Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study.
Jionghui GuTong TongChang HeMin XuXin YangJie TianTianan JiangKun WangPublished in: European radiology (2021)
• We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. • Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. • The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.
Keyphrases
- neoadjuvant chemotherapy
- deep learning
- early stage
- end stage renal disease
- transcription factor
- sentinel lymph node
- newly diagnosed
- locally advanced
- ejection fraction
- lymph node
- chronic kidney disease
- peritoneal dialysis
- primary care
- prognostic factors
- magnetic resonance imaging
- machine learning
- contrast enhanced
- computed tomography
- radiation therapy
- optical coherence tomography
- genome wide analysis