Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma:a multicenter study

Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets(699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital)between January 2005 and December 2019.The Cancer Hospital dataset was randomly split into a training cohort(559 patients)and a validation cohort(140 patients)to train and tune a deep learning model based on a deep residual network(ResNet).The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model.Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography(HRCT)features for the model.The predictive performance was assessed by area under the curves(AUCs),accuracy,precision,recall,and F1 score.Subgroup analysis was performed to evaluate the potential bias of the study population.Results:A total of 1,009 patients were included in this study;409(40.5%)were male and 600(59.5%)were female.The median age was 57.0 years(inter-quartile range,IQR:50.0-64.0).The deep learning model achieved AUCs of 0.906(95%CI:0.873-0.938)and 0.893(95%CI:0.857-0.930)for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule(non-pGGN)testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.622).The precisions of this model for predicting pN0 disease were 0.979(95%CI:0.963-0.995)and 0.983(95%CI:0.967-0.998)in the testing cohort and the non-pGGN testing cohort,respectively.The deep learning model achieved AUCs of 0.848(95%CI:0.798-0.898)and 0.831(95%CI:0.776-0.887)for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.657).The recalls of this model for predicting pN2 disease were 0.903(95%CI:0.870-0.936)and 0.931(95%CI:0.901-0.961)in the testing cohort and the non-pGGN testing cohort,respectively.Conclusions:The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features extraction, a radiomics signature was constructed in derivation cohort（346 CRC patients）. A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen（CEA） level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation（separate from the cohort used to build the model） was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results： The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell＇s concordance index（c-index）： 0.817; 95% confidence interval（95% CI）： 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination（c-index： 0.803; 95% CI： 0.794–0.812）.Conclusions： Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

从信号到知识——基于人工智能的医学影像裸数据诊断价值初探

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology.Modern medical care and imaging technology are becoming increasingly inseparable.However,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows.In this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing images.Moreover,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not possess.Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

3旋流燃烧室头部冷态流场数值计算与试验验证

为了探究单管燃烧室壁面开设光学观察窗与3维倾斜冷却孔对燃烧室冷态头部流场结构的影响规律,利用CFD软件对某型发动机单头部燃烧室结构及简化结构的流场特性进行数值计算,并利用粒子测速仪(PIV)对开设光学观察窗的单管燃烧室头部冷态流场进行了试验验证。结果表明:单管燃烧室冷态流场数值计算结果与PIV测量结果基本相同,验证了数值计算的准确性。燃烧室进口空气流量的增加不影响燃烧室回流区的大小;单管燃烧室壁面冷却孔的布置位置对冷态流场中心回流区几乎无影响;在单管燃烧室水平方向壁面上开设光学观察窗,对水平方向的回流区影响较大,而垂直平面上的回流区几乎不受影响。

吴颢昕运用补气活血法治疗心系疾病经验

《灵枢》言:“心者,五脏六腑之大主也”[1]。吴颢昕从事心脑血管疾病治疗研究工作近40年,善于运用传统中医理论并结合现代临床药理研究。结合心系疾病多虚多瘀的特点,他提出“气不足无以行血液,脉不通无以安五脏”的观点,认为心系疾病多有气虚、血瘀的表现。治疗在辨证的基础上重用补气活血之法并结合相关方药的应用,在临床中多取得满意的疗效,其遣方用药思路和对疾病的独特认识值得进一步研究和探索。该文基于气虚血瘀理论梳理了吴颢昕辨证治疗心血管疾病的经验,阐述了“扶正培本”与“活血消瘀”之间的辨证关系,提出了以益气活血通脉为主,结合辨证和调五脏的心病治疗原则,从扩张型心肌病、冠心病、高血压病三方面对吴颢昕治疗心血管疾病的用药规律及学术经验进行论述,并列举典型门诊案例进行讨论,为临床辨证治疗心系疾病提供指导与参考。

获各琦铜矿利用采空区及废旧巷道预热冬季入风流的研究

获各琦铜矿利用地温预热的原理,设计了冬季井筒防冻通风系统。该预热系统经过一年多的运行,采空区及废旧巷道可使约63m^3/s的冷空气由-26℃预热到+2℃以上,有效地降低了冬季期间进风井的预热压力及预热成本,节省了大量能源消耗。在实践上证明了利用采空区及废旧巷道预热冬季入风流在技术上的有效性、经济上的合理性和安全上的可靠性。

