您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2023, Vol. 61 ›› Issue (3): 14-20.doi: 10.6040/j.issn.1671-7554.0.2022.0956

• 专家综述 • 上一篇    下一篇

人工智能在脊柱畸形诊疗中的应用

吴南1,2,3,4,*(),仉建国1,2,3,4,朱源棚1,2,4,陈癸霖1,2,4,陈泽夫1,2,4   

  1. 1. 中国医学科学院北京协和医院骨科, 北京 100730
    2. 骨骼畸形遗传学研究北京市重点实验室, 北京 100730
    3. 疑难重症及罕见病国家重点实验室，北京协和医院, 北京 100730
    4. 中国医学科学院脊柱畸形大数据研究与应用重点实验室, 北京 100730
  • 收稿日期:2022-08-10 出版日期:2023-03-10 发布日期:2023-03-24
  • 通讯作者: 吴南 E-mail:dr.wunan@pumch.cn
  • 作者简介:吴南，医学博士，博士后，博士研究生导师，国家优秀青年科学基金获得者，中国医学科学院北京协和医学院研究员、骨科副主任医师。兼任中国医学科学院脊柱畸形大数据研究与应用重点实验室主任、北京协和医院青年工作部部长。师从我国著名骨科专家邱贵兴院士，并于美国贝勒医学院分子和人类遗传系James R. Lupski院士课题组从事博士后工作。组织建立的“系统解析脊柱侧凸及相关合并症(Deciphering Disorders Involving Scoliosis and Comorbidities, DISO，http://www.discostudy.org/)”国际多中心协作组，为全面揭示世界范围内脊柱畸形及相关疾病的遗传学病因及机制、实现脊柱畸形的精准诊疗打下坚实基础。是国际上较早将基因组学技术应用于脊柱畸形研究及精准诊疗的学者之一，多次受邀在国际和国内著名临床机构、科研院所及大会做专题报告|在脊柱畸形的分子遗传学及精准诊疗方面，发表论文128篇，其中中文核心12篇，SCI 116篇，总IF共832.2分，总引用3400余次(Google Scholar)。其中以(共同)第一/通信作者在《New England Journal of Medicine》《Molecular Cancer》 《Genome Medicine》 《American Journal of Human Genetics 》《American Journal of Human Genetics》《Kidney International》《Journal of Nanobiotechnology》等发表SCI论文72篇，总IF共482.2分，篇均IF=6.8。以(共同)第一/通信作者发表JCR 1区文章35篇，其中IF>10的文章7篇。近5年主持项目15项，授权国家发明专利8项，参编中英文书籍9部

Application of artificial intelligence in the diagnosis and treatment of spinal deformity

Nan WU1,2,3,4,*(),Jianguo ZHANG1,2,3,4,Yuanpeng ZHU1,2,4,Guilin CHEN1,2,4,Zefu CHEN1,2,4   

  1. 1. Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
    2. Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
    3. State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing 100730, China
    4. Key Laboratory of Spinal Deformity Research and Application of Big Data, Beijing 100730, China
  • Received:2022-08-10 Online:2023-03-10 Published:2023-03-24
  • Contact: Nan WU E-mail:dr.wunan@pumch.cn

RichHTML

10

PDF (PC)

358

摘要：

脊柱畸形是一种高致畸致残性疾病，其发病年龄可覆盖全生命周期。随着计算机技术的快速发展，人工智能有了显著的进步。目前人工智能在疾病的诊疗方面有着巨大的应用潜能，也逐渐被应用于脊柱畸形的筛查、诊治、手术决策、术中操作和并发症预测等多个方面。近年来，许多研究对此方向进行了探索，提出了很多具有优秀表现和广阔应用前景的方案与模型，本文就此做一综述。

关键词: 人工智能, 脊柱畸形, 脊柱侧凸, 神经网络, 机器学习

Abstract:

Spinal deformity is a highly teratogenic and disabling disease, whose age of onset covers the entire life cycle. With the rapid development of computer technology, artificial intelligence has made remarkable progress, and has huge application potential in the diagnosis and treatment of diseases, especially in the screening, diagnosis and treatment, surgical decision-making, intraoperative operations and complication prediction of spinal deformities. In the past few years, a large number of researches have explored in the related fields and proposed plentiful well-working and promising projects and models. This paper will review the latest advances.

