Establishment of a Multi-omics Prediction Model for Early Triple-negative Breast Cancer Based on UPGRADE-TNBC Study
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…
Conditions studied
Triple -Negative Breast Cancertriple, Meta-Learning, Predictive Models, Multimodal
About this study
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.
Interventions
- Other: Collect data — Collect multi-source data from patients, including clinical information, radiomics, pathological images, and molecular sequencing
Primary outcomes
- Model building (2 years)
Eligibility information
Study locations
- Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, Beijing Municipality 100021 China
Source: ClinicalTrials.gov. Record last refreshed by Varda Clinical: 2026-10-15.