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The diagnostic value of multimodal MRI and dual-energy CT based on machine learning in breast cancer and the prediction of axillary lymph node status

The diagnostic value of multimodal MRI and dual-energy CT based on machine learning in breast cancer and the prediction of axillary lymph node status

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200065443
Enrollment
Unknown
Registered
2022-11-04
Start date
2022-11-15
Completion date
Unknown
Last updated
2023-05-15

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

Conditions

Breast Cancer

Interventions

Gold Standard:Histopathology
Index test:Clinical model, MRI imageomics model, dual energy CT imageomics model and combined model

Sponsors

Nanhua University First Affiliated Hospital
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
20 Years to 90 Years

Inclusion criteria

Inclusion criteria: 1. Patients with mass detected by various adjunctive examinations but without any intervention; 2. Patients who can perform breast MRI and CT and are willing to undergo surgical resection or biopsy; 3. Participants voluntarily participated in the study and signed the informed consent form.

Exclusion criteria

Exclusion criteria: 1. Breast cancer patients treated (including chemoradiotherapy and surgery); 2. MRI examination of contraindications; 3. There were no patients with pathological data or incomplete data.

Design outcomes

Primary

MeasureTime frame
Breast 3.0 T MRI semiquantitative parameters;Chest Dual-energy CT quantitative parameters;Histopathology;—

Secondary

MeasureTime frame
Breast 3.0 T MRI morphological parameters;Chest Dual-energy CT morphological parameters;—

Countries

China

Contacts

Public ContactGuanghua Luo

Nanhua University First Affiliated Hospital

1579839814@qq.com+86 18973407238

Outcome results

None listed

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