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Prediction of Non-sentinel Lymph Node Metastatic Status of Breast Cancer Based on Pathology-MRI Images

NCT06510738 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 700

Last updated 2024-08-13

No results posted yet for this study

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.

Conditions

  • Breast Cancer
  • Artificial Intelligence
  • Lymph Node Metastasis
  • Digital Pathology

Sponsors & Collaborators

  • Affiliated Cancer Hospital of Shantou University Medical College

    collaborator OTHER

  • Yunnan Cancer Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
75 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-08-31
Primary Completion
2025-06-30
Completion
2025-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06510738 on ClinicalTrials.gov