Recognition and Management of Venous Leg Ulcers

The management of venous leg ulcers can pose quite a challenge to patients and health care providers. This condition can significantly impact quality of life and impose a huge financial burden on the patient and health care system. This review article will discuss the importance of early recognition of high-risk patients, with evaluation and initiation of appropriate multimodality treatment, which must be done in a timely and efficient manner.     

微导管在肝癌介入栓塞治疗中的应用

目的分析探讨肝癌介入栓塞治疗中微导管的作用。方法将肝癌介入栓塞治疗的患者分为普通栓塞组和微导管栓塞组,普通组179例;微导管组47例。普通栓塞组常规采用RH导管或Cobra导管行超选择栓塞治疗,微导管栓塞组采用SP3F同轴微导管作超选择肝段或者亚肝段的肿瘤供血血管栓塞治疗,其中28例行肝段栓塞,19例行肝亚段栓塞治疗。结果微导管组术后无1例误栓异位动脉而致并发症,肝功能损害轻微,疗效较普通组有明显提高。结论微导管的使用可提高栓塞的准确率增强疗效,避免并发症,肝功能损害轻微。     

鼻泪管阻塞的内涵管治疗

目的介绍鼻泪管阻塞的内涵管治疗。方法对9例11只鼻泪管阻塞（鼻泪管与泪囊连接处）置放Song601型内涵管治疗，按Song方法顺行引入导丝，逆行放入内涵管。术前与术后做泪囊造影。结果所有鼻泪管在术后造影均通畅，最长随访日为377天，放内涵管前，所有病例均有Ⅳ～Ⅴ度流泪程度，放内涵管后半年内，10只（91％）流泪明显好转，其中7只完全正常；1只5个月后无效，取出内涵管重新置放，症状消失至今3个月。结论介入治疗界泪管阻塞是一种安全、简单及有效的方法，不影响局部解剖结构。     

iFlow软件量化评估下肢动脉阻塞性疾病血流改变的价值

目的 探讨iFlow软件量化评估下肢动脉阻塞性疾病介入治疗前后血流动力学改变的价值.方法 回顾性分析经CTA明确诊断,且行介入治疗成功的16例症状性外周动脉疾病患者.患者均分4段行下肢动脉血管造影,而后行狭窄闭塞部位的球囊扩张和(或)支架植入治疗.采用量化分析iFlow软件对血管造影数据进行分析.测量术前和术后即刻股总动脉分叉部、腘动脉分叉部和踝关节水平的达峰时间(TTP)值,并计算腘动脉分叉部TTP与股总动脉分叉部TTP的差值(腘动脉分叉部TTP差值)和踝关节水平TTP和股总动脉分叉部TTP的差值(踝关节水平TTP差值).采用t检验比较上述参数值的变化.结果 16例介入手术均顺利完成,未出现与介入操作相关的并发症.股总动脉分叉部术前TTP为(5.46±2.69)s,术后为(4.64±0.92)s,差异无统计学意义(t=-1.161,P＞0.05).腘动脉分叉部、踝关节水平术前TTP分别为(13.49±3.96)、(22.84±7.27)s,术后分别为(9.84±2.44)、(14.88±4.56)s,差异均有统计学意义(t值分别为-3.142和-3.710,P均＜0.01).腘动脉分叉部、踝关节水平术前 TTP差值分别为(8.03±4.04)、(17.38±6.17)s,术后分别为(5.20±2.42)、(10.25±4.64)s,差异均有统计学意义(t值分别为2.404和3.746,P均＜0.05).结论 通过iFlow软件量化分析评估下肢动脉阻塞性疾病血管成形术前后的血流动力学变化准确、可行.     

A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow

Ultrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)–powered ultrasound is becoming more mature and getting closer to routine clinical applications in recent times because of an increased need for efficient and objective acquisition and evaluation of ultrasound images. Because ultrasound images involve operator-, patient-, and scanner-dependent variations, the adaptation of classical machine learning methods to clinical applications becomes challenging. With their self-learning ability, deep-learning (DL) methods are able to harness exponentially growing graphics processing unit computing power to identify abstract and complex imaging features. This has given rise to tremendous opportunities such as providing robust and generalizable AI models for improving image acquisition, real-time assessment of image quality, objective diagnosis and detection of diseases, and optimizing ultrasound clinical workflow. In this report, the authors review current DL approaches and research directions in rapidly advancing ultrasound technology and present their outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow.     

E-learning and education in radiology

Purpose

To evaluate current applications of e-learning in radiology.

Material and methods

A Medline search was performed using PubMed (National Library of Medicine, Bethesda, MD) for publications discussing the applications of e-learning in radiology. The search strategy employed a single combination of the following terms: (1) e-learning, and (2) education and (3) radiology. This review was limited to human studies and to English-language literature. We reviewed all the titles and subsequent the abstract of 29 articles that appeared pertinent. Additional articles were identified by reviewing the reference lists of relevant papers. Finally, the full text of 38 selected articles was reviewed.

Results

Literature data shows that with the constant development of technology and global spread of computer networks, in particular of the Internet, the integration of multimedia and interactivity introduced into electronic publishing has allowed the creation of multimedia applications that provide valuable support for medical teaching and continuing medical education, specifically for radiology. Such technologies are valuable tools for collaboration, interactivity, simulation, and self-testing. However, not everything on the World Wide Web is useful, accurate, or beneficial: the quality and veracity of medical information on the World Wide Web is variable and much time can be wasted as many websites do not meet basic publication standards.

Conclusion

E-learning will become an important source of education in radiology.     

原发性肝癌并门静脉癌栓的肝动脉化疗栓塞治疗

目的评价经肝动脉化疗栓塞术治疗原发性肝癌并门静脉癌栓的疗效和安全性。方法原发性肝癌并门静脉癌栓患者83例,其中53例行肝动脉化疗栓塞术(栓塞组),30例行肝动脉化学药物灌注(对照组)。对两组的疗效和并发症进行比较。结果经介入治疗后,栓塞组患者 6、12、24、36个月的生存率分别为97．8％、85．2％、45．8％、4．4％,均高于对照组(37．6％、5．3％、0、0． P均<0．01)。两组患者术后并发症的发生率差异无统计学意义。结论原发性肝癌并门静脉癌栓行经肝动脉化疗栓塞术较化疗灌注术的疗效好。     

Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy

PurposeTo evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy.MethodsForty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics’ diagnostic performance for lesion histology, T-stage, N-stage, and grade.ResultsWhole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers (P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean_0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean_10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean_25-50) (AUC = 0.829-0.829). For mean_10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10^?3 mm²/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N₁ than N₀ for both readers (P ≤ .043) were mean_0-10 (AUC = 0.789-0.822) and mean_10-25 (AUC = 0.800-0.822). For mean_10-25 (metric with highest AUC for identifying N₀), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage (P > .195) or grade (P > .215).ConclusionVolumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean_10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.