Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1785079604673773568 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10377791 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kotelesmariamagdalena comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach AT vigdorovitsalon comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach AT kumardarshan comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach AT mihaiioanamaria comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach AT jurescuaura comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach AT ghejuadelina comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach AT bucuradeline comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach AT harichoctaviaoana comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach AT olteanugheorgheemilian comparativeevaluationofbreastductalcarcinomagradingadeeplearningmodelandgeneralpathologistsassessmentapproach |