Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers

IF 2.4 3区医学 Q2 ACOUSTICS

Ultrasound in Medicine and Biology Pub Date : 2024-12-03 DOI:10.1016/j.ultrasmedbio.2024.11.003

Meiying Yan , Dilin He , Yu Sun , Long Huang , Linli Cai , Chen Wang , Jincao Yao , Xiangyang Li , Hongping Song , Chen Yang

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引用次数: 0

Abstract

Objective

Early and accurate prediction of axillary lymph node metastasis (ALNM) is crucial in determining appropriate treatment strategies for patients with early-stage breast cancer. The aim of this study was to evaluate the efficacy of radiomic features extracted from ultrasound (US) images combined with machine learning (ML) methods in predicting ALNM to improve diagnostic accuracy and patient prognosis.

Methods

In this retrospective study, data of 282 early-stage breast cancer patients from two centers were analyzed. We considered clinicopathological characteristics, conventional US features, contrast-enhanced ultrasound (CEUS) characteristics, and radiomics features. Radiomics features were extracted from US images, and using least absolute shrinkage and selection operator (LASSO) regression, 12 key features were selected to compute a Radiomics score (Rad-score). A nomogram was developed based on these features, alongside five ML models: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using metrics such as the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), negative predictive value (NPV), and positive predictive value (PPV).

Results

Both the nomogram and ML models, including the Rad-score combined with histologic type, significantly predicted ALNM. Among all models, the XGBoost model showed the best performance with an AUC of 0.810 and an accuracy of 84.1% in the external test set, surpassing the nomogram and other ML models. SHapley Additive exPlanations (SHAP) analysis further provided insights into the influence of individual radiomics features on ALNM prediction.

Conclusions

While the nomogram provides a useful traditional statistical approach, integrating radiomics features with ML, particularly the XGBoost model enhanced by SHAP interpretability, offers superior predictive accuracy for ALNM in early-stage breast cancer patients.

查看原文本刊更多论文

Nomogram和Machine Learning模型预测早期乳腺癌腋窝淋巴结转移的比较分析：两中心腋窝临床和超声阴性病例的研究。

目的：早期准确预测乳腺癌腋窝淋巴结转移（ALNM）对早期乳腺癌患者确定合适的治疗策略至关重要。本研究的目的是评估从超声（US）图像中提取的放射学特征结合机器学习（ML）方法预测ALNM的有效性，以提高诊断准确性和患者预后。方法：回顾性分析两个中心282例早期乳腺癌患者的资料。我们考虑了临床病理特征、常规超声特征、对比增强超声（CEUS）特征和放射组学特征。从美国图像中提取放射组学特征，并使用最小绝对收缩和选择算子（LASSO）回归，选择12个关键特征来计算放射组学评分（Rad-score）。基于这些特征开发了一个nomogram，以及5个ML模型：Logistic Regression （LR）、Naive Bayes （NB）、Support Vector Machine （SVM）、K-Nearest Neighbors （KNN）和Extreme Gradient Boosting （XGBoost）。使用曲线下面积（AUC）、准确性（ACC）、敏感性（SEN）、特异性（SPE）、阴性预测值（NPV）和阳性预测值（PPV）等指标评估模型的性能。结果：nomogram和ML模型（包括rad评分结合组织学分型）对ALNM均有显著预测作用。在所有模型中，XGBoost模型在外部测试集中的AUC为0.810，准确率为84.1%，表现最佳，超过了nomogram和其他ML模型。SHapley加性解释（SHAP）分析进一步揭示了个体放射组学特征对ALNM预测的影响。结论：虽然nomogram提供了一种有用的传统统计学方法，但将放射组学特征与ML相结合，特别是通过SHAP可解释性增强的XGBoost模型，为早期乳腺癌患者的ALNM预测提供了更高的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ultrasound in Medicine and Biology 医学-核医学

CiteScore

6.20

自引率

6.90%

发文量

325

审稿时长

70 days

期刊介绍： Ultrasound in Medicine and Biology is the official journal of the World Federation for Ultrasound in Medicine and Biology. The journal publishes original contributions that demonstrate a novel application of an existing ultrasound technology in clinical diagnostic, interventional and therapeutic applications, new and improved clinical techniques, the physics, engineering and technology of ultrasound in medicine and biology, and the interactions between ultrasound and biological systems, including bioeffects. Papers that simply utilize standard diagnostic ultrasound as a measuring tool will be considered out of scope. Extended critical reviews of subjects of contemporary interest in the field are also published, in addition to occasional editorial articles, clinical and technical notes, book reviews, letters to the editor and a calendar of forthcoming meetings. It is the aim of the journal fully to meet the information and publication requirements of the clinicians, scientists, engineers and other professionals who constitute the biomedical ultrasonic community.