Accurate differentiation of metastatic and ultrasound-atypical reactive hyperplastic lymph nodes using a fusion model integrating ultrasound radiomics and habitatomics: A multicenter study.
Authors
Affiliations (8)
Affiliations (8)
- Graduate School, Zhejiang Chinese Medical University, Hangzhou 310053, China.
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China.
- Wenling Institute of Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang, China.
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China.
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, 310022, China.
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China.
- Center of Intelligent Diagnosis and Therapy (Taizhou), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Taizhou, Zhejiang, China.
- Research Center of Interventional Medicine and Engineering, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310000, China.
Abstract
The significant sonographic overlap between metastatic and ultrasound-atypical reactive hyperplastic lymph nodes remains a challenge in subjective, experience-based ultrasound diagnosis. Consequently, clinicians rely on biopsy, which is an invasive procedure associated with complications such as bleeding and infection, as well as the risk of false-negatives that exacerbate patient anxiety. In this multicenter retrospective study, patients with suspicious lymph nodes from two institutions were enrolled. Conventional and habitat-based radiomic features, capturing intratumoral heterogeneity, were extracted from ultrasound images. After feature selection via ElasticNet regression and multicollinearity removal, four machine learning models were developed. Hyperparameters were optimized using fivefold cross-validation. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA), and the model was interpreted using SHapley Additive exPlanation (SHAP) analysis. A total of 1,230 patients (training set: 702; internal set: 302; external set: 226) were included in this study. Eight independent risk factors (e.g., age, long-to-short axis ratio, and cortical morphology) were identified. The random forest fusion model achieved an area under the curve (AUC) of 0.910 and an F1 score of 0.806 in the external set, significantly surpassing the clinical model (AUC = 0.690). Compared with conventional radiomics, the fusion model showed superior reclassification (net reclassification improvement = 0.562, integrated discrimination improvement = 0.144). SHAP analysis linked malignancy risk to higher gray-level nonuniformity and lower elongation, ensuring clinical plausibility. DCA confirmed robust clinical net benefit across all cohorts. The fusion model, integrating radiomic and habitat features, enables noninvasive suspicious lymph node prediction. It may reduce unnecessary biopsies in low-risk patients and provide incremental value for individualized preoperative management by quantifying spatial characteristics.