Deep learning‐based prediction of H3K27M alteration in diffuse midline gliomas based on whole‐brain MRI

Autor: Bowen Huang, Yuekang Zhang, Qing Mao, Yan Ju, Yanhui Liu, Zhengzheng Su, Yinjie Lei, Yanming Ren
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Cancer Medicine, Vol 12, Iss 16, Pp 17139-17148 (2023)
Druh dokumentu: article
ISSN: 2045-7634
DOI: 10.1002/cam4.6363
Popis: Abstract Background H3K27M mutation status significantly affects the prognosis of patients with diffuse midline gliomas (DMGs), but this tumor presents a high risk of pathological acquisition. We aimed to construct a fully automated model for predicting the H3K27M alteration status of DMGs based on deep learning using whole‐brain MRI. Methods DMG patients from West China Hospital of Sichuan University (WCHSU; n = 200) and Chengdu Shangjin Nanfu Hospital (CSNH; n = 35) who met the inclusion and exclusion criteria from February 2016 to April 2022 were enrolled as the training and external test sets, respectively. To adapt the model to the human head MRI scene, we use normal human head MR images to pretrain the model. The classification and tumor segmentation tasks are naturally related, so we conducted cotraining for the two tasks to enable information interaction between them and improve the accuracy of the classification task. Results The average classification accuracies of our model on the training and external test sets was 90.5% and 85.1%, respectively. Ablation experiments showed that pretraining and cotraining could improve the prediction accuracy and generalization performance of the model. In the training and external test sets, the average areas under the receiver operating characteristic curve (AUROCs) were 94.18% and 87.64%, and the average areas under the precision‐recall curve (AUPRC) were 93.26% and 85.4%. Conclusions The developed model achieved excellent performance in predicting the H3K27M alteration status in DMGs, and its good reproducibility and generalization were verified in the external dataset.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje