AIMC Topic: Breast Neoplasms

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Two-step beam geometry optimization for volumetric modulated arc therapy gantry angles in breast treatments.

Medical physics
BACKGROUND: In partial arc volumetric modulated arc therapy (VMAT) for treating breast cancer, setting up the limiting gantry positions of the treatment machine is a nontrivial yet repetitive and time-consuming task during planning. Templatized solut...

Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.

Breast cancer research : BCR
BACKGROUND: Treatment with HER2-targeted therapies is recommended for HER2-positive breast cancer patients with HER2 gene amplification or protein overexpression. Interestingly, recent clinical trials of novel HER2-targeted therapies demonstrated pro...

Development of a GPT-4-Powered Virtual Simulated Patient and Communication Training Platform for Medical Students to Practice Discussing Abnormal Mammogram Results With Patients: Multiphase Study.

JMIR formative research
BACKGROUND: Standardized patients (SPs) prepare medical students for difficult conversations with patients. Despite their value, SP-based simulation training is constrained by available resources and competing clinical demands. Researchers are turnin...

Sensitivity of a deep-learning-based breast cancer risk prediction model.

Physics in medicine and biology
When it comes to the implementation of deep-learning based breast cancer risk (BCR) prediction models in clinical settings, it is important to be aware that these models could be sensitive to various factors, especially those arising from the acquisi...

[Artificial intelligence and civil liability in cancerology: The breast cancer example].

Gynecologie, obstetrique, fertilite & senologie
For some years now, artificial intelligence has been investing in the field of healthcare, in both technical and clinical disciplines. While this technological advance represents a real opportunity for the doctors of today and tomorrow, the fact rema...

Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration.

Technology in cancer research & treatment
IntroductionThe study aims to evaluate the performance of an interpretable machine learning model in predicting preoperative axillary lymph node metastasis using primary breast cancer and lymph node features derived from contrast-enhanced mammography...

An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer.

Technology in cancer research & treatment
IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and...

Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity.

Scientific reports
In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores the integration of Federated Learning (FL) and Differential Privacy (DP) for breast cancer detection, lever...

Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.

Frontiers in immunology
BACKGROUND: Breast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. T...

An integrated approach of feature selection and machine learning for early detection of breast cancer.

Scientific reports
Breast cancer ranks among the most prevalent cancers in women globally, with its treatment efficacy heavily reliant on the early identification and diagnosis of the disease. The importance of early detection and diagnosis cannot be overstated in enha...