Accurately predicting the upgrade of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) is crucial for surgical planning. However, traditional deep learning methods face challenges due to limited ultrasound data and poor generalization ability. This study proposes the DiffKD-DCIS framework, integrating conditional diffusion modeling with teacher-student knowledge distillation. The framework operates in three stages: First, a conditional diffusion model generates high-fidelity ultrasound images using multimodal conditions for data augmentation. Then, a deep teacher network extracts robust features from both original and synthetic data. Finally, a compact student network learns from the teacher via knowledge distillation, balancing generalization and computational efficiency. Evaluated on a multi-center dataset of 1,435 cases, the synthetic images were of good quality. The student network had fewer parameters and faster inference. On external test sets, it outperformed partial combinations, and its accuracy was comparable to senior radiologists and superior to junior ones, showing significant clinical potential.

翻译：准确预测导管原位癌（DCIS）升级为浸润性导管癌（IDC）对于手术规划至关重要。然而，传统深度学习方法因超声数据有限及泛化能力不足而面临挑战。本研究提出DiffKD-DCIS框架，将条件扩散建模与师生知识蒸馏相结合。该框架分三个阶段运行：首先，条件扩散模型利用多模态条件生成高保真超声图像以进行数据增强；随后，深度教师网络从原始数据与合成数据中提取鲁棒特征；最后，紧凑的学生网络通过知识蒸馏向教师网络学习，在泛化能力与计算效率之间取得平衡。在一个包含1,435例病例的多中心数据集上的评估表明，合成图像质量良好。学生网络参数量更少、推理速度更快。在外部测试集上，其性能优于部分组合方法，其准确率与资深放射科医师相当且优于初级医师，显示出显著的临床潜力。