AIMC Topic: Carcinoma, Ductal, Breast

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A deep learning model for breast ductal carcinoma in situ classification in whole slide images.

Virchows Archiv : an international journal of pathology Jan 25, 2022
The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pat...
Humans Biopsy Carcinoma, Intraductal, Noninfiltrating Pathologists Carcinoma, Ductal, Breast Female Breast Neoplasms Deep Learning Area Under Curve
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The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

The British journal of radiology Dec 15, 2021
OBJECTIVES: The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors affecting its efficacy.
Carcinoma, Ductal, Breast Breast Density Carcinoma, Lobular Case-Control Studies Algorithms Deep Learning Retrospective Studies Pilot Projects Female Ultrasonography, Mammary Humans Aged Adult Sensitivity and Specificity Breast Neoplasms Middle Aged
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Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models.

Computers in biology and medicine Oct 13, 2021
Invasive ductal carcinoma (IDC) breast cancer is a significant health concern for women all around the world and early detection of the disease may increase the survival rate in patients. Therefore, Computer-Aided Diagnosis (CAD) based systems can as...
Humans Deep Learning Carcinoma, Ductal, Breast Carcinoma, Ductal Female Survival Rate Breast Neoplasms Diagnosis, Computer-Assisted
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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...
Breast Neoplasms Biomarkers, Tumor China Predictive Value of Tests Automation, Laboratory Retrospective Studies Female Reproducibility of Results Immunohistochemistry Receptor, ErbB-2 Carcinoma, Ductal, Breast Microscopy In Situ Hybridization, Fluorescence Image Interpretation, Computer-Assisted Observer Variation Humans Artificial Intelligence
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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...
Carcinoma, Intraductal, Noninfiltrating Image Interpretation, Computer-Assisted Carcinoma, Ductal, Breast Margins of Excision Neoplasm, Residual Mastectomy, Segmental Humans Female Breast Neoplasms Deep Learning
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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...
Positron Emission Tomography Computed Tomography Radiopharmaceuticals Carcinoma, Ductal, Breast Fluorodeoxyglucose F18 Humans Machine Learning Female Sensitivity and Specificity Breast Neoplasms Lymphatic Metastasis
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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...
Humans Mammography Carcinoma, Ductal, Breast Adult Breast Neoplasms Middle Aged Algorithms Deep Learning Female Aged Aged, 80 and over
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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...
Image Processing, Computer-Assisted Follow-Up Studies Magnetic Resonance Imaging Neoplasm Invasiveness Carcinoma, Ductal, Breast Breast Density Carcinoma, Lobular Humans Neural Networks, Computer Adult Algorithms Breast Neoplasms Prospective Studies Middle Aged Deep Learning Prognosis Female Aged Aged, 80 and over
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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...
Humans Image Processing, Computer-Assisted Female Carcinoma, Ductal, Breast Breast Neoplasms Mitotic Index 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...
Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Receptor, ErbB-2 Machine Learning Breast Neoplasms Prognosis Humans Middle Aged Tomography, X-Ray Computed Aged Adolescent Female Aged, 80 and over ROC Curve Carcinoma, Ductal, Breast Young Adult Receptors, Estrogen Adult Receptors, Progesterone Carcinoma, Lobular
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