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<p>The presence of axillary lymph node metastasis (ALNM) in breast cancer patients is an important factor in deciding whether to have axillary surgery or pursue alternative treatments. Based on axillary ultrasound (US) and histopathologic data, three graph neural network models were compared to predict ALNM in early-stage breast cancer. The patients were randomly divided into two data sets: training (80%) and testing (20%). Predictive performance was measured using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and area under the curve (AUC). In the test cohort, the graph convolutional network (GCN) performed the best in predicting ALNM, with an AUC of 0.77 (95% confidence interval [CI]: 0.69–0.84). In conclusion, the GCN model has the potential to provide a noninvasive tool for detecting ALNM and can aid in clinical decision-making. Prospective studies are expected to provide high-level evidence for clinical usage in future investigations.</p>

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A comparative analysis of three graph neural network models for predicting axillary lymph node metastasis in early-stage breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月22日

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  • Enock Adjei Agyekum,
  • Wentao Kong,
  • Yong-zhen Ren,
  • Eliasu Issaka,
  • Josephine Baffoe,
  • Wang Xian,
  • Gongxun Tan,
  • Chunjing Xiong,
  • Zhangye Wang,
  • Xiaoqin Qian,
  • Xiangjun Shen

摘要

The presence of axillary lymph node metastasis (ALNM) in breast cancer patients is an important factor in deciding whether to have axillary surgery or pursue alternative treatments. Based on axillary ultrasound (US) and histopathologic data, three graph neural network models were compared to predict ALNM in early-stage breast cancer. The patients were randomly divided into two data sets: training (80%) and testing (20%). Predictive performance was measured using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and area under the curve (AUC). In the test cohort, the graph convolutional network (GCN) performed the best in predicting ALNM, with an AUC of 0.77 (95% confidence interval [CI]: 0.69–0.84). In conclusion, the GCN model has the potential to provide a noninvasive tool for detecting ALNM and can aid in clinical decision-making. Prospective studies are expected to provide high-level evidence for clinical usage in future investigations.