Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study

Cancer Imaging

Jingjing Xiao¹  Meng Li¹  Jia Yang²  Zhuoli Zhang²  Qiandong Yao³  Yuan Qu⁴  Xianqi Wang⁴  Kang Chen⁴  Haoran Luo⁴  Ke Li⁴  Mingshan Du⁴  Wenjing Hou⁴  Jing Li⁴  Wei Chen⁴  Lian Li⁴  Jiali Yang⁵ 

[1] Department of Medical Engineering, Xinqiao Hospital, Army Medical University;Department of Radiology, Feinberg School of Medicine, Northwestern University;Department of Radiology, Sichuan Science City Hospital;Department of Radiology, Southwest Hospital, Army Medical University;Hepatopancreatobiliary Surgery, Southwest Hospital, Army Medical University;

关键词: Pancreatic ductal adenocarcinoma;    Radiomics;    CT;    Lymph node metastasis;   

DOI  :  10.1186/s40644-020-0288-3

来源: DOAJ

【 摘 要 】

Abstract Background 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). Methods 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. Results 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. Conclusions 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.

【 授权许可】

Unknown