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Deep learning-based multi-task neoadjuvant chemotherapy treatment response prediction with whole slide images in breast cancer: a multicenter study

Deep learning-based multi-task neoadjuvant chemotherapy treatment response prediction with whole slide images in breast cancer: a multicenter study

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400080821
Enrollment
Unknown
Registered
2024-02-08
Start date
2024-02-10
Completion date
Unknown
Last updated
2024-02-11

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

Conditions

Breast cancer

Interventions

Sponsors

Yantai Yuhuangding Hospital
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
22 Years to 78 Years

Inclusion criteria

Inclusion criteria: 1. the patients were biopsy-confirmed breast cancer and defined as locally advanced breast cancer; 2. NAC followed by surgery; 3. treatment response confirmed by experienced pathologists after surgery; 4. WSIs of biopsy were available; 5. and biopsy was conducted within 2 weeks before NAC.

Exclusion criteria

Exclusion criteria: 1. history of chemotherapy, surgery or malignancy prior to NAC; 2. the patients received non-standard treatment or did not complete the NAC regimen; 3. no surgery was performed or the results of postoperative pathology were incomplete; 4. lack of clinicopathological information and WSIs for analysis requirements; 5. insufficient quality of WSIs.

Design outcomes

Primary

MeasureTime frame
Area under receiver operating characteristic curve;

Secondary

MeasureTime frame
Sensitivity;Specificity;Accuracy;

Countries

China

Contacts

Public ContactNing Mao

Yantai Yuhuangding Hospital

maoning@pku.edu.cn+86 131 0535 1972

Outcome results

None listed

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