(1)获奖情况 1、2021年,云南省肿瘤医院优秀职工; 2、2021年,昆明医科大学PBL案例大赛一等奖; 3、2022年,云南省肿瘤医院优秀职工; 4、2022年,共青团昆明医科大学委员会“青年岗位能手”称号; 5、2023年,云南省肿瘤医院优秀科主任。 (2)教学/科研项目 [1]李振辉,云南省科技厅-昆明医科大学基础研究联合专项杰出青年培育项目,202401AY070001-316,病理-影像组学早期预测胃癌术前免疫治疗疗效的研究,2024-08至2027-09,40万元,在研,主持。 [2]李振辉,国家自科科学基金地区项目,82360345,多时序CT联合多区域数字病理早期预测胃癌新辅助化疗抵抗的研究,32万元,2024-01至2027-12,在研,主持。 [3]李振辉,云南省“兴滇英才支持计划”青年人才专项项目,基于多模态纵向影像的直肠癌术后复发风险智能量化的研究,88万,2023-01至2027-12,在研,主持。 [4]李振辉,昆明医科大学校级创新团队,CXTD202110,胃肠肿瘤影像学智能研究科技创新团队,30万,2022-04至2025-03,在研,主持。 [5]李振辉,云南省应用基础研究专项面上项目,202201AT070010,基于纵向影像-病理组学构建局部进展期直肠癌术后远处转移风险智能量化模型的研究, 10万,2022-04至2025-03,在研,主持。 [6]李振辉,云南省应用基础研究专项优秀青年项目,202101AW070001,胃癌影像-病理组学,30万,2021-04至2024-03,在研,主持。 [7]李振辉,国家自科科学基金青年科学基金项目,82001986,基于影像-病理组学预测局部进展期胃癌新辅助化疗疗效的研究,24万元,2021-01至2023-12,已结题,主持。 [8]李振辉,云南省重大科技专项(生物医药),202002AA100007,昆医大第三附属医院肿瘤影像组学数据库,300万,2020-07至2023-06,在研,主持。 [9]李振辉,云南省科技厅-昆明医科大学应用基础研究联合专项面上项目,2019FE001(-083),局部进展期直肠癌术后远处转移预测模型的构建及验证,2019-08至2022-09,10万元,已结题,主持。 [10]李振辉,云南省教育厅基金面上项目,2018JS223,基于MRI 的影像组学对局部进展期直肠癌术后远处转移的预测价值研究,起止年月:2018-05至2021-06,4万,已结题,主持。 [11]李振辉,云南省卫生和计划生育委员会医学后备人才,H2017-007,起止年月:2019-01至2021-12,27万,已结题,主持。 [12]李振辉,云南省科技厅-昆明医科大学应用基础研究联合专项资金项目,2014FB062,放射学网络信息资源的用户评价方法及其实证研究,2014-10至2017-09,10万元,已结题,主持。 (3)论文 英文论文: [1]Li Zhenhui#, Zhang D#, Pang X#, Yan S#, Lei M, Cheng X, Song Q, Cai L, Wang Z*, You D*. Association Between Serum Carcinoembryonic Antigen Levels at Different Perioperative Time Points and Colorectal Cancer Outcomes.Front Oncol, 2021;11:722883.(第一作者,SCI检索,中科院二区,IF =3.5) [2]Li Zhenhui#, Li Chunxia#, Pu Hongjiang#, Pang Xiaolin#, Wang Yingyi#, Zhang Dafu, Lei Ming, Cheng Xianshuo, Zhao Yanrong, Lu Guiyu, Ding Yingying, Cai Le, Liu Zaiyi*, Zhang Tao*, You Dingyun*. Trajectories of perioperative serum carcinoembryonic antigen and colorectal cancer outcome: A retrospective, multicenter longitudinal cohort study.Clin Transl Med, 2021;11(2):e293.(第一作者,SCI检索,中科院一区,IF =7.9) [3]Zhou X#, Liu Z#, Zhang D, Wu L, Sun K, Shao L, Huang L,Li ZH*, Tian J*. Improving initial nodal staging of T3 rectal cancer using quantitative image features.Br J Surg, 2020;107(11): e541-e542.(通讯作者,SCI检索,中科院一区,IF =8.6) [4]Zhang C, Chen J, Liu Y, Yang Y, Xu Y, You R, Li Y, Liu L, Yang L, Li H, Wang G, Li W,Li Z*.Amide proton transfer-weighted MRI for assessing rectal adenocarcinoma T-staging and perineural invasion: a prospective study. Eur Radiol. 2024 Aug 9. doi: 10.1007/s00330-024-11000-2. Epub ahead of print. PMID: 39122854.(通讯作者,SCI检索,中科院二区,TOP期刊,IF=4,7) [5]Liu M#, Yan G#, Li Y#, You R#, Liu L#, Zhang D, Yang G, Dong X, Ding Y, Yan S*, You D*,Li Z*. Preoperative splenic area as a prognostic biomarker of early-stage non-small cell lung cancer.Cancer Imaging, 2023;23(1):116.