Axillary Lymph Node Metastasis, Breast Cancer
Conditions
Keywords
breast cancer, multi-modal ultrasound, radiomics
Brief summary
The project is proposed based on multimodal ultrasonic imaging omics building used for accurate prediction of the breast cancer and axillary lymph node metastasis load artificial intelligence forecasting model, this method can dig the hidden features of ultrasonic image is not visible to the naked eye, make up the subjectivity in the process of clinical doctors in diagnosis and treatment, provide accurate, objective basis for clinical decision making.
Interventions
OTHERUltrasonic image analysis
Preoperative conventional ultrasound (US), elastic ultrasound (UE) and contrast-enhanced ultrasound (CEUS) images were analyzed. Histopathological results were used as the gold standard. The cases were randomly divided into training set and test set with a ratio of 7:3. The US image, UE image and CEUS image of the maximum long-axis section of each lesion were selected, and the region of interest (ROI) of the lesion was manually delineated.
Sponsors
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 90 Years
Healthy volunteers
No
Inclusion criteria
* patients who have not received breast puncture biopsy or neoadjuvant chemotherapy, etc. prior to ultrasonography; * patients who are not allergic to the ultrasound contrast agent (SonoVue) and have signed an informed consent form for ultrasonography * patients with breast tumors who have undergone surgical treatment at our hospital and have complete case histories.
Exclusion criteria
* patients with incomplete case data; * cases with large breast masses that do not completely show the region of interest (ROI); * patients who have undergone breast aspiration biopsy or other treatments such as neoadjuvant chemotherapy or endocrine therapy before ultrasound examination.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of differential diagnosis between benign and malignant | 2022.12.15-2023.12.30 | ROC curve, sensitivity, specificity, accuracy, decision curve |
Contacts
Primary Contactbaojie wen, doctor
Outcome results
None listed