获各琦铜矿井下粉尘治理的研究与应用

详细介绍了获各琦铜矿在治理井下粉尘方面的实践与创新,通过采用水封爆破、喷雾降尘、密闭尘源、喷洒抑尘剂等符合矿山实际的治理措施,有效减少在爆破作业、斜坡道运输及深溜井卸放矿过程中产生的粉尘量,并控制其在空气中的扩散,成效显著且经济可行,可供其他矿山治理同类产尘问题采纳应用。

Semi-supervised Long-tail Endoscopic Image Classification

Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited annotations.Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse classes.Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling.After splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited annotations.Results The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall evaluation.SSL algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,respectively.With a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average performance.Through analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail class.Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume hospitals.However,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.

急性胆管炎患者胆汁培养结果和药敏分析

目的研究急性胆管炎患者胆汁培养的病原学现状,病原体分布特点以及药敏情况为急性胆管炎的治疗提供更为合理的抗生素指导方案。方法回顾性分析2015年1月~2018年1月湛江地区我院收治符合病例研究纳入标准共314例急性胆管炎患者的临床病例资料,对其胆汁培养的病原菌分布及耐药性进行分析。结果314例急性胆管炎胆汁培养的病原菌阳性率为82.80%,共检出病原菌358株,革兰氏阴性菌230株,革兰氏阳性菌122株,真菌6株。最常见的细菌是大肠埃希菌129株(36.0%)、粪肠球菌59株(16.5%)、铜绿假单胞菌27株(7.5%)、肺炎克雷伯菌肺炎亚种24株(6.7%)以及屎肠球菌17株(4.7%)。胆道培养为革兰氏阴性致病菌总体上对碳氢酶烯类(厄他培南耐药率0%,亚胺培南耐药率8.41%,美罗培南耐药率22.22%)及氨基糖苷类(阿米卡星耐药率1.35%,庆大霉素耐药率20.00%、妥布霉素10.19%)相对敏感度较高。胆道培养为革兰氏阳性致病菌总体上对临床上常用的大部分抗生素耐药率较低,譬如对米诺环素、莫西沙星、氨苄西林、头孢曲松耐药率均低于10%。而对于临床上较少应用的抗菌素如克林霉素、红霉素耐药率则高于40%。结论急性胆管炎患者胆汁培养的病原学现状仍然以革兰阴性菌为主,而药敏以碳氢酶烯类及氨基糖苷类相对敏感度较高,故临床上对其应用抗菌素治疗时要重视加强管理及监控。

The Applications of Artificial Intelligence in Digestive System Neoplasms:A Review

Importance:Digestive system neoplasms(DSNs)are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%.Subjective evaluation of medical images including endoscopic images,whole slide images,computed tomography images,and magnetic resonance images plays a vital role in the clinical practice of DSNs,but with limited performance and increased workload of radiologists or pathologists.The application of artificial intelligence(AI)in medical image analysis holds promise to augment the visual interpretation of medical images,which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.Highlights:We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis,assessment of treatment response,and prognosis prediction on 4 typical DSNs including esophageal cancer,gastric cancer,colorectal cancer,and hepatocellular carcinoma.Conclusion:AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs.Several technical issues should be overcome before its application into clinical practice of DSNs.

高温强余旋非均匀来流条件模拟方法研究

针对新一代一体化加力燃烧室进口高温强余旋非均匀进气特点,设计了一种可用于模拟一体化加力燃烧室真实进气条件的高温强余旋非均匀进口流场发生装置,该装置能够模拟真实的一体化加力燃烧室进口来流条件。同时开展了耦合进口马赫数、温度和余旋角度的不均匀流场数值模拟研究,评估了非均匀来流生成装置设计、测量方法与评价指标的有效性,并通过试验结果验证了数值模拟的准确性。结果表明:数值计算所得余旋角误差在壁面附近约为±2°,这是由于数值计算低估了壁面的耗散作用,而在中心区域则在优于±2°;同样在马赫数不均匀模拟中,在主流区域误差在10%以内;此外在对于温度不均匀性的模拟中,数值仿真表现出了较大的误差,这是因为数值计算中未考虑壁面向外界的传热过程。综合来看,本文所提出的高温强余旋非均匀生成装置可生成用于模拟下一代一体化加力燃烧室进口所面临的真实复杂不均匀进口流场条件,所采用的数值模拟方法可以较为准确地揭示流场非均匀特性。

