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CT radiomics can help screen the coronavirus disease2019(COVID-19): a preliminary study

Science China(Information Sciences) 2020年第7期63卷 222-229页

作者： mengjie fang Bingxi HE Li LI Di DONG Xin YANG Cong LI Lingwei MENG Lianzhen ZHONG Hailin LI Hongjun LI Jie TIAN School of Artificial Intelligence University of Chinese Academy of Sciences CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Department of Radiology Beijing Youan Hospital Capital Medical University Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang University

The coronavirus disease 2019(COVID-19) is raging across the world. The radiomics, which explores huge amounts of features from medical image for disease diagnosis, may help the screen of the COVID-19. In this study, we aim to develop a radiomic signature to screen COVID-19 from CT *** retrospectively collect 75 pneumonia patients from Beijing Youan Hospital, including 46 patients with COVID-19 and 29 other types of pneumonias. These patients are divided into training set(n = 50) and test set(n = 25) at random. We segment the lung lesions from the CT images, and extract 77 radiomic features from the lesions. Then unsupervised consensus clustering and multiple cross-validation are utilized to select the key features that are associated with the COVID-19. In the experiments, while twenty-three radiomic features are found to be highly associated with COVID-19, four key features are screened and used as the inputs of support vector machine to build the radiomic signature. We use area under the receiver operating characteristic curve(AUC) and calibration curve to assess the performance of our model. It yields AUCs of 0.862 and 0.826 in the training set and the test set respectively. We also perform the stratified analysis and find that its predictive ability is not affected by gender, age, chronic disease and degree of *** conclusion, we investigate the value of radiomics in screening COVID-19, and the experimental results suggest the radiomic signature could be a potential tool for diagnosis of COVID-19.

关键词： coronavirus disease 2019 radiomics pneumonia diagnosis

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Artificial intelligence in gastric cancer:applications and challenges

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Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang mengjie fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** 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.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 329-342页

作者： Hailin Li Weiyuan Huang Siwen Wang Priya SBalasubramanian Gang Wu mengjie fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Psychiatry Weill Cornell MedicineNew YorkNY 10065USA Department of Radiotherapy Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China Department of Radiology Kiang Wu HospitalSanto AntónioMacao 999078China Department of Radiology Zhuhai People’s Hospital(Zhuhai Hospital Affiliated With Jinan University)ZhuhaiGuangdong 519000China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxi 710126China Zhuhai Precision Medical Center Zhuhai People’s HospitalZhuhaiGuangdong 519000China.

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

关键词： Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction

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

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Health Data Science 2023年第1期3卷 1-16页

作者： Shuaitong Zhang Wei Mu Di Dong Jingwei Wei mengjie fang Lizhi Shao Yu Zhou Bingxi He Song Zhang Zhenyu Liu Jianhua Liu Jie Tian School of Engineering Medicine Beihang UniversityBeijingChina. Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina. CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina. Department of Oncology Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical SciencesGuangzhouGuangdongChina.

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 *** 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 ***: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 ***:AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of *** technical issues should be overcome before its application into clinical practice of DSNs.

关键词： diagnosis neoplasms holds

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Semi-supervised Long-tail Endoscopic Image Classification

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Chinese Medical Sciences Journal 2022年第3期37卷 171-180,I0002页

作者： Runnan Cao mengjie fang Hailing Li Jie Tian Di Dong School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Advaneed Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'an 710126China

Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited *** We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse ***-supervised learning algorithm FixMatch was applied based on consistency regularization and *** 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 *** The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall *** 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,*** 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 *** 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 *** 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 ***,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.

关键词： endoscopic image artificial intelligence semi-supervised learning long-tail distribution image classification

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中国行业间上下行风险溢出效应研究

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国际金融研究 2024年第4期 24-36页

作者：李政李薇武坤方梦洁天津财经大学金融学院

本文将行业波动分解为低波动和高波动,分别代表上行风险和下行风险,基于Elastic-Net-VARX模型,在控制国际冲击的影响下,构建了中国经济金融体系的行业风险溢出网络,从静态和动态两个层面分析中国行业间风险总溢出及方向性溢出水平,并探... 详细信息

