Preoperative Prediction of Lymphovascular Invasion of Node-Negative Gastric Cancer Based on CT Radiomics
10.3969/j.issn.1005-5185.2024.01.013
- VernacularTitle:基于CT影像组学术前预测淋巴结阴性胃癌淋巴血管侵犯
- Author:
Feifei LOU
1
;
Qingqing CHEN
;
Hao HUANG
;
Fang WANG
;
Jie HE
;
Enhui XIN
;
Hongjie HU
Author Information
1. 浙江大学医学院附属邵逸夫医院放射科,浙江 杭州 310000
- Keywords:
Stomach neoplasms;
Radiomics;
Node-negative;
Lymphovascular invasion;
Tomography,X-ray computed
- From:
Chinese Journal of Medical Imaging
2024;32(1):73-80
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To explore the value of CT-based radiomics in the preoperative prediction of lymphatic invasion of node-negative gastric cancer,and to construct a nomogram combined with clinical variables.Materials and Methods The clinical and CT imaging data of 173 gastric cancer patients with lymph node negative and pathologically confirmed gastric cancer in the Sir Run Run Shaw Hospital from January 2019 to June 2021 were retrospectively analyzed.A total of 60 cases with lymphovascular invasion(LVI)positive patients and 113 cases with LVI negative patients were included,and randomly divided into train cohort(n=121)and test cohort(n=52)at 7∶3.Based on the train cohort,the clinical model,the radiomics model,the fusion model were constructed and verified in the test cohort.Clinical data and conventional CT features included age,gender,tumor marker,tumor location,tumor morphology,enhancement range,etc.The clinical significant variables were selected through univariate and multivariate analysis to establish the clinical model.The tumor regions of interest were segmented and radiomics features were extracted by using the 3D-Slicer software.Key features were screened through least absolute shrinkage and selection operator regression analysis,and then the radiomics model was constructed with random forest algorithm,and converted to random forest score(RF score).The fusion model was constructed via combining clinical significant variables and RF score,and visualized as a nomogram.The receiver operator characteristic curve and area under curve(AUC)were used to evaluate the prediction performance of the models.Decision curve analysis was used to calculate the clinical practicability.Results The radiomics model was superior to the clinical model.The radiomics model AUC of the train cohort and the test cohort were 0.872(0.810 to 0.935)and 0.827(0.707 to 0.947),the clinical model AUC were 0.767(0.682 to 0.852)and 0.761(0.610 to 0.913).The nomogram further improved the predictive efficiency,the AUC in train cohort and test cohort reached 0.898(0.842 to 0.953)and 0.844(0.717 to 0.971),respectively.Decision curve analysis demonstrated clinical benefits of nomogram.Conclusion The radiomics model can be used to preoperatively predict LVI of node-negative gastric cancer.The nomogram can further improve the prediction efficiency.
