Cargando…
Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics
OBJECTIVE: To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC). METHODS: Between November...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520481/ https://www.ncbi.nlm.nih.gov/pubmed/36185309 http://dx.doi.org/10.3389/fonc.2022.890659 |
_version_ | 1784799634029281280 |
---|---|
author | Feng, Bao Liu, Zhuangsheng Liu, Yu Chen, Yehang Zhou, Haoyang Cui, Enming Li, Xiaoping Chen, Xiangmeng Li, Ronggang Yu, Tianyou Zhang, Ling Long, Wansheng |
author_facet | Feng, Bao Liu, Zhuangsheng Liu, Yu Chen, Yehang Zhou, Haoyang Cui, Enming Li, Xiaoping Chen, Xiangmeng Li, Ronggang Yu, Tianyou Zhang, Ling Long, Wansheng |
author_sort | Feng, Bao |
collection | PubMed |
description | OBJECTIVE: To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC). METHODS: Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI. RESULTS: In the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions (AUC: 0.852 vs. 0.726, p < 0.001; IDI = 0.092, p < 0.001; NRI = 0.554, p < 0.001). The diagnostic performance of TLS was not affected by the menstrual state, molecular subtype, or contrast agent type (all p > 0.05). Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility. CONCLUSIONS: An AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC. |
format | Online Article Text |
id | pubmed-9520481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95204812022-09-30 Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics Feng, Bao Liu, Zhuangsheng Liu, Yu Chen, Yehang Zhou, Haoyang Cui, Enming Li, Xiaoping Chen, Xiangmeng Li, Ronggang Yu, Tianyou Zhang, Ling Long, Wansheng Front Oncol Oncology OBJECTIVE: To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC). METHODS: Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI. RESULTS: In the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions (AUC: 0.852 vs. 0.726, p < 0.001; IDI = 0.092, p < 0.001; NRI = 0.554, p < 0.001). The diagnostic performance of TLS was not affected by the menstrual state, molecular subtype, or contrast agent type (all p > 0.05). Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility. CONCLUSIONS: An AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC. Frontiers Media S.A. 2022-09-15 /pmc/articles/PMC9520481/ /pubmed/36185309 http://dx.doi.org/10.3389/fonc.2022.890659 Text en Copyright © 2022 Feng, Liu, Liu, Chen, Zhou, Cui, Li, Chen, Li, Yu, Zhang and Long https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Feng, Bao Liu, Zhuangsheng Liu, Yu Chen, Yehang Zhou, Haoyang Cui, Enming Li, Xiaoping Chen, Xiangmeng Li, Ronggang Yu, Tianyou Zhang, Ling Long, Wansheng Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics |
title | Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics |
title_full | Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics |
title_fullStr | Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics |
title_full_unstemmed | Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics |
title_short | Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics |
title_sort | predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: transfer learning vs. radiomics |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520481/ https://www.ncbi.nlm.nih.gov/pubmed/36185309 http://dx.doi.org/10.3389/fonc.2022.890659 |
work_keys_str_mv | AT fengbao predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT liuzhuangsheng predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT liuyu predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT chenyehang predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT zhouhaoyang predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT cuienming predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT lixiaoping predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT chenxiangmeng predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT lironggang predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT yutianyou predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT zhangling predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics AT longwansheng predictinglymphovascularinvasioninclinicallynodenegativebreastcancerdetectedbyabbreviatedmagneticresonanceimagingtransferlearningvsradiomics |