AIMC Topic: Carcinoma, Ductal, Breast

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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...
Magnetic Resonance Imaging Carcinoma, Ductal, Breast Carcinoma, Lobular Middle Aged ROC Curve Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Humans Machine Learning Prognosis Retrospective Studies Female Breast Neoplasms
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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...
Logistic Models Lymph Nodes Nomograms Lymphatic Metastasis Neoplasm Invasiveness Lymph Node Excision Aged, 80 and over Adult Breast Neoplasms Middle Aged ROC Curve Sentinel Lymph Node Biopsy, Fine-Needle Sentinel Lymph Node Biopsy Axilla Regression Analysis Breast Tumor Burden Carcinoma, Ductal, Breast Humans Machine Learning Retrospective Studies Female 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 Machine Learning Algorithms Datasets as Topic Carcinoma, Ductal, Breast Microarray Analysis Gene Ontology RNA, Neoplasm RNA-Seq Databases, Genetic Early Detection of Cancer Neoplasm Staging Models, Biological Supervised Machine Learning Female Reproducibility of Results Breast Neoplasms Transcriptome Protein Interaction Maps
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Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.

Thoracic cancer Jan 16, 2020
BACKGROUND: The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer.
Carcinoma, Lobular Follow-Up Studies Neoplasm Invasiveness Carcinoma, Ductal, Breast Chemotherapy, Adjuvant Carcinoma, Papillary Humans Prognosis Retrospective Studies Female Breast Neoplasms Middle Aged Deep Learning ROC Curve Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy
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Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.

Breast cancer research : BCR Jan 13, 2020
BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification ...
Transcriptome ROC Curve Carcinoma, Intraductal, Noninfiltrating Spatial Analysis Carcinoma, Ductal, Breast Molecular Typing Humans Machine Learning Female Breast Neoplasms Biomarkers, Tumor
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Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Computers in biology and medicine Oct 16, 2019
PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.
Humans Biopsy, Fine-Needle Middle Aged Deep Learning Support Vector Machine Magnetic Resonance Imaging Predictive Value of Tests Carcinoma, Ductal, Breast Retrospective Studies Female Aged Adult Breast Neoplasms
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Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score.

Journal of magnetic resonance imaging : JMRI Aug 21, 2018
BACKGROUND: Oncotype Dx is a validated genetic analysis that provides a recurrence score (RS) to quantitatively predict outcomes in patients who meet the criteria of estrogen receptor positive / human epidermal growth factor receptor-2 negative (ER+/...
ROC Curve Imaging, Three-Dimensional Image Processing, Computer-Assisted Treatment Outcome Magnetic Resonance Imaging Area Under Curve Neoplasm Recurrence, Local Receptor, ErbB-2 Carcinoma, Ductal, Breast Estrogen Receptor alpha Female Reproducibility of Results Aged Adult Breast Neoplasms Middle Aged Humans Neural Networks, Computer Algorithms Retrospective Studies
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Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm.

Annals of surgical oncology Jul 5, 2018
OBJECTIVES: In the postneoadjuvant chemotherapy (NAC) setting, conventional radiographic complete response (rCR) is a poor predictor of pathologic complete response (pCR) of the axilla. We developed a convolutional neural network (CNN) algorithm to b...
Carcinoma, Lobular Young Adult Adult Survival Rate Breast Neoplasms Biomarkers, Tumor Middle Aged Prognosis Retrospective Studies Female Aged Aged, 80 and over Neoplasm Invasiveness Axilla Receptor, ErbB-2 Humans Carcinoma, Ductal, Breast Neural Networks, Computer Receptors, Estrogen Algorithms Chemotherapy, Adjuvant ROC Curve Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Magnetic Resonance Imaging
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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...
Image Processing, Computer-Assisted Mammography Ultrasonography, Mammary Breast Carcinoma, Intraductal, Noninfiltrating Aged Pattern Recognition, Automated Humans Aged, 80 and over Carcinoma, Ductal, Breast Neural Networks, Computer Young Adult Carcinoma, Lobular Algorithms Adult Prospective Studies Breast Neoplasms Female Middle Aged
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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...
Humans Retrospective Studies Female Magnetic Resonance Imaging Predictive Value of Tests Aged Breast Adult Carcinoma, Intraductal, Noninfiltrating Breast Neoplasms Image Interpretation, Computer-Assisted Middle Aged Carcinoma, Ductal, Breast Deep Learning Contrast Media
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