Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning

• Published in: Frontiers in neurology Vol. 14; p. 1100933
• Main Authors: Luo, Chenhua, Yang, Jiyan, Liu, Zhengzheng, Jing, Di
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 31-03-2023
• Subjects: deep learning; glioma; Neurology; overall survival; pathomics; recurrence; recurrence; overall survival; deep learning; glioma; pathomics
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2023.1100933

A deep learning (DL) model based on representative biopsy tissues can predict the recurrence and overall survival of patients with glioma, leading to optimized personalized medicine. This research aimed to develop a DL model based on hematoxylin-eosin (HE) stained pathological images and verify its diagnostic accuracy.
Our study retrospectively collected 162 patients with glioma and randomly divided them into a training set (
= 113) and a validation set (
= 49) to build a DL model. The HE-stained slide was segmented into a size of 180 × 180 pixels without overlapping. The patch-level features were extracted by the pre-trained ResNet50 to predict the recurrence and overall survival. Additionally, a
was introduced where low-size digital biopsy images with clinical information were inputted into the DL model to ensure minimum memory occupation.
Our study extracted 512 histopathological features from the HE-stained slides of each glioma patient. We identified 36 and 18 features as significantly related to disease-free survival (DFS) and overall survival (OS), respectively, (
< 0.05) using the univariate Cox proportional-hazards model. Pathomics signature showed a C-index of 0.630 and 0.652 for DFS and OS prediction, respectively. The time-dependent receiver operating characteristic (ROC) curves, along with nomograms, were used to assess the diagnostic accuracy at a fixed time point. In the validation set (
= 49), the area under the curve (AUC) in the 1- and 2-year DFS was 0.955 and 0.904, respectively, and the 2-, 3-, and 5-year OS were 0.969, 0.955, and 0.960, respectively. We stratified the patients into low- and high-risk groups using the median hazard score (0.083 for DFS and-0.177 for OS) and showed significant differences between these groups (
< 0.001).
Our results demonstrated that the DL model based on the HE-stained slides showed the predictability of recurrence and survival in patients with glioma. The results can be used to assist oncologists in selecting the optimal treatment strategy in clinical practice.