Journal of cancer research and therapeutics · 2026
Accurate differentiation of metastatic and ultrasound-atypical reactive hyperplastic lymph nodes using a fusion model integrating ultrasound radiomics and habitatomics: A multicenter study.
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Shen Jiafei, Jin Zhiyan, Li Xiaoxian, Yao Jincao, Zhang Yang, Jiang Tian, Zhang Ke, Lv Lujiao, Li Bin, Zhou Jianhua, Wang Liping, Xu Dong, Chen Liyu
Аннотация
BACKGROUND: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.