Key words: Artificial intelligence, Spinal deformity, Scoliosis, Neural network, Machine learning

中图分类号: 

  • R681.5
1 Smith JS , Shaffrey CI , Bess S , et al. Recent and emerging advances in spinal deformity[J]. Neurosurgery, 2017, 80 (3s): S70- S85.
doi: 10.1093/neuros/nyw048
2 Diebo BG , Shah NV , Boachie-Adjei O , et al. Adult spinal deformity[J]. Lancet, 2019, 394 (10193): 160- 172.
doi: 10.1016/S0140-6736(19)31125-0
3 Shakil H , Iqbal ZA , Al-Ghadir AH . Scoliosis: review of types of curves, etiological theories and conservative treatment[J]. J Back Musculoskelet Rehabil, 2014, 27 (2): 111- 115.
doi: 10.3233/BMR-130438
4 Dunn J , Henrikson NB , Morrison CC , et al. Screening for adolescent idiopathic scoliosis: evidence report and systematic review for the US preventive services task force[J]. JAMA, 2018, 319 (2): 173- 187.
doi: 10.1001/jama.2017.11669
5 Weinstein SL , Dolan LA , Cheng JC , et al. Adolescent idiopathic scoliosis[J]. Lancet, 2008, 371 (9623): 1527- 1537.
doi: 10.1016/S0140-6736(08)60658-3
6 Dick S . Artificial Intelligence[J]. Harvard Data Science Review, 2019, 1 (1): 1- 3.
7 Bhinder B , Gilvary C , Madhukar NS , et al. Artificial intelligence in cancer research and precision medicine[J]. Cancer Discov, 2021, 11 (4): 900- 915.
doi: 10.1158/2159-8290.CD-21-0090
8 Schmidt-Erfurth U , Sadeghipour A , Gerendas BS , et al. Artificial intelligence in Retina[J]. Prog Retin Eye Res, 2018, 67, 1- 29.
doi: 10.1016/j.preteyeres
9 Nensa F , Demircioglu A , Rischpler C . Artificial intelligence in nuclear medicine[J]. J Nucl Med, 2019, 60 (Suppl 2): 29- 37.
10 Ramirez L , Durdle NG , Raso VJ , et al. A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography[J]. IEEE Trans Inf Technol Biomed, 2006, 10 (1): 84- 91.
doi: 10.1109/TITB.2005.855526
11 Watanabe K , Aoki Y , Matsumoto M . An application of artificial intelligence to diagnostic imaging of spine disease: estimating spinal alignment from moiré images[J]. Neurospine, 2019, 16 (4): 697- 702.
doi: 10.14245/ns.1938426.213
12 Yang J , Zhang K , Fan H , et al. Development and validation of deep learning algorithms for scoliosis screening using back images[J]. Commun Biol, 2019, 2, 390.
doi: 10.1038/s42003-019-0635-8
13 Hong A , Jaswal N , Westover L , et al. Surface topography classification trees for assessing severity and monitoring progression in adolescent idiopathic scoliosis[J]. Spine (Phila Pa 1976), 2017, 42 (13): E781- E787.
doi: 10.1097/BRS.0000000000001971
14 Kokabu T , Kanai S , Kawakami N , et al. An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection[J]. Spine J, 2021, 21 (6): 980- 987.
doi: 10.1016/j.spinee.2021.01.022
15 Greer H , Gerber S , Niethammer M , et al. Scoliosis screening and monitoring using self contained ultrasound and neural networks[J]. Proc IEEE Int Symp Biomed Imaging, 2018, 2018, 1500- 1503.
doi: 10.1109/isbi.2018.8363857
16 Kim KH , Choi WJ , Sohn MJ . Feature importance analysis for postural deformity detection system using explainable predictive modeling technique[J]. Applied Sciences, 2022, 12 (2): 925- 926.
doi: 10.3390/app12020925
