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Research on a Multi-Modal Deep Learning Model for Breast Cancer Invasiveness and Prognosis Prediction Based on Imaging and Pathology

Research on a Multi-Modal Deep Learning Model for Breast Cancer Invasiveness and Prognosis Prediction Based on Imaging and Pathology

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400092835
Enrollment
Unknown
Registered
2024-11-25
Start date
2024-12-01
Completion date
Unknown
Last updated
2024-12-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast Cancer

Interventions

Progression Group:None
Non-Progression Group:None

Sponsors

The Fifth Affiliated Hospital of Sun Yat-sen University
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Retrospective Study: 1) Age over 18; 2) Complete mammography and MRI images within 2 weeks prior to treatment; 3) Biopsy or postoperative pathology confirmation of breast cancer; 4) 5-year follow-up data with clear evaluation of disease progression. 2.Prospective Study: 1) Voluntary signing of informed consent; 2) Age over 18; 3) Complete mammography and MRI images within 2 weeks prior to treatment; 4) Biopsy or postoperative pathology confirmation of breast cancer.

Exclusion criteria

Exclusion criteria: 1.Incomplete clinical or pathological data, or MRI image quality does not meet the requirements for image evaluation. 2.Patients with major diseases such as heart failure or other malignancies.

Design outcomes

Primary

MeasureTime frame
Progression-free survival within 5 years;—

Secondary

MeasureTime frame
Overall survival;—

Countries

China

Contacts

Public ContactYaqin Zhang

The Fifth Affiliated Hospital of Sun Yat-sen University

zhyaqin@mail.sysu.edu.cn+86 756 2528321

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026