Breast Cancer Detection

This study aims to evaluate the clinical utility of \[68Ga\]Ga-FAP-2268 PET/CT for disease staging and assessment in patients with high-risk primary breast cancer. By targeting fibroblast activation protein (FAP), this novel imaging approach may offer improved tumor visualization compared to conventional imaging, which may help improve treatment planning.

Cancer continues to be one of the most significant public health issues worldwide. Thanks to early detection, the mortality rate of certain types of cancer has decreased significantly. However, the lack of accessible, low-cost, non-invasive, non-toxic, and easy-to-implement diagnostic methods results in late diagnoses, leading to 1 in every 6 people dying from cancer today. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and are found both inside cells and in bodily fluids such as blood and urine. Several studies have demonstrated their role in human diseases, especially cancer, where tumors release miRNAs into the bloodstream that can be used as biomarkers for early detection. The research and development team at Oncoliq SAS identified a panel of miRNAs associated with breast cancer, with a detection performance of 82% sensitivity and 83% specificity, validated in exploratory and preliminary confirmatory studies. These results led to the filing of a patent based on liquid biopsy technology. The objective of this protocol is to establish a panel of miRNAs for the early detection of breast cancer using samples from breast cancer patients and a cancer-free control group. Validation of the miRNAs will be carried out through RT-qPCR. This stage constitutes the analytical validation of the biomarkers, with the goal of developing an algorithm capable of classifying individuals with and without breast cancer. In future stages, a pilot clinical trial is planned to evaluate test implementation and clinical validation.

This study aims to develop AI models to better read diagnostic mammograms for various populations and types of breast cancer, using the images that participants donate and their responses from study questionnaire to improve patient outcomes. This study also aims to provide mammography images to participants.

Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with dense breast tissue. Contrast-enhanced MRI offers higher diagnostic performance but its use is limited by high costs, safety concerns with gadolinium-based contrast agents, and limited accessibility. This investigator-initiated trial aims to evaluate the clinical application of non-contrast multiparametric MRI, combined with advanced artificial intelligence algorithms, for the early detection and diagnosis of breast cancer. The study will collect MRI imaging data from multiple centers and integrate radiomic features across T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. A deep learning-based model will be developed and validated to improve lesion detection, differential diagnosis, and risk stratification. The ultimate goal of this project is to establish a safe, accurate, and scalable breast cancer screening pathway suitable for Chinese women. By reducing dependence on invasive procedures and contrast agents, and by leveraging AI for standardization and efficiency, this approach may significantly improve early detection rates and contribute to better patient outcomes.

The goal of this pilot clinical trial is to demonstrate supplemental screening MBI (molecular breast imaging) in women with dense breasts. The main questions it aims to answer are: * Does screening MBI find more cancer than screening DBT (3D mammography, digital breast tomosynthesis? * Does screening MBI result in more call-backs for biopsy than DBT? * How well does MBI-guided biopsy conform with pathology reports? Researchers will compare screening MBI to screening DBT to see if MBI is more sensitive to detecting cancer in women with dense breasts. Participants will * Receive both screening DBT and screening MBI * Receive either DBT-guided or MBI-guided biopsy (randomly assigned), if required by the screening images