Artificial intelligence (AI)-systems can improve cancer diagnosis, yet their development often relies on subjective histological features as ground truth for training. Here, we developed an AI-model applied to histological whole-slide images (WSIs) using CDH1 bi-allelic mutations, pathognomonic for invasive lobular carcinoma (ILC) in breast neoplasms, as ground truth. The model accurately predicted CDH1 bi-allelic mutations (accuracy=0.95) and diagnosed ILC (accuracy=0.96). A total of 74% of samples classified by the AI-model as having CDH1 bi-allelic mutations but lacking these alterations displayed alternative CDH1 inactivating mechanisms, including a deleterious CDH1 fusion gene and non-coding CDH1 genetic alterations. Analysis of internal and external validation cohorts demonstrated 0.95 and 0.89 accuracy for ILC diagnosis, respectively. The latent features of the AI-model correlated with human-explainable histopathologic features. Taken together, this study reports the construction of an AI-algorithm trained using a genetic rather than histologic ground truth that can robustly classify ILCs and uncover CDH1 inactivating mechanisms, providing the basis for orthogonal ground truth utilization for development of diagnostic AI-models applied to WSI.

中文翻译：

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基于基因组学驱动的基于人工智能的模型对乳腺浸润性小叶癌进行分类并发现 CDH1 失活机制


人工智能 （AI） 系统可以改进癌症诊断，但它们的开发通常依赖于主观组织学特征作为训练的基本事实。在这里，我们开发了一个应用于组织学全玻片图像 （WSI） 的 AI 模型，使用 CDH1 双等位基因突变作为乳腺肿瘤浸润性小叶癌 （ILC） 的特征，作为基本事实。该模型准确预测了 CDH1 双等位基因突变 （准确率 = 0.95） 并诊断了 ILC （准确率 = 0.96）。共有 74% 的样本被 AI 模型归类为具有 CDH1 双等位基因突变但缺乏这些改变，显示出替代的 CDH1 失活机制，包括有害的 CDH1 融合基因和非编码 CDH1 基因改变。内部和外部验证队列的分析表明，ILC 诊断的准确性分别为 0.95 和 0.89。AI 模型的潜在特征与人类可解释的组织病理学特征相关。综上所述，本研究报告了使用遗传而非组织学真实训练的 AI 算法的构建，该算法可以稳健地对 ILC 进行分类并揭示 CDH1 失活机制，为正交真实利用开发应用于 WSI 的诊断 AI 模型提供了基础。