A fully automated deep-learning model for predicting the molecular subtypes of posterior fossa ependymomas using T2-weighted images.

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 I Sair Harrison X Bai Frederik Barkhof Xing Liu Yaou Liu

Published in: Clinical cancer research : an official journal of the American Association for Cancer Research (2023)

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.

Keyphrases

deep learning
convolutional neural network
artificial intelligence
decision making
machine learning
magnetic resonance
high throughput
single molecule
magnetic resonance imaging
computed tomography