AI Medical Compendium Topic:
Breast Neoplasms

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Deep learning-based transcription factor activity for stratification of breast cancer patients.

Biochimica et biophysica acta. Gene regulatory mechanisms
Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activ...

Prediction for Distant Metastasis of Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Images under Deep Learning.

Computational intelligence and neuroscience
This research aimed to explore the effect of using magnetic resonance imaging (MRI) radiomic features to establish a model for predicting distant metastasis under dynamic contrast-enhanced MRI imaging with deep learning algorithms. The deep learning ...

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches.

Computational intelligence and neuroscience
Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer, breast cancer is the second most common cancer among women. It...

Diagnosing Cancer Using IOT and Machine Learning Methods.

Computational intelligence and neuroscience
Breast cancer affects one in every eight women and is the most common cancer. . To diagnose breast cancer, a potentially fatal condition, using microarray technology, large datasets can now be used. . This study used machine learning algorithms and I...

Analysis of robot-assisted nipple-sparing mastectomy using the da Vinci SP system.

Journal of surgical oncology
BACKGROUND: As patients tend to be diagnosed with breast cancer at an early stage, the demand for better cosmetic outcomes has increased. Several studies revealed that robot-assisted nipple-sparing mastectomy (RNSM) shows favorable outcomes. The aim ...

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time.

European radiology
OBJECTIVES: To investigate the feasibility of automatically identifying normal scans in ultrafast breast MRI with artificial intelligence (AI) to increase efficiency and reduce workload.

Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model.

European journal of radiology
OBJECTIVE: Mammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study ...

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

Journal of the American College of Radiology : JACR
OBJECTIVE: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary wid...

Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
AIM: To train and validate a comprehensive deep-learning (DL) segmentation model for loco-regional breast cancer with the aim of clinical implementation.

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological class...