Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models.
Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models.
复制标题
使用多分辨率深度学习模型从组织病理学图像预测子宫内膜癌亚型和分子特征。
DOI：
10.1016/j.xcrm.2021.100400
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发表时间：
2021-09-21
期刊：
影响因子：
--
通讯作者：
Fenyö D
中科院分区：
文献类型：
--
作者：
Hong R;Liu W;DeLair D;Razavian N;Fenyö D
子宫内膜癌组织学亚型、分子亚型和突变状态的确定对于诊断过程至关重要，并直接影响患者的预后和治疗。测序虽然速度较慢且成本较高，但可以提供有关分子亚型和突变的额外信息，可用于更好地选择治疗方法。在这里，我们实现了一个定制的多分辨率深度卷积神经网络 Panoptes，它不仅可以预测组织学亚型，还可以根据数字化 H&E 染色病理图像预测分子亚型和 18 种常见基因突变。该模型实现了高精度，并且在独立数据集上具有良好的泛化能力。我们的结果表明，经过进一步完善，Panoptes 具有临床应用潜力，可帮助病理学家无需测序即可确定子宫内膜癌的分子亚型和突变。 CNN 模型根据 H&E 图像预测子宫内膜癌的亚型和突变 多分辨率 CNN 模型在 H&E 图像上的表现优于单分辨率模型 特征提取表明 CNN 模型使用人类可解释的肿瘤特征 肿瘤等级将 CNV-H 分子亚型与子宫内膜样组织学样本区分开来 Hong 等人。开发并实现定制的多分辨率深度卷积神经网络，基于数字化 H&E 染色病理图像预测子宫内膜癌的分子亚型和 18 种常见基因突变。这些模型学习人类可解释和可概括的特征，表明无需测序分析即可进行潜在的临床应用。
The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is critical for the diagnostic process, and directly affects patients’ prognosis and treatment. Sequencing, albeit slower and more expensive, can provide additional information on molecular subtypes and mutations that can be used to better select treatments. Here, we implement a customized multi-resolution deep convolutional neural network, Panoptes, that predicts not only the histological subtypes but also the molecular subtypes and 18 common gene mutations based on digitized H&E-stained pathological images. The model achieves high accuracy and generalizes well on independent datasets. Our results suggest that Panoptes, with further refinement, has the potential for clinical application to help pathologists determine molecular subtypes and mutations of endometrial carcinoma without sequencing. CNN models predict subtypes and mutations in endometrial cancer based on H&E images Multi-resolution CNN models perform better on H&E images than single-resolution ones Feature extraction suggests CNN models use human interpretable tumor features Tumor grade distinguishes CNV-H molecular subtype from endometrioid histology samples Hong et al. develop and implement a customized multi-resolution deep convolutional neural network that predict molecular subtypes and 18 common gene mutations in endometrial cancer based on digitized H&E-stained pathological images. The models learn human interpretable and generalizable features, indicating potential clinical application without sequencing analysis.
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