文章：

利用机器学习预测雌激素受体阳性、HER2阴性乳腺癌中多基因检测（Oncotype DX与Mammaprint）复发风险分级——BRAIN研究

Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer—The BRAIN Study

原文发布日期：13 February 2024

DOI: 10.3390/cancers16040774

类型: Article

开放获取: 是

英文摘要：

This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2− breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2− breast cancer.

摘要翻译： 

本研究旨在开发一种基于机器学习的预测模型，用于预测多基因检测（MGA）风险类别。研究利用接受过Oncotype DX（ODX）或MammaPrint（MMP）检测的雌激素受体阳性（ER+）/HER2−乳腺癌患者数据构建预测模型。开发队列共纳入2565例患者，其中2039例接受ODX检测，526例接受MMP检测。MMP风险预测模型采用单一XGBoost模型，ODX风险预测模型通过软投票方式整合LightGBM、CatBoost和XGBoost模型。此外，结合MMP与ODX的集成模型通过软投票方式整合CatBoost和XGBoost模型。研究从建模数据集中随机抽取10组样本（占数据总量10%）进行交叉验证，评估各验证集的预测精度。结果显示：MMP模型预测准确率为84.8%，ODX模型为87.9%，集成模型为86.8%。在集成队列中，预测低风险类别的敏感性、特异性和精确度分别为0.91、0.66和0.92。多个亚组的预测准确率超过90%，其中Ki-67<20且组织学分级1~2级且绝经前状态亚组的预测准确率最高，达95.7%。本研究表明，基于机器学习的预测模型有望为ER+/HER2−乳腺癌的现有多基因检测提供补充。

原文链接：

Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer—The BRAIN Study