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Machine Learning Models for Predicting Long-Term Clinical Outcomes in Chinese Female Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

A deep learning model based on MRI and pathological images predicts the prognosis of patients with breast cancer receiving neoadjuvant therapy

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500098023
Enrollment
Unknown
Registered
2025-02-28
Start date
2024-12-10
Completion date
Unknown
Last updated
2025-03-03

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

Conditions

Breast Cacner

Interventions

Observation group:N/A

Sponsors

Harbin Medical University Cancer Hospital
Lead Sponsor

Eligibility

Sex/Gender
Female

Inclusion criteria

Inclusion criteria: Collecting the medical history, imaging, and pathological image data of patients with invasive breast cancer who received NAC treatment at our hospital from February 2015 to December 2025 requires approval from the Ethics Committee of our hospital. (1) Primary breast cancer confirmed by needle biopsy; (2) Those who have not received NAC treatment before; (3) Underwent surgery after completing a full cycle of NAC at our hospital; (4) Underwent MRI examination before starting NAC treatment and before surgery at our hospital.

Exclusion criteria

Exclusion criteria: None

Design outcomes

Primary

MeasureTime frame
5-year survival rate;—

Countries

China

Contacts

Public ContactMing Niu

Harbin Medical University Cancer Hospital

niuming2024@126.com+86 138 9572 1945

Outcome results

None listed

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