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Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach

Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies amo...

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Autores principales: Köteles, Maria Magdalena, Vigdorovits, Alon, Kumar, Darshan, Mihai, Ioana-Maria, Jurescu, Aura, Gheju, Adelina, Bucur, Adeline, Harich, Octavia Oana, Olteanu, Gheorghe-Emilian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377791/
https://www.ncbi.nlm.nih.gov/pubmed/37510069
http://dx.doi.org/10.3390/diagnostics13142326
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author Köteles, Maria Magdalena
Vigdorovits, Alon
Kumar, Darshan
Mihai, Ioana-Maria
Jurescu, Aura
Gheju, Adelina
Bucur, Adeline
Harich, Octavia Oana
Olteanu, Gheorghe-Emilian
author_facet Köteles, Maria Magdalena
Vigdorovits, Alon
Kumar, Darshan
Mihai, Ioana-Maria
Jurescu, Aura
Gheju, Adelina
Bucur, Adeline
Harich, Octavia Oana
Olteanu, Gheorghe-Emilian
author_sort Köteles, Maria Magdalena
collection PubMed
description Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model’s grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.
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spelling pubmed-103777912023-07-29 Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach Köteles, Maria Magdalena Vigdorovits, Alon Kumar, Darshan Mihai, Ioana-Maria Jurescu, Aura Gheju, Adelina Bucur, Adeline Harich, Octavia Oana Olteanu, Gheorghe-Emilian Diagnostics (Basel) Article Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model’s grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach. MDPI 2023-07-10 /pmc/articles/PMC10377791/ /pubmed/37510069 http://dx.doi.org/10.3390/diagnostics13142326 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Köteles, Maria Magdalena
Vigdorovits, Alon
Kumar, Darshan
Mihai, Ioana-Maria
Jurescu, Aura
Gheju, Adelina
Bucur, Adeline
Harich, Octavia Oana
Olteanu, Gheorghe-Emilian
Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach
title Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach
title_full Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach
title_fullStr Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach
title_full_unstemmed Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach
title_short Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach
title_sort comparative evaluation of breast ductal carcinoma grading: a deep-learning model and general pathologists’ assessment approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377791/
https://www.ncbi.nlm.nih.gov/pubmed/37510069
http://dx.doi.org/10.3390/diagnostics13142326
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