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

• DOI: 10.1186/s13244-025-01969-9
• Publication Date: 2025-04-24T12:49:00Z
• Authors: Pingfan Jia; Yueying Li; Haonan Li; Yuan Li; Huijuan Qin; Anyu Xie; Yuru Li; Luyao Wang; Luqin Ke; Huijie Feng; Hongwei Yu; Juan Li; Ning Yuan; Xing Guo

Abstract

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’ diagnostic capabilities in daily clinical practice.

Critical relevance statement The habitat radiomics model could accurately predict the lymph node status of adenocarcinoma in the esophagogastric junction and assist radiologists in improving diagnostic efficacy, which lays the foundation for accurate staging and effective treatment.

Key Points

Accurate lymph node diagnosis in esophagogastric junction adenocarcinoma is beneficial for prognosis.

Habitat radiomics model accurately predicted and assisted physicians in diagnosing lymph nodes.

The habitat model effectively reclassified false-negative lymph nodes at Node-RADS 1 and 2.

Graphical Abstract