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学术成果

作为课题负责人，承担过十多项科研项目，其中包括国家自然科学基金、上海市科委重大项目子课题、国家教委重点项目等。在国内外核心期刊上累计发表学术论文170多篇，其中SCI论文95篇。申请国家发明专利十多项。目前的研究重点主要集中在医学影像人工智能与磁共振成像技术研发两个方面。

1 医学影像人工智能

近年来，与医院广泛合作，建立基于医学影像的诊断模型，发表相关SCI论文14篇。在磁共振成像领域的顶级会议ISMRM 2020年会上，与医院合作发表报告19篇，其中课题组口头报告3篇。此外，为了提高医工结合的科研效率、促进科研成果在临床落地应用，课题组与医院、公司合作开发了多款软件，也为课题的实施创造了良好的条件：

l影像组学科研平台

课题组开发的开源项目，为影像组学的研究建立了半自动的工具，覆盖影像组学研究的整个流程，包括特征提取、数据预处理、降维、特征选择、分类、结果查看与报告生成等。软件已在多家医院的科研中获得应用，课题组基于此平台，已有10多篇学术论文发表。

l智能影像工作站

智能影像工作站在普通的PACS工作站的基础上，提供了开放的框架，可以集成深度学学模型、影像组学模型与传统的图像后处理算法。在有适当的权限与配置的前提下，系统可以自动从医院的影像系统获取医生关心的图像，调用相关的模型进行数据处理，并将处理后的结果回传到医院的PACS系统，或在智能工作站上查看。该软件为医学影像人工智能模型的应用提供了便利的平台。

目前基于该系统实现的“前列腺AI计算机辅助诊断系统”中包含了前列腺的分割、肿瘤检测、评分、良恶性鉴别等多个模型，已在多家医院部署，进行临床前研究。

l多任务标记软件

提供了便捷的半自动标记工具、自动插值工具，可大大提高影像数据的标记效率，同时，也支持病人、检查、系列、影像、病灶等不同层次的文本信息的标记，便于数据的标注与管理。整个环境可以根据项目需要进行配置。

图1： FeatureExplorer软件界面。软件可以帮助医学影像人工智能的研究人员迅速地从大量的机器学习模型中发掘出最优模型，完成疾病的分类、治疗决策、预后评估等机器学习任务。

图2：智能影像工作站。集成的前列腺人工智能辅助诊断系统，可以自动对前列腺图像进行分割（蓝线）、癌灶检测（黄线）与评分。右下为癌灶检测的热图。

图3 多任务标记软件。可在影像上同时标记多种ROI，并同时输入病人、检查、影像、病灶相关的信息。标记的ROI的内容和临床信息的内容都可以根据项目定制。软件还提供了多种半自动标记工具和插值工具，可大大提高标记效率。

2 磁共振成像系统研发

　　从2005年起，与上海康达卡勒幅医疗科技有限公司合作，进行磁共振成像系统的研发。先后负责过系统软件与算法的研发、系统研制、产品质量体系等工作。参与并主持了0.35T、0.5T、1.5T、0.7T等一系列磁共振成像系统的研制开发与产业化工作，多款产品获得NMPA颁发的产品注册证，成功地进行了产业化。主持开发了完全自主知识产权的软件系统，包括系统应用软件、谱仪控制软件和系统调试测试软件等。

图4 主持或参与研发的磁共振成像系统。产品均获得国家药监局办法的产品注册证。

图5 主持研发的磁共振成像系统应用软件。

[发表论文（2021-）]

1. 杨鸿玺，高安康, 王一达, 白洁, 张勇, 程敬亮, 杨光,“基于影像组学和多序列 MRI 的胶质瘤相关癫痫预测”，《中国医学物理学杂志》，1005-202X（2023）卷40，期11, 1350-1355, 2023年11月, DOI:10.3969/j.issn.1005-202X.2023.11.006

