融合深度学习与影像组学，提升乳腺癌早期筛查准确率
Ensemble of radiomics and ConvNeXt for breast cancer diagnosis
- 用卷积神经网络和影像组学分别建模，再通过集成方法融合预测结果
- 集成模型在两个数据集上均达AUC 0.87，优于单一模型的0.83和0.80
- 适合医学影像分析、辅助诊断系统研发人员参考
乳腺癌早期诊断对提高生存率至关重要。影像组学与深度学习（DL）在辅助放射科医生早期发现癌症方面展现出显著潜力。本文旨在评估影像组学、深度学习及集成技术在筛查乳腺钼靶中检测癌症的表现。使用两个独立数据集：RSNA 2023乳腺癌检测挑战赛数据集（11,913名患者）和墨西哥TecSalud队列数据集（19,400名患者）。以RSNA数据集训练ConvNeXtV1-small深度学习模型，并在TecSalud数据集上验证；影像组学模型基于TecSalud数据集构建，并采用留一年外验证法评估。集成方法采用统一策略融合并校准预测结果。结果显示，集成方法在所有测试中达到最高AUC 0.87，优于ConvNeXtV1-small的0.83和影像组学的0.80。结论表明，结合深度学习与影像组学预测的集成方法可显著提升乳腺钼靶图像中的癌症诊断性能。
原文摘要 · Abstract (English)
Early diagnosis of breast cancer is crucial for improving survival rates. Radiomics and deep learning (DL) have shown significant potential in assisting radiologists with early cancer detection. This paper aims to critically assess the performance of radiomics, DL, and ensemble techniques in detecting cancer from screening mammograms. Two independent datasets were used: the RSNA 2023 Breast Cancer Detection Challenge (11,913 patients) and a Mexican cohort from the TecSalud dataset (19,400 patients). The ConvNeXtV1-small DL model was trained on the RSNA dataset and validated on the TecSalud dataset, while radiomics models were developed using the TecSalud dataset and validated with a leave-one-year-out approach. The ensemble method consistently combined and calibrated predictions using the same methodology. Results showed that the ensemble approach achieved the highest area under the curve (AUC) of 0.87, compared to 0.83 for ConvNeXtV1-small and 0.80 for radiomics. In conclusion, ensemble methods combining DL and radiomics predictions significantly enhance breast cancer diagnosis from mammograms.
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