Habitat radiomics assists radiologists in accurately diagnosing lymph node metastasis of adenocarcinoma of the esophagogastric junction

作者：Pingfan Jia, Yueying Li, Haonan Li, Yuan Li, Yuan Li, Yuan Li, Huijuan Qin, AnYu Xie, Yuru Li, Yuru Li, Luyao Wang, Luqin Ke, Huijie Feng, Hongwei Yu, Juan Li, Ning Yuan, Xing Guo · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-01969-9 · 被引用次数：7 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Esophageal Cancer Research and Treatment、Gastric Cancer Management and Outcomes

OBJECTIVES: This study aimed to develop a habitat radiomics (HR) model capable of preoperatively predicting lymph node metastasis (LNM) in adenocarcinoma of the esophagogastric junction (AEG) and to implement its use in clinical practice. METHODS: In this retrospective analysis, 337 patients from three centers were enrolled and divided into three cohorts: training, validation, and test (208, 52, and 77 patients, respectively). We constructed HR models, conventional radiomics models, and combined models to identify LNM in AEG. The area under the curve (AUC) was employed to identify the optimal model, which was then evaluated for assisting radiologists in the empirical and RADS groups in diagnosing LNM. Finally, the prediction process of the optimal model was visualized using SHAP plots. RESULTS: The HR model demonstrated superior performance, achieving the highest AUC values of 0.876, 0.869, and 0.795 in the training, validation, and test cohorts, respectively. Regardless of seniority, the empirical group of radiologists showed a significant improvement in the AUC and accuracy when using the HR model, compared to working alone (p < 0.05). Furthermore, the RADS group radiologists exhibited strong reclassification ability, effectively reevaluating patients with false-negative LN initially classified as Node-RADS score 1 or 2 by themselves. CONCLUSION: The HR model facilitates the accurate prediction of LNM in AEG and holds potential as a valuable tool to augment radiologists' di...