2. Jie Bai; Mengyang He; Eryuan Gao; Guang Yang; Chengxiu Zhang; Hongxi Yang; Jie Dong; Xiaoyue Ma; Yufei Gao; Huiting Zhang; Yang Song; Xu Yan; Yong Zhang; Jingliang Cheng; Guohua Zhao, “Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis”, BMC Cancer (2023) 23:1231，https://doi.org/10.1186/s12885-023-11718-0

3. Chenglong Wang, Yun Liu, Fen Wang, Chengxiu Zhang, Yida Wang Mei Yuan, Guang Yang*, “Towards reliable and explainable AI model for pulmonary nodule diagnosis”, Biomedical Signal Processing and Control 88 (2024) 105646 (IF2022=5.076)

4. Mingxi Jiang, Zihao Yang, Ting Lu, Jiabao Li*, Chenglong Wang*, Guang Yang, Likun Pan*, “Machine learning accelerated study for predicting the lattice constant and substitution energy of metal doped titanium dioxide”, Ceramics International (2023), accepted, (IF2002=5.532)

5. Guangsheng Xu, Yajuan Zhang, Mingxi Jiang, Jinliang Li*, Hengchao Sun, Jiabao Li, Ting Lu, Chenglong Wang*, Guang Yang, Likun Pan*, “A Machine learning assisted study on organic solvents in electrolytes for expanding the electrochemical stable window of Zinc-ion batteries”, Chemical Engineering Journal (2023), DOI: 10.1016/j.cej.2023.146676 (IF2022=16.744)

6. Xiumei Li#, Chengxiu Zhang#, Tingting Li, Xiuqiang Lin, Dongmei Wu, Guang Yang*, and Dairong Cao*, “Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies, Scientific Reports (2023)13:15586, DOI:10.1038/s41598-023-42916-2 (IF2022=4.996)

7. Mingxi Jiang, Yajuan Zhang, Zihao Yang, Haibo Li, Jinliang Li, Jiabao Li, Ting Lu, Chenglong Wang*, Guang Yang*, Likun Pan*, “A Data-driven Interpretable Method to Predict Capacities of Metal ion Doped TiO2 Anode Materials for Lithium-ion Batteries Using Machine Learning Classifiers”, Inorg. Chem. Front., 2023, DOI:10.1039/D3QI01705B. (IF2022=7.779)

8. Eryuan Gao; Peipei Wang; Jie Bai; Xiaoyue Ma; Yufei Gao; Jinbo Qi; Kai Zhao; Huiting Zhang; Xu Yan; Guang Yang; Jingliang Cheng; Zhao Guohua, Radiomic analysis of diffusion kurtosis imaging: Distinguishing between glioblastoma multiforme and single brain metastasis, Academic Radiology (2023), https://doi.org/10.1016/j.acra.2023.07.023 (IF2022=5.483)

9. Shuang Lu#, Chenglong Wang#, Yun Liu, Funing Chu, Zhengyan Jia, Hongkai Zhang, Zhaoqi Wang, Yanan Lu, Shuting Wang, Guang Yang*, Jinrong Qu*, “The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma”, European Radiology (2023). https://doi.org/10.1007/s00330-023-10040-4 (IF2022=7.034)

10. Feng Wang, Cheng-Long Wang, Yin-Qiao Yi, Teng Zhang, Yan Zhong, Jia-Jia Zhu, Hai Li, Guang Yang, Tong-Fu Yu, Mei Yuan. “Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features”, Scientific Reports 13, 9302 (2023). DOI: 10.1038/s41598-023-36409-5 (IF2022=4.996)