高温高压下平板预膜喷嘴初次雾化试验

为了深入探究预膜喷嘴燃油初次雾化机理,试验研究了高温高压条件下不同工况参数对平板预膜喷嘴初次雾化特性的影响规律。采用高速摄影可视化技术和马尔文粒度仪获取了预膜平板液膜厚度、液膜波动形态以及索泰尔平均直径(SMD)等雾化特性。利用图像后处理技术提取液膜厚度,试验结果表明:液膜厚度随进气压力、进气温度和进口韦伯数的增加而减小,随燃油流量增加而增加。采用本征正交分解(POD)对液膜波动主要模态时间系数进行快速傅里叶变换,结果表明在相同工况下,液膜波动频率表现出和液膜厚度变化相反的结果。当进气压力改变时,进口韦伯数保持不变,试验结果表明进气压力对最终液滴平均尺寸影响不明显,证实了进口韦伯数是更适合的表征燃油雾化效果的参数,在工况范围内液滴尺寸随着进口韦伯数的增加而减小。

加力用扇形喷嘴雾化特性试验

根据加力燃烧室内锥凹腔点火与联焰要求,设计了扇形喷嘴并开展相应的雾化试验,研究了供油压差、扇形角度及扇形出口高度等参数对流量特性和雾化特性的影响以及加力环境下横向气流的温度、速度和供油压差对索太尔平均直径(SMD)及穿透深度的影响。采用称质量法测量流量系数,利用马尔文粒度仪和高速摄影仪对下游SMD、雾化角度及穿透深度进行测量。结果表明:①供油压差增大,流量系数先减少,后稳定;②供油压差一定,扇形出口角度越大,流量系数和雾化角度也越大;③扇形出口高度增加,雾化效果变好;④出口位置对雾化特性影响不大;⑤供油压差越大,穿透深度越大,SMD减小;⑥横向气流速度越大、温度越高,穿透深度越浅,油雾场越靠近下游;⑦横向气流温度越高,SMD越小。

Impact of an innovative tuberculosis financing and payment model on health service utilization by tuberculosis patients in China:do the poor fare better than the rich?

Background:Tuberculosis(TB)prevalence is closely associated with poverty in China,and poor patients face more barriers to treatment.Using an insurance-based approach,the China-Gates TB program Phase II was implemented between 2012 and 2014 in three cities in China to improve access to TB care and reduce the financial burden on patients,particularly among the poor.This study aims to assess the program effects on service use,and its equity impact across different income groups.Methods:Data from 788 and 775 patients at baseline and final evaluation were available for analysis respectively.Inpatient and outpatient service utilization,treatment adherence,and patient satisfaction were assessed before and after the program,across different income groups(extreme poverty,moderate poverty and non-poverty),and in various program cities,using descriptive statistics and multi-variate regression models.Key stakeholder interviews were conducted to qualitatively evaluate program implementation and impacts.Results:After program implementation,the hospital admission rate increased more for the extreme poverty group(48.5 to 70.7%)and moderate poverty group(45.0 to 68.1%),compared to the non-poverty group(52.9 to 643%).The largest increase in the number of outpatient visits was also for the extreme poverty group(4.6 to 5.7).The proportion of patients with good medication adherence increased by 15 percentage points in the extreme poverty group and by ten percentage points in the other groups.Satisfaction rates were high in all groups.Qualitative feedback from stakeholders also suggested that increased reimbursement rates,easier reimbursement procedures,and allowance improved patients'service utilization.Implementation of case-based payment made service provision more compliant to clinical pathways.Conclusion:Patients in extreme or moderate poverty benefited more from the program compared to a non-poverty group,indicating improved equity in TB service access.The pro-poor design of the program provides important丨essons to other TB programs in China and other countries to better address TB care for the poor.

Impact of an innovative financing and payment model on tuberculosis patients'financial burden:is tuberculosis care more affordable for the poor?

