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Ultrasound Radiomics for Predicting Breast Cancer and Axillary Lymph Node Metastasis

Not yet recruiting
Conditions
Axillary Lymph Node Metastasis
Breast Cancer
Interventions
Other: Ultrasonic image analysis
Registration Number
NCT05768451
Lead Sponsor
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
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.

Detailed Description

Not available

Recruitment & Eligibility

Status
NOT_YET_RECRUITING
Sex
Female
Target Recruitment
200
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.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
benignUltrasonic image analysis-
malignantUltrasonic image analysis-
Primary Outcome Measures
NameTimeMethod
Accuracy of differential diagnosis between benign and malignant2022.12.15-2023.12.30

ROC curve, sensitivity, specificity, accuracy, decision curve

Secondary Outcome Measures
NameTimeMethod
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