AIMC Topic: Carcinoma, Ductal, Breast

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SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics.

Genome biology Jul 29, 2025
Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introdu...
Deep Learning Unsupervised Machine Learning Carcinoma, Ductal, Breast Female Breast Neoplasms Gene Expression Profiling Transcriptome Humans
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Recurrence prediction of invasive ductal carcinoma from preoperative contrast-enhanced computed tomography using deep convolutional neural network.

Biomedical physics & engineering express Jul 10, 2025
Predicting the risk of breast cancer recurrence is crucial for guiding therapeutic strategies, including enhanced surveillance and the consideration of additional treatment after surgery. In this study, we developed a deep convolutional neural networ...
Tomography, X-Ray Computed Female Aged Adult Humans Neural Networks, Computer Retrospective Studies Carcinoma, Ductal, Breast Preoperative Period ROC Curve Breast Neoplasms Contrast Media Middle Aged Area Under Curve Convolutional Neural Networks Neoplasm Recurrence, Local
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A Comprehensive AI-Based Approach in Classifying Breast Lesions: Focusing on Improving Pathologists' Accuracy and Efficiency.

Clinical breast cancer Mar 26, 2025
BACKGROUND: Accurate classification of breast lesions is essential for effective clinical decision-making and patient management. In this study, we evaluated an artificial intelligence (AI) solution to classify whole slide images (WSIs) of breast les...
Humans Artificial Intelligence Female Sensitivity and Specificity Pathologists Image Interpretation, Computer-Assisted Carcinoma, Ductal, Breast Carcinoma, Lobular Breast Neoplasms Middle Aged Breast Carcinoma, Intraductal, Noninfiltrating
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Deep learning-assisted identification and localization of ductal carcinoma from bulk tissue in-silico models generated through polarized Monte Carlo simulations.

Biomedical physics & engineering express Mar 4, 2025
Despite significant progress in diagnosis and treatment, breast cancer remains a formidable health challenge, emphasizing the continuous need for research. This simulation study uses polarized Monte Carlo approach to identify and locate breast cancer...
Deep Learning Image Processing, Computer-Assisted Carcinoma, Ductal, Breast Monte Carlo Method Computer Simulation Female Breast Neoplasms Humans Neural Networks, Computer Algorithms
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Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.

PloS one Feb 26, 2025
More than 90% of deaths due to breast cancer (BC) are due to metastasis-related complications, with invasive ductal carcinoma (IDC) of the breast being the most common pathologic type of breast cancer and highly susceptible to metastasis to distant o...
Breast Neoplasms Machine Learning ROC Curve Algorithms Neoplasm Metastasis Prognosis Carcinoma, Ductal, Breast Female Humans
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Histopathologic Differential Diagnosis and Estrogen Receptor/Progesterone Receptor Immunohistochemical Evaluation of Breast Carcinoma Using a Deep Learning-Based Artificial Intelligence Architecture.

The American journal of pathology Sep 4, 2024
In breast carcinoma, invasive ductal carcinoma (IDC) is the most common histopathologic subtype, and ductal carcinoma in situ (DCIS) is a precursor of IDC. These two often occur concomitantly. The immunohistochemical staining of estrogen receptor (ER...
Receptors, Estrogen Receptors, Progesterone Breast Neoplasms Biomarkers, Tumor Deep Learning Diagnosis, Differential Carcinoma, Intraductal, Noninfiltrating Immunohistochemistry Carcinoma, Ductal, Breast Humans Artificial Intelligence Female
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Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

Journal of magnetic resonance imaging : JMRI May 10, 2024
BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new a...
Image Interpretation, Computer-Assisted Receptor, ErbB-2 Carcinoma, Ductal, Breast Middle Aged ROC Curve Algorithms Magnetic Resonance Imaging Retrospective Studies Breast Female Humans Aged Reproducibility of Results Machine Learning Adult Breast Neoplasms
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Characterization and classification of ductal carcinoma tissue using four channel based stokes-mueller polarimetry and machine learning.

Lasers in medical science May 4, 2024
Interaction of polarized light with healthy and abnormal regions of tissue reveals structural information associated with its pathological condition. Even a slight variation in structural alignment can induce a change in polarization property, which ...
Humans Machine Learning Female Breast Neoplasms Microscopy, Polarization Image Processing, Computer-Assisted Support Vector Machine Carcinoma, Ductal, Breast
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Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.

Cancer imaging : the official publication of the International Cancer Imaging Society Apr 5, 2024
BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal ca...
Mammography Carcinoma, Intraductal, Noninfiltrating Watchful Waiting Carcinoma, Ductal, Breast Female Breast Neoplasms Humans Deep Learning Retrospective Studies Patient Participation
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Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients.

Science bulletin Mar 29, 2024
An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specime...
Deep Learning Intraoperative Period Tomography, Optical Coherence Carcinoma, Intraductal, Noninfiltrating Carcinoma, Ductal, Breast Aged Adult Breast Neoplasms Middle Aged Humans Prospective Studies Female
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