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Early Evaluation of Neoadjuvant Chemotherapy for breast cancer Based on Deep Learning Multimodal Multisectional Ultrasound and Pathological Image Analysis

Early Evaluation of Neoadjuvant Chemotherapy for breast cancer Based on Deep Learning Multimodal Multisectional Ultrasound and Pathological Image Analysis

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400081777
Enrollment
Unknown
Registered
2024-03-11
Start date
2024-03-11
Completion date
Unknown
Last updated
2024-03-18

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

Conditions

breast cancer

Interventions

Index test:Computer-aided diagnosis, CAD

Sponsors

Renji Hospital, Shanghai Jiao Tong University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: ? Age = 18 years old; ? Complete full cycle chemotherapy; ? Before NAC, hollow needle biopsy was performed and confirmed as breast cancer; ? Surgery was performed after NAC, and the postoperative pathological information was complete; ? No history of breast cancer and radiotherapy of the same side chest wall; ? Has not received radiotherapy, chemotherapy, or endocrine therapy before NAC; ? The patient underwent grayscale ultrasound, CDFI, and elastography in both NAC0 and NAC1 stages, and the images were well preserved and of good quality There was no distant metastasis or primary tumor in other areas during the chemotherapy process.

Exclusion criteria

Exclusion criteria: ? Have a history of malignant tumors or chemotherapy; ? Incomplete or missing preoperative or postoperative pathological data; ? Incomplete or poor quality multimodal ultrasound images; ? With distant metastasis and bilateral breast cancer; ? Multiple primary tumors.

Design outcomes

Primary

MeasureTime frame
Ultrasonic image features;AUC of ROC;Accuracy ;Sensitivity;Specificity;

Secondary

MeasureTime frame
Positive predicative value;Negative predictive value;

Countries

China

Contacts

Public ContactCaifeng Wan

Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine

wancaifeng@renji.com+86 159 2186 3320

Outcome results

None listed

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