AIMC Topic: Breast Neoplasms

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Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study.

JMIR cancer Jan 28, 2025
BACKGROUND: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information a...
Natural Language Processing Mastectomy Mastectomy, Segmental Emotions Decision Making Social Media Humans Female Breast Neoplasms
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Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions.

IEEE reviews in biomedical engineering Jan 28, 2025
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep l...
Humans Image Interpretation, Computer-Assisted Female Breast Neoplasms Magnetic Resonance Imaging Mammography Deep Learning Breast
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Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach.

Magnetic resonance imaging Jan 27, 2025
BACKGROUND: Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectivene...
Magnetic Resonance Imaging Neoplasm Invasiveness Breast Aged Image Interpretation, Computer-Assisted Deep Learning Breast Neoplasms Adult Preoperative Care Image Processing, Computer-Assisted Humans Radiomics Sensitivity and Specificity Diffusion Magnetic Resonance Imaging Contrast Media Retrospective Studies Middle Aged Female
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A multi-model machine learning framework for breast cancer risk stratification using clinical and imaging data.

Journal of X-ray science and technology Jan 27, 2025
PurposeThis study presents a comprehensive machine learning framework for assessing breast cancer malignancy by integrating clinical features with imaging features derived from deep learning.MethodsThe dataset included 1668 patients with documented b...
Risk Assessment Aged Humans Mammography Breast Neoplasms Machine Learning Breast Middle Aged Female
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QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics.

Analytical chemistry Jan 27, 2025
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reli...
Mass Spectrometry Metabolomics Liquid Chromatography-Mass Spectrometry Female Chromatography, Liquid Breast Neoplasms Humans Deep Learning Neural Networks, Computer Algorithms
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Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.

Scientific reports Jan 27, 2025
The impact of mitochondrial and lysosomal co-dysfunction on breast cancer patient outcomes is unclear. The objective of this study is to develop a predictive machine learning (ML) model utilizing mitochondrial and lysosomal co-regulators in order to ...
Lysosomes Mitochondria Polymorphism, Single Nucleotide Gene Expression Regulation, Neoplastic DNA Copy Number Variations Prognosis Female Breast Neoplasms Humans Machine Learning
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Unrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data.

Medical & biological engineering & computing Jan 25, 2025
Photoacoustic tomography (PAT) has emerged as a promising imaging modality for breast cancer detection, offering unique advantages in visualizing tissue composition without ionizing radiation. However, limited-view scenarios in clinical settings pres...
Humans Image Processing, Computer-Assisted Algorithms Photoacoustic Techniques Female Tomography Breast Neoplasms Deep Learning
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A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.

Scientific reports Jan 25, 2025
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretatio...
Mammography Breast Neoplasms Diagnosis, Computer-Assisted Support Vector Machine Humans Algorithms Databases, Factual Female
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Comment on 'CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images'.

Physics in medicine and biology Jan 24, 2025
An analysis of the methodology used by the authors of the commented article is presented and errors related to data preparation are pointed out.
Breast Neoplasms Humans Ultrasonography Neural Networks, Computer Image Processing, Computer-Assisted Ultrasonography, Mammary Female Automation
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Breast radiation therapy fluence painting with multi-agent deep reinforcement learning.

Medical physics Jan 23, 2025
BACKGROUND: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, the manual fluence painting process presents a challenge for efficient clinical operation.
Humans Retrospective Studies Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Reinforcement Machine Learning Female Breast Neoplasms Deep Learning
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