Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.
Jionghui GuTong TongDong XuFang ChengChengyu FangChang HeJing WangBaohua WangXin YangKun WangZhenyu ZhangTian An JiangPublished in: Cancer (2022)
In this study, we proposed two deep learning radiomics nomogram models based on pre-neoadjuvant chemotherapy (NAC) and preoperative ultrasonography images for independently predicting the status of tumor and axillary lymph node (ALN) after NAC. A more comprehensive assessment of the patient's condition after NAC can be achieved by predicting the status of the tumor and ALN separately. Our model can potentially provide a noninvasive and personalized method to offer decision support for organ preservation and avoidance of excessive surgery.
Keyphrases
- neoadjuvant chemotherapy
- lymph node
- deep learning
- sentinel lymph node
- locally advanced
- transcription factor
- contrast enhanced
- lymph node metastasis
- magnetic resonance imaging
- convolutional neural network
- artificial intelligence
- minimally invasive
- machine learning
- squamous cell carcinoma
- patients undergoing
- early stage
- rectal cancer
- physical activity
- radiation therapy
- body mass index
- coronary artery bypass
- acute coronary syndrome
- coronary artery disease
- percutaneous coronary intervention