Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas.
Dongling PeiFangzhan GuanXuanke HongZhen LiuWeiwei WangYuning QiuWenchao DuanMinkai WangChen SunWenqing WangXiangxiang WangYu GuoZilong WangZhongyi LiuAoqi XingZhixuan GuoLin LuoXianzhi LiuJingliang ChengBin ZhangZhenyu ZhangJing YanPublished in: European radiology (2023)
• The multimodal radiomic model outperformed conventional MRI when predicting both the IDH wild type and IDH mutant and 1p/19q-codeleted subtypes of gliomas. • The multimodal radiomic model showed comparable performance to the combined model in the prediction of the three molecular subtypes. • Radiomic features from T1-weighted gadolinium contrast-enhanced and relative cerebral blood volume images played an important role in the prediction of molecular subtypes.
Keyphrases
- contrast enhanced
- wild type
- diffusion weighted
- magnetic resonance imaging
- magnetic resonance
- computed tomography
- diffusion weighted imaging
- low grade
- high grade
- high resolution
- single molecule
- pain management
- deep learning
- optical coherence tomography
- dual energy
- machine learning
- mass spectrometry
- convolutional neural network