A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images

Autor: Lei Jin, Tianyang Sun, Xi Liu, Zehong Cao, Yan Liu, Hong Chen, Yixin Ma, Jun Zhang, Yaping Zou, Yingchao Liu, Feng Shi, Dinggang Shen, Jinsong Wu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: iScience, Vol 26, Iss 11, Pp 108041- (2023)
Druh dokumentu: article
ISSN: 2589-0042
DOI: 10.1016/j.isci.2023.108041
Popis: Summary: Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. The application of deep learning techniques holds promise for automated histological pathology diagnosis. In this study, we collected 733 whole slide images from four medical centers, of which 456 were used for model training, 150 for internal validation, and 127 for multi-center testing. The study includes 5 types of common gliomas.A subtask-guided multi-instance learning image-to-label training pipeline was employed. The pipeline leveraged “patch prompting” for the model to converge with reasonable computational cost. Experiments showed that an overall accuracy of 0.79 in the internal validation dataset. The performance on the multi-center testing dataset showed an overall accuracy to 0.73. The findings suggest a minor yet acceptable performance decrease in multi-center data, demonstrating the model’s strong generalizability and establishing a robust foundation for future clinical applications.
Databáze: Directory of Open Access Journals