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A machine learning model based on multimodal imaging to predict lymphovascular invasion in invasive breast cancer

A machine learning model based on multimodal imaging to predict lymphovascular invasion in invasive breast cancer

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400094756
Enrollment
Unknown
Registered
2024-12-26
Start date
2025-01-01
Completion date
Unknown
Last updated
2025-01-06

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

Conditions

Breast cancer

Interventions

Training set:None

Sponsors

Xuzhou Central Hospital
Lead Sponsor

Eligibility

Sex/Gender
Female

Inclusion criteria

Inclusion criteria: 1. All patients underwent preoperative mammography and ultrasound examination, and complete pathological examination results and clinical data were available; 2. The quality of X-ray and ultrasound images of patients meet the requirements of post-processing; 3. No medical treatment was given prior to the examination;

Exclusion criteria

Exclusion criteria: 1. Did not undergo surgery at our hospital or did not have postoperative pathological evaluation results; 2. Where only one position of the X-ray or ultrasound image contains the lesion or is so large that the image acquisition is incomplete or affects the delineation of the area of interest; 3. The patient has a history of previous treatment, such as puncture, adjuvant chemotherapy, radiotherapy or surgery; 4. With other malignancies;

Design outcomes

Primary

MeasureTime frame
Lymphovascular invasion;

Countries

China

Contacts

Public ContactShao Guoqing

Xuzhou Central Hospital

362116537@qq.com+86 516 83956415

Outcome results

None listed

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