基于放射组学的泌乳素腺瘤和生长激素腺瘤分类研究  

Classification of Prolactin Adenoma and Growth Hormone Adenoma Based onRadiomics

作　　者：代湖明 陈彦如 袁平 卓碧华 DAI Hu-ming;CHEN Yan-ru;YUAN Ping;ZHUO Bi-hua(College of Computer Science,Sichuan University,Chengdu 610065;College of Mathematics and Information Engineering,Chongqing University of Education,Chongqing 400067;Information Technology Section,Officers College of PAP,Chengdu 610213)

机构地区：[1]四川大学计算机学院,成都610065 [2]重庆市第二师范学院技术与信息工程学院,重庆400067 [3]武警警官学院信息技术教研室,成都610213

出　　处：《现代计算机》2021年第8期28-31,共4页Modern Computer

基　　金：四川省国际科技合作基地(川科外[2018]12号)。

摘　　要：泌乳素细胞和生长激素细胞是腺垂体祖细胞的同一种转录因子PIT-1分化而成,但是泌乳素腺瘤和生长激素腺瘤在治疗方法上有较大的差异,对两种肿瘤的正确分类在医学上有较大的价值。通过影像组学从两种肿瘤的MRI图像中提取出大量的影像特征,然后利用提取出的影像特征训练分类模型,测试结果表明训练出的机器学习模型在医学上有较高的实用价值。Prolactin cells and growth hormone cells are differentiated from PIT-1,the same transcription factor of adenohypophysial progenitor cells.However,there is a great difference in the treatment of prolactin adenoma and growth hormone adenoma,the correct classification of the two tumors is of great medical value.A large number of imaging features were extracted from MRI images of the two tumors by radiomics,use these extracted image features to train a classification model.The test results show that the trained machine learning model has high practical value in medicine.

关 键 词：机器学习 放射组学 泌乳素腺瘤 生长激素腺瘤 

分 类 号：R736.4[医药卫生—肿瘤]