| Item type |
文献 / Documents(1) |
| 公開日 |
2020-08-04 |
| アクセス権 |
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|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1038/s41598-019-55922-0 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1038/s41598-019-55922-0 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis |
|
言語 |
en |
| 著者 |
ナカモト, タカヒロ
タカハシ, ワタル
芳賀, 昭弘
タカハシ, サトシ
キリュウ, シゲル
ナワ, カナブ
オオタ, タケシ
オザキ, ショウ
ノザワ, ユウキ
タナカ, ショウタ
ムカサ, アキタケ
ナカガワ, ケイイチ
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2-weighted magnetic resonance images (T2WIs). We proposed a framework and applied it to CE-T1WIs and T2WIs (with tumor region data) acquired preoperatively from 157 patients with malignant glioma (grade III: 55, grade IV: 102) as the primary dataset and 67 patients with malignant glioma (grade III: 22, grade IV: 45) as the validation dataset. Radiomic features such as size/shape, intensity, histogram, and texture features were extracted from the tumor regions on the CE-T1WIs and T2WIs. The Wilcoxon–Mann–Whitney (WMW) test and least absolute shrinkage and selection operator logistic regression (LASSO-LR) were employed to select the radiomic features. Various machine learning (ML) algorithms were used to construct prediction models for the malignant glioma grades using the selected radiomic features. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the prediction models in the primary dataset. The selected radiomic features for all folds in the LOOCV of the primary dataset were used to perform an independent validation. As evaluation indices, accuracies, sensitivities, specificities, and values for the area under receiver operating characteristic curve (or simply the area under the curve (AUC)) for all prediction models were calculated. The mean AUC value for all prediction models constructed by the ML algorithms in the LOOCV of the primary dataset was 0.902 ± 0.024 (95% CI (confidence interval), 0.873–0.932). In the independent validation, the mean AUC value for all prediction models was 0.747 ± 0.034 (95% CI, 0.705–0.790). The results of this study suggest that the malignant glioma grades could be sufficiently and easily predicted by preparing the CE-T1WIs, T2WIs, and tumor delineations for each patient. Our proposed framework may be an effective tool for preoperatively grading malignant gliomas. |
|
言語 |
en |
| 書誌情報 |
en : Scientific Reports
巻 9,
p. 19411,
発行日 2019-12-19
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| 収録物ID |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
20452322 |
| 出版者 |
|
|
出版者 |
Springer Nature |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| EID |
|
|
識別子 |
363960 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
srep_9_19411.pdf (4.39 MB)
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