Journal Article

Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction

Huang, Chencui, Zhang, Yanqiang, Wang, Yi, Tong, Yahan, Cheng, Xiangdong, Yu, Pengcheng, Xu, Ruixin, Cao, Mengxuan, Chen, Wujie, You, Yi, Zhang, Ruolan, Zhang, Lei

2025

Overview

Purpose In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph node metastasis is crucial. Methods We retrospectively collected 256 patients with AEG from two centres. The radiomics features were extracted from the preoperative diagnostic CT images, and the feature selection method and machine learning method were applied to reduce the feature size and establish the predictive imaging features. By comparing the three machine learning methods, the best radiomics nomogram was selected, and the average AUC was obtained by 20 repeats of fivefold cross-validation for comparison. The fusion model was constructed by logistic regression combined with clinical factors. On this basis, ROC curve, calibration curve and decision curve of the fusion model are constructed. Results The predictive efficacy of fusion model for tumour invasion depth was higher than that of radiomics nomogram, with an AUC of 0.764 vs. 0.706 in the test set, P  < 0.001, internal validation set 0.752 vs. 0.697, P  < 0.001, and external validation set 0.756 vs. 0.687, P  < 0.001, respectively. The predictive efficacy of the lymph node metastasis fusion model was higher than that of the radiomics nomogram, with an AUC of 0.809 vs. 0.732 in the test set, P  < 0.001, internal validation set 0.841 vs. 0.718, P  < 0.001, and external validation set 0.801 vs. 0.680, P  < 0.001, respectively. Conclusion We have developed a fusion model combining radiomics and clinical risk factors, which is crucial for the accurate preoperative diagnosis and treatment of AEG, advancing precision medicine. It may also spark discussions on the imaging feature differences between AEG and GC (Gastric cancer).

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Adenocarcinoma / Adenocarcinoma - diagnostic imaging / Adenocarcinoma - pathology / Adenocarcinoma - surgery / Adenocarcinoma of esophagogastric junction (AEG) / Aged / Business metrics / Computed tomography / CT imaging / Diagnosis / Effectiveness / Esophageal Neoplasms - diagnostic imaging / Esophageal Neoplasms - pathology / Esophageal Neoplasms - surgery / Esophagogastric Junction - diagnostic imaging / Esophagogastric Junction - pathology / Female / Fusion model / Gastric cancer / Humans / Imaging / Learning algorithms / Lymph node metastasis / Lymph nodes / Lymphatic Metastasis - diagnostic imaging / Lymphatic system / Machine Learning / Magnetic resonance imaging / Male / Medical imaging / Medical prognosis / Medicine / Medicine & Public Health / Metastases / Metastasis / Middle Aged / Neoplasm Invasiveness / Nomograms / Precision medicine / Prediction / Prediction models / Radiology / Radiomics / Radiomics nomogram / Retrospective Studies / Risk factors / Serous invasion / Stomach Neoplasms - diagnostic imaging / Stomach Neoplasms - pathology / Support vector machines / Surgery / Survival analysis / Test sets / Tomography / Tomography, X-Ray Computed - methods