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A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI
BACKGROUND: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature. METHODS: Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard....
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6987284/ https://www.ncbi.nlm.nih.gov/pubmed/31993839 http://dx.doi.org/10.1186/s41747-019-0131-4 |
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author | D’Amico, Natascha C. Grossi, Enzo Valbusa, Giovanni Rigiroli, Francesca Colombo, Bernardo Buscema, Massimo Fazzini, Deborah Ali, Marco Malasevschi, Ala Cornalba, Gianpaolo Papa, Sergio |
author_facet | D’Amico, Natascha C. Grossi, Enzo Valbusa, Giovanni Rigiroli, Francesca Colombo, Bernardo Buscema, Massimo Fazzini, Deborah Ali, Marco Malasevschi, Ala Cornalba, Gianpaolo Papa, Sergio |
author_sort | D’Amico, Natascha C. |
collection | PubMed |
description | BACKGROUND: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature. METHODS: Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method (“training with input selection and testing”) was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs). RESULTS: A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87–100%), a specificity of 37/41 (90%, 95% CI 77–97%), and an accuracy of 64/68 (94%, 95% CI 86–98%). CONCLUSION: This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI. |
format | Online Article Text |
id | pubmed-6987284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-69872842020-02-11 A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI D’Amico, Natascha C. Grossi, Enzo Valbusa, Giovanni Rigiroli, Francesca Colombo, Bernardo Buscema, Massimo Fazzini, Deborah Ali, Marco Malasevschi, Ala Cornalba, Gianpaolo Papa, Sergio Eur Radiol Exp Original Article BACKGROUND: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature. METHODS: Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method (“training with input selection and testing”) was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs). RESULTS: A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87–100%), a specificity of 37/41 (90%, 95% CI 77–97%), and an accuracy of 64/68 (94%, 95% CI 86–98%). CONCLUSION: This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI. Springer International Publishing 2020-01-28 /pmc/articles/PMC6987284/ /pubmed/31993839 http://dx.doi.org/10.1186/s41747-019-0131-4 Text en © The Author(s) 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article D’Amico, Natascha C. Grossi, Enzo Valbusa, Giovanni Rigiroli, Francesca Colombo, Bernardo Buscema, Massimo Fazzini, Deborah Ali, Marco Malasevschi, Ala Cornalba, Gianpaolo Papa, Sergio A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI |
title | A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI |
title_full | A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI |
title_fullStr | A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI |
title_full_unstemmed | A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI |
title_short | A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI |
title_sort | machine learning approach for differentiating malignant from benign enhancing foci on breast mri |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6987284/ https://www.ncbi.nlm.nih.gov/pubmed/31993839 http://dx.doi.org/10.1186/s41747-019-0131-4 |
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