11. Yida Wang, Naying He, Chunyan Zhang, Youmin Zhang, Chenglong Wang, Pei Huang, Zhijia Jin, Yan Li, Zenghui Cheng, Yu Liu, Xinhui Wang, Chen Chen, Jingliang Cheng*, Fangtao Liu, Ewart Mark Haacke, Shengdi Chen, Guang Yang*, Fuhua Yan*, “An automatic interpretable deep learning pipeline for accurate Parkinson’s disease diagnosis using quantitative susceptibility mapping and T1-weighted images”, Human Brain Mapping 2023, 10.1002/hbm.26399 (IF2022=5.399) 19 June 2023 Volume44, Issue12, 4426-4438, 15 August 2023

12. Haijie Wang, Yida Wang, He Zhang, Xuan Yin, Chenglong Wang, Yuanyuan Lu, Yang Song, Hao Zhu*, Guang Yang*, “A deep learning pipeline using prior knowledge for automatic evaluation of placenta accreta spectrum disorders with MRI”, Journal of Magnetic Resonance Imaging (IF2022=5.119), 2023, April, DOI: 10.1002/jmri.28770

13. Xie J, Li C, Chen Y, Zhang H, Lin H, Yang G, Long L, “Potential Value of the Stretched Exponential and Fractional Order Calculus Model in Discriminating Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: An Animal Experiment of Orthotopic Xenograft Nude Mice”, Current Medical Imaging, 22 Mar 2023, DOI: 10.2174/1573405619666230322123117 (IF2022=1.315)

14. Yida Wang, He Zhang, Tianping Wang, Liangqing Yao, Guofu Zhang, Xuefen Liu, Guang Yang*, and Lei Yuan*, “Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging”, Scientific Reports (2023), 13:2770, DOI: 10.1038/s41598-023-29814-3 (IF2022=4.996)

15. Jia-Xiang Xin, Guang Yang, Huojun Zhang, Jianqi Li, Caixia Fu, Jiachen Wang, Rui Tong, Yan Ren*, Da-Xiu Wei*, Yefeng Yao*, Optimal Control of Spin Singlet Order - Towards Accurately Targeting Molecular Signal in Magnetic Resonance Imaging and Spectroscopy, Scientific Reports (2023), 13:2212, DOI: 10.1038/s41598-023-28425-2 (IF2022=4.996)

16. Ziyu Le, Dongmei Wu, Xuming Chen, Lei Wang, Yi Xu, Guoqi Zhao, Chengxiu Zhang, Ying Chen, Ye Hu, Shengyu Yao, Tingfeng Chen, Jianping Ren*, Guang Yang*, Yong Liu*, “A radiomics approach for predicting acute hematologic toxicity in patients with cervical or endometrial cancer undergoing external-beam radiotherapy”, Radiotherapy and Oncology, 2023, Jan. 182, 109489, DOI: 10.1016/j.radonc.2023.109489 (IF2022=6.901)

17. Jing Zhang, Chenao Zhan, Chenxiu Zhang, Yang Song, Xu Yan, Yihao Guo, Tao Ai, and Guang Yang*, “Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features”, La Radiologia Medica (2023), DOI: 10.1007/s11547-023-01594-w (IF2022=6.313)

18. Sihong Huang, Xianglin Zhou, Wei Zhao, Yanyao Du, Danhui Yang, Yijie Huang, Yanjing Chen, Huiting Zhang, Guang Yang, Jun Liu*, and Hong Luo*, “Dynamic white matter changes in recovered COVID-19 patients: a two-year follow-up study”, Theranostics 2023; 13(2): 724-735. doi: 10.7150/thno.79902 (IF2022=11.6)

19. Long Cui, Yang Song, Yida Wang, Rui Wang, Dongmei Wu, Haibin Xie, Jianqi Li, Guang Yang*, “Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis”, PLoS One (2023), 18(1):e0278668, DOI: 10.1371/journal.pone.0278668 (IF2022=3.752)

20. Ruiqi Yu, Ke-wen Jiang, Jie Bao, Ying Hou, Yinqiao Yi, Dongmei Wu, Yang Song, Chun-Hong Hu*, Guang Yang* & Yu-Dong Zhang*, PI-RADSAI: introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI. British Journal of Cancer (2023) 128, 1019–1029 https://doi.org/10.1038/s41416-022-02137-2 (IF2022=9.075)

21. 刘雲，王一达，张成秀，杨光，王成龙*，“基于深度学习结合解剖学注意力机制的肺结节良恶性分类”，《中国医学物理学杂志》，2022年第11期1441-1447

22. Jia-Xiang Xin, Da-Xiu Wei, Yan Ren, Jun-Long Wang, Guang Yang, Huojun Zhang, Jianqi Li, Caixia Fu, Ye-Feng Yao. Distinguishing glutamate and glutamine in in vivo 1H MRS based on nuclear spin singlet order filtering. Magnetic Resonance in Medicine. 2022; 89(5), 1728-1740, doi:10.1002/mrm.29562 (IF2022=3.737)

23. 汤伟，徐倩，高婷婷，董辽，杨光，杜小霞*，陈伟*，“额颞叶和小脑及感觉运动区局部的度中心度值异常与帕金森患者冻结步态有关”，《磁共振成像》, 2022,13(7) : 84-89. DOI: 10.12015/issn.1674-8034.2022.07.015

24. 王颖珊; 邓奥琦; 毛瑾玲; 朱中旗; 石洁*; 杨光; 马伟伟; 路青*; 汪红志*，“基于3D VNetTrans的膝关节滑膜磁共振图像自动分割”，《波谱学杂志》, 2022, 39(03): 303-315.

25. 谢金桓; 龙莉玲*; 李晨晖; 张会婷; 杨光，“非高斯扩散加权成像：拉伸指数模型与分数阶微积分模型对术前预测肝细胞癌微血管侵犯的价值”，《临床放射学杂志》, 2022, 41(12): 2250-2256.

26. Hongyue Tao; Yibo Dan; Yiwen Hu; Yuxue Xie; Rong Lu; Xiangwen Li; Qianru Li; Chenglong Wang; Chengxiu Zhang; Guang Yang*; Shuang Chen*, Using radiomics to detect subtle architecture changes of cartilage and subchondral bone in hindfoot joints in chronic lateral ankle instability patients based on MRI PD-FS images，Academic Radiology (2022) 30(8), 1667-1677, DOI：10.1016/j.acra.2022.11.014 (IF2022=5.482) 2022.8

27. 常晓; 蔡昕; 杨光; 聂生东*，“生成对抗网络在医学图像转换领域的应用”，《波谱学杂志》, 2022, 39(03): 366-380

28. Ke-Wen Jiang, Yang Song, Ying Hou, Rui Zhi, Jing Zhang, Mei-Ling Bao, Hai Li, Xu Yan, Wei Xi, Cheng-Xiu Zhang, Ye-Feng Yao, Guang Yang*, Yu-Dong Zhang*, “Performance of artificial intelligence-aided diagnosis system for clinically significant prostate cancer with MRI: a diagnostic comparison study”, Journal of Magnetic Resonance Imaging, 57(5), 1352-1364, (IF2022=5.119), DOI: 10.1002/jmri.28427

29. Jinbo Qi, Peipei Wang, Guohua Zhao, Eryuan Gao, Kai Zhao, Ankang Gao, Jie Bai, Huiting Zhang, Guang Yang, Yong Zhang, Xiaoyue Ma* and Jingliang Cheng*, Histogram Analysis Based on Neurite Orientation Dispersion and Density MR Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements, Journal of Magnetic Resonance Imaging (2022, IF=5.119), DOI: 10.1002/jmri.28419

30. Teng Zhang, Chengxiu Zhang, Yan Zhong, Yingli Sun, Haijie Wang, Hai Li, Guang Yang, Quan Zhu and Mei Yuan, A Radiomics nomogram for invasiveness prediction in lung adenocarcinoma manifesting as part-solid nodules with solid components smaller than 6 mm”, Frontiers in Oncology (2022), 12:900049. DOI: 10.3389/fonc.2022.900049

