AIMC Topic: Ki-67 Antigen
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Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study.
Histopathology
Jun 24, 2021
AIMS: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article w...
The human-in-the-loop: an evaluation of pathologists' interaction with artificial intelligence in clinical practice.
Histopathology
May 30, 2021
AIMS: One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and...
Ki-67 Antigen
Pathology, Clinical
Data Accuracy
Observer Variation
Immunohistochemistry
Calibration
Artificial Intelligence
Research Design
Retrospective Studies
Reproducibility of Results
Diagnosis, Computer-Assisted
Image Processing, Computer-Assisted
Breast Neoplasms
Pathologists
Humans
Biomarkers, Tumor
PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer.
Scientific reports
Apr 19, 2021
The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting both tumor progression and probable response to chemotherapy. The value of Ki-67 index and TILs in approach to heterogeneous ...
Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.
Scientific reports
Jul 6, 2020
The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the ...
Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.
The journal of pathology. Clinical research
Jun 27, 2020
The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we p...
CD8 Antigens
Antigens, CD20
Ki-67 Antigen
Tissue Array Analysis
Antigens, Differentiation, Myelomonocytic
Risk Assessment
Deep Learning
Time Factors
Cell Proliferation
Neoplasm Staging
Image Interpretation, Computer-Assisted
Immunohistochemistry
Risk Factors
Predictive Value of Tests
Stomach Neoplasms
Antigens, CD
Humans
Tumor Microenvironment
Biomarkers, Tumor
Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics.
IEEE journal of biomedical and health informatics
Nov 28, 2019
OBJECTIVE: Histologic grade and Ki-67 proliferation status are important clinical indictors for breast cancer prognosis and treatment. The purpose of this study is to improve prediction accuracy of these clinical indicators based on tumor radiomic an...
Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes.
Scientific reports
Nov 28, 2019
Radiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of m...
Cytokeratin-Supervised Deep Learning for Automatic Recognition of Epithelial Cells in Breast Cancers Stained for ER, PR, and Ki-67.
IEEE transactions on medical imaging
Aug 7, 2019
Immunohistochemistry (IHC) of ER, PR, and Ki-67 are routinely used assays in breast cancer diagnostics. Determination of the proportion of stained cells (labeling index) should be restricted on malignant epithelial cells, carefully avoiding tumor inf...
Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.
Neuroradiology
Aug 2, 2019
PURPOSE: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker whic...
Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.
IEEE transactions on bio-medical engineering
Feb 22, 2019
OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeli...
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