AIMC Topic: Carcinoma, Intraductal, Noninfiltrating

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Automated and real-time segmentation of suspicious breast masses using convolutional neural network.

PloS one May 16, 2018
In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review B...
Carcinoma, Ductal, Breast Carcinoma, Lobular Breast Carcinoma, Intraductal, Noninfiltrating Pattern Recognition, Automated Adult Breast Neoplasms Middle Aged Image Processing, Computer-Assisted Mammography Ultrasonography, Mammary Aged Aged, 80 and over Young Adult Algorithms Prospective Studies Female Humans Neural Networks, Computer
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Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Journal of the American College of Radiology : JACR Feb 2, 2018
PURPOSE: The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) o...
Humans Female Aged Adult Neural Networks, Computer Prognosis Retrospective Studies Monte Carlo Method Biopsy, Large-Core Needle Carcinoma in Situ Breast Neoplasms Mammography Middle Aged Neoplasm Staging Deep Learning Carcinoma, Intraductal, Noninfiltrating
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High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision.

Radiology Oct 17, 2017
Purpose To develop a machine learning model that allows high-risk breast lesions (HRLs) diagnosed with image-guided needle biopsy that require surgical excision to be distinguished from HRLs that are at low risk for upgrade to cancer at surgery and t...
Prognosis Clinical Decision-Making Female Aged Aged, 80 and over Young Adult Watchful Waiting Risk Assessment Humans Unnecessary Procedures Precancerous Conditions Follow-Up Studies Machine Learning Image-Guided Biopsy Adult Carcinoma, Intraductal, Noninfiltrating Breast Neoplasms Middle Aged
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Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.

Scientific reports Apr 25, 2017
Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differ...
Cellular Microenvironment Humans Machine Learning Tumor Suppressor Proteins Support Vector Machine Female Aged Epithelial Cells Immunohistochemistry Breast Neoplasms Carcinoma, Intraductal, Noninfiltrating Hyperplasia Transcription Factors
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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 Sep 1, 2024
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...
Middle Aged Humans Deep Learning Aged Carcinoma, Intraductal, Noninfiltrating Magnetic Resonance Imaging Retrospective Studies Contrast Media Adult Predictive Value of Tests Female Image Interpretation, Computer-Assisted Breast Neoplasms Breast Carcinoma, Ductal, Breast
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Active Surveillance for Atypical Ductal Hyperplasia and Ductal Carcinoma In Situ.

Journal of breast imaging Jul 28, 2023
Atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are relatively common breast lesions on the same spectrum of disease. Atypical ductal hyperblasia is a nonmalignant, high-risk lesion, and DCIS is a noninvasive malignancy. While a...
Mammography Female Humans Carcinoma, Intraductal, Noninfiltrating Breast Neoplasms Artificial Intelligence Watchful Waiting Early Detection of Cancer
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Prediction of Atypical Ductal Hyperplasia Upgrades Through a Machine Learning Approach to Reduce Unnecessary Surgical Excisions.

JCO clinical cancer informatics Dec 1, 2018
PURPOSE: Surgical excision is currently recommended for all occurrences of atypical ductal hyperplasia (ADH) found on core needle biopsies for malignancy diagnoses and treatment of lesions. The excision of all ADH lesions may lead to overtreatment, w...
Cohort Studies Humans Machine Learning Carcinoma, Intraductal, Noninfiltrating Female Hyperplasia
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