(通讯作者,SCI检索,中科院二区,IF =3.5) [6]Zheng X#, Li C#, Ai J#, Dong G, Long M, Li M, Qiu S, Huang Y, Yang G, Zhang T,Li Z*.No prognostic impact of staging bone scan in patients with stage IA non-small cell lung cancer.Ann Nucl Med, 2024. doi: 10.1007/s12149-12024-01927-12143. Online ahead of print.(唯一通讯作者,SCI,IF=2.5) [7]Wang S#, Yu H#, Gan Y#, Wu Z, Li E, Li X, Cao J, Zhu Y, Wang L, Deng H, Xie M, Wang Y, Ma X, Liu D, Chen B, Tian P, Qiu Z, Xian J, Ren J, Wang K, Wei W, Xie F*,Li Z*, Wang Q*, Xue X*, Liu Z*, Shi J*, Li W*, Tian J*. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study.Lancet Digital Health,2022;4(5):e309-e319.(共同通讯作者,SCI检索,中科院一区,TOP期刊,IF =23.8) [8]Feng LL#,Liu ZY#, Li CF#,Li ZH#, Lou XY#, Shao LZ#, Wang YL, Huang Y, Chen MD HY, Pang XL, Liu S, He F, Zheng J, Meng XC, Xie PY, Yang GY, Ding Y,Wei MB, Yun JP, Hung M, Zhou WH, Wahl DR, Lan P*, Tian J*, Wan XB*.Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter observational study.Lancet Digital Health,2022; 4(1): e8-e17.(并列第一作者,SCI检索,中科院一区,TPO期刊,IF =23.8) [9]Liu ZY#, Meng XC#, Zhang ZM#,Li ZH#, Liu JG, Sun K, Meng YK, Dai WX, Xie PY, Ding YY, Wang MY*, Cai GX*, Tian J*. Predicting Distant Metastasis and Chemotherapy Benefit in Locally Advanced Rectal Cancer: A Multicenter Radiomic Study.Nat Commun, 2020;11(1):4308.(并列第一作者,SCI检索,中科院一区,TOP期刊,IF =14.7) [10]Chen Q, Zhang J, Meng R, Zhou L,Li Z*, Feng Q*, Shen D*. Modality-Specific Information Disentanglement From Multi-Parametric MRI for Breast Tumor Segmentation and Computer-Aided Diagnosis.IEEE Trans Med Imaging, 2024;43(5):1958-1971.(共同通讯作者,SCI检索,中科院一区,TOP期刊,IF =8.9) [11]Li Y, Shen Y, Zhang J, Song S,Li Z*, Ke J*, Shen D*. A Hierarchical Graph V-Net with Semi-supervised Pre-training for Histological Image based Breast Cancer Classification.IEEE Trans Med Imaging, 2023;42(12):3907-3918.(共同通讯作者,SCI检索,中科院一区,TOP期刊,IF =8.9) [12]Zhang J#, Cui Z, Zhou L*, Sun Y,Li Z*, Liu Z, Shen D*. Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification with Iterative Cycle-consistent Semi-supervised Learning.IEEE Trans Med Imaging, 2023; 42(12):3944-3955.(共同通讯作者,SCI检索,中科院一区,TOP期刊,IF =8.9) [13]Li C#, Liu L#, You R, Li Y, Pu H, Lei M, Fan B, Lv J, Liu M, Yan G,Li Z*, You D*, Zhang T*.Trajectory patterns and cumulative burden of CEA during follow-up with non-small cell lung cancer outcomes: A retrospective longitudinal cohort study.Br J Cancer, 2024. doi: 10.1038/s41416-41024-02678-41418. Online ahead of print.(共同通讯作者,SCI检索,中科院一区,IF=6.4) [14]Cai M#, Zhao K#, Wu L#, Huang Y, Zhao M, Hu Q, Chen Q, Yao S,Li Z*, Fan X*, Liu Z*. Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis.Chin Med J (Engl), 2024;137(4):421-430.