AIMC Topic: Carcinoma, Ductal, Breast
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Deep-learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy.
Histopathology
Jan 30, 2024
AIMS: Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low...
Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning.
Asian Pacific journal of cancer prevention : APJCP
Feb 1, 2023
OBJECTIVE: Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep...
Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy.
Scientific reports
Feb 10, 2022
In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric image...
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...
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.
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...
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...
Female
Reproducibility of Results
Breast Neoplasms
Biomarkers, Tumor
China
Predictive Value of Tests
Microscopy
Humans
Immunohistochemistry
Artificial Intelligence
Receptor, ErbB-2
Automation, Laboratory
In Situ Hybridization, Fluorescence
Carcinoma, Ductal, Breast
Retrospective Studies
Image Interpretation, Computer-Assisted
Observer Variation
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...
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...
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...
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