Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration

Q: What does this research mean for the field?

An interpretable machine learning model integrating contrast-enhanced mammography (CEM) and ultrasound BI-RADS features accurately predicts preoperative axillary lymph node metastasis in breast cancer patients. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Weimin Xu; Bowen Zheng; Chanjuan Wen; Hui Zeng; Sina Wang; Zilong He; Xin Liao; Weiguo Chen; Yingjia Li; Genggeng Qin

doi:10.1177/15330338251334735

April 17, 2025Technology in Cancer Research & TreatmentOpen Access

Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration

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Key Points

This research aims to improve the accuracy of predicting preoperative axillary lymph node metastasis in breast cancer using machine learning.
Retrospective study involving 292 female patients with primary breast cancer.
Radiologists extracted BI-RADS features from CEM and US images to create datasets for training machine learning models.
Model performance was evaluated using ROC and AUC metrics.
The combination of CEM and ultrasound BI-RADS achieved the highest AUC of 0.762 and specificity of 86.67%.
The ensemble model utilizing Random Forest as a meta-model had an AUC of 0.754 and specificity of 83.33%.
Key features for prediction included lymph node long diameters and complete morphology from CEM images.

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Implication

Evaluates a machine learning model predicting axillary lymph node metastasis, suggesting improved clinical decision-making.

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Cite This Study

Xu et al. (2025) studied this question.

synapsesocial.com/papers/6a2295df23d43a3f28118bd4 https://doi.org/10.1177/15330338251334735

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Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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