journal contribution
posted on 2024-01-05, 08:20 authored by Dan Cheng, Zhizheng Zhuo, Jiang Du, Jinyuan Weng, Chengzhou Zhang, Yunyun Duan, Ting Sun, Minghao Wu, Min Guo, Tiantian Hua, Ying Jin, Boyang Peng, Zhaohui Li, Mingwang Zhu, Maliha Imami, Chetan Bettegowda, Haris Sair, Harrison X. Bai, Frederik Barkhof, Xing Liu, Yaou Liu <p>Details of the MR acquisition protocols</p>
Funding
National Science Foundation of China
Beijing Municipal Natural Science Foundation for Distinguished Young Scholars
Beijing Youth Scholar, and the Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospital Authority
History
ARTICLE ABSTRACT
We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images.
We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed.
For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95).
A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.