31. Peipei Wang, Eryuan Gao, Jinbo Qi, Xiaoyue Ma, Kai Zhao, Jie Bai, Yong Zhang, Huiting Zhang, Guang Yang, Jingliang Cheng, Guohua Zhao, “Quantitative analysis of mean apparent propagator-magnetic resonance imaging for distinguishing glioblastoma from solitary brain metastasis”, European Journal of Radiology 154 (2022), 110430, (IF2022=4.531), DOI: 10.1016/j.ejrad.2022.110430

32. 董辽; 梁怀彬; 杨光; 刘建仁*; 杜小霞*，“基于概率性纤维追踪的躯体症状障碍患者脑网络初步探究”，《磁共振成像》, 2022, 13(07): 80-83+130

33. 高婷婷，王梦星，汤伟，董辽，杨光，杜小霞，马骏,“夜间遗尿症儿童静息态脑功能磁共振成像研究”，《磁共振成像》，2022, 13(6):71-75

34. Xiao Chang, Xin Cai, Guang Yang*, Shengdong Nie*, “Self-supervised learning for multi-center magnetic resonance imaging harmonization without traveling phantoms”, Physics in Medicine and Biology (2022), (2022 IF=4.174), DOI:10.1088/1361-6560/ac7b66

35. Hao Zhu, Xuan Yin, Haijie Wang, Yida Wang, Xuefen Liu, Chenglong Wang, Xiaotian Li, Yuanyuan Lu, Guang Yang*, and He Zhang*, “A computerized diagnostic model for automatically evaluating placenta accrete spectrum disorders based on the combined MR radiomics-clinical signatures”, Scientific Reports (2022)12:10130, (IF2022=4.996), DOI: 10.1038/s41598-022-14454-w

36. Qiong Ma; Yinqiao Yi; Tiejun Liu; Xinnian Wen; Fei Shan; Feng Feng; Qinqin Yan; Jie Shen; Guang Yang*; Yuxin Shi*, MRI-based radiomics signature for identification of invisible basal cisterns changes in tuberculosis meningitis: a multicenter study, European Radiology (2022), 32(12), 8659-‌8669 (IF2022=7.034), 10.1007/s00330-022-08911-3, 2022.6

37. Hu Guo, Jun Liu, Junjiao Hu, Huiting Zhang, Wei Zhao, Min Gao, Yi Zhang, Guang Yang, and Yan Cui, Diagnostic performance of gliomas grading and IDH status decoding: A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models, Journal of Magnetic Resonance Imaging (2022) 56:1834–1844, (IF2022=5.119), DOI:10.1002/jmri.28211

38. 刘绅，淡一波，王昭琦，鲁亚南，曲金荣，杨光*，“基于影像组学与深度学习的食管癌磁共振成像的T分期”，信息技术，（2022）4, 35-42

39. 淡一波，陶虹月，王一达，王成龙，陈爽，杨光*，“基于预建模的影像组学特征选择方法”，信息技术，（2022）4,1-6

40. Funing Chu#, Yun Liu#, Qiuping Liu, Weijia Li, Zhengyan Jia, Chenglong Wang, Zhaoqi Wang, Shuang Lu, Ping Li, Yuanli Zhang, Yubo Liao, Mingzhe Xu, Xiaoqiang Yao, Shuting Wang, Cuicui Liu, Hongkai Zhang, Shaoyu Wang, Xu Yan, Ihab R. Kamel, Haibo Sun, Guang Yang, Yudong Zhang, Jinrong Qu*, Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in esophageal squamous cell carcinoma, European Radiology (2022), (IF2022=7.034), DOI: 10.1007/s00330-022-08776-6

