目的:使用新辅助治疗前(NAT前)的MRI特征,开发和验证HER2零和低的机器学习模型。
方法:516名NAT手术后的乳腺癌患者被随机分为训练集(n=362)和内部验证集(n=154)进行模型构建和评估。MRI特征(肿瘤直径,增强类型,背景实质增强,增强模式,增强的百分比,信号增强率,乳房水肿,和ADC)进行了审查。Logistic回归(LR),支持向量机(SVM),k-最近邻(KNN),和极端梯度增强(XGBoost)模型利用MRI特征在训练和验证数据集中进行HER2状态评估。表现最好的模型产生了HER2评分,随后与病理完全缓解(pCR)和无病生存期(DFS)相关。
结果:XGBoost模型优于LR,SVM,和KNN,在验证数据集中,ROC曲线下面积(AUC)为0.783(95%CI:0.733-0.833)和0.787(95%CI:0.709-0.865)。其用于预测pCR的HER2评分在训练数据集中具有0.708的AUC和在验证数据集中具有0.695的AUC。此外,在验证数据集中,低HER2评分与较短的DFS显著相关(HR:2.748,95%CI:1.016~7.432,P=0.037).
结论:XGBoost模型可以帮助区分HER2-zero和HER2-low乳腺癌,并有潜力预测接受NAT的乳腺癌患者的pCR和预后。
结论:低表达人表皮生长因子受体2(HER2)的乳腺癌可以从HER2靶向治疗中获益。预测HER2低表达对于适当的管理至关重要。MRI特征为该临床问题提供了解决方案。
OBJECTIVE: To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features pre-neoadjuvant therapy (NAT).
METHODS: Five hundred and sixteen breast cancer patients post-NAT surgery were randomly divided into training (n = 362) and internal validation sets (n = 154) for model building and evaluation. MRI features (tumour diameter, enhancement type, background parenchymal enhancement, enhancement pattern, percentage of enhancement, signal enhancement ratio, breast oedema, and apparent diffusion coefficient) were reviewed. Logistic regression (LR), support vector machine (SVM), k-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) models utilized MRI characteristics for HER2 status assessment in training and validation datasets. The best-performing model generated a HER2 score, which was subsequently correlated with pathological complete response (pCR) and disease-free survival (DFS).
RESULTS: The XGBoost model outperformed LR, SVM, and KNN, achieving an area under the receiver operating characteristic curve (AUC) of 0.783 (95% CI, 0.733-0.833) and 0.787 (95% CI, 0.709-0.865) in the validation dataset. Its HER2 score for predicting pCR had an AUC of 0.708 in the training datasets and 0.695 in the validation dataset. Additionally, the low HER2 score was significantly associated with shorter DFS in the validation dataset (hazard ratio: 2.748, 95% CI, 1.016-7.432, P = .037).
CONCLUSIONS: The XGBoost model could help distinguish HER2-zero and HER2-low breast cancers and has the potential to predict pCR and prognosis in breast cancer patients undergoing NAT.
CONCLUSIONS: HER2-low-expressing breast cancer can benefit from the HER2-targeted therapy. Prediction of HER2-low expression is crucial for appropriate management. MRI features offer a solution to this clinical issue.