本文将行业波动分解为低波动和高波动,分别代表上行风险和下行风险,基于Elastic-Net-VARX模型,在控制国际冲击的影响下,构建了中国经济金融体系的行业风险溢出网络,从静态和动态两个层面分析中国行业间风险总溢出及方向性溢出水平,并探究各行业上下行风险溢出差异及传染特征。研究发现:第一,中国经济金融体系的上下行风险呈现明显的跨行业传染特征,工业是系统中最主要的风险输出者,可选消费是最主要的风险接收者;第二,上下行风险溢出效应具有显著的非对称性,下行风险溢出溢入水平高于上行风险,并且上行风险的溢出源头是金融行业,下行风险的溢出源头是实体行业;第三,相较于溢入效应,下行与上行风险溢出效应的差异更明显,其中,信息技术、通信服务与可选消费行业的下行风险溢出明显高于上行风险,但金融行业的上行风险溢出显著高于下行风险。本文为监管部门准确把握系统性风险传染源头,制定差异化的上下行风险防控措施提供可行建议。

关键词：高低波动上下行风险波动溢出效应系统性风险

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清热解毒汤联合伐昔洛韦治疗复发性生殖器疱疹临床研究

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新中医 2023年第21期55卷 108-112页

作者：方慧倩方梦婕淳安县第一人民医院皮肤科浙江杭州311700

目的:观察清热解毒汤联合伐昔洛韦治疗复发性生殖器疱疹(RGH)的临床疗效。方法:选取84例RGH患者,按随机数字表法分为治疗组44例与对照组40例。对照组给予伐昔洛韦治疗,治疗组给予清热解毒汤联合伐昔洛韦治疗,2组均连续治疗10 d。观察2... 详细信息

目的:观察清热解毒汤联合伐昔洛韦治疗复发性生殖器疱疹(RGH)的临床疗效。方法:选取84例RGH患者,按随机数字表法分为治疗组44例与对照组40例。对照组给予伐昔洛韦治疗,治疗组给予清热解毒汤联合伐昔洛韦治疗,2组均连续治疗10 d。观察2组临床疗效、疱疹愈合时间及复发率,比较2组治疗前后生殖器疱疹症状积分、免疫功能的变化。结果:治疗后,2组生殖器疱疹症状积分均较治疗前下降(P<0.05),治疗组生殖器疱疹症状积分低于对照组(P<0.05)。治疗组临床疗效总有效率为93.18%,对照组为77.50%,2组比较,差异有统计学意义(P<0.05)。治疗后,治疗组止疱时间、结痂时间、脱痂时间均早于对照组(P<0.05)。治疗后,2组CD3^(^(+))、CD4^(^(+))、CD4^(^(+))/CD8^(^(+))水平均较治疗前升高,CD8^(^(+))水平均较治疗前下降,治疗组前后比较,差异有统计学意义(P<0.05);治疗组CD3^(^(+))、CD4^(^(+))、CD4^(^(+))/CD8^(^(+))水平均高于对照组(P<0.05),CD8^(^(+))水平低于对照组(P<0.05)。治疗组复发率为4.88%,对照组复发率为22.58%,2组比较,差异有统计学意义(P<0.05)。结论:清热解毒汤联合伐昔洛韦治疗RGH疗效显著,可缓解临床症状,促进疱疹愈合,提高机体免疫力及降低复发率。

关键词：复发性生殖器疱疹清热解毒汤伐昔洛韦免疫功能复发率

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丹参素对UVB诱导的皮肤光老化小鼠的保护作用和抗氧化机制研究

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日用化学工业（中英文） 2024年第1期54卷 65-72页

作者：王安娜方梦婕唐超岳天翔江西省医药学校江西南昌330020 江西中医药大学江西南昌330004

研究丹参素(DSS)对紫外线B(UVB)诱导的皮肤光老化(SP)小鼠的保护作用和抗氧化机制。建立了UVB诱导的SP小鼠模型,使用3个剂量的DSS(20,40和80 mg/(kg·d))治疗小鼠8周,结束后分别测定了各组小鼠的表皮含水量。通过HE染色评价皮肤组织形态... 详细信息

