Carcinoma, Ductal, Breast

Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.

Technology in cancer research & treatment 39431304

PURPOSE: Our study aimed to investigate the potential of radiomics with DCE-MRI for predicting Ki-67 expression in invasive ductal breast cancer.

Adult Aged Area Under Curve Biomarkers, Tumor Breast Neoplasms Carcinoma, Ductal, Breast Female Humans Image Interpretation, Computer-Assisted Ki-67 Antigen Machine Learning Magnetic Resonance Imaging Middle Aged Prognosis Radiomics Retrospective Studies ROC Curve

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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 38702279

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...

Adult Aged Breast Neoplasms Carcinoma, Ductal, Breast Carcinoma, Intraductal, Noninfiltrating Deep Learning Female Humans Intraoperative Period Middle Aged Prospective Studies Tomography, Optical Coherence

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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 38576031

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...

Breast Neoplasms Carcinoma, Ductal, Breast Carcinoma, Intraductal, Noninfiltrating Deep Learning Female Humans Mammography Patient Participation Retrospective Studies Watchful Waiting

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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 38726477

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...

Adult Aged Algorithms Breast Breast Neoplasms Carcinoma, Ductal, Breast Female Humans Image Interpretation, Computer-Assisted Machine Learning Magnetic Resonance Imaging Middle Aged Receptor, ErbB-2 Reproducibility of Results Retrospective Studies ROC Curve

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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 38703302

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 ...

Breast Neoplasms Carcinoma, Ductal, Breast Female Humans Image Processing, Computer-Assisted Machine Learning Microscopy, Polarization Support Vector Machine

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CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.

Cancer medicine 39215495

OBJECTIVE: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. ...

Breast Neoplasms Carcinoma, Ductal, Breast Deep Learning Female Humans Neoplasm Metastasis Neural Networks, Computer

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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 39241826

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...

Artificial Intelligence Biomarkers, Tumor Breast Neoplasms Carcinoma, Ductal, Breast Carcinoma, Intraductal, Noninfiltrating Deep Learning Diagnosis, Differential Female Humans Immunohistochemistry Receptors, Estrogen Receptors, Progesterone

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Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Radiology. Artificial intelligence 38900042

Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI withou...

Adult Aged Breast Breast Neoplasms Carcinoma, Ductal, Breast Carcinoma, Intraductal, Noninfiltrating Contrast Media Deep Learning Female Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Middle Aged Predictive Value of Tests Retrospective Studies

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Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.

PloS one 40009584

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...

Algorithms Breast Neoplasms Carcinoma, Ductal, Breast Female Humans Machine Learning Neoplasm Metastasis Prognosis ROC Curve

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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 39933195

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...

Algorithms Breast Neoplasms Carcinoma, Ductal, Breast Computer Simulation Deep Learning Female Humans Image Processing, Computer-Assisted Monte Carlo Method Neural Networks, Computer

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Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.

Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients.

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.

Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

Characterization and classification of ductal carcinoma tissue using four channel based stokes-mueller polarimetry and machine learning.

CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.

Histopathologic Differential Diagnosis and Estrogen Receptor/Progesterone Receptor Immunohistochemical Evaluation of Breast Carcinoma Using a Deep Learning-Based Artificial Intelligence Architecture.

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.

Deep learning-assisted identification and localization of ductal carcinoma from bulk tissue in-silico models generated through polarized Monte Carlo simulations.

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