用于评估新辅助化疗后食管癌淋巴结转移的计算机断层扫描支持向量机模型
Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy.
作者信息
Wang Zhi-Long, Zhou Zhi-Guo, Chen Ying, Li Xiao-Ting, Sun Ying-Shi
机构信息
From the *Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China; and †Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas.
出版信息
J Comput Assist Tomogr. 2017 May/Jun;41(3):455-460. doi: 10.1097/RCT.0000000000000555.
OBJECTIVE
The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography.
MATERIALS AND METHODS
A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis.
RESULTS
Support vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively.
CONCLUSIONS
The support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy.
目的
本研究旨在通过基于计算机断层扫描的支持向量机模型诊断食管癌的淋巴结转移情况。
材料与方法
纳入131例接受术前化疗及根治性手术的食管癌患者。记录新辅助化疗前后CT图像上的各项指标（肿瘤厚度、肿瘤长度、肿瘤CT值、淋巴结总数以及最大淋巴结的长径和短径）。基于这些CT指标建立支持向量机模型以预测淋巴结转移情况。
结果
支持向量机模型在诊断淋巴结转移方面优于术前CT上最大淋巴结的短径。受试者操作特征曲线下面积分别为0.887和0.705。
结论
CT图像的支持向量机模型有助于诊断接受术前化疗的食管癌患者的淋巴结转移情况。
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