研究丹参素(DSS)对紫外线B(UVB)诱导的皮肤光老化(SP)小鼠的保护作用和抗氧化机制。建立了UVB诱导的SP小鼠模型,使用3个剂量的DSS(20,40和80 mg/(kg·d))治疗小鼠8周,结束后分别测定了各组小鼠的表皮含水量。通过HE染色评价皮肤组织形态,Masson三色染色评价胶原蛋白沉积。按照试剂盒说明测定皮肤组织中氧化应激指标(SOD、CAT、GSH-Px和MDA)和炎症指标(TNF-α和IL-6)水平。通过RT-qPCT和Western blotting检测了皮肤组织中MMP-1、Collagen I、Nrf2、Keap1、HO-1、NF-κB p65和p-NF-κB p65的mRNA或蛋白水平。结果显示,DSS剂量依赖性地提高了UVB诱导的SP小鼠表皮含水量,减轻皮肤损伤,促进胶原形成(P<0.05)。DSS抑制了UVB诱导的SP小鼠皮肤组织中MMP-1的转录和表达,促进了Collagen的转录和表达(P<0.05)。DSS升高了UVB诱导的SP小鼠皮肤组织中SOD、CAT和GSH-Px的水平,降低了MDA的水平(P<0.05)。DSS降低了UVB诱导的SP小鼠皮肤组织中TNF-α和IL-6的水平(P<0.05)。DSS促进了UVB诱导的SP小鼠皮肤组织中Nrf2和HO-1的转录和表达,抑制了Keap1的转录和表达(P<0.05)。DSS抑制了UVB诱导的SP小鼠皮肤组织中p-NF-κB p65的表达(P<0.05)。本研究表明DSS可有效改善UVB诱导的小鼠SP,其机制与Nrf2和NF-κB信号通路有关。

关键词：丹参素紫外线B 皮肤光老化小鼠抗氧化

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基于能谱CT的影像组学术前预测进展期胃癌淋巴结转移的价值

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中华医学杂志 2020年第21期100卷 1617-1622页

作者：王睿李靖方梦捷董迪梁盼高剑波郑州大学第一附属医院放射科郑州450052 郑州大学附属肿瘤医院放射科郑州450008 中国科学院自动化研究所分子影像重点实验室北京100190

目的探讨基于能谱CT的影像组学模型术前预测进展期胃癌淋巴结转移的价值。方法回顾性收集2014年1月至2018年10月郑州大学第一附属医院共196例经手术病理确诊的胃腺癌患者的临床及能谱CT影像资料,其中男143例,女53例,年龄28~81(59±11)岁... 详细信息

目的探讨基于能谱CT的影像组学模型术前预测进展期胃癌淋巴结转移的价值。方法回顾性收集2014年1月至2018年10月郑州大学第一附属医院共196例经手术病理确诊的胃腺癌患者的临床及能谱CT影像资料,其中男143例,女53例,年龄28~81(59±11)岁,按照1∶1的比例随机分为训练集和验证集,并根据病理资料细分为淋巴结转移组和非转移组。测量并计算胃腺癌能谱参数值并进行淋巴结CT评估。基于不同能级(40、65和100 keV)的双期图像提取273个影像组学特征并建立组学标签。对能谱参数和组学特征进行单因素分析,将差异有统计学意义的变量纳入多因素Logistic回归分析中构建联合预测模型并绘制诺模图。受试者工作特征(ROC)曲线分析用于评价预测模型诺模图的诊断性能,不同数据集之间的曲线下面积(AUC)比较采用Delong检验。结果训练集和验证集中,淋巴结转移组的肿瘤静脉期碘基值均大于非转移组[训练集:22.98(100 mg/L)>20.31(100 mg/L),P=0.086;验证集:25.14(100 mg/L)>21.07(100 mg/L),P=0.009];淋巴结CT评估在两组间的差异均有统计学意义(P0.05)。结论基于能谱CT的影像组学对术前定量预测进展期胃癌淋巴结转移状态具有较好的应用价值。

关键词：肿瘤,胃淋巴结能谱成像影像组学

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影像组学的发展与应用

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中华放射学杂志 2017年第1期51卷 75-77页

作者：张利文方梦捷臧亚丽朱永北董迪刘侠田捷中国科学院自动化研究所分子影像重点实验室北京100190

近年来大数据技术与医学影像辅助诊断的有机融合产生了新的影像组学方法，其通过从影像中提取海量特征来量化肿瘤等重大疾病，可以有效解决肿瘤异质性难以定量评估的问题，具有重要的临床价值。影像组学技术来源于计算机辅助诊断（compu... 详细信息

近年来大数据技术与医学影像辅助诊断的有机融合产生了新的影像组学方法，其通过从影像中提取海量特征来量化肿瘤等重大疾病，可以有效解决肿瘤异质性难以定量评估的问题，具有重要的临床价值。影像组学技术来源于计算机辅助诊断（computer aided diagnosis，CAD），目前已经发展成为融合影像、基因、临床等信息的辅助诊断、分析和预测的方法。影像组学作为医工交叉的产物，其应用先进的计算机方法解决临床具体问题，将有广阔的应用前景。笔者将对新兴的影像组学技术进行介绍，阐述影像组学的相关概念、具体处理流程及其面临的挑战、应用领域及未来的发展方向。

关键词：医学影像应用计算机辅助诊断 diagnosis 肿瘤异质性临床价值组学技术计算机方法

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