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

Condition(s) studied

Investigational drug(s) / intervention(s)

Collect data: Collect multi-source data from patients, including clinical information, radiomics, pathological images, and molecular sequencing

Study summary

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

Primary outcome measure(s)

• Model building — 2 years
  A predictive model for neoadjuvant therapy in TNBC was successfully established

Trial sites (1)

More Cancer Institute and Hospital, Chinese Academy of Medical Sciences trials in China