Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.
Yiwei TongZhaoyu HuHaoyu WangJiahui HuangYing ZhanWeimin ChaiYinhui DengYing YuanKunwei ShenYuanyuan WangXiaosong ChenJinhua YuPublished in: European radiology (2024)
• DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.
Keyphrases
- free survival
- positive breast cancer
- deep learning
- randomized controlled trial
- systematic review
- convolutional neural network
- healthcare
- artificial intelligence
- machine learning
- open label
- stem cells
- magnetic resonance
- squamous cell carcinoma
- contrast enhanced
- magnetic resonance imaging
- optical coherence tomography
- replacement therapy
- study protocol
- mesenchymal stem cells