View clinical trials related to Breast Cancer.
Filter by:GrannyFit is a prospective, national, multicenter, single-arm open-label study. It will include a total of 200 participants over the age of 70 years treated for de novo or recurrent (local or distant) BC. Participants will receive a Withing Steel activity tracker, which they will be asked to wear 24 h per day for 12 months. The principal assessments will be performed at baseline, at 6 months and at 12 months. The investigators will evaluate clinical (e.g. comorbidities), lifestyle, quality of life, fatigue, and physical activity parameters. All questionnaires will be completed on a REDCap form, via a secure internet link.
To find a recommended dose of valemetostat that can be given in combination with trastuzumab deruxtecan to patients with low/ultra-low HER2-expressing metastatic breast cancer.
With existing evidence showing the difference in miRNA expression levels between non-cancer and cancer groups, the investigators assume that levels of DNA methylation, RNA expression as well as protein concentration will also be dysregulated during disease progression. Combining the power of multi-omic cancer biomarkers, the investigators hypothesize that the sensitivity and specificity of MiRXES MCST can be significantly improved compared to existing multi-cancer diagnostic tests. In this study, the investigators propose to develop and validate blood-based, multi-cancer screening tests through a multi-omics approach.
This is a Phase III, randomized, open-label, 3-arm, multicenter, international study assessing the efficacy and safety of Dato-DXd with or without durvalumab compared with ICT in participants with stage I to III TNBC with residual invasive disease in the breast and/or axillary lymph nodes at surgical resection following neoadjuvant systemic therapy.
We obtained the occurrence of pain sensation, pain mood, sleep, etc. during endocrine therapy in breast cancer patients through telephone follow-up, and analyzed risk factors through artificial intelligence
The over-arching goal of the Contrast-Enhanced Spectral Mammography Imaging Screening Trial (CMIST) is to determine if dual-energy contrast-enhanced spectral mammography (CESM) can detect more cancers with fewer false positives than digital breast tomosynthesis (DBT) in women with dense breasts. Aim 1: To evaluate the performance of CESM compared to DBT at baseline for breast-cancer screening in women with dense breasts. Aim 2: To evaluate the performance of CESM compared to DBT at the 1-year follow up for breast-cancer screening in women with dense breasts.
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.