Zuo Mingrong, Xing Xiang, Zheng Linmao, Wang Hao, Yuan Yunbo, Chen Siliang, Yu Tianping, Zhang ShuXin, Yang Yuan, Mao Qing, Yu Yongbin, Chen Ni, Liu Yanhui
Department of Neurosurgery, West China Hospital, Sichuan University, 37 Guoxue Avenue, Chengdu, 610041, People's Republic of China.
Department of Pediatric Neurosurgery, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
Sci Rep. 2025 Jan 2;15(1):265. doi: 10.1038/s41598-024-84238-x.
Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glioma diagnosis. We analyzed 472 whole slide images (WSIs) from 226 patients in West China Hospital (WCH) and 1604 WSIs from 880 patients in The Cancer Genome Atlas (TCGA). We utilized the OpenSlide library to load WSIs, segmented them into small patches using the DeepZoom module, and then normalized the color using the Reinhard method. A weakly supervised deep learning model was developed using ResNet-50 combined with an attention mechanism. We investigated the performance of the model by calculating area under the curve (AUC) in a ten-fold cross-validation setting. Heatmap visualizations showed the prediction mechanism of the model. The results were promising, with high AUC values for differentiating grades of astrocytomas, oligodendrogliomas, all gliomas, and glioma types in the TCGA dataset (0.9419, 0.8659, 0.9904, and 0.9298, respectively), and in the WCH cohort (0.9048, 0.7423, 0.9510, and 0.7098, respectively). The model demonstrated a strong ability to infer IDH status in the TCGA dataset (AUC = 0.9488). The weakly supervised deep learning model proved to be an effective and reliable tool for neuropathological diagnosis, making it an attractive auxiliary tool.
已经创建了多个人工智能系统，以利用苏木精-伊红染色切片促进肿瘤的准确和快速组织病理学诊断。我们旨在研究弱监督深度学习是否有助于胶质瘤诊断。我们分析了来自华西医院（WCH）226例患者的472张全切片图像（WSIs）和来自癌症基因组图谱（TCGA）880例患者的1604张WSIs。我们使用OpenSlide库加载WSIs，使用DeepZoom模块将它们分割成小斑块，然后使用Reinhard方法对颜色进行归一化。使用ResNet-50结合注意力机制开发了一个弱监督深度学习模型。我们通过在十折交叉验证设置中计算曲线下面积（AUC）来研究该模型的性能。热图可视化展示了该模型的预测机制。结果很有前景，在TCGA数据集中区分星形细胞瘤、少突胶质细胞瘤、所有胶质瘤的等级以及胶质瘤类型的AUC值较高（分别为0.9419、0.8659、0.9904和0.9298），在WCH队列中（分别为0.9048、0.7423、0.9510和0.7098）。该模型在TCGA数据集中显示出很强的推断IDH状态的能力（AUC = 0.9488）。弱监督深度学习模型被证明是神经病理学诊断的有效且可靠的工具，使其成为一个有吸引力的辅助工具。
