用低分辨率图像提升乳腺癌HER2分型准确率
Contrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning
- 用端到端的ConvNeXt模型处理低分辨率病理图
- 三分类准确率达83.56%，F1分数超传统方法5.35%以上
- 适合临床辅助诊断，提升病理分型一致性
乳腺癌是女性中最常见的恶性肿瘤，精确检测与分类对治疗至关重要。免疫组化（IHC）生物标志物如HER2、ER和PR对于识别乳腺癌亚型具有关键作用。然而，传统的IHC分类依赖病理医生经验，存在工作量大且观察者间差异显著的问题。为此，本研究构建了印度病理学乳腺癌数据集（IPD-Breast），包含1,272张IHC切片（涵盖HER2、ER和PR），旨在实现受体状态分类的自动化。重点在于开发用于HER2三分类（0、低、高）的预测模型以改善预后评估。多种深度学习模型的评估表明，采用低分辨率IHC图像的端到端ConvNeXt网络在三分类任务中达到AUC 91.79%、F1分数83.52%、准确率83.56%，相比基于补丁的方法在F1分数上提升超过5.35%。该研究展示了简单而有效的深度学习方法在提高乳腺癌分类准确性与可重复性方面的潜力，支持其融入临床工作流程以改善患者预后。
原文摘要 · Abstract (English)
Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Immunohistochemistry (IHC) biomarkers like HER2, ER, and PR are critical for identifying breast cancer subtypes. However, traditional IHC classification relies on pathologists' expertise, making it labor-intensive and subject to significant inter-observer variability. To address these challenges, this study introduces the India Pathology Breast Cancer Dataset (IPD-Breast), comprising of 1,272 IHC slides (HER2, ER, and PR) aimed at automating receptor status classification. The primary focus is on developing predictive models for HER2 3-way classification (0, Low, High) to enhance prognosis. Evaluation of multiple deep learning models revealed that an end-to-end ConvNeXt network utilizing low-resolution IHC images achieved an AUC, F1, and accuracy of 91.79%, 83.52%, and 83.56%, respectively, for 3-way classification, outperforming patch-based methods by over 5.35% in F1 score. This study highlights the potential of simple yet effective deep learning techniques to significantly improve accuracy and reproducibility in breast cancer classification, supporting their integration into clinical workflows for better patient outcomes.
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