AIMC Topic: Carcinoma, Intraductal, Noninfiltrating

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Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.

JAMA network open
IMPORTANCE: Following recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerize...

A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.

Breast cancer research : BCR
BACKGROUND: Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mas...

Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Journal of the American College of Radiology : JACR
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...

High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision.

Radiology
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...

Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.

Scientific reports
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...

Spatially Discontinuous Mutation Topographies in Ductal Carcinoma In Situ Reveal Noncompetitive Growth Dynamics.

Cancer research
UNLABELLED: Preinvasive breast cancer, or ductal carcinoma in situ (DCIS), shares many morphologic and genomic features with invasive breast cancer, yet most DCIS tumors remain indolent over decades. In this study, we performed spatial analyses of so...

Advancements in Detection and Management of Ductal Carcinoma in Situ.

Radiographics : a review publication of the Radiological Society of North America, Inc
Ductal carcinoma in situ (DCIS) is a noninvasive breast cancer characterized by neoplastic epithelial cells confined to the ductal system by the basement membrane without invasion of adjacent tissue. Its progression to invasive carcinoma is not under...

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

Radiology. Artificial intelligence
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...

Active Surveillance for Atypical Ductal Hyperplasia and Ductal Carcinoma In Situ.

Journal of breast imaging
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...