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Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method
BACKGROUND: In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unneces...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10265786/ https://www.ncbi.nlm.nih.gov/pubmed/37312026 http://dx.doi.org/10.1186/s12880-023-01023-4 |
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author | Meng, Mingzhu Li, Hong Zhang, Ming He, Guangyuan Wang, Long Shen, Dong |
author_facet | Meng, Mingzhu Li, Hong Zhang, Ming He, Guangyuan Wang, Long Shen, Dong |
author_sort | Meng, Mingzhu |
collection | PubMed |
description | BACKGROUND: In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions. METHODS: A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2. RESULTS: The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110). CONCLUSION: The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-023-01023-4. |
format | Online Article Text |
id | pubmed-10265786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102657862023-06-15 Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method Meng, Mingzhu Li, Hong Zhang, Ming He, Guangyuan Wang, Long Shen, Dong BMC Med Imaging Research BACKGROUND: In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions. METHODS: A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2. RESULTS: The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110). CONCLUSION: The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-023-01023-4. BioMed Central 2023-06-13 /pmc/articles/PMC10265786/ /pubmed/37312026 http://dx.doi.org/10.1186/s12880-023-01023-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Meng, Mingzhu Li, Hong Zhang, Ming He, Guangyuan Wang, Long Shen, Dong Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method |
title | Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method |
title_full | Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method |
title_fullStr | Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method |
title_full_unstemmed | Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method |
title_short | Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method |
title_sort | reducing the number of unnecessary biopsies for mammographic bi-rads 4 lesions through a deep transfer learning method |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10265786/ https://www.ncbi.nlm.nih.gov/pubmed/37312026 http://dx.doi.org/10.1186/s12880-023-01023-4 |
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