To study the application value of machine learning mammography and serum Raman spectroscopy in the prediction of triple negative breast cancer and histological grade

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2400092187

Enrollment

Unknown

Registered

2024-11-12

Start date

2024-11-15

Completion date

Unknown

Last updated

2024-11-18

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

Conditions

breast cancer

Interventions

Triple Negative Breast Cancer:None

Non-triple-negative breast cancer:None

Eligibility

Sex/Gender

All

Age

20 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) age =18 years old; (2) Breast cancer confirmed by pathology and immunohistochemical analysis; (3) have complete clinical and molybdenum target imaging data, and the image quality, imaging conditions and imaging location meet the diagnostic criteria; (4) Routine mammography images including bilateral complete cephalo-caudal (CC) and medio-lateral oblique (MLO) positions; (5) Patients did not receive chemotherapy, radiotherapy or surgery before admission.

Exclusion criteria

Exclusion criteria: (1) The image quality does not meet the requirements; (2) patients with biopsy before mammography and after surgery; (3) patients after breast prosthesis implantation, breast injection and neoadjuvant chemotherapy; (4) patients with other immune diseases and malignant tumors.

Design outcomes

Primary

Measure	Time frame
Raman spectrum;Mammographic images;AUC;	—

Countries

China

Contacts

Public ContactXiaohong Lyu

The First Affiliated Hospital of Jinzhou Medical University

rainbow_dl@163.com+86 150 4268 6069

Outcome results

None listed

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