The complex diagnostic criteria for gliomas pose great challenges for making accurate diagnoses with computational pathology methods. There are no in-depth analyses of the accuracy, reliability and auxiliary capability of present approaches from a clinical perspective. Previous studies have overlooked the exploration of molecular and morphological correlations. To overcome these limitations, we propose ROAM, a multiple-instance learning model based on large regions of interest and a pyramid transformer. ROAM enlarges regions of interest to facilitate the consideration of tissue contexts. It utilizes the pyramid transformer to model both intrascale and interscale correlations of morphological features and leverages class-specific multiple-instance learning based on attention to extract slide-level visual representations that can be used to diagnose gliomas. Through comprehensive experiments on both in-house and external glioma datasets, we demonstrate that ROAM can automatically capture key morphological features consistent with the experience of pathologists and thus provide accurate, reliable and adaptable clinical-grade diagnoses of gliomas. Moreover, ROAM has clinical value for auxiliary diagnoses and could pave the way for the study of molecular and morphological correlations.

神经胶质瘤复杂的诊断标准给计算病理学方法的准确诊断带来了巨大的挑战。目前还没有从临床角度对现有方法的准确性、可靠性和辅助能力进行深入分析。先前的研究忽视了分子和形态学相关性的探索。为了克服这些限制，我们提出了 ROAM，一种基于大兴趣区域和金字塔变压器的多实例学习模型。 ROAM 扩大了感兴趣区域，以方便考虑组织环境。它利用金字塔变换器对形态特征的尺度内和尺度间相关性进行建模，并利用基于注意力的特定类多实例学习来提取可用于诊断神经胶质瘤的幻灯片级视觉表示。通过对内部和外部胶质瘤数据集的综合实验，我们证明 ROAM 可以自动捕获与病理学家经验一致的关键形态特征，从而提供准确、可靠和适应性强的胶质瘤临床级诊断。此外，ROAM具有辅助诊断的临床价值，并可为分子和形态学相关性的研究铺平道路。