深度学习和病理组学分析可预测高级别胶质瘤的预后。

Deep learning and pathomics analyses predict prognosis of high-grade gliomas.

作者信息

Zhu Yuchen, Gong Yuxi, Xu Weilin, Sun Xingjian, Jiang Gefei, Qiu Lei, Shi Kexin, Wu Mengxing, Fei Yinjiao, Yuan Jinling, Luo Jinyan, Li Yurong, Cao Yuandong, Pan Minhong, Zhou Shu

机构信息

Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China.

出版信息

Front Neurol. 2025 Aug 11;16:1614678. doi: 10.3389/fneur.2025.1614678. eCollection 2025.

OBJECTIVE

Utilizing pathomics to analyze high-grade gliomas and provide prognostic insights.

METHODS

Regions of Interest (ROIs) in tumor areas were identified in whole-slide images (WSI). Tumor patches underwent cropping, white space removal, and normalization. A deep learning model trained on these patches aggregated predictions for WSIs. Pathological features were extracted using Pearson correlation, univariate Cox regression, and LASSO-Cox regression. Three models were developed: a Pathomics-based model, a clinical model, and a combined model integrating both.

RESULTS

Pathological and Clinical Features were used to build two models, leading to a predictive model with a C-index of 0.847 (train) and 0.739 (test). High-risk patients had a median progression-free survival (PFS) of 10 months (p<0.001), while low-risk patients had not reached median PFS. Stratification by IDH status revealed significant PFS differences.

CONCLUSION

The combined model effectively predicts high-grade glioma prognosis.

目的

利用病理组学分析高级别胶质瘤并提供预后见解。

方法

在全切片图像（WSI）中识别肿瘤区域的感兴趣区域（ROI）。对肿瘤切片进行裁剪、去除空白区域并进行归一化处理。在这些切片上训练的深度学习模型汇总了对WSI的预测。使用Pearson相关性、单变量Cox回归和LASSO-Cox回归提取病理特征。开发了三个模型：基于病理组学的模型、临床模型以及整合两者的联合模型。