AIMC Topic: Carcinoma, Ductal, Breast

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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...
Prospective Studies Prognosis Female Aged Aged, 80 and over Humans Neural Networks, Computer Algorithms Follow-Up Studies Magnetic Resonance Imaging Neoplasm Invasiveness Carcinoma, Ductal, Breast Breast Density Carcinoma, Lobular Adult Breast Neoplasms Middle Aged Deep Learning Image Processing, Computer-Assisted
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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 Female Breast Neoplasms Deep Learning Image Processing, Computer-Assisted Carcinoma, Ductal, Breast Mitotic Index
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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...
Receptors, Progesterone Carcinoma, Lobular Breast Neoplasms Female Middle Aged Aged Adolescent Aged, 80 and over ROC Curve Humans Young Adult Antineoplastic Combined Chemotherapy Protocols Machine Learning Adult Prognosis Neoadjuvant Therapy Tomography, X-Ray Computed Follow-Up Studies Receptor, ErbB-2 Carcinoma, Ductal, Breast Receptors, Estrogen
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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...
Female Breast Neoplasms Middle Aged ROC Curve Antineoplastic Combined Chemotherapy Protocols Humans Machine Learning Prognosis Retrospective Studies Neoadjuvant Therapy Follow-Up Studies Magnetic Resonance Imaging Carcinoma, Ductal, Breast Carcinoma, Lobular
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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...
Biopsy, Fine-Needle Sentinel Lymph Node Biopsy Humans Lymph Nodes Nomograms Lymphatic Metastasis Neoplasm Invasiveness Lymph Node Excision Adult Tumor Burden Breast Neoplasms Carcinoma, Ductal, Breast Middle Aged Machine Learning Axilla Sentinel Lymph Node ROC Curve Retrospective Studies Regression Analysis Logistic Models Female Breast Aged Aged, 80 and over
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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 ...
RNA, Neoplasm RNA-Seq Databases, Genetic Early Detection of Cancer Neoplasm Staging Models, Biological Supervised Machine Learning Datasets as Topic Carcinoma, Ductal, Breast Microarray Analysis Gene Ontology Female Reproducibility of Results Breast Neoplasms Transcriptome Protein Interaction Maps Humans Machine Learning Algorithms
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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.
Chemotherapy, Adjuvant Carcinoma, Papillary Carcinoma, Lobular Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Retrospective Studies Neoplasm Invasiveness Female Carcinoma, Ductal, Breast Breast Neoplasms Middle Aged Deep Learning ROC Curve Humans Prognosis
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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 ...
Female Breast Neoplasms Biomarkers, Tumor Transcriptome ROC Curve Carcinoma, Intraductal, Noninfiltrating Spatial Analysis Carcinoma, Ductal, Breast Molecular Typing Humans Machine Learning
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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.
Breast Neoplasms Predictive Value of Tests Middle Aged Humans Deep Learning Carcinoma, Ductal, Breast Retrospective Studies Support Vector Machine Biopsy, Fine-Needle Female Magnetic Resonance Imaging Aged Adult
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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 Reproducibility of Results Humans Area Under Curve Aged Neural Networks, Computer Imaging, Three-Dimensional Neoplasm Recurrence, Local Adult Algorithms Image Processing, Computer-Assisted Receptor, ErbB-2 Breast Neoplasms Retrospective Studies Treatment Outcome Carcinoma, Ductal, Breast Middle Aged Female Magnetic Resonance Imaging Estrogen Receptor alpha
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