Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously assessed an attention-based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51) patients and corresponding hematoxylin-eosin-stained histological sections. Pairwise analysis of sample cohorts from three meningioma methylation classes demonstrated high accuracy in two combinations. The performance of our approach was validated using an independent set of 51 meningioma patient samples. Importantly, attention map visualization revealed that the algorithm primarily focuses on tumor regions deemed significant by neuropathologists, offering insights into the decision-making process of the CNN. Our findings highlight the capacity of CNNs to effectively harness phenotypic information from histological sections through computerized images for precision medicine. Notably, this study is the first demonstration of predicting clinically relevant DNA methylome information using computer vision applied to standard histopathology. The introduced AI framework holds great potential in supporting, augmenting, and expediting meningioma classification in the future.
卷积神经网络（CNNs）正日益成为高级计算病理学中的重要工具，凭借其卓越的视觉解码能力推动精准医学的发展。脑膜瘤作为最常见的原发性颅内肿瘤，其准确分级与分类对临床决策至关重要。近年来，基于DNA甲基化的脑膜瘤分子分类方法在预测肿瘤复发方面显示出优于传统组织病理学方法的效能。然而，DNA甲基化谱分析成本高昂、操作复杂且尚未普及。因此，基于数字病理学预测DNA甲基化分型具有重要价值，可作为分子分类的有效补充。本研究开发并严格评估了一种基于注意力机制的多实例深度神经网络，利用142例（+51例）患者的肿瘤甲基化组数据及对应苏木精-伊红染色组织切片进行脑膜瘤甲基化分型预测。对三种脑膜瘤甲基化分型样本队列的配对分析显示，其中两种组合具有高预测精度。我们使用独立的51例脑膜瘤患者样本验证了该方法的性能。值得注意的是，注意力图谱可视化显示算法主要聚焦于神经病理学家判定的关键肿瘤区域，为理解CNN的决策过程提供了新视角。本研究结果凸显了CNN通过计算机图像处理有效提取组织切片表型信息以服务精准医学的能力。特别指出，这是首次利用计算机视觉技术对标准组织病理学图像进行临床相关DNA甲基化组信息预测的实证研究。该人工智能框架在未来脑膜瘤分类的辅助、增强与加速方面展现出巨大潜力。
肿瘤(癌症)患者之家