[1]刘渊,程玉玉,贺睿敏,等.基于机器学习的鼻咽癌转移淋巴结鉴别模型[J].中国医学物理学杂志,2019,36(11):1350-1355.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.020]
 LIU Yuan,CHENG Yuyu,HE Ruimin,et al.Machine learning-based classification model of lymph node metastasis in nasopharyngeal carcinoma[J].Chinese Journal of Medical Physics,2019,36(11):1350-1355.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.020]
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基于机器学习的鼻咽癌转移淋巴结鉴别模型()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第11期
页码:
1350-1355
栏目:
其他(数字医学等)
出版日期:
2019-11-25

文章信息/Info

Title:
Machine learning-based classification model of lymph node metastasis in nasopharyngeal carcinoma
文章编号:
1005-202X(2019)11-1350-06
作者:
刘渊12程玉玉3贺睿敏4周卫兵1贺秋冬4肖若冰4贺阳4谢常军4谢海辉4文洪永4陈娟4何尧林4
1.中南大学湘雅医院肿瘤科, 湖南 长沙410008; 2.郴州市第一人民医院核医学科, 湖南 郴州423000; 3.南华大学船山学院, 湖南 衡阳 421001; 4.南华大学附属第二医院放射治疗科, 湖南 衡阳 421001
Author(s):
LIU Yuan12 CHENG Yuyu3 HE Ruimin4 ZHOU Weibing1 HE Qiudong4 XIAO Ruobing4 HE Yang4 XIE Changjun4 XIE Haihui4 WEN Hongyong4 CHEN Juan4 HE Yaolin4
1. Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China; 2. Department of Nuclear Medicine, Chenzhou No.1 People’s Hospital, Chenzhou 423000, China; 3. Chuanshan College, University of South China, Hengyang 421001, China; 4. Department of Radiotherapy, the Second Affiliated Hospital, University of South China, Hengyang 421001, China
关键词:
机器学习鼻咽癌转移淋巴结辅助诊断模型
Keywords:
Keywords: machine learning nasopharyngeal carcinoma metastatic lymph node auxiliary diagnosis model
分类号:
R730.49;R318
DOI:
DOI:10.3969/j.issn.1005-202X.2019.11.020
文献标志码:
A
摘要:
目的:研究使用机器学习与影像组学建立用于鼻咽癌CT图像中鉴别转移淋巴结的模型。方法:选择50例鼻咽癌患者初诊CT平扫及静脉灌注增强图像及18F-FGD-PET图像,患者均经病理及PET检查证实为鼻咽癌伴局部淋巴结转移。手动勾画患者CT图像中体积>1 cm3的淋巴结,由18F-FGD-PET图像中对应区域SUVmax>2.5及现行影像学标准作为转移与否的分类标准。研究中共获得143枚淋巴结,其中转移淋巴结103枚。使用机器学习方法对上述分类结果进行训练,其中列入训练组淋巴结100枚,验证组43枚,分组方式为随机分组以避免特定的分组方式造成的系统误差。结果:机器学习过程中获得由淋巴结体积、最大横截面短轴及数个影像组学特征构建模型,模型对转移淋巴结的鉴别准确率可达86%。特征选择结果得出:最大横截面直径、平均宽度、灰度强度能量、像素数量、频度、形态密实度等可作为诊断转移淋巴结的重要特征。结论:研究中建立的鉴别模型可在CT图像中实现辅助诊断转移淋巴结,为影像检查中快速判定鼻咽癌患者淋巴结是否转移提供一种新思路,有利于个体化放疗中靶区的精准勾画。
Abstract:
To establish a model for identifying metastatic lymph nodes in CT images of nasopharyngeal carcinoma with the use of machine learning and radiomics. Methods The plain CT, intravenous perfusion enhancement and 18F-FGD-PET images of 50 pre-treatment patients with nasopharyngeal carcinoma were analyzed in the study. All patients were confirmed by pathological and PET examinations as nasopharyngeal carcinoma with local lymph node metastasis. The lymph nodes with a volume >1 cm3 in CT image were manually delineated, and the maximum standardized uptake value (SUVmax)>2.5 of the corresponding area in 18F-FGD-PET image and the current standard were used as the diagnose criteria. A total of 143 lymph nodes were obtained, of which 103 were metastatic lymph nodes. The above classification results were trained and verified by a machine learning model. There were 100 lymph nodes in the training data and 43 in the test data. The lymph nodes were randomly grouped in order to avoid system errors caused by specific grouping. Results During the machine learning, a model was constructed from several imaging ensembles (lymph node volume, maximum cross-sectional minor axis and several radiomics features). The accuracy of the model for the classification of metastatic lymph nodes reached 86%. The result of feature selection showed that the maximum cross-sectional diameter, mean breadth, intensity direct energy, number of voxels, busyness and shape-compactness could be used as important features in the classification of metastatic lymph nodes. Conclusion The established classification model can be used for the auxiliary diagnose of metastatic lymph nodes in CT images, which provides a new idea for the rapid determination of metastatic lymph node in patients with nasopharyngeal carcinoma, and it’s helpful for the accurate segmentation of target areas in indi vidualized radiotherapy.

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备注/Memo

备注/Memo:
【收稿日期】2019-06-09 【基金项目】国家自然科学基金(81770928) 【作者简介】刘渊,在职研究生,主治医师,主要研究方向:核素影像与内放射治疗,E-mail: qlyliuyuan@163.com 【通信作者】周卫兵,博士,博士后,副教授,主要研究方向:胸腹部肿瘤综合治疗及放疗,E-mail: zhouweibing298@163.com
更新日期/Last Update: 2019-11-28