41. Xue Yang, Kai-Rui Hua, Jia-Xiang Xin, Yu-Xiao Li, Guang Yang, Da-Xiu Wei, Ye-Feng Yao, “Multiple-targeting NMR Signal Selection by Optimal Control of Nuclear Spin Singlet”, Journal of Magnetic Resonance (2022), (2022 IF=2.734), DOI:10.1016/j.jmr.2022.107188

42. Jingyu Zhong, Chengxiu Zhang, Yangfan Hu, Jing Zhang, Yun Liu, Liping Si, Yue Xing, Defang Ding, Jia Geng, Qiong Jiao, Huizhen Zhang, Guang Yang*, Weiwu Yao*, “Automated Prediction of the Chemotherapy Response in Osteosarcoma with Deep Learning and an MRI-Based Radiomics nomogram”, European Radiology (2022), (2022 IF=7.034), DOI: 10.1007/s00330-022-08735-1

43. Jie Bai, Hongxi Yang, Ankang Gao, Yida Wang, Guohua Zhao, Xiaoyue Ma, Chenglong Wang, Haijie Wang, Xiaonan Zhang, Guang Yang, Yong Zhang, Jingliang Cheng, “Radiomics nomogram improve the prediction of epilepsy in patients with cerebral gliomas”, Frontiers in Oncology (2022), (2022 IF=5.738), 12:856359

44. Weiwei Zhao; Yida Wang; Fangfang Zhou; Gaiying Li; Zhichao Wang; Haodong Zhong; Yang Song; Kelly M. Gillen; Yi Wang; Guang Yang*，Jianqi Li*,“Automated Segmentation of Midbrain Structures in Quantitative Susceptibility Maps Based on Deeply-Supervised Convolutional Neural Network and Transfer Learning”，Frontiers in Neuroscience (2022), (2022 IF=5.152), 16, 801618, DOI: 10.3389/fnins.2022.801618

45. Ruiqi Yu, Wei Liu, Yang Song, Jing Zhang, Xiao-hang Liu, Liangping Zhou, Guang Yang, “Identification of ISUP Grade of Clear Cell Renal Cell Carcinoma by Radiomics on Multi-phase CT Images”, Chinese Journal of Academic Radiology (2022), https://doi.org/10.1007/s42058-022-00087-5

46. Tianping Wang, Haijie Wang, Yida Wang, Xuefen Liu, Lei Ling, Guofu Zhang, Guang Yang, He Zhang, “MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols”, Journal of Ovarian Research (2022), 15, 6. (2022 IF=5.506), DOI: 10.1186/s13048-021-00941-7 (复旦大学附属妇产科医院)

47. Shuyi Yang, Yida Wang, Yuxin Shi, Guang Yang, Qinqin Yan, Jie Shen, Qingle Wang, Haoling Zhang, Shan Yang, Fei Shan*, Zhiyong Zhang*, “Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation”, Magnetic Resonance Imaging (2022), 85, 80-86, 10.1016/j.mri.2021.10.010, 2022 IF=3.13

48. 吴洁，张师天，谢海滨，杨光*, “基于多影像中心磁共振数据的半监督膝盖异常分类”, 《计算机应用》2022, 42(1) :316-324

49. Li, Qiong; Feng, Qiu-Xia; Qi, Liang; Liu, Chang; Zhang, Jing ; Yang, Guang ; Ge, Ying-Qian ; Zhang, Yu-Dong; Liu, Xi-sheng, “Prognostic Aspects of Lymphovascular Invasion in Localized Gastric Cancer: New Insights into the Radiomics and Deep Transfer Learning from Contrast-Enhanced CT Imaging, Abdominal Radiology (2022), 47, 496-507，DOI: https://doi.org/10.1007/s00261-021-03309-z, 2022 IF=2.886

50. 周敏雄，张会婷，王一达，杨光，姚旭峰，高安康，程敬亮，白洁，严序，“数据采集方案对神经扩散模型影响的评估”，《波谱学杂志》, 2022, 39(2): 220-229. DOI: 10.11938/cjmr20202870

