慢性泪囊炎患者应用鼻内镜下鼻腔泪囊造口术的临床效果及对安全性的影响

目的探讨慢性泪囊炎患者应用鼻内镜下鼻腔泪囊造口术的临床效果及安全性。方法选取2018年1月至2019年12月因慢性泪囊炎于本院接受治疗的46例患者为研究对象,随机分为研究组与对照组,各23例。对照组接受泪囊鼻腔造口治疗,研究组取鼻内镜下鼻腔泪囊造口术治疗,比较两组临床效果、手术指标以及并发症发生情况。结果研究组治疗总有效率高于对照组(P<0.05);研究组术中出血量少于对照组,手术及住院时间均短于对照组(P<0.05);研究组并发症总发生率低于对照组(P<0.05)。结论慢性泪囊炎患者采用鼻内镜下鼻腔泪囊造口术治疗效果显著,并发症较少,安全性较高,值得临床推广应用。     

Silver Diamine Fluoride Protocol for Reducing Preventable Dental Hospitalisations in Victorian Children

IntroductionThis study was designed to assess whether a dental caries management protocol combining a single application of 38% silver diamine fluoride (SDF) with comprehensive oral health education will successfully divert high-risk children from dental treatment under dental general anaesthesia (DGA), arrest active caries in primary teeth, and improve parent-reported child oral health–related quality of life (OHRQoL).MethodsChildren aged 2 to 10 years, who attended two public dental agencies in Victoria, Australia, and were unable to tolerate restorative treatments in the clinic setting, elected to participate in either a 38% SDF intervention protocol or, alternatively, referral for DGA. Follow-up examinations were completed at 6 months to assess caries progression, decayed missing filled tooth index, PUFA index (pulpal involvement, ulceration, fistula, abscess), DGA referral rates, and OHRQoL (Early Childhood Oral Health Impact Scale [ECOHIS]).ResultsOf the total sample, 89.5% of children (n = 102) [mean (SD) age, 4.1 (1.0) years] with 401 active carious lesions elected to participate in the 38% SDF protocol; 10.5% (n = 12) of parents opted for referral for treatment under DGA. The proportion of active caries subsequently arrested at follow-up (number of arrested lesions/number of lesions treated) was 0.78 (95% CI, 0.69 to 0.87). There was an 88% reduction in referrals for DGA in eligible children over the 6-month period. The 38% SDF intervention group showed a significant improvement in ECOHIS scores at follow-up (P < .001).DiscussionAdoption of the 38% SDF intervention protocol resulted in a significant reduction in the rate of preventable dental hospitalisations. Most parents opted against referral for DGA. Parent-reported OHRQoL for children improved significantly.     

上海市浦东新区基层全科医师心身医学项目培训效果评估*

目的在上海市浦东新区全科医师队伍中开展心身医学知识培训。方法采用问卷星平台进行问卷调查,选取辖区内45家社区卫生服务中心参加培训的135名学员作为培训组,未参加培训的100名全科医师作为对照组,进行培训效果评估。结果培训组总分高于对照组,差异有显著统计学意义(P0.001),培训后,提升最快的模块依次是心身医学知识的应用(P0.001)、精神科问诊及治疗(P0.001)、社区随访沟通技巧(P0.001)和儿童青少年心身疾病(P=0.002),但焦虑症模块的变化差异无统计学意义(P=0.213);相对于城区的全科医师,乡镇的全科医师培训后,精神科问诊及治疗(P=0.471)和儿童青少年心身疾病(P=0.06)模块的变化均无统计学意义。结论心身医学培训能显著提升全科医师对相应知识的掌握,城区和乡镇的全科医师对心身医学知识的需求有差别,需要进一步精确设计培训模式和方向。     

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

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%.