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Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network
The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three cl...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221636/ https://www.ncbi.nlm.nih.gov/pubmed/35741157 http://dx.doi.org/10.3390/diagnostics12061347 |
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author | Abel, Frederik Landsmann, Anna Hejduk, Patryk Ruppert, Carlotta Borkowski, Karol Ciritsis, Alexander Rossi, Cristina Boss, Andreas |
author_facet | Abel, Frederik Landsmann, Anna Hejduk, Patryk Ruppert, Carlotta Borkowski, Karol Ciritsis, Alexander Rossi, Cristina Boss, Andreas |
author_sort | Abel, Frederik |
collection | PubMed |
description | The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms. |
format | Online Article Text |
id | pubmed-9221636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92216362022-06-24 Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network Abel, Frederik Landsmann, Anna Hejduk, Patryk Ruppert, Carlotta Borkowski, Karol Ciritsis, Alexander Rossi, Cristina Boss, Andreas Diagnostics (Basel) Article The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms. MDPI 2022-05-29 /pmc/articles/PMC9221636/ /pubmed/35741157 http://dx.doi.org/10.3390/diagnostics12061347 Text en © 2022 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 Abel, Frederik Landsmann, Anna Hejduk, Patryk Ruppert, Carlotta Borkowski, Karol Ciritsis, Alexander Rossi, Cristina Boss, Andreas Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network |
title | Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network |
title_full | Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network |
title_fullStr | Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network |
title_full_unstemmed | Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network |
title_short | Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network |
title_sort | detecting abnormal axillary lymph nodes on mammograms using a deep convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221636/ https://www.ncbi.nlm.nih.gov/pubmed/35741157 http://dx.doi.org/10.3390/diagnostics12061347 |
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