Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, 400038, China.
Department of Radiology, Sichuan Science City Hospital, Mianyang, Sichuan, China.
Cancer Imaging. 2020 Jan 30;20(1):12. doi: 10.1186/s40644-020-0288-3.
We developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).
This retrospective study included 159 patients with PDAC (118 in the primary cohort and 41 in the validation cohort) who underwent preoperative contrast-enhanced computed tomography examination between 2012 and 2015. All patients underwent surgery and lymph node status was determined. A total of 2041 radiomics features were extracted from venous phase images in the primary cohort, and optimal features were extracted to construct a radiomics signature. A combined prediction model was built by incorporating the radiomics signature and clinical characteristics selected by using multivariable logistic regression. Clinical prediction models were generated and used to evaluate both cohorts.
Fifteen features were selected for constructing the radiomics signature based on the primary cohort. The combined prediction model for identifying preoperative lymph node metastasis reached a better discrimination power than the clinical prediction model, with an area under the curve of 0.944 vs. 0.666 in the primary cohort, and 0.912 vs. 0.713 in the validation cohort.
This pilot study demonstrated that a noninvasive radiomics signature extracted from contrast-enhanced computed tomography imaging can be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC.
我们开发了一种计算模型，该模型整合了来自增强型计算机断层扫描（CECT）图像的临床数据和影像学特征，以预测胰腺导管腺癌（PDAC）患者的淋巴结（LN）转移。
本回顾性研究纳入了 159 例 PDAC 患者（118 例来自初级队列，41 例来自验证队列），这些患者于 2012 年至 2015 年间接受了术前增强型计算机断层扫描检查。所有患者均接受了手术，且淋巴结状态得到了确定。在初级队列的静脉相图像中提取了总共 2041 个放射组学特征，并提取了最佳特征来构建放射组学特征。通过使用多变量逻辑回归选择放射组学特征和临床特征，构建了一个联合预测模型。生成了临床预测模型并用于评估两个队列。
基于初级队列，选择了 15 个特征来构建放射组学特征。用于识别术前淋巴结转移的联合预测模型的鉴别能力优于临床预测模型，在初级队列中的曲线下面积为 0.944 对 0.666，在验证队列中的曲线下面积为 0.912 对 0.713。
这项初步研究表明，从增强型计算机断层扫描成像中提取的非侵入性放射组学特征可方便地用于预测 PDAC 患者的术前淋巴结转移。
