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Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data

NCT07671690 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 1800

Last updated 2026-06-26

No results posted yet for this study

Summary

This study aims to develop a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information based on multicenter retrospective data. To externally validate the model in an independent prospective cohort, and evaluate its accuracy in predicting pathological complete response (pCR), 3-year and 5-year disease-free survival (DFS). To establish visual tools such as nomograms, assisting clinicians in identifying patients with chemoresistance and facilitating individualized de-escalation or escalation treatment strategies.

Conditions

  • Breast Carcinoma

Interventions

DIAGNOSTIC_TEST

To explore the value of a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information in predicting pCR and long-term prognosis.

MRI and ultrasound were performed in addition to conventional treatment regimens

Sponsors & Collaborators

  • Yunnan Cancer Hospital

    lead OTHER

Principal Investigators

  • Lianhua Ye · Ethics Committee of Yunnan Provincial Cancer Hospital

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-01
Primary Completion
2026-12-31
Completion
2029-06-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07671690 on ClinicalTrials.gov