| Item type |
デフォルトアイテムタイプ_（フル）(1) |
| 公開日 |
2023-03-18 |
| タイトル |
|
|
タイトル |
Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method |
|
言語 |
en |
| 作成者 |
Kawahara, Daisuke
Tang, Xueyan
Lee, Chung K.
Nagata, Yasushi
Watanabe, Yoichi
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利情報 |
|
|
権利情報 |
Copyright © 2021 Kawahara, Tang, Lee, Nagata and Watanabe. This is an openaccess 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. |
| 主題 |
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|
主題Scheme |
Other |
|
主題 |
radiomics |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
machine learning |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
brain metastases |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
gamma knife |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
radiosurgery |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
local control |
| 内容記述 |
|
|
内容記述 |
Purpose: The current study proposed a model to predict the response of brain metastases (BMs) treated by Gamma knife radiosurgery (GKRS) using a machine learning (ML) method with radiomics features. The model can be used as a decision tool by clinicians for the most desirable treatment outcome. Methods and Material: Using MR image data taken by a FLASH (3D fast, low-angle shot) scanning protocol with gadolinium (Gd) contrast-enhanced T1-weighting, the local response (LR) of 157 metastatic brain tumors was categorized into two groups (Group I: responder and Group II: non-responder). We performed a radiomics analysis of those tumors, resulting in more than 700 features. To build a machine learning model, first, we used the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of radiomics features to the minimum number of features useful for the prediction. Then, a prediction model was constructed by using a neural network (NN) classifier with 10 hidden layers and rectified linear unit activation. The training model was evaluated with five-fold cross-validation. For the final evaluation, the NN model was applied to a set of data not used for model creation. The accuracy and sensitivity and the area under the receiver operating characteristic curve (AUC) of the prediction model of LR were analyzed. The performance of the ML model was compared with a visual evaluation method, for which the LR of tumors was predicted by examining the image enhancement pattern of the tumor on MR images. Results: By the LASSO analysis of the training data, we found seven radiomics features useful for the classification. The accuracy and sensitivity of the visual evaluation method were 44 and 54%. On the other hand, the accuracy and sensitivity of the proposed NN model were 78 and 87%, and the AUC was 0.87. Conclusions: The proposed NN model using the radiomics features can help physicians to gain a more realistic expectation of the treatment outcome than the traditional method. |
|
言語 |
en |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
The portions of the current study were presented as an e-poster at the 19th Leksell Gamma Knife Society Meeting, Dubai, UAE, March 4–8, 2018, and as a short oral talk at the 2019 ASTRO Annual Meeting, Chicago, IL, September 15–18, 2019. |
| 出版者 |
|
|
出版者 |
Frontiers Media |
| 言語 |
|
|
言語 |
eng |
| 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 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 関連情報 |
|
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.3389/fonc.2020.569461 |
| 関連情報 |
|
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|
識別子タイプ |
PMID |
|
|
関連識別子 |
33505904 |
| 関連情報 |
|
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3389/fonc.2020.569461 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2234-943X |
| 開始ページ |
|
|
開始ページ |
569461 |
| 書誌情報 |
Frontiers in Oncology
Frontiers in Oncology
巻 10,
p. 569461,
発行日 2021-01-11
|
| 旧ID |
50459 |
FrontOncol_10_569461.pdf (977.2 KB)
