View clinical trials related to Breast Cancer.
Filter by:After an initial screening phase (SAFIR 03 - SCREENING) to identify patients with blood circulating mutated-PIK3CA tumours persistent, patients will be enrolled in the treatment phase of SAFIR 03 (SAFIR 03 - ARRIBA) that was designed as a randomised, open-label, multicentre, phase II study, for comparison of alpelisib to ribociclib in combination with fulvestrant (as the continuation of the CDK4/6 inhibitor-fulvestrant strategy) in terms of progression-free survival (PFS).
Phase 1 - Safety and Proof of Concept
"Deep-learning" is a fast-growing method of machine learning (artificial intelligence, AI) which is arousing the interest of the scientific committee in many medical fields. These methods make it possible to generate matches between raw inputs (such as the digital signal from the ECG) and the desired outputs (for example, the measurement of QTc). Unlike traditional machine learning methods, which require manual extraction of structured and predefined data from raw input, deep-learning methods learn these functionalities directly from raw data, without pre-defined guidelines. With the advent of big-data and the recent exponential increase in computing power, these methods can produce models with exceptional performance. The investigators recently used this type of method using multi-layered artificial neural networks, to create an application based on a model that directly transforms the raw digital data of ECGs (.xml) into a measure of QTc comparable to those respecting the highest standards concerning reproducibility. The main purpose of this trial is to study the performance of our DL-AI model for QTc measurement (vs. best standards of QTc measurements, TCM) applied to the recommended ECG monitoring following ribociclib prescription for breast cancer patients in routine clinical care. The investigators will acquire ECG with diverse devices including simplified devices (one/three lead acquisition, low frequency sampling rate: 125-500 Htz) to determine if they'll be equally performant versus 12-lead acquisition machine to evaluate QTc in this setting.
The purpose of this study is to analysis the fluorescence image of the breast sentinel lymph node (SLN) using Indocyanine green (ICG). Moreover, to investigate whether an artificial intelligence protocol was suitable for identifying metastatic status of SLN during the surgery, and evaluate the diagnosis consistency of the AI technique and pathological examinations for lymph node with and without metastasis.
Evaluation of the diagnostic accuracy of Mammography in the morpho-structural analysis of mammographic images in breast cancer-diagnosed patients or patients in follow-up for breast cancer by extracting a number of features describing the texture and morphology of the lesions reported in the form of a structured report explicitly developed for the study. Correlation of the data obtained from evaluating the primary endpoint with the genetic/molecular analysis on liquid biopsy.
This is an open-label single photon emission tomography/computed tomography (SPECT/CT) study to investigate the imaging performance of 99mTc-MIRC213 in breast cancer patients. A single dose of 11.1-14.8Mega-Becquerel (MBq) per kilogram body weight 99mTc-MIRC213 will be injected intravenously. Visual and semiquantitative method will be used to assess the SPECT/CT images.
PSMA is highly expressed on the cell surface of the microvasculature of several solid tumors, including breast cancer. This makes it a potentially imaging target for the detection and grading of breast cancer. This pilot study was designed to evaluate the diagnostic performance of 68Ga-P16-093, a novel radiopharmaceutical targeting PSMA, which was compared with 18F-FDG in the same group of breast cancer patients.
The Serpentine (Stratify cancER PatiENTs by ImmuNosupprEssion) project, represents the most consistent effort so far attempted to translate MDSC into clinical practise by producing an off-the-shelf compliant assay for quantifying these cells in peripheral blood.
This is a prospective and retrospective study to evaluate the effect of pre-operative therapy on response and survival, and compare the difference in response and survival by pre-operative regimen or by patient's clinicopathological characteristic in early or advanced breast cancer.
Research with biospecimens such as blood, tissue, or body fluids can help researchers understand how the human body works. Researchers can make new tests to find diseases, understand how treatments work, or find new ways to treat a disease. The purpose of this study is to collect biospecimens for research from patients with known or suspected lung cancer. The information learned from the biospecimens may be used in future treatments. The purpose of this protocol is to create a pleural fluid registry for use in future studies.