Patient test set mass margins.

An external validation of IAIA-BL—a deep-learning based, inherently interpretable breast lesion malignancy prediction model—was performed on two patient populations: 207 women ages 31 to 96, (425 mammograms) from iCAD, and 58 women (104 mammograms) from Emory University. This is the first external validation of an inherently interpretable, deep learning-based lesion classification model. IAIA-BL and black-box baseline models had lower mass margin classification performance on the external datasets than the internal dataset as measured by AUC. These losses correlated with a smaller reduction in malignancy classification performance, though AUC 95% confidence intervals overlapped for all sites. However, interpretability, as measured by model activation on relevant portions of the lesion, was maintained across all populations. Together, these results show that model interpretability can generalize even when performance does not.本研究针对IAIA-BL——一款基于深度学习的固有可解释性乳腺病变良恶性预测模型——开展外部验证，验证人群分为两组：来自iCAD的207名年龄介于31至96岁的女性（共425例乳腺钼靶影像），以及来自埃默里大学（Emory University）的58名女性（共104例乳腺钼靶影像）。这是首次针对固有可解释性深度学习病变分类模型开展的外部验证研究。以受试者工作特征曲线下面积（AUC）为评估指标，IAIA-BL与黑箱基线模型在外部数据集上的肿块边缘分类性能均低于其在内部数据集上的表现。尽管所有受试中心的AUC 95%置信区间存在重叠，但上述性能损失与良恶性分类性能的小幅下降存在相关性。不过，通过模型在病变相关区域的激活程度所评估的可解释性，在所有受试人群中均得以保持。综上，本研究结果表明，即便模型的分类性能无法实现泛化，其可解释性仍可保持泛化能力。