51. Qi Wan, Yuze Wang, Jianfeng Hu, Peng Wang, Yu Peng, Tianjing Zhang, Jianqing Sun, Yang Song, Guang Yang, Xinchun Li, Changhong Liang, Diagnostic performance of 2D and 3D T2WI-based radiomics features with machine learning approaches in patients with solid solitary pulmonary lesion, Front. Oncol. (2021), DOI: 10.3389/fonc.2021.683587

52. 张一鸿,侯莹,包婕,王成龙，宋阳，张玉东，杨光，“基于注意力区域不同组分特征的磁共振成像前列腺癌包膜侵犯诊断研究”，《磁共振成像》2021, 12(12) : 39-43, 66. DOI:10.12015/issn.1674-8034.2021.12.008

53. Ankang Gao, Hongxi Yang, Yida Wang, Guohua Zhao, Chenglong Wang, Haijie Wang, Xiaonan Zhang, Yong Zhang, Jingliang Cheng*, Guang Yang* and Jie Bai*, “Radiomics for prediction of epilepsy in patients with frontal glioma”, Frontiers in Oncology: Neuro-Oncology and Neurosurgical Oncology (2021), Vol.11, Article 725926, DOI: 10.3389/fonc.2021.725926

54. Qinqin Yan, Yinqiao Yi, Jie Shen, Fei Shan, Zhiyong Zhang, Guang Yang*, Yuxin Shi*, Preliminary study of 3T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses, Cancer Cell International, (2021) 21:539 https://doi.org/10.1186/s12935-021-02195-1

55. Yuxue Xie, Yibo Dan, Hongyue Tao, Chenglong Wang, Chengxiu Zhang, Yida Wang, Jiayu Yang, Guang Yang*, and Shuang Chen*, “Radiomics feature analysis of cartilage and subchondral bone in differentiating knees predisposed to posttraumatic osteoarthritis after anterior cruciate ligament reconstruction from healthy knees”, BioMed Research International, Volume 2021, 4351499, https://doi.org/10.1155/2021/4351499

56. Ying Hou, Jie Bao, Yang Song, Mei-Ling Bao, Ke-Wen Jiang, Jing Zhang, Guang Yang, Chun-hong Hu, Hai-Bin Shi, Xi-Ming Wang, and Yu-Dong Zhang, Integration of Clinicopathologic Identification and Deep Transferrable Image Feature Representation Improves Predictions of Lymph Node Metastasis in Prostate Cancer, EBioMedicine 68 (2021) 103395

57. 易音巧，王一达，宋阳，杨永贵，汪劭川，郭岗*，杨光，“基于深度学习的胸部数字X线质量控制方法”，《计算机应用》2021, 41(S1):237-242

58. Y Hou, YH Zhang, J Bao, ML Bao, G Yang, HB Shi, Y Song, “Artificial Intelligence Is a Promising Prospect for the Detection of Prostate Cancer Extracapsular Extension with mp-MRI: A Two-center Comparative Study”, Eur J Nucl Med Mol Imaging (2021). https://doi.org/10.1007/s00259-021-05381-5

59. 陈静, 黄浦江, 杨光*, 刘志远，“个体前额叶与丘脑之间的功能连接与其对网游渴求程度的关系：一项静息态 fMRI研究，[J]. 磁共振成像, 2021, 12(4): 45-50

60. Zhicong Li; Jing Zhang; Yang Song; Xiaorui Yin; An Chen; Na Tang; Martin R Prince; Han Wang, Guang Yang, “Utilization of Radiomics to Predict Long Term Outcome of Magnetic Resonance Guided Focused Ultrasound Ablation Therapy in Adenomyosis Corresponding”, European Radiology (2021), https://doi.org/10.1007/s00330-020-07076-1. 31(1):392-402

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