胞11.2×109/L,血红蛋白72g/L。CT平扫:右额颞顶部颅骨内板下方见一新月形高低密度影,范围约11.0cm×1.0cm,边界清楚,前方呈低密度灶,CT值19Hu,后方呈高密度灶,CT值61Hu。右侧脑室及中线受压左移,后纵裂池内亦呈高密度铸形,CT值64Hu(图1),骨窗示颅骨结构完整,未见外伤性改变(图2)。CT诊断:右额颞顶部硬膜下血肿(亚急性期)并蛛网膜下腔出血。2讨…     

新生儿缺氧缺血性脑病143例颅脑CT分析

目的:评价CT检查对新生儿缺氧缺血性脑病(HIE)的诊断价值。方法:使用CT扫描仪,用OM基准线、0度角、层厚及层距5mm连续平扫至颅顶。结果:189例临床怀疑HIE的新生儿,CT阳性发现HIE143例,合并蛛网膜下腔出血22例,分别占受检者75·66%和11·64%。结论:对可疑HIE患儿行颅脑CT扫描,对临床上早期诊断HIE提供了可靠依据。它不但可以直观地提供病损部位与程度,而且对推测预后具有重要意义。