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Dual-Energy CT Radiomics with Machine Learning for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer

Dual-Energy CT Radiomics with Machine Learning for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500098626
Enrollment
Unknown
Registered
2025-03-11
Start date
2025-03-11
Completion date
Unknown
Last updated
2025-03-17

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:Pathological results
Index test:Lymphovascular Invasion

Sponsors

Lishui Central Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
35 Years to 90 Years

Inclusion criteria

Inclusion criteria: (i) Patients with primary invasive breast cancer confirmed by postoperative pathology; (ii) Patients who underwent dual-energy CT two weeks before surgery; (iii) Patients whose postoperative pathological results were used to evaluate the lymphovascular invasion status.

Exclusion criteria

Exclusion criteria: (i) Patients with a history of other tumors; (ii) Patients who received other anti-tumor treatments before surgery, such as neoadjuvant chemotherapy; (iii) patients with multiple or non-massive lesions; (iv) Patients with poor CT imaging quality that cannot be measured (e.g., severe artifacts); and (v) Patients with incomplete clinical pathological data.

Design outcomes

Primary

MeasureTime frame
DECT image quantitative parameters;Accuracy;Sensitivity;Specificity;

Secondary

MeasureTime frame
Pathological evaluation index;

Countries

China

Contacts

Public ContactWeiyue Chen

Lishui Central Hospital

lsszxyycwy@163.com+86 158 5779 9259

Outcome results

None listed

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