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Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography

Introduction Early breast cancer detection with screening mammography has been shown to reduce mortality and improve breast cancer survival. This study aims to evaluate the ability of an artificial intelligence computer-aided detection (AI CAD) system to detect biopsy-proven invasive lobular carcino...

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Autores principales: Arce, Sylvia, Vijay, Arunima, Yim, Eunice, Spiguel, Lisa R, Hanna, Mariam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249706/
https://www.ncbi.nlm.nih.gov/pubmed/37303390
http://dx.doi.org/10.7759/cureus.38770
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author Arce, Sylvia
Vijay, Arunima
Yim, Eunice
Spiguel, Lisa R
Hanna, Mariam
author_facet Arce, Sylvia
Vijay, Arunima
Yim, Eunice
Spiguel, Lisa R
Hanna, Mariam
author_sort Arce, Sylvia
collection PubMed
description Introduction Early breast cancer detection with screening mammography has been shown to reduce mortality and improve breast cancer survival. This study aims to evaluate the ability of an artificial intelligence computer-aided detection (AI CAD) system to detect biopsy-proven invasive lobular carcinoma (ILC) on digital mammography. Methods This retrospective study reviewed mammograms of patients who were diagnosed with biopsy-proved ILC between January 1, 2017, and January 1, 2022. All mammograms were analyzed using cmAssist(®) (CureMetrix, San Diego, California, United States), which is an AI CAD for mammography. The AI CAD sensitivity for detecting ILC on mammography was calculated and further subdivided by lesion type, mass shape, and mass margins. To account for the within-subject correlation, generalized linear mixed models were implemented to investigate the association between age, family history, and breast density and whether the AI detected a false positive or true positive. Odds ratios, 95% confidence intervals, and p-values were also calculated. Results A total of 124 patients with 153 biopsy-proven ILC lesions were included. The AI CAD detected ILC on mammography with a sensitivity of 80%. The AI CAD had the highest sensitivity for detecting calcifications (100%), masses with irregular shape (82%), and masses with spiculated margins (86%). However, 88% of mammograms had at least one false positive mark with an average number of 3.9 false positive marks per mammogram. Conclusion The AI CAD system evaluated was successful in marking the malignancy in digital mammography. However, the numerous annotations confounded the ability to determine its overall accuracy and this reduces its potential use in real-life practice.
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spelling pubmed-102497062023-06-09 Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography Arce, Sylvia Vijay, Arunima Yim, Eunice Spiguel, Lisa R Hanna, Mariam Cureus Radiology Introduction Early breast cancer detection with screening mammography has been shown to reduce mortality and improve breast cancer survival. This study aims to evaluate the ability of an artificial intelligence computer-aided detection (AI CAD) system to detect biopsy-proven invasive lobular carcinoma (ILC) on digital mammography. Methods This retrospective study reviewed mammograms of patients who were diagnosed with biopsy-proved ILC between January 1, 2017, and January 1, 2022. All mammograms were analyzed using cmAssist(®) (CureMetrix, San Diego, California, United States), which is an AI CAD for mammography. The AI CAD sensitivity for detecting ILC on mammography was calculated and further subdivided by lesion type, mass shape, and mass margins. To account for the within-subject correlation, generalized linear mixed models were implemented to investigate the association between age, family history, and breast density and whether the AI detected a false positive or true positive. Odds ratios, 95% confidence intervals, and p-values were also calculated. Results A total of 124 patients with 153 biopsy-proven ILC lesions were included. The AI CAD detected ILC on mammography with a sensitivity of 80%. The AI CAD had the highest sensitivity for detecting calcifications (100%), masses with irregular shape (82%), and masses with spiculated margins (86%). However, 88% of mammograms had at least one false positive mark with an average number of 3.9 false positive marks per mammogram. Conclusion The AI CAD system evaluated was successful in marking the malignancy in digital mammography. However, the numerous annotations confounded the ability to determine its overall accuracy and this reduces its potential use in real-life practice. Cureus 2023-05-09 /pmc/articles/PMC10249706/ /pubmed/37303390 http://dx.doi.org/10.7759/cureus.38770 Text en Copyright © 2023, Arce et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Radiology
Arce, Sylvia
Vijay, Arunima
Yim, Eunice
Spiguel, Lisa R
Hanna, Mariam
Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography
title Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography
title_full Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography
title_fullStr Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography
title_full_unstemmed Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography
title_short Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography
title_sort evaluation of an artificial intelligence system for detection of invasive lobular carcinoma on digital mammography
topic Radiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249706/
https://www.ncbi.nlm.nih.gov/pubmed/37303390
http://dx.doi.org/10.7759/cureus.38770
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