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.
The UPGRADE-TNBC Study Population
Populations outside the UPGRADE-TNBC study
The participants in the UPGRADE-TNBC study were all patients with early-stage triple-negative breast cancer who received neoadjuvant therapy.
Other
Prospective
Collect data
Other interventionCollect multi-source data from patients, including clinical information, radiomics, pathological images, and molecular sequencing
Model building
A predictive model for neoadjuvant therapy in TNBC was successfully established
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