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Establishment of a Multi-omics Prediction Model for Early Triple-negative Breast Cancer Based on UPGRADE-TNBC Study

Establishment of a Multi-omics Prediction Model for Early Triple-negative Breast Cancer Based on UPGRADE-TNBC Study

Recruiting
18-75 years
Female
Phase N/A
  • Technical (Original)
  • Simplified (AI rewritten)

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Overview

Based on the UPGRADE-TNBC study, a high-quality TNBC sample repository was established. By integrating multi-source data-including clinical information, radiomics, pathological images, and molecular sequencing-and innovatively incorporating a meta-learning strategy, a treatment response prediction model based on multimodal small-sample learning was developed. This approach aims to optimize drug combinations and precisely identify patient subgroups likely to benefit from treatment, thereby providing a new paradigm for personalized therapy in early-stage TNBC.

Eligibility

Inclusion Criteria:

  • The UPGRADE-TNBC Study Population

Exclusion Criteria:

  • Populations outside the UPGRADE-TNBC study

Study details
    Triple -Negative Breast Cancertriple
    Meta-Learning
    Predictive Models
    Multimodal

NCT07773818

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

22 August 2026

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