

FOLLOWUS
Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing 100191, China
[ "Dr. Jie Tian received his PhD degree (with honors) from Chinese Academy of Sciences in 1993. He has been a professor at the Institute of Automation, Chinese Academy of Sciences since 1997, and the director of the Beijing Advanced Innovation Center for Big Data-Based Precision Medicine in Beihang University since 2018. Dr. Tian has been elected as the Fellow of AAAS, ISMRM, AIMBE, IAMBE, IEEE, OSA, SPIE, and IAPR. He serves as the editorial board member of European Journal of Nuclear Medicine and Molecular Imaging, IEEE Transactions on Medical Imaging, and Photoacoustics. He is the author of over 400 peer-reviewed journal articles, which have received over 36,000 Google Scholar citations (H-index 93). He has received numerous awards, including five national top awards for his outstanding work in medical imaging. Dr. Tian is recognized as a pioneer and a leader in China in the field of molecular imaging. He has developed a series of new optical imaging models and reconstruction algorithms for in vivo optical tomographic imaging. He has also explored novel artificial intelligence strategies for medical image quantitative analysis, and constructed several open-access softwares." ]
Published:30 September 2021
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Tian Jie. The Next-Level Precision Medicine in Cancer Management Using Artificial Intelligence[J]. Chinese medical sciences journal, 2021, 36(3): 171-172.
Tian Jie. The Next-Level Precision Medicine in Cancer Management Using Artificial Intelligence[J]. Chinese medical sciences journal, 2021, 36(3): 171-172. DOI: 10.24920/004013.
医学大数据的快速积累和人工智能技术的进步正在推动癌症精准医学发展到更高的水平。人工智能技术在辅助癌症诊断、疗效评估、预测预后等方面的研究已取得了显著进展。高质量、前瞻性和大样本的队列研究是未来探索人工智能在精准肿瘤学中进一步应用的关键。我们呼吁不同领域的研究人员对人工智能新技术的发展及这些技术在临床中的合理应用予以关注,加强多中心合作,设计和建立完善的数据库及标准化的数据采集协议。相信人工智能将在癌症精准诊疗的临床应用领域取得实质性进展。
He J , Baxter SL , Xu J , et al . The practical implementation of artificial intelligence technologies in medicine . Nat Med 2019 ; 25 ( 1 ): 30 - 6 . doi: 10.1038/s41591-018-0307-0 1001-9294-36-3-171/s41591-018-0307-0 . DOI: 10.1038/s41591-018-0307-0 http://doi.org/10.1038/s41591-018-0307-0
Dong D , Tang L , Li ZY , et al , Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer . Ann Oncol 2019 ; 30 ( 3 ): 431 - 8 . doi: 10.1093/annonc/mdz001 1001-9294-36-3-171/annonc/mdz001 . DOI: 10.1093/annonc/mdz001 http://doi.org/10.1093/annonc/mdz001
Wei J , Jiang H , Gu D , et al . Radiomics in liver diseases: current progress and future opportunities . Liver int 2020 ; 40 ( 9 ): 2050 - 63 . doi: 10.1111/liv.14555 1001-9294-36-3-171/liv.14555 . DOI: 10.1111/liv.14555 http://doi.org/10.1111/liv.14555
Segal E , Sirlin CB , Ooi C , et al . Decoding global gene expression programs in liver cancer by noninvasive imaging . Nat Biotechnol 2007 ; 25 ( 6 ): 675 - 80 . doi: 10.1038/nbt1306 1001-9294-36-3-171/nbt1306 . DOI: 10.1038/nbt1306 http://doi.org/10.1038/nbt1306
Zhang F , Zhong L , Zhao X , et al . A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study . Ther Adv Med Oncol 2020 ; 12 : 1758835920971416 . doi: 10.1177/1758835920971416 1001-9294-36-3-171/1758835920971416 . DOI: 10.1177/1758835920971416 http://doi.org/10.1177/1758835920971416
Dong D , Fang M , Tang L , et al . Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study . Ann Oncol 2020 ; 31 ( 7 ): 912 - 20 . doi: 10.1016/j.annonc.2020.04.003 1001-9294-36-3-171/j.annonc.2020.04.003 . DOI: 10.1016/j.annonc.2020.04.003 http://doi.org/10.1016/j.annonc.2020.04.003
Sun R , Limkin EJ , Vakalopoulou M , et al . A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study . Lancet Oncol 2018 ; 19 ( 9 ): 1180 - 91 . doi: 10.1016/S1470-2045(18)30413-3 1001-9294-36-3-171/S1470-2045(18)30413-3 . DOI: 10.1016/S1470-2045(18)30413-3 http://doi.org/10.1016/S1470-2045(18)30413-3
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