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Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer

The clinical benefit of neoadjuvant chemotherapy (NACT) before concurrent chemoradiotherapy (CCRT) vs. adjuvant chemotherapy after CCRT is debated. Non-response to platinum-based NACT is a major contributor to poor prognosis, but there is currently no reliable method for predicting the response to N...

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Autores principales: Huang, Yibao, Zhu, Qingqing, Xue, Liru, Zhu, Xiaoran, Chen, Yingying, Wu, Mingfu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9001844/
https://www.ncbi.nlm.nih.gov/pubmed/35425697
http://dx.doi.org/10.3389/fonc.2022.817250
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author Huang, Yibao
Zhu, Qingqing
Xue, Liru
Zhu, Xiaoran
Chen, Yingying
Wu, Mingfu
author_facet Huang, Yibao
Zhu, Qingqing
Xue, Liru
Zhu, Xiaoran
Chen, Yingying
Wu, Mingfu
author_sort Huang, Yibao
collection PubMed
description The clinical benefit of neoadjuvant chemotherapy (NACT) before concurrent chemoradiotherapy (CCRT) vs. adjuvant chemotherapy after CCRT is debated. Non-response to platinum-based NACT is a major contributor to poor prognosis, but there is currently no reliable method for predicting the response to NACT (rNACT) in patients with locally advanced cervical cancer (LACC). In this study we developed a machine learning (ML)-assisted model to accurately predict rNACT. We retrospectively analyzed data on 636 patients diagnosed with stage IB2 to IIA2 cervical cancer at our hospital between January 1, 2010 and December 1, 2020. Five ML-assisted models were developed from candidate clinical features using 2-step estimation methods. Receiver operating characteristic curve (ROC), clinical impact curve, and decision curve analyses were performed to evaluate the robustness and clinical applicability of each model. A total of 30 candidate variables were ultimately included in the rNACT prediction model. The areas under the ROC curve of models constructed using the random forest classifier (RFC), support vector machine, eXtreme gradient boosting, artificial neural network, and decision tree ranged from 0.682 to 0.847. The RFC model had the highest predictive accuracy, which was achieved by incorporating inflammatory factors such as platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, neutrophil-to-albumin ratio, and lymphocyte-to-monocyte ratio. These results demonstrate that the ML-based prediction model developed using the RFC can be used to identify LACC patients who are likely to respond to rNACT, which can guide treatment selection and improve clinical outcomes.
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spelling pubmed-90018442022-04-13 Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Huang, Yibao Zhu, Qingqing Xue, Liru Zhu, Xiaoran Chen, Yingying Wu, Mingfu Front Oncol Oncology The clinical benefit of neoadjuvant chemotherapy (NACT) before concurrent chemoradiotherapy (CCRT) vs. adjuvant chemotherapy after CCRT is debated. Non-response to platinum-based NACT is a major contributor to poor prognosis, but there is currently no reliable method for predicting the response to NACT (rNACT) in patients with locally advanced cervical cancer (LACC). In this study we developed a machine learning (ML)-assisted model to accurately predict rNACT. We retrospectively analyzed data on 636 patients diagnosed with stage IB2 to IIA2 cervical cancer at our hospital between January 1, 2010 and December 1, 2020. Five ML-assisted models were developed from candidate clinical features using 2-step estimation methods. Receiver operating characteristic curve (ROC), clinical impact curve, and decision curve analyses were performed to evaluate the robustness and clinical applicability of each model. A total of 30 candidate variables were ultimately included in the rNACT prediction model. The areas under the ROC curve of models constructed using the random forest classifier (RFC), support vector machine, eXtreme gradient boosting, artificial neural network, and decision tree ranged from 0.682 to 0.847. The RFC model had the highest predictive accuracy, which was achieved by incorporating inflammatory factors such as platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, neutrophil-to-albumin ratio, and lymphocyte-to-monocyte ratio. These results demonstrate that the ML-based prediction model developed using the RFC can be used to identify LACC patients who are likely to respond to rNACT, which can guide treatment selection and improve clinical outcomes. Frontiers Media S.A. 2022-03-29 /pmc/articles/PMC9001844/ /pubmed/35425697 http://dx.doi.org/10.3389/fonc.2022.817250 Text en Copyright © 2022 Huang, Zhu, Xue, Zhu, Chen and Wu 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
Huang, Yibao
Zhu, Qingqing
Xue, Liru
Zhu, Xiaoran
Chen, Yingying
Wu, Mingfu
Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer
title Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer
title_full Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer
title_fullStr Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer
title_full_unstemmed Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer
title_short Machine Learning-Assisted Ensemble Analysis for the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer
title_sort machine learning-assisted ensemble analysis for the prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9001844/
https://www.ncbi.nlm.nih.gov/pubmed/35425697
http://dx.doi.org/10.3389/fonc.2022.817250
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