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Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

BACKGROUND: In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence). METHODS: An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logis...

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Autores principales: Akrami, Majid, Arasteh, Peyman, Eghbali, Tannaz, Shahraki, Hadi Raeisi, Tahmasebi, Sedigheh, Zangouri, Vahid, Rezaianzadeh, Abbas, Talei, Abdolrasoul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136222/
https://www.ncbi.nlm.nih.gov/pubmed/30208904
http://dx.doi.org/10.1186/s12957-018-1489-0
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author Akrami, Majid
Arasteh, Peyman
Eghbali, Tannaz
Shahraki, Hadi Raeisi
Tahmasebi, Sedigheh
Zangouri, Vahid
Rezaianzadeh, Abbas
Talei, Abdolrasoul
author_facet Akrami, Majid
Arasteh, Peyman
Eghbali, Tannaz
Shahraki, Hadi Raeisi
Tahmasebi, Sedigheh
Zangouri, Vahid
Rezaianzadeh, Abbas
Talei, Abdolrasoul
author_sort Akrami, Majid
collection PubMed
description BACKGROUND: In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence). METHODS: An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logistic regression model. RESULTS: For recurrence < 5 years, age (OR 0.96, 95% CI = 0.95–0.97), number of pregnancies (OR 0.94, 95% CI = 0.89–0.99), family history of other cancers (OR 0.73, 95% CI = 0.60–0.89), hormone therapy (OR 0.76, 95% CI = 0.61–0.96), dissected lymph nodes (OR 0.98, 95% CI = 0.97–0.99), right-sided BC (OR 0.87, 95% CI = 0.77–0.99), diabetes (OR 0.77, 95% CI = 0.60–0.98), history of breast operations (OR 0.38, 95% CI = 0.17–0.88), smoking (OR 5.72, 95% CI = 2.11–15.55), history of breast disease (OR 3.32, 95% CI = 1.92–5.76), in situ component (OR 1.58, 95% CI = 1.35–1.84), tumor necrosis (OR 1.87, 95% CI = 1.57–2.22), sentinel lymph node biopsy (SLNB) (OR 2.90, 95% CI = 2.05–4.11) and SLNB+axillary node dissection (OR 3.50, 95% CI = 2.26–5.42), grade 3 (OR 1.79, 95% CI = 1.46–2.21), stage 2 (OR 2.71, 95% CI = 2.18–3.35), stages 3 and 4 (OR 5.01, 95% CI = 3.52–7.13), and mastectomy+radiotherapy (OR 2.97, 95% CI = 2.39–3.68) were predictors of recurrence < 5 years. Moreover, relative to mastectomy without radiotherapy (as reference for comparison), quadrantectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence < 5 years. (OR 17.58, 95% CI = 6.70–46.10 vs. OR: 2.50, 95% CI = 2–3.12). Accuracy, sensitivity, and specificity of the model were 82%, 75.6%, and 74.9%, respectively. For recurrence > 5 years, stage 2 cancer (OR 1.67, 95% CI = 1.31–2.14) and radiotherapy+mastectomy (OR 2.45, 95% CI = 1.81–3.32) were significant predictors; furthermore, relative to mastectomy without radiotherapy (as reference for comparison), quadranectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence > 5 years (OR 7.62, 95% CI = 1.52–38.15 vs. OR 1.75, 95% CI = 1.32–2.32). Accuracy, sensitivity, and specificity of the model were 71%, 78.8%, and 55.8%, respectively. CONCLUSION: For the first time, we constructed models for estimating recurrence based on timing of recurrence which are among the most applicable models with excellent accuracy (> 80%). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12957-018-1489-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-61362222018-09-15 Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different? Akrami, Majid Arasteh, Peyman Eghbali, Tannaz Shahraki, Hadi Raeisi Tahmasebi, Sedigheh Zangouri, Vahid Rezaianzadeh, Abbas Talei, Abdolrasoul World J Surg Oncol Research BACKGROUND: In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence). METHODS: An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logistic regression model. RESULTS: For recurrence < 5 years, age (OR 0.96, 95% CI = 0.95–0.97), number of pregnancies (OR 0.94, 95% CI = 0.89–0.99), family history of other cancers (OR 0.73, 95% CI = 0.60–0.89), hormone therapy (OR 0.76, 95% CI = 0.61–0.96), dissected lymph nodes (OR 0.98, 95% CI = 0.97–0.99), right-sided BC (OR 0.87, 95% CI = 0.77–0.99), diabetes (OR 0.77, 95% CI = 0.60–0.98), history of breast operations (OR 0.38, 95% CI = 0.17–0.88), smoking (OR 5.72, 95% CI = 2.11–15.55), history of breast disease (OR 3.32, 95% CI = 1.92–5.76), in situ component (OR 1.58, 95% CI = 1.35–1.84), tumor necrosis (OR 1.87, 95% CI = 1.57–2.22), sentinel lymph node biopsy (SLNB) (OR 2.90, 95% CI = 2.05–4.11) and SLNB+axillary node dissection (OR 3.50, 95% CI = 2.26–5.42), grade 3 (OR 1.79, 95% CI = 1.46–2.21), stage 2 (OR 2.71, 95% CI = 2.18–3.35), stages 3 and 4 (OR 5.01, 95% CI = 3.52–7.13), and mastectomy+radiotherapy (OR 2.97, 95% CI = 2.39–3.68) were predictors of recurrence < 5 years. Moreover, relative to mastectomy without radiotherapy (as reference for comparison), quadrantectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence < 5 years. (OR 17.58, 95% CI = 6.70–46.10 vs. OR: 2.50, 95% CI = 2–3.12). Accuracy, sensitivity, and specificity of the model were 82%, 75.6%, and 74.9%, respectively. For recurrence > 5 years, stage 2 cancer (OR 1.67, 95% CI = 1.31–2.14) and radiotherapy+mastectomy (OR 2.45, 95% CI = 1.81–3.32) were significant predictors; furthermore, relative to mastectomy without radiotherapy (as reference for comparison), quadranectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence > 5 years (OR 7.62, 95% CI = 1.52–38.15 vs. OR 1.75, 95% CI = 1.32–2.32). Accuracy, sensitivity, and specificity of the model were 71%, 78.8%, and 55.8%, respectively. CONCLUSION: For the first time, we constructed models for estimating recurrence based on timing of recurrence which are among the most applicable models with excellent accuracy (> 80%). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12957-018-1489-0) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-12 /pmc/articles/PMC6136222/ /pubmed/30208904 http://dx.doi.org/10.1186/s12957-018-1489-0 Text en © The Author(s). 2018 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Akrami, Majid
Arasteh, Peyman
Eghbali, Tannaz
Shahraki, Hadi Raeisi
Tahmasebi, Sedigheh
Zangouri, Vahid
Rezaianzadeh, Abbas
Talei, Abdolrasoul
Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?
title Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?
title_full Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?
title_fullStr Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?
title_full_unstemmed Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?
title_short Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?
title_sort introducing novel and comprehensive models for predicting recurrence in breast cancer using the group lasso approach: are estimates of early and late recurrence different?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136222/
https://www.ncbi.nlm.nih.gov/pubmed/30208904
http://dx.doi.org/10.1186/s12957-018-1489-0
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