Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.
Hai-Qing LiuSi-Ying LinYi-Dong SongSi-Yao MaiYue-Dong YangKai ChenZhuo WuHui-Ying ZhaoPublished in: European radiology (2022)
• Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..
Keyphrases
- deep learning
- machine learning
- contrast enhanced
- convolutional neural network
- artificial intelligence
- magnetic resonance imaging
- rectal cancer
- diffusion weighted imaging
- lymph node
- locally advanced
- computed tomography
- big data
- stem cells
- squamous cell carcinoma
- single molecule
- risk assessment
- optical coherence tomography
- human health