17 LeCun Y , Bengio Y , Hinton G . Deep learning[J]. Nature, 2015, 521 (7553): 436- 444.
doi: 10.1038/nature14539
18 Sarvamangala DR , Kulkarni RV . Convolutional neural networks in medical image understanding: a survey[J]. Evol Intell, 2022, 15 (1): 1- 22.
doi: 10.1007/s12065-020-00540-3
19 Duong L , Cheriet F , Labelle H . Automatic detection of scoliotic curves in posteroanterior radiographs[J]. IEEE Trans Biomed Eng, 2010, 57 (5): 1143- 1151.
doi: 10.1109/TBME.2009.2037214
20 Horng MH , Kuok CP , Fu MJ , et al. Cobb angle measurement of spine from X-ray images using convolutional neural network[J]. Comput Math Methods Med, 2019, 2019, 6357171.
doi: 10.1155/2019/6357171
21 Weng CH , Wang CL , Huang YJ , et al. Artificial intelligence for automatic measurement of sagittal vertical axis using ResUNet framework[J]. J Clin Med, 2019, 8 (11): E1826.
doi: 10.3390/jcm8111826
22 Wu H , Bailey C , Rasoulinejad P , et al. Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net[J]. Med Image Anal, 2018, 48, 1- 11.
doi: 10.1016/j.media.2018.05.005
23 Wang L , Xu Q , Leung S , et al. Accurate automated Cobb angles estimation using multi-view extrapolation net[J]. Med Image Anal, 2019, 58, 101542.
doi: 10.1016/j.media.2019.101542
24 Cina A , Bassani T , Panico M , et al. 2-step deep learning model for landmarks localization in spine radiographs[J]. Sci Rep, 2021, 11 (1): 9482.
doi: 10.1038/s41598-021-89102-w
25 Zhang K , Xu N , Guo C , et al. MPF-net: an effective framework for automated Cobb angle estimation[J]. Med Image Anal, 2022, 75, 102277.
doi: 10.1016/j.media.2021.102277
26 Zhang T , Li Y , Cheung JPY , et al. Learning-based coronal spine alignment prediction using smartphone-acquired scoliosis radiograph images[J]. IEEE Access, 2021, 9, 38287- 38295.
doi: 10.1109/ACCESS.2021.3061090
27 Meng N , Cheung JPY , Wong KK , et al. An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation[J]. E Clinical Medicine, 2022, 43, 101252.
doi: 10.1016/j.eclinm.2021.101252
28 Gajny L , Ebrahimi S , Vergari C , et al. Quasi-automatic 3D reconstruction of the full spine from low-dose biplanar X-rays based on statistical inferences and image analysis[J]. Eur Spine J, 2019, 28 (4): 658- 664.
doi: 10.1007/s00586-018-5807-6
29 Aubert B , Vazquez C , Cresson T , et al. Toward automated 3D spine reconstruction from biplanar radiographs using CNN for statistical spine model fitting[J]. IEEE Trans Med Imaging, 2019, 38 (12): 2796- 2806.
doi: 10.1109/TMI.2019.2914400
30 Lenke LG , Betz RR , Harms J , et al. Adolescent idiopathic scoliosis: a new classification to determine extent of spinal arthrodesis[J]. J Bone Joint Surg Am, 2001, 83 (8): 1169- 1181.
doi: 10.2106/00004623-200108000-00006
31 Zhuang Q , Qiu G , Li Q , et al. Modified PUMC classification for adolescent idiopathic scoliosis[J]. Spine J, 2019, 19 (9): 1518- 1528.
doi: 10.1016/j.spinee.2019.03.008
32 Phan P , Mezghani N , Nault ML , et al. A decision tree can increase accuracy when assessing curve types according to Lenke classification of adolescent idiopathic scoliosis[J]. Spine (Phila Pa 1976), 2010, 35 (10): 1054- 1059.
