AIMC Topic: Carcinoma, Ductal, Breast

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Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.

Virchows Archiv : an international journal of pathology Jul 19, 2021
The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the prognosis of breast cancer patients. Several investigations have shown high intraobserver and interobserve...
Automation, Laboratory Retrospective Studies Female Reproducibility of Results Breast Neoplasms Humans Artificial Intelligence Image Interpretation, Computer-Assisted Observer Variation Immunohistochemistry Receptor, ErbB-2 Carcinoma, Ductal, Breast Biomarkers, Tumor China Predictive Value of Tests Microscopy In Situ Hybridization, Fluorescence
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Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Apr 26, 2021
The surgical margin status of breast lumpectomy specimens for invasive carcinoma and ductal carcinoma in situ (DCIS) guides clinical decisions, as positive margins are associated with higher rates of local recurrence. The "cavity shave" method of mar...
Neoplasm, Residual Mastectomy, Segmental Humans Female Breast Neoplasms Deep Learning Carcinoma, Intraductal, Noninfiltrating Image Interpretation, Computer-Assisted Carcinoma, Ductal, Breast Margins of Excision
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A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.

Breast cancer (Tokyo, Japan) Jan 17, 2021
OBJECTIVE: The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomog...
Carcinoma, Ductal, Breast Fluorodeoxyglucose F18 Sensitivity and Specificity Breast Neoplasms Lymphatic Metastasis Humans Positron Emission Tomography Computed Tomography Machine Learning Radiopharmaceuticals Female
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Visualizing "featureless" regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiology.

Japanese journal of radiology Nov 16, 2020
PURPOSE: To demonstrate how artificial intelligence (AI) can expand radiologists' capacity, we visualized the features of invasive ductal carcinomas (IDCs) that our algorithm, developed and validated for basic pathological classification on mammogram...
Adult Breast Neoplasms Middle Aged Deep Learning Mammography Humans Algorithms Female Aged Aged, 80 and over Carcinoma, Ductal, Breast
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Volumetric breast density estimation on MRI using explainable deep learning regression.

Scientific reports Oct 22, 2020
To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast densi...
Magnetic Resonance Imaging Neoplasm Invasiveness Carcinoma, Ductal, Breast Breast Density Carcinoma, Lobular Middle Aged Deep Learning Image Processing, Computer-Assisted Follow-Up Studies Humans Neural Networks, Computer Algorithms Prospective Studies Prognosis Female Aged Aged, 80 and over Adult Breast Neoplasms
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Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses.

Diagnostic pathology Jul 4, 2020
BACKGROUND: The mitotic count in breast carcinoma is an important prognostic marker. Unfortunately substantial inter- and intra-laboratory variation exists when pathologists manually count mitotic figures. Artificial intelligence (AI) coupled with wh...
Image Processing, Computer-Assisted Carcinoma, Ductal, Breast Mitotic Index Humans Female Breast Neoplasms Deep Learning
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A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning.

Scientific reports Jul 2, 2020
Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early prediction of responsiveness to NAC could facilitate treatment adjustments on an individual patient...
Receptor, ErbB-2 Carcinoma, Ductal, Breast Receptors, Estrogen Receptors, Progesterone Carcinoma, Lobular ROC Curve Prognosis Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Tomography, X-Ray Computed Follow-Up Studies Female Aged Aged, 80 and over Young Adult Adult Breast Neoplasms Middle Aged Adolescent Humans Machine Learning
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A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.

Breast cancer research : BCR May 28, 2020
BACKGROUND: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and va...
Female Breast Neoplasms Middle Aged ROC Curve Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Magnetic Resonance Imaging Carcinoma, Ductal, Breast Carcinoma, Lobular Humans Machine Learning Prognosis Retrospective Studies
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Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Acta oncologica (Stockholm, Sweden) Mar 9, 2020
The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-mak...
Sentinel Lymph Node Biopsy Tumor Burden ROC Curve Carcinoma, Ductal, Breast Sentinel Lymph Node Logistic Models Biopsy, Fine-Needle Neoplasm Invasiveness Lymph Nodes Aged Lymph Node Excision Humans Nomograms Aged, 80 and over Machine Learning Axilla Adult Lymphatic Metastasis Regression Analysis Retrospective Studies Breast Neoplasms Breast Female Middle Aged
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Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.

Scientific reports Mar 5, 2020
Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need ...
Humans RNA, Neoplasm Machine Learning Algorithms Datasets as Topic Female Carcinoma, Ductal, Breast Microarray Analysis Gene Ontology Databases, Genetic Early Detection of Cancer Neoplasm Staging Models, Biological Supervised Machine Learning Reproducibility of Results Breast Neoplasms Transcriptome Protein Interaction Maps RNA-Seq
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