Background:In response to the high financial burden of health services facing tuberculosis(TB)patients in China,the China-Gates TB project,PhaseⅡ,has implemented a new financing and payment model as an important component of the overall project in three cities in eastern,central and western China.The model focuses on increasing the reimbursement rate for TB patients and reforming provider payment methods by replacing fee-for-service with a case-based payment approach.This study investigated changes in out-of-pocket(OOP)health expenditure and the financial burden on TB patients before and after the interventions,with a focus on potential differential impacts on patients from different income groups.Methods:Three sample counties in each of the three prefectures:Zhenjiang,Yichang and Hanzhong were chosen as study sites.TB patients who started and completed treatment before,and during the intervention period,were randomly sampled and surveyed at the baseline in 2013 and final evaluation in 2015 respectively.OOP health expenditure and percentage of patients incurring catastrophic health expenditure(CHE)were calculated for different income groups.OLS regression and Iogit regression were conducted to explore the intervention's impacts on patient OOP health expenditure and financial burden after adjusting for other covariates.Key-informant interviews and focus group discussions were conducted to understand the reasons for any observed changes.Results:Data from 738(baseline)and 735(evaluation)patients were available for analysis.Patient mean OOP health expenditure increased from RMB 3576 to RMB 5791,and the percentage of patients incurring CHE also increased after intervention.The percentage increase in OOP health expenditure and the likelihood of incurring CHE were significantly lower for patients from the highest income group as compared to the lowest.Qualitative findings indicated that increased use of health services not covered by the standard package of the model was likely to have caused the increase in financial burden.Conclusions:The implementation of the new financing and payment model did not protect patients,especially those from the lowest income group,from financial difficulty,due partly to their increased use of health service.More financial resources should be mobilized to increase financial protection,particularly for poor patients,while cost containment strategies need to be developed and effectively implemented to improve the effective coverage of essential healthcare in China.

Artificial intelligence in gastric cancer:applications and challenges

Gastric cancer(GC)is one of the most common malignant tumors with high mortality.Accurate diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical images.However,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer discrepancies.Recently,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data automatically.Researchers are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including GC.Herein,we aim to provide a broad framework to summarize current research on AI in GC.In the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation.In the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype classification.For treatment decisions,AI can help with surgical margin determination and prognosis prediction.Meanwhile,current approaches are challenged by data scarcity and poor interpretability.To tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

进口不均匀来流下典型稳定器流场特性研究

为探究加力燃烧室典型稳定器在近似真实来流进口条件下的流场特性,通过用户自定义函数给定进口边界非均匀进气条件(包括速度不均匀和余旋角不均匀),采用雷诺平均方法对某一体化模型加力燃烧室典型稳定器的冷态流场进行了数值模拟,获得了一体化加力燃烧室流场结构、总压损失、流阻损失系数等特征。数值计算结果表明:1)凹腔-中心锥回流区轮廓随速度不均匀度增加而逐渐增大,支板后回流区轮廓随速度不均匀度增加而减小;随余旋角不均匀度增大,凹腔-中心锥回流区轮廓向内缩小,而支板后回流区沿径向向内增大;2)余旋角度大于15°时,支板整流性能显著减弱,在支板压力面气流分离形成回流区,增大燃烧室流动损失;3)随着速度不均匀度或余旋角不均匀度的增加,总压损失及流阻损失系数增大;此外,余旋角度将会增加速度的变化率。

Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method

Sea surface temperature （SST） is an important variable for understanding interactions between the ocean and the atmosphere. SST fusion is crucial for acquiring SST products of high spatial resolution and coverage. This study introduces a Bayesian maximum entropy （BME） method for blending daily SSTs from multiple satellite sensors. A new spatiotemporal covariance model of an SST field is built to integrate not only single-day SSTs but also time-adjacent SSTs. In addition, AVHRR 30-year SST climatology data are introduced as soft data at the estimation points to improve the accuracy of blended results within the BME framework. The merged SSTs, with a spatial resolution of 4 km and a temporal resolution of 24 hours, are produced in the Western Pacific Ocean region to demonstrate and evaluate the proposed metho- dology. Comparisons with in situ drifting buoy observations show that the merged SSTs are accurate and the bias and root-mean-square errors for the comparison are 0.15℃ and 0.72℃, respectively.