doi: 10.1097/BRS.0b013e3181bf280e
33 Stokes IA , Sangole AP , Aubin CE . Classification of scoliosis deformity three-dimensional spinal shape by cluster analysis[J]. Spine (Phila Pa 1976), 2009, 34 (6): 584- 590.
doi: 10.1097/BRS.0b013e318190b914
34 Thong W , Parent S , Wu J , et al. Three-dimensional morphology study of surgical adolescent idiopathic scoliosis patient from encoded geometric models[J]. Eur Spine J, 2016, 25 (10): 3104- 3113.
doi: 10.1007/s00586-016-4426-3
35 Pasha S , Hassanzadeh P , Ecker M , et al. A hierarchical classification of adolescent idiopathic scoliosis: identifying the distinguishing features in 3D spinal deformities[J]. PLoS One, 2019, 14 (3): e0213406.
doi: 10.1371/journal.pone.0213406
36 He Z , Wang Y , Qin X , et al. Classification of neurofibromatosis-related dystrophic or nondystrophic scoliosis based on image features using Bilateral CNN[J]. Med Phys, 2021, 48 (4): 1571- 1583.
doi: 10.1002/mp.14719
37 Mezghani N , Phan P , Mitiche A , et al. A Kohonen neural network description of scoliosis fused regions and their corresponding Lenke classification[J]. Int J Comput Assist Radiol Surg, 2012, 7 (2): 257- 264.
doi: 10.1007/s11548-011-0667-0
38 Lafage R , Ang B , Alshabab BS , et al. Predictive model for selection of upper treated vertebra using a machine learning approach[J]. World Neurosurg, 2021, 146, e225- e232.
doi: 10.1016/j.wneu
39 Ames CP , Smith JS , Pellisé F , et al. Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value[J]. Spine (Phila Pa 1976), 2019, 44 (13): 915- 926.
doi: 10.1097/BRS.0000000000002974
40 Raman T , Vasquez-Montes D , Varlotta C , et al. Decision tree-based modelling for identification of predictors of blood loss and transfusion requirement after adult spinal deformity surgery[J]. Int J Spine Surg, 2020, 14 (1): 87- 95.
doi: 10.14444/7012
41 Pasha S , Flynn J . Data-driven classification of the 3D spinal curve in adolescent idiopathic scoliosis with an application in surgical outcome prediction[J]. Sci Rep, 2018, 8 (1): 16296.
doi: 10.1038/s41598-018-34261-6
42 Guo XY, Xu SX, Wang YZ, et al. Prediction model of scoliosis progression bases on deep learning[C]//Cyberspace Data Intell Cyber Living Syndr Heal. Singapore: Springer, 2019: 431-440. doi: 10.1007/978-981-15-1925-3_31.
43 García-Cano E , Arámbula Cosío F , Duong L , et al. Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression[J]. Comput Biol Med, 2018, 103, 34- 43.
doi: 10.1016/j.compbiomed.2018.09.029
44 Pellisé F , Vila-Casademunt A , Núñez-Pereira S , et al. Surgeons' risk perception in ASD surgery: the value of objective risk assessment on decision making and patient counselling[J]. Eur Spine J, 2022, 31 (5): 1174- 1183.
doi: 10.1007/s00586-022-07166-2
45 Kantelhardt SR , Martinez R , Baerwinkel S , et al. Perioperative course and accuracy of screw positioning in conventional, open robotic-guided and percutaneous robotic-guided, pedicle screw placement[J]. Eur Spine J, 2011, 20 (6): 860- 868.
doi: 10.1007/s00586-011-1729-2
46 Gao S , Lv Z , Fang H . Robot-assisted and conventional freehand pedicle screw placement: a systematic review and meta-analysis of randomized controlled trials[J]. Eur Spine J, 2018, 27 (4): 921- 930.
