AUTHOR=Bi Lei , Liu Yubo , Xu Jingxu , Wang Ximing , Zhang Tong , Li Kaiguo , Duan Mingguang , Huang Chencui , Meng Xiangjiao , Huang Zhaoqin 

TITLE=A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.632176

DOI=10.3389/fonc.2021.632176

ISSN=2234-943X

ABSTRACT=Purpose: To establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.
Materials and Methods: A total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.
Results: The radiomics signature, constructed by seven selected features, was closely related to LN metastasis in training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in training set (area under the curve [AUC], 0.853) and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram. 
Conclusion: Our CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and noninvasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.