秦磊博士學術報告

時間:2019-06-22浏覽:13

報告題目:Brain MRI: From Bench to Bedside

報告人介紹:

秦磊,东南大学生物医学工程96级学士(王雪梅教授),00级硕士(影像实验室),美国马里兰大学生物工程博士,美国哈佛医学院博士后,美国哈佛医学院助理教授,项目负责人,博士生导师。哈佛医学院是全美排名第一的医学院,丹纳法伯癌症研究院是哈佛医学院附属医院,是主要从事癌症研究和治疗的专科医院。目前在成人癌症治疗方面排名全美第四,在儿童癌症治疗方面排名全美第一。秦磊博士目前任丹纳法伯医院影像科物理师主任,主要负责核磁共振,CT,核医学的临床物理工作,解决临床影像的技术问题,开发新的成像技术,培训技术员理解和操作仪器,培训影像科实习医生和住院医生。另外还带领博士后和实习医生从事肿瘤成像方面的研究,发表论文二十余篇,获专利一项。曾两次获得IEEE最佳论文奖(两千多篇投稿中评选出最优秀的五篇获奖), 以及ISMRM的最优秀论文奖(四千多篇投稿中最优秀的3%获奖)。

報告摘要:

Our mission is to optimize the care of cancer patients by using imaging as a biomarker to accelerate drug development and promote successful and timely translation of cancer discovery into clinical practice. In this context, we have established the Center for Biomedical Imaging in Oncology with bidirectional exchanges between the mouse hospital at the Lurie Family Imaging Center, and the Clinical Imaging Research laboratory at the Cancer Center. Multiple co-clinical trials have been successfully carried out to facilitate translational cancer imaging for drug development and precision medicine.

Patient survival in high grade gliomas (HGG) remains poor. HGG is the largest and most genetically and phenotypically heterogeneous category of primary brain tumor. Numerous novel chemical, targeted molecular and immunoactive therapies in trial produce promising responses in a small disparate subsets of patients but which patient will respond to which therapy remains unpredictable. Reliable imaging biomarkers for prediction and early detection of treatment response and survival are a critical need in neuro-oncology. Methods: Brain tumor MRI 'deep features' extracted by a convolutional neural network (CNN) transfer learning were combined with features derived from an explicitly-designed radiomics model to search for MRI markers predictive of overall survival (OS) in HGG patients. Brain MRI of 128 HGG patients from the Cancer Genome Atlas (TCGA) were used as the discovery data set and the brain MRI of 52 local HGG patients as the validation data set. We extracted 8192 deep features and 348 radiomics features from each cohort and used feature selection and elastic net-Cox model to distinguish longer from shorter-term survivors. Results: The combined feaure framework was predictive of OS in both the discovery set (log-rank test p value=0.0227) and validation cohort (log-rank test p value=0.0148).  Conclusion: Combined deep and radiomics features can predict OS in HGG patients and merit further stufy for reproducible prediction of treatment response.

報告時間:

2019年6月24日(星期一)下午3:30-5:00

報告地點:

生物電子學國家重點實驗室逸夫科技館三樓會議室


生物科學與醫學工程學院