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Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study

OBJECTIVE: We aimed to develop an ovarian cancer-specific predictive framework for clinical use platinum-sensitivity and prognosis using machine learning methods based on multiple biomarkers, including circulating tumor cells (CTCs). PATIENTS AND METHODS: We enrolled 156 epithelial ovarian cancer (E...

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Autores principales: Ma, Jun, Yang, Jiani, Jin, Yue, Cheng, Shanshan, Huang, Shan, Zhang, Nan, Wang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140950/
https://www.ncbi.nlm.nih.gov/pubmed/34040391
http://dx.doi.org/10.2147/OTT.S307546
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author Ma, Jun
Yang, Jiani
Jin, Yue
Cheng, Shanshan
Huang, Shan
Zhang, Nan
Wang, Yu
author_facet Ma, Jun
Yang, Jiani
Jin, Yue
Cheng, Shanshan
Huang, Shan
Zhang, Nan
Wang, Yu
author_sort Ma, Jun
collection PubMed
description OBJECTIVE: We aimed to develop an ovarian cancer-specific predictive framework for clinical use platinum-sensitivity and prognosis using machine learning methods based on multiple biomarkers, including circulating tumor cells (CTCs). PATIENTS AND METHODS: We enrolled 156 epithelial ovarian cancer (EOC) patients, randomly assigned into the training and validation cohorts. Eight machine learning classifiers, including Random Forest (RF), Support Vector Machine, Gradient Boosting Machine, Conditional RF, Neural Network, Naive Bayes, Elastic Net, and Logistic Regression, were used to derive predictive information from 11 peripheral blood parameters, including CTCs. Through the advanced CanPatrol CTC-enrichment technique, we detect CTCs and classify them into subpopulations: epithelial, mesenchymal, and hybrids. Survival curves were generated by Kaplan–Meier method and calculated through the Log rank test. RESULTS: Machine learning techniques, especially the Random Forest classifier, were superior to conventional regression-based analyses in predicting multiple clinical parameters related to EOC. The values for the receiver operating characteristic (ROC) curve for segregating EOC with advanced clinical stages and platinum-sensitivity were 0.796 (95% CI, 0.727–0.866) and 0.809 (95% CI, 0.742–0.876), respectively. Stepwise, we used the unsupervised clustering analysis to identify EOC subgroups with significantly worse overall survival (OS), especially in the advanced-stage group with the p-value of 0.0018 (HR, 2.716; 95% CI, 1.602–4.605) for progression-free survival (PFS) and 0.0037 (HR, 2.359; 95% CI, 1.752–6.390) for overall survival (OS). CONCLUSION: Machine learning systems could provide risk stratification for EOC patients before initial intervention through blood variables, including circulating tumor cells. The predictive algorithms could facilitate personalized treatment options through promising pre-treatment stratification of EOC patients. TRIAL REGISTRATION: ChiCTR-DDD-16009601 Registered 25 October 2016.
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spelling pubmed-81409502021-05-25 Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study Ma, Jun Yang, Jiani Jin, Yue Cheng, Shanshan Huang, Shan Zhang, Nan Wang, Yu Onco Targets Ther Original Research OBJECTIVE: We aimed to develop an ovarian cancer-specific predictive framework for clinical use platinum-sensitivity and prognosis using machine learning methods based on multiple biomarkers, including circulating tumor cells (CTCs). PATIENTS AND METHODS: We enrolled 156 epithelial ovarian cancer (EOC) patients, randomly assigned into the training and validation cohorts. Eight machine learning classifiers, including Random Forest (RF), Support Vector Machine, Gradient Boosting Machine, Conditional RF, Neural Network, Naive Bayes, Elastic Net, and Logistic Regression, were used to derive predictive information from 11 peripheral blood parameters, including CTCs. Through the advanced CanPatrol CTC-enrichment technique, we detect CTCs and classify them into subpopulations: epithelial, mesenchymal, and hybrids. Survival curves were generated by Kaplan–Meier method and calculated through the Log rank test. RESULTS: Machine learning techniques, especially the Random Forest classifier, were superior to conventional regression-based analyses in predicting multiple clinical parameters related to EOC. The values for the receiver operating characteristic (ROC) curve for segregating EOC with advanced clinical stages and platinum-sensitivity were 0.796 (95% CI, 0.727–0.866) and 0.809 (95% CI, 0.742–0.876), respectively. Stepwise, we used the unsupervised clustering analysis to identify EOC subgroups with significantly worse overall survival (OS), especially in the advanced-stage group with the p-value of 0.0018 (HR, 2.716; 95% CI, 1.602–4.605) for progression-free survival (PFS) and 0.0037 (HR, 2.359; 95% CI, 1.752–6.390) for overall survival (OS). CONCLUSION: Machine learning systems could provide risk stratification for EOC patients before initial intervention through blood variables, including circulating tumor cells. The predictive algorithms could facilitate personalized treatment options through promising pre-treatment stratification of EOC patients. TRIAL REGISTRATION: ChiCTR-DDD-16009601 Registered 25 October 2016. Dove 2021-05-18 /pmc/articles/PMC8140950/ /pubmed/34040391 http://dx.doi.org/10.2147/OTT.S307546 Text en © 2021 Ma et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Ma, Jun
Yang, Jiani
Jin, Yue
Cheng, Shanshan
Huang, Shan
Zhang, Nan
Wang, Yu
Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study
title Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study
title_full Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study
title_fullStr Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study
title_full_unstemmed Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study
title_short Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study
title_sort artificial intelligence based on blood biomarkers including ctcs predicts outcomes in epithelial ovarian cancer: a prospective study
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140950/
https://www.ncbi.nlm.nih.gov/pubmed/34040391
http://dx.doi.org/10.2147/OTT.S307546
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