AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Carcinoma, Ductal, Breast

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A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.

Breast cancer (Tokyo, Japan)
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...

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
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...

Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.

Virchows Archiv : an international journal of pathology
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...

Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models.

Computers in biology and medicine
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...

A deep learning model for breast ductal carcinoma in situ classification in whole slide images.

Virchows Archiv : an international journal of pathology
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...

Ultrasonographic morphological characteristics determined using a deep learning-based computer-aided diagnostic system of breast cancer.

Medicine
To investigate the correlations between ultrasonographic morphological characteristics quantitatively assessed using a deep learning-based computer-aided diagnostic system (DL-CAD) and histopathologic features of breast cancer.This retrospective stud...

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy.

Scientific reports
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...

Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning.

Asian Pacific journal of cancer prevention : APJCP
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 model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy.

Histopathology
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...