Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram.
Jingyu ZhongChengxiu ZhangYangfan HuJing ZhangYun LiuLiping SiYue XingDefang DingJia GengQiong JiaoHuizhen ZhangGuang YangWeiwu YaoPublished in: European radiology (2022)
• The nnU-Net trained by manual labels enables the use of an automatic segmentation tool for ROI delineation of osteosarcoma. • A pipeline using automatic lesion segmentation and followed by a radiomics classifier could aid the evaluation of NAC response of osteosarcoma. • A predictive nomogram composed of clinical variables and MRI-based radiomics score provides support for individualised treatment planning.
Keyphrases
- deep learning
- contrast enhanced
- lymph node metastasis
- neoadjuvant chemotherapy
- convolutional neural network
- magnetic resonance imaging
- artificial intelligence
- machine learning
- locally advanced
- magnetic resonance
- diffusion weighted imaging
- computed tomography
- lymph node
- sentinel lymph node
- transcription factor
- early stage
- radiation therapy
- genome wide analysis