doi: 10.1007/s00586-017-5333-y
47 Peng YN , Tsai LC , Hsu HC , et al. Accuracy of robot-assisted versus conventional freehand pedicle screw placement in spine surgery: a systematic review and meta-analysis of randomized controlled trials[J]. Ann Transl Med, 2020, 8 (13): 824.
doi: 10.21037/atm-20-1106
48 Lieberman IH , Kisinde S , Hesselbacher S . Robotic-assisted pedicle screw placement during spine surgery[J]. JBJS Essent Surg Tech, 2020, 10 (2): e0020.
doi: 10.2106/JBJS.ST.19.00020
49 Esfandiari H , Newell R , Anglin C , et al. A deep learning framework for segmentation and pose estimation of pedicle screw implants based on C-arm fluoroscopy[J]. Int J Comput Assist Radiol Surg, 2018, 13 (8): 1269- 1282.
doi: 10.1007/s11548-018-1776-9
50 Guiot BH , Khoo LT , Fessler RG . A minimally invasive technique for decompression of the lumbar spine[J]. Spine (Phila Pa 1976), 2002, 27 (4): 432- 438.
doi: 10.1097/00007632-200202150-00021
51 Liu G , Liu S , Zuo YZ , et al. Recent advances in technique and clinical outcomes of minimally invasive spine surgery in adult scoliosis[J]. Chin Med J (Engl), 2017, 130 (21): 2608- 2615.
doi: 10.4103/0366-6999.212688
52 Shamji MF , Goldstein CL , Wang M , et al. Minimally invasive spinal surgery in the elderly: does it make sense?[J]. Neurosurgery, 2015, 77 (Suppl 4): S108- S115.
53 de Bodman C , Ansorge A , Tabard A , et al. Clinical and radiological outcomes of minimally-invasive surgery for adolescent idiopathic scoliosis at a minimum two years' follow-up[J]. Bone Joint J, 2020, 102-B (4): 506- 512.
doi: 10.1302/0301-620X.102B4.BJJ-2019-0447.R1
54 von Atzigen M , Liebmann F , Hoch A , et al. HoloYolo: a proof-of-concept study for marker-less surgical navigation of spinal rod implants with augmented reality and on-device machine learning[J]. Int J Med Robot, 2021, 17 (1): 1- 10.
55 Luthi M , Gerig T , Jud C , et al. Gaussian process morphable models[J]. IEEE Trans Pattern Anal Mach Intell, 2018, 40 (8): 1860- 1873.
doi: 10.1109/TPAMI.2017.2739743
56 Scheer JK , Osorio JA , Smith JS , et al. Development of validated computer-based preoperative predictive model for proximal junction failure (PJF) or clinically significant PJK with 86% accuracy based on 510 ASD patients with 2-year follow-up[J]. Spine (Phila Pa 1976), 2016, 41 (22): E1328- E1335.
doi: 10.1097/BRS.0000000000001598
57 Peng L , Lan L , Xiu P , et al. Prediction of proximal junctional kyphosis after posterior scoliosis surgery with machine learning in the lenke 5 adolescent idiopathic scoliosis patient[J]. Front Bioeng Biotechnol, 2020, 8, 559387.
doi: 10.3389/fbioe.2020.559387
58 Yagi M , Fujita N , Okada E , et al. Fine-tuning the predictive model for proximal junctional failure in surgically treated patients with adult spinal deformity[J]. Spine (Phila Pa 1976), 2018, 43 (11): 767- 773.
doi: 10.1097/BRS.0000000000002415
59 Yagi M , Hosogane N , Fujita N , et al. The patient demographics, radiographic index and surgical invasiveness for mechanical failure (PRISM) model established for adult spinal deformity surgery[J]. Sci Rep, 2020, 10 (1): 9341.
