Development of a Diagnostic Prediction Model for de Novo Metastatic Breast Cancer Using Routinely Available Baseline Parameters
The goal of this observational study is to create a tool to estimate the risk of macroscopic distant tumor spread in women with newly diagnosed breast cancer. The main question it aims to answer is:
• Can a model, which combines routine medical information collected at diagnosis, accurately predict if a patient has metastasis large enough to be found during systemic imaging? Researchers will review the past medical records of participants who were treated at a university hospital. Because this study looks at past data, participants will not be asked to do any new tasks, take new tests, or change their medical care.
Using Imaging Data and Genomic Data to Predict Metastasis of Breast Cancer After Treatment
Artificial Intelligence-Based Early Warning for Distant Metastasis in Malignant Tumors
Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
Detection of Germline and Somatic Pathogenic Variants in Patients With de Novo Metastatic Breast Cancer