View clinical trials related to Breast Diseases.
Filter by:The present study will assess real-world clinical outcomes, adverse events and economics from treatment with endocrine therapy combined with abemaciclib in patients with HR-positive breast cancer.
The extent of breast cancer is an important prognostic factor in patients diagnosed with this disease. Therefore, adequate staging at diagnosis is a requisite for optimal treatment. In all patients diagnosed with locally advanced breast cancer (LABC), distant staging using 18F-FDG PET/CT is recommended. However, the degree of metabolic uptake in the primary breast tumor is significantly lower in the ER+ subtype compared to HER2+ and triple negative breast cancer (TNBC). As a consequence, a suboptimal 18F-FDG uptake in ER+ breast cancer patients can potentially lead to missed distant metastases. Fibroblast-activating protein inhibitor (FAPI) is a recently developed radiotracer that binds to FAP, a stromal antigen overexpressed in more than 90% of epithelial-derived tumors and their metastases. Previous studies all show 68Ga-FAPI PET/CT to have a higher detection rate compared to 18F-FDG PET/CT. However, all previous studies were performed without considering breast cancer subtype. If the metabolic uptake by 68Ga-FAPI-46 is higher in ER+ breast cancer patients, more lesions will be detected, resulting in a more appropriate treatment for these patients. Therefore, in this pilot study, the investigators aim to compare the diagnostic performance of 18F-FDG with 68Ga-FAPI-46 as PET-tracer in ER+ breast cancer patients.
The goal of this observational study is to investigate the ability of a the Z-scanner to identify and differentiate cancer and benign lesions from healthy breast tissue based on permittivity. The main questions it aims to answer are: 1. Determine the permittivity values of the Z-scanner associated with healthy, benign, and malignant tissue in human breasts. 2. Determine the repeatability, reproducibility, inter-and intra-operator variability of the Z-scanner.
The accuracy of breast examinations and ultrasonography performed clinically to detect breast mass varies greatly depending on the physician's skill level, and the accuracy of breast examinations by non-experts is particularly low. In this study, we aimed to validate whether the concurrent use of ultrasound sensor technology is an efficient strategy for the purpose of improving the sensitivity of detecting breast masses through breast examination.
This study is a single-centre prospective observational cohort study designed to assess and compare the sensitivity and specificity of a Lunit INSIGHT MMG assisted human reading to the standard care double human reading process within mammography review at a "one-stop" breast clinic (non-inferiority study). The current imaging reporting process is a sequential double read of mammography and ultrasound (if available) images, by consultant radiologists or radiographers. The first reader produces a report which is then sent to the second reader who reviews it. If the second reader agrees with the first, this is reflected in the second reader's report which translates into a decision for further action; in the event of disagreement, a third reader arbitrates and produces the final report. In the past, breast clinics have had to resort to single reader reporting due to staff shortages and high demand. This results in delays to any further assessments that may be required. It is worth noting however that despite difficulties in meeting the target, the current clinical pathway has proven to be cost effective. The Lunit INSIGHT MMG tool could generate benefits and potential efficiencies if it were introduced to the clinical service as an assistant reader within the mammography reporting process, by replacing one of the two human readers in the current standard of care. Before this can be assessed however, its non-inferiority in combination with a human reader in comparison to standard of care (double human reading) must first be established. This study will aim to address this issue in the first instance, maintaining standard of care for all patients seen within the 2 week wait pathway, by introducing the use of Lunit INSIGHT MMG into one of two arms within this prospective, observational parallel cohort study.
Human soft tissue such as breast tissue has viscoelasticity property. However, most ultrasound has only been measured for elasticity and viscosity has been neglected. Shear wave elastography (SWE) is a ultrasound technique that quantifies tissue elasticity. Shear wave dispersion (SWD) imaging is a newly developed ultrasound technique that evaluates the dispersion slope of shear waves, which is related to the viscosity of biological tissues. The goal of this retrospective study was to compare diagnostic performance between SWE and SWD to distinguish benign from malignant breast masses and to investigate additional role of SWD. Using histological diagnosis of breast lesions as a standard reference, quantitative indices of SWE and SWD were evaluated to diagnose breast cancer, and the diagnostic performance of SWE and SWD was compared.
The overall aim of the project is to investigate how artificial intelligence (AI) can be used to streamline and at the same time increase diagnostic safety in breast cancer screening with mammography. AI has been shown in a number of studies to have great potential for both increasing diagnostic certainty (e.g. reduced occurrence of interval cancers) and at the same time reducing the workload for doctors. However, much research remains to clinically validate these new tools and to increase the understanding of how they affect the work of doctors. The specific goal of the project is to investigate whether the implementation of AI in breast cancer screening in Östergötland, Sweden, can increase the sensitivity (the mammography examination's ability to find breast cancer) and the specificity (that is, the right case is selected for further investigation: a minimum of healthy women are recalled but so many breast cancer cases that are possible are selected for further investigation) and at the same time make screening more efficient through reduced workload. AI will be implemented in the clinical routine and performance metrics such as cancer detection rate etc will be closely monitored. The study do not assign specific interventions to the study participants.
This comparative study will recruit 30 females who are scheduled for mammography and ultrasound assessment. The clinical 2D ultrasound is performed routinely, and the research portion of this study will add a few extra 3-D ultrasound images during the procedure. The ultrasound imaging laboratory under the direction of Dr. Aaron Fenster has developed a customized device designed to acquire 3D ultrasound of the breast using a commercial ultrasound machine. The purpose is to see how well 3-dimensional ultrasound acquire from that device is able to visualize tumours and other key features in comparison to the clinical system InveniaTM developed by GE Medical.
Value of New Mammography Techniques in Comparison to Dynamic Contrast-Enhanced MRI of the Breast in the Detection and Diagnosis of Breast lesions
This retrospective study aimed to create a prediction model using deep learning and radiomics features extracted from intratumoral and peritumoral regions of breast lesions in ultrasound images, to diagnose benign and malignant breast lesions with BI-RADS 4 classification. Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.