doi: 10.1038/s41598-020-66353-7
60 Kim JS , Merrill RK , Arvind V , et al. Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion[J]. Spine (Phila Pa 1976), 2018, 43 (12): 853- 860.
doi: 10.1097/BRS.0000000000002442
61 Karhade AV , Schwab JH , Del Fiol G , et al. SMART on FHIR in spine: integrating clinical prediction models into electronic health records for precision medicine at the point of care[J]. Spine J, 2020, 21 (10): 1649- 1651.
62 Park JH , Stegall PR , Roye DP , et al. Robotic spine exoskeleton (RoSE): characterizing the 3-D stiffness of the human torso in the treatment of spine deformity[J]. IEEE Trans Neural Syst Rehabil Eng, 2018, 26 (5): 1026- 1035.
doi: 10.1109/TNSRE.2018.2821652
[1] 王韶卿 刘毅慧 付婷婷 成金勇 刘强. 肝癌31磷磁共振波谱数据的分类[J]. 山东大学学报(医学版), 2209, 47(6): 42-46.
[2] 刘新宇,李冬来,赵文龙,王政,李超,王连雷,原所茂,田永昊. 机器人/导航辅助下椎弓根螺钉植入在脊柱畸形矫正中的应用[J]. 山东大学学报 (医学版), 2023, 61(3): 21-28.
[3] 王辉,王连雷,吴天驰,田永昊,原所茂,王霞,吕维加,刘新宇. 人工智能辅助设计3D打印手术导板在脊柱侧凸矫形术中的应用[J]. 山东大学学报 (医学版), 2023, 61(3): 127-133.
[4] 黄霖,车圳,李明,李玉希,宁庆. 人工智能在骨科疾病诊治中的研究进展[J]. 山东大学学报 (医学版), 2023, 61(3): 37-45.
[5] 刘亚军,郎昭,郭安忆,刘文勇. 骨科冲击波治疗的智能化发展现状及趋势分析[J]. 山东大学学报 (医学版), 2023, 61(3): 7-13.
[6] 冯世庆. 计算机视觉与腰椎退行性疾病[J]. 山东大学学报 (医学版), 2023, 61(3): 1-6.
[7] 巨艳丽,王丽华,成芳,黄凤艳,陈学禹,贾红英. 基于机器学习构建放射性碘治疗疗效的预测模型[J]. 山东大学学报 (医学版), 2023, 61(1): 94-99.
[8] 况利,徐小明,曾琪. 机器学习用于自杀研究的综述[J]. 山东大学学报 (医学版), 2022, 60(4): 10-16.
[9] 冯一平,孙大鹏,王显军,纪伊曼,刘云霞. DLNM和LSTM神经网络对临沂市手足口病发病的预测效果比较[J]. 山东大学学报 (医学版), 2022, 60(2): 96-101.
[10] 姜震,孙静,邹雯,王唱唱,高琦. 基于两种机器学习算法的双相情感障碍患者自杀行为影响因素模型比较研究[J]. 山东大学学报 (医学版), 2022, 60(1): 101-108.
[11] 王琳琳,孙玉萍. 从临床医生角度,看人工智能在癌症精准诊疗中的应用及思考[J]. 山东大学学报 (医学版), 2021, 59(9): 89-96.
[12] 田瑶天,王宝,李叶琴,王滕,田力文,韩波,王翠艳. 基于可解释性心脏磁共振参数的机器学习模型预测儿童心肌炎的预后[J]. 山东大学学报 (医学版), 2021, 59(7): 43-49.
[13] 李新钢,张鑫,陈安静. 当代脑计划研究进展[J]. 山东大学学报 (医学版), 2020, 1(8): 5-9, 21.
[14] 吴强,何泽鲲,刘琚,崔晓萌,孙双,石伟. 基于机器学习的脑胶质瘤多模态影像分析[J]. 山东大学学报 (医学版), 2020, 1(8): 81-87.
[15] 刘琚,吴强,于璐跃,林枫茗. 基于深度学习的脑肿瘤图像分割[J]. 山东大学学报 (医学版), 2020, 1(8): 42-49, 73.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 索东阳,申飞,郭皓,刘力畅,杨惠敏,杨向东. Tim-3在药物性急性肾损伤动物模型中的表达及作用机制[J]. 山东大学学报 (医学版), 2020, 1(7): 1 -6 .
[2] 马青源,蒲沛东,韩飞,王超,朱洲均,王维山,史晨辉. miR-27b-3p调控SMAD1对骨肉瘤细胞增殖、迁移和侵袭作用的影响[J]. 山东大学学报 (医学版), 2020, 1(7): 32 -37 .
[3] 龙婷婷,谢明,周璐,朱俊德. Noggin蛋白对小鼠脑缺血再灌注损伤后学习和记忆能力与齿状回结构的影响[J]. 山东大学学报 (医学版), 2020, 1(7): 15 -23 .
[4] 李宁,李娟,谢艳,李培龙,王允山,杜鲁涛,王传新. 长链非编码RNA AL109955.1在80例结直肠癌组织中的表达及对细胞增殖与迁移侵袭的影响[J]. 山东大学学报 (医学版), 2020, 1(7): 38 -46 .
[5] 徐玉香,刘煜东,张蓬,段瑞生. 101例脑小血管病患者脑微出血危险因素的回顾性分析[J]. 山东大学学报 (医学版), 2020, 1(7): 67 -71 .
[6] 张宝文,雷香丽,李瑾娜,罗湘俊,邹容. miR-21-5p靶向调控TIMP3抑制2型糖尿病肾病小鼠肾脏系膜细胞增殖及细胞外基质堆积[J]. 山东大学学报 (医学版), 2020, 1(7): 7 -14 .
[7] 付洁琦,张曼,张晓璐,李卉,陈红. Toll样受体4抑制过氧化物酶体增殖物激活受体γ加重血脂蓄积的分子机制[J]. 山东大学学报 (医学版), 2020, 1(7): 24 -31 .
[8] 丁祥云,于清梅,张文芳,庄园,郝晶. 胰岛素样生长因子II在84例多囊卵巢综合征患者颗粒细胞中的表达和促排卵结局的相关性[J]. 山东大学学报 (医学版), 2020, 1(7): 60 -66 .
[9] 史爽,李娟,米琦,王允山,杜鲁涛,王传新. 胃癌miRNAs预后风险评分模型的构建与应用[J]. 山东大学学报 (医学版), 2020, 1(7): 47 -52 .
[10] 肖娟,肖强,丛伟,李婷,丁守銮,张媛,邵纯纯,吴梅,刘佳宁,贾红英. 两种甲状腺超声数据报告系统诊断效能的比较[J]. 山东大学学报 (医学版), 2020, 1(7): 53 -59 .
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发