(共同通讯作者,SCI检索,中科院一区,IF=7.5) [15]Zhang J#, Cui Z#, Shi Z, Jiang Y, Zhang Z, Dai X, Yang Z, Gu Y, Zhou L, Han C, Huang X, Ke C, Li S, Xu Z, Gao F, Zhou L, Wang R, Liu J, Zhang J, Ding Z*, Sun K*,Li Z*, Liu Z*, Shen D*. A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.Patterns,2023;4(9):100826.(共同通讯作者,SCI检索,中科院一区,IF = 6.7) [16]Li C#, Zhao K#, Zhang D, Pang X, Pu H, Lei M, Fan B, Lv J, You D*,Li Z*, Zhang T*.Prediction models of colorectal cancer prognosis incorporating perioperative longitudinal serum tumor markers: a retrospective longitudinal cohort study.BMC Medicine, 2023;21(1):63.(共同通讯作者,SCI检索,中科院一区,IF =7) [17]Liu Y#, Wang Y#, Wang Y#, Xie Y#, Cui Y, Feng S, Yao M, Qiu B, Shen W, Chen D, Du G, Chen X, Liu Z,Li Z*, Yang X*, Liang C*, Wu L*. Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study.EClinicalMedicine, 2022;52:101562.(共同通讯作者,SCI检索,中科院一区,IF =9.6) [18]Wang Y#, Pan X#, Lin H#, Han C#, An Y, Qiu B, Feng Z, Huang X, Xu Z, Shi Z, Chen X, Li B, Yan L, Lu C*,Li Z*, Cui Y*, Liu Z*, Liu Z*. Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study.J Transl Med, 2022;20(1):595.(共同通讯作者,SCI检索,中科院一区,IF =6.1) [19]Lin H#, Pan X#, Feng Z#, Yan L#, Hua J, Liang Y, Han C, Xu Z, Wang Y, Wu L, Cui Y, Huang X, Shi Z, Chen X, Chen X, Zhang Q, Liang C*, Zhao K*,Li Z*, Liu Z*. Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification.J Transl Med,2022;20(1):261.(共同通讯作者,SCI检索,中科院一区,IF =6.1) [20]Zhang J#, Cui Y#, Wei K#,Li Z#, Li D, Song R, Ren J, Gao X*, Yang X*. Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study.Gastric Cancer, 2022;25(6):1050-1059.(并列第一作者,SCI检索,中科院一区,IF =6) [21]Cui Y#, Zhang J#,Li Z#, Wei K#, Lei Y, Ren J, Wu L, Shi Z, Meng X*, Yang X*, Gao X*. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.eClinicalMedicine, 2022; 46: 101348.(并列第一作者,SCI检索,中科院一区,IF=9.6) [22]Huang Y#, He L#,Li Z#, Chen X, Han C, Zhao K, Zhang Y, Qu J, Mao Y, Liang C*, Liu Z*. Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer.Chin J Cancer Res, 2022;34(1):40-52.(并列第一作者,SCI检索,中科院一区,IF =7) [23]Kong X#, Zhang Q#, Wu X, Zou T, Duan J, Song S, Nie J, Tao C, Tang M, Wang M, Zou J, Xie Y,Li Z*, Li Z*. Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer.Front Oncol,2022;12:816297.(共同通讯作者,SCI检索,中科院二区,IF =3.5) [24]Xie Kun#, Cui Yanfan#, Zhang Dafu#, He Weiyang, He Yinfu, Gao Depei, Zhang Zhiping, Dong Xingxiang, Yang Guangjun, Dai Youguo*,Li Zhenhui*. Pretreatment Contrast-Enhanced Computed Tomography Radiomics for Prediction of Pathological Regression Following Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer: A Preliminary Multicenter Study.Front Oncol, 2021;11:770758.(通讯作者,SCI检索,中科院二区,IF =3.5) [25]Li Chunxia#, Zhang Dafu#, Pang Xiaolin#, Pu Hongjiang, Lei Ming, Fan Bingbing, Lv Jiali, You Dingyun*,Li Zhenhui*, Zhang Tao*.Trajectories of Perioperative Serum Tumor Markers and Colorectal Cancer Outcomes: A Retrospective, Multicenter Longitudinal Cohort Study.EBioMedicine, 2021, 74: 103706.