Deep learning model based on PET/CT and combination with Cox proportional hazard model for predicting progression of lung invasive adenocarcinoma after surgery

Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years after surgery.Methods The clinical,PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed.According to PD or not,the patients were divided into the PD group(n=71)and non-PD group(n=179).The basic data and PET/CT findings were compared between groups,among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic(ROC)curve and corresponding cut-off value.Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery.The patients were divided into training,validation and test sets at the ratio of 6∶2∶2,and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model,and the combination model was built in serial connection of DL model and the predictive factors.In test set,the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve(AUC).Results Patients'gender and smoking status,as well as the long diameter,SUV max and SUV mean of lesions measured on PET images,the long diameter,short diameter and type of lesions showed on CT were statistically different between groups(all P<0.05).Smoking(HR=1.787[1.053,3.031],P=0.031)and lesion SUV max>4.15(HR=5.249[1.062,25.945],P=0.042)were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery.In test set,the AUC of PET/CT DL model for predicting PD was 0.847,of the combination model was 0.890,of the latter was higher than of the former(P=0.036).Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery.Combining with Cox proportional hazard model could further improve its predicting efficacy.

基于文献调查分析中医药全方位深度参与疾病管理的研究

目的:基于文献调查分析中医药全方位深度参与疾病管理的应用效果。方法:通过文献调查分析2010年1月—2022年1月期间各医疗机构急诊科患者在临床疾病管理中的方法,分析其中医药全方位深度参与疾病管理现状,统计其中医药全方位深度参与疾病管理的频次,并根据分析结果总结相应的中医药疾病管理方案,对比其他文献在急诊科患者疾病管理中,常规疾病管理下和中医药疾病管理下急诊科患者疾病管理前后的疾病管理质量变化,同时比较常规疾病管理下和中医药疾病管理下急诊科患者疾病管理后的疾病管理满意度。结果:通过统计分析发现该数据库共有中医药技术项目10种,参与疾病管理总应用频次为184次,其中单种中医药技术应用频次最多的为穴位按摩,其次为循经推拿、中药熏洗、耳穴埋豆、中医艾灸,其为应用频次排名前5的中医药技术项目。基于文献调查分析显示,管理前,两组急诊科患者中医体质辨析指标评分、中医技术应用指标评分、中医病情观察指标评分及中医康复保健指标评分对比,差异均无统计学意义(P>0.05);管理后,中医药疾病管理患者中医体质辨析指标评分、中医技术应用指标评分、中医病情观察指标评分及中医康复保健指标评分均明显高于常规疾病管理患者(P<0.05)。基于文献调查分析显示,中医药疾病管理患者疾病管理满意度高于常规疾病管理患者(P<0.05)。结论:穴位按摩、循经推拿、中药熏洗、耳穴埋豆及中医艾灸等在疾病管理中参与度较深,基于文献调查分析中医药全方位深度参与疾病管理现状为患者进行相应的中医药疾病管理方案可以在更大程度上提高患者疾病管理质量及疾病管理满意度。

我生病,丢了谁的面子?以“脸面”为路径的叙事分析与病耻感的转化机制

本研究以“深度疾病”患者的叙事分析为方法,以中国本土社会学的“脸面”模型为框架,尝试从面子折损与获得的社交情境出发,探究以白化病为代表的这类“被看见”的病症,其患者从感知污名到污名内化为耻感的转化机制。研究发现,“月亮孩子之家”微信公众号的患者叙事呈现了“脸面”传导的三种效应:一致效应,家庭对“面子”的无视成为污名内化为耻感的重要“防线”;混合效应,白化病患者在获得社会支持后努力通过个人进取、为自己“挣脸”的方式以“回馈”帮助者、维护帮助者的“面子”,社会支持在一定程度上阻滞了病耻感的内化;异质效应,社会关系中的他人要求患者维护“面子”时,来自家庭与社会他者的双重压力使患者陷入习惯性自我怀疑与否定,污名转化为病耻感。在理论与方法层面,本研究尝试勾连中国本土面子理论与健康叙事的内容与结构分析;在应用层面,希望藉此唤起健康干预对患者因面子问题而陷入社交、失业等恶性循环困境的关照,亦探讨了研究对患者群体的意义、局限性以及可能进路。