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Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas

PURPOSE: The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas. METHODS: This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant...

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Autores principales: Fan, Ziwen, Sun, Zhiyan, Fang, Shengyu, Li, Yiming, Liu, Xing, Liang, Yucha, Liu, Yukun, Zhou, Chunyao, Zhu, Qiang, Zhang, Hong, Li, Tianshi, Li, Shaowu, Jiang, Tao, Wang, Yinyan, Wang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290517/
https://www.ncbi.nlm.nih.gov/pubmed/34295805
http://dx.doi.org/10.3389/fonc.2021.616740
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author Fan, Ziwen
Sun, Zhiyan
Fang, Shengyu
Li, Yiming
Liu, Xing
Liang, Yucha
Liu, Yukun
Zhou, Chunyao
Zhu, Qiang
Zhang, Hong
Li, Tianshi
Li, Shaowu
Jiang, Tao
Wang, Yinyan
Wang, Lei
author_facet Fan, Ziwen
Sun, Zhiyan
Fang, Shengyu
Li, Yiming
Liu, Xing
Liang, Yucha
Liu, Yukun
Zhou, Chunyao
Zhu, Qiang
Zhang, Hong
Li, Tianshi
Li, Shaowu
Jiang, Tao
Wang, Yinyan
Wang, Lei
author_sort Fan, Ziwen
collection PubMed
description PURPOSE: The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas. METHODS: This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status. RESULTS: Six valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733–0.8755) and 0.758 (0.6879–0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201–0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group. CONCLUSION: Combined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.
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spelling pubmed-82905172021-07-21 Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas Fan, Ziwen Sun, Zhiyan Fang, Shengyu Li, Yiming Liu, Xing Liang, Yucha Liu, Yukun Zhou, Chunyao Zhu, Qiang Zhang, Hong Li, Tianshi Li, Shaowu Jiang, Tao Wang, Yinyan Wang, Lei Front Oncol Oncology PURPOSE: The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas. METHODS: This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status. RESULTS: Six valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733–0.8755) and 0.758 (0.6879–0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201–0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group. CONCLUSION: Combined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology. Frontiers Media S.A. 2021-07-06 /pmc/articles/PMC8290517/ /pubmed/34295805 http://dx.doi.org/10.3389/fonc.2021.616740 Text en Copyright © 2021 Fan, Sun, Fang, Li, Liu, Liang, Liu, Zhou, Zhu, Zhang, Li, Li, Jiang, Wang and Wang 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
Fan, Ziwen
Sun, Zhiyan
Fang, Shengyu
Li, Yiming
Liu, Xing
Liang, Yucha
Liu, Yukun
Zhou, Chunyao
Zhu, Qiang
Zhang, Hong
Li, Tianshi
Li, Shaowu
Jiang, Tao
Wang, Yinyan
Wang, Lei
Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas
title Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas
title_full Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas
title_fullStr Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas
title_full_unstemmed Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas
title_short Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas
title_sort preoperative radiomics analysis of 1p/19q status in who grade ii gliomas
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290517/
https://www.ncbi.nlm.nih.gov/pubmed/34295805
http://dx.doi.org/10.3389/fonc.2021.616740
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