(共同通讯作者,SCI检索,中科院一区,IF =9.7) [26]Zhao K#, Wu X#,Li ZH#, Wang YY#, Xu ZY, Li YJ, Wu L, Yao S*, Huang YQ*, Liang CH*, Liu ZY*. Prognostic value of a modified Immunoscore in patients with stage I−III resectable colon cancer.Chin J Cancer Res, 2021;33(3):379-390.(并列第一作者,SCI检索,中科院一区,IF =7) [27]He L#,Li ZH#, Chen X#, Huang Y, Yan L, Liang C*, Liu Z*. A radiomics prognostic scoring system for predicting progression-free survival in patients with stage IV non-small cell lung cancer treated with platinum-based chemotherapy.Chin J Cancer Res,2021;33(5):592-605.(并列第一作者,SCI检索,中科院一区,IF =7) [28]Liu XY#, Zhang DF#, Liu ZY#,Li ZH#, Xie PY, Sun K, Wei W, Dai WX, Tang ZC, Ding YY, Cai GX, Tong T*, Meng XC*, Tian J*.Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study.EBioMedicine, 2021, 69: 103442-103442.(并列第一作者,SCI检索,中科院一区,IF =9.7) 中文论文: [1] 金怀平, 薛飞跃,李振辉, 陶海波, 王彬. 基于病理图像集成深度学习的胃癌预后预测方法[J].电子与信息学报, 2023, 45(7): 2623-2633. doi: 10.11999/JEIT220655(EI) [2] 李清婉,张治平,高德培,张大福,谢锟,崔艳芬,代佑果,李振辉.CT影像组学标签预测局部进展期胃癌新辅助化疗疗效的多中心分析[J].医学影像学杂志,2022,32(4):619-625.(EI) [3] 谢锟,张治平,田川,等.扩散加权成像预测胃腺癌淋巴结转移的价值初探[J].医学影像学杂志, 2022(002):032.(EI) [4] 代佑果, 王嘉鑫, 张大福, 刘宥苡, 吕煜, 胡义波, 韩潇, 栾利昆, 刘琴,李振辉*. 肠腔内导管截流加腹腔自制双套管冲洗引流治疗十二指肠瘘.中华胃肠外科杂志, 2021;24(8):718-721.(EI) [5] 李振辉, 高德培, 吴琳, 董兴祥, 杨光军, 张大福*. 肠系膜侵袭性纤维瘤病的CT特征.中国医学影像学杂志,2021;29(3):244-247.(EI) [6] 张大福,李振辉*, 高德培, 董兴祥, 杨光军. 原发肺黏液表皮样癌CT诊断(附14例报告).放射学实践,2020;35(10):1253-1257.(EI) [7] 马焕,李振辉#, 李鹍*, 胡早秀, 杨义豪, 陶海波. 动态增强MRI定量参数评估骨肉瘤新辅助化疗疗效价值.中华肿瘤防治杂志,2019;26(06):421-426.(EI) [8] 李振辉, 马焕*, 李鹍, 王洪波, 陶海波. DWI在中央型软骨性肿瘤鉴别诊断中的价值.放射学实践,2018;33(02):187-191.(EI) [9] 李振辉, 张治平, 董兴祥, 杨光军, 高德培, 张大福*. 结直肠原发神经内分泌癌的CT表现.中国临床医学影像杂志,2017;28(06):430-433+437.(EI) [10]李振辉, 张治平, 董兴祥, 高德培, 张大福*. 结直肠原发性黏液腺癌与印戒细胞癌的CT表现比较.中华胃肠外科杂志,2017;3(20):315-319.(EI) [11]蒋洁智, 丁莹莹,李振辉*. 基于治疗前MR-DWI影像组学预测肺癌化疗疗效的初步研究.放射学实践,2017;12(32):1221-1224.(EI) [12]李振辉, 胡玉川*. MOOC平台给影像医师带来了什么?放射学实践,2014;29(12):1371-1372.(EI) [13]李振辉, 李鹍, 董兴祥, 高德培, 杨光军, 张大福*. 结直肠黏液腺癌的MRI表现.放射学实践,2017;32(07):726-729.(EI) [14]江登科, 汪珍元, 周合群,李振辉*, 张大福. 腕关节腱鞘巨细胞瘤7例影像学特征分析.中国临床医学影像杂志,2016;27(06):423-426.(EI) [15]李振辉, 张大福, 高德培, 王关顺, 段学昆, 杨光军*. LI-RADS CT分类标准对肝细胞癌的诊断效能评价.放射学实践, 2016;31(04):307-310.(EI) [16]马焕,李鹍,李振辉,等.磁共振扩散加权成像评估骨肉瘤新辅助化疗疗效的价值简[J].中华肿瘤防治杂志, 2016, 23(16):4.(EI) (4)知识产权 无 (5)专著、教材 [1]游顶云,李振辉(副主编),等.实用临床流行病学研究设计,人民卫生出版社,2021.11.01 [2]何波,李振辉(副主译),等.MRI基础(第4版),人民卫生出版社,2019.12.01 [3]何波,李振辉(参编),等.骨关节创伤影像征象解析,人民卫生出版社,2019.06.01 [4]何波,李振辉(参译),等.骨关节创伤影像学必读,科学出版社,2019.09.01 [5]王其军,李振辉(参编),等. 泌尿系统多层螺旋CT诊断学, 人民卫生出版社, 2017.3.1 [6]丁莹莹,李振辉(副主译),等. 淋巴结影像解剖与诊断, 人民军医出版社, 2015.08.01 [7]王骏,李振辉(参编),等.医学影像信息学, 北京大学医学出版社, 2014.12.01 |