融合超声与临床数据，提升罕见乳腺肿瘤分类准确率。
Multimodal Deep Learning for Phyllodes Tumor Classification from Ultrasound and Clinical Data
- 双分支网络分别处理超声图像和患者临床数据，实现多模态特征融合。
- 在81例患者中，模型AUC达0.9427，F1-score达0.7294，优于单一模态。
- 适合临床辅助诊断，减少不必要的活检与手术。
叶状肿瘤（PTs）是罕见的纤维上皮性乳腺病变，因其影像学表现与良性纤维腺瘤相似，术前分类困难，常导致不必要的手术切除。为此，我们提出一种融合乳腺超声（BUS）图像与结构化临床数据的多模态深度学习框架。基于81例经确诊的PT患者数据，构建双分支神经网络，提取并融合超声图像与患者元数据特征。采用类感知采样与受试者分层5折交叉验证，以缓解类别不平衡与数据泄露问题。结果表明，所提多模态方法在区分良性与交界/恶性PT方面优于单模态基线。在六种图像编码器中，ConvNeXt与ResNet18表现最佳，多模态设置下AUC-ROC分别为0.9427和0.9349，F1-score分别为0.6720和0.7294。本研究证明了多模态AI作为无创诊断工具的潜力，有助于减少不必要的活检，优化乳腺肿瘤管理决策。
原文摘要 · Abstract (English)
Phyllodes tumors (PTs) are rare fibroepithelial breast lesions that are difficult to classify preoperatively due to their radiological similarity to benign fibroadenomas. This often leads to unnecessary surgical excisions. To address this, we propose a multimodal deep learning framework that integrates breast ultrasound (BUS) images with structured clinical data to improve diagnostic accuracy. We developed a dual-branch neural network that extracts and fuses features from ultrasound images and patient metadata from 81 subjects with confirmed PTs. Class-aware sampling and subject-stratified 5-fold cross-validation were applied to prevent class imbalance and data leakage. The results show that our proposed multimodal method outperforms unimodal baselines in classifying benign versus borderline/malignant PTs. Among six image encoders, ConvNeXt and ResNet18 achieved the best performance in the multimodal setting, with AUC-ROC scores of 0.9427 and 0.9349, and F1-scores of 0.6720 and 0.7294, respectively. This study demonstrates the potential of multimodal AI to serve as a non-invasive diagnostic tool, reducing unnecessary biopsies and improving clinical decision-making in breast tumor management.
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