Artificial Intelligence, Breast Cancer, Digital Pathology, Lymph Node Metastasis
Conditions
Brief summary
The goal of this observational study is to develop an artificial intelligence model based on pathology and magnetic resonance imaging (MRI) images to predict the metastatic status of non-sentinel lymph nodes in patients with breast cancer sentinel lymph node metastasis. The main questions it aims to answer are: Can an artificial intelligence model based on MRI images of breast cancer patients predict the non-sentinel lymph node metastatic status in patients with breast cancer sentinel lymph node metastasis? Can an artificial intelligence model based on intraoperative frozen section images of sentinel lymph nodes in breast cancer patients predict the non-sentinel lymph node metastasis status in patients with sentinel lymph node metastasis from breast cancer? Can artificial intelligence models based on preoperative MRI and intraoperative frozen section images of sentinel lymph nodes in breast cancer patients predict the non-sentinel lymph node metastatic status in patients with sentinel lymph node metastasis from breast cancer? Researchers will retrospectively and prospectively collect preoperative MRI and intraoperative sentinel lymph node section images from breast cancer patients.
Interventions
None listed
Sponsors
Affiliated Cancer Hospital of Shantou University Medical College
Yunnan Cancer Hospital
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
1. Patients with primary breast cancer 2. Positive sentinel lymph node biopsy and axillary lymph node dissection. 3. No neoadjuvant chemotherapy, radiotherapy or ablation prior to surgery. 4. MRI within 3 weeks prior to surgery.
Exclusion criteria
1. recurrent breast cancer or history of surgery or radiation therapy in the axilla 2. Inflammatory breast cancer 3. Bilateral breast cancer or metastatic breast cancer 4. previous or current history of other malignant tumors 5. Sentinel lymph node count \> 5
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Non-sentinel lymph node metastatic status | 2024-2025 | metastasis or non-metastasis |
| Accuracy, Sensitivity, Specificity，Area under the curve | 2024-2025 | The performance of predicting non-sentinel lymph node metastatic status |
| Positive predictive value，Negative predictive value | 2024-2025 | The performance of predicting non-sentinel lymph node metastatic status |
Outcome results
None listed