4.3 Article

Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in early-stage breast cancer

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Mitosis domain generalization in histopathology images - The MIDOG

Marc Aubreville et al.

Summary: The density of mitotic figures (MF) in tumor tissue is an important marker for tumor grading, but its recognition by pathologists is biased and limited. Deep learning methods can support the recognition, but their performance deteriorates in different clinical environments due to variability caused by using different whole slide scanners. The MICCAI MIDOG 2021 challenge aimed to develop scanner-agnostic MF detection algorithms and the winning algorithm outperformed six experts on the same task.

MEDICAL IMAGE ANALYSIS (2023)

Article Multidisciplinary Sciences

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images

Mario Verdicchio et al.

Summary: This study proposed a novel pathomic approach for the classification of tumor-infiltrating lymphocytes (TILs) in breast cancer histopathological whole slide images. By extracting pathomic features and using machine learning models, the researchers achieved a good classification performance for TILs.

HELIYON (2023)

Article Oncology

Breast Cancer, Version 3.2022

William J. Gradishar et al.

Summary: This article discusses the complex therapeutic options for patients with noninvasive or invasive breast cancer, as well as the recommendations provided by the NCCN Clinical Practice Guidelines for Breast Cancer. It focuses on the overall management of ductal carcinoma in situ and the workup and locoregional management of early stage invasive breast cancer.

JOURNAL OF THE NATIONAL COMPREHENSIVE CANCER NETWORK (2022)

Article Oncology

Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years

Gerardo Fernandez et al.

Summary: This study evaluated a digital artificial intelligence assay for improving breast cancer grading and predicting recurrence risk. The results showed that the assay was effective in predicting the risk of breast cancer recurrence and had the potential to enhance treatment decision-making by improving gene expression analysis.

BREAST CANCER RESEARCH (2022)

Review Computer Science, Interdisciplinary Applications

Computational Nuclei Segmentation Methods in Digital Pathology: A Survey

Tomohiro Hayakawa et al.

Summary: Pathology is crucial in modern medicine, with nuclei segmentation being particularly important in cancer analysis, diagnosis, and grading. Traditional cancer diagnosis relies on cell morphology and architecture distribution, but computerized approaches in digital pathology are rapidly advancing, offering new avenues for nuclei detection, segmentation, and classification.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2021)

Article Pathology

Histologic grading of breast carcinoma: a multi-institution study of interobserver variation using virtual microscopy

Paula S. Ginter et al.

Summary: The study evaluated interobserver variability among a multi-institutional group of breast pathologists using digital whole slide imaging for breast cancer grading, showing moderate concordance overall, with variations in observer agreement for different grades and components. Discrepancies in grading infrequently led to clinically meaningful changes in prognostic stage, with tubule formation showing the best observer agreement.

MODERN PATHOLOGY (2021)

Article Oncology

SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images

Konstantinos Zormpas-Petridis et al.

Summary: SuperHistopath is an efficient framework for digital pathology image analysis which combines segmentation and classification methods to accurately map the heterogeneity of tumor morphologies. It can classify different types of tissues accurately and discover significant differences in research.

FRONTIERS IN ONCOLOGY (2021)

Article Computer Science, Artificial Intelligence

Recognition of overlapping elliptical objects in a binary image

Tong Zou et al.

Summary: The method proposed in this paper utilizes ellipses to identify overlapping objects, while using polygon approximation and a greedy algorithm to find the optimal ellipses. Experimental results demonstrate that this method achieves higher accuracy and flexibility compared to other methods in cell images and bloodstain patterns.

PATTERN ANALYSIS AND APPLICATIONS (2021)

Article Oncology

Breast Cancer, Version 3.2020

William J. Gradishar et al.

JOURNAL OF THE NATIONAL COMPREHENSIVE CANCER NETWORK (2020)

Article Computer Science, Interdisciplinary Applications

Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map

Peter Naylor et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Computer Science, Artificial Intelligence

BACH: Grand challenge on breast cancer histology images

Guilherme Aresta et al.

MEDICAL IMAGE ANALYSIS (2019)

Article Computer Science, Artificial Intelligence

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Simon Graham et al.

MEDICAL IMAGE ANALYSIS (2019)

Article Pathology

Breast cancer histologic grading using digital microscopy: concordance and outcome association

Emad A. Rakha et al.

JOURNAL OF CLINICAL PATHOLOGY (2018)

Article Engineering, Biomedical

Machine learning approaches to analyze histological images of tissues from radical prostatectomies

Arkadiusz Gertych et al.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2015)

Correction Medicine, General & Internal

Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens (vol 313, pg 1122, 2015)

Joann G. Elmore et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2015)

Article Medicine, General & Internal

Diagnostic Concordance Among Pathologists interpreting Breast Biopsy Specimens

Joann G. Elmore et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2015)

Article Multidisciplinary Sciences

Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images

Mitko Veta et al.

PLOS ONE (2013)