| Item type |
デフォルトアイテムタイプ（梨大）(1) |
| 公開日 |
2025-01-06 |
| タイトル |
|
|
タイトル |
Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study |
|
言語 |
en |
| 作成者 |
根本, 光
齋藤, 正英
Satoh, Yoko
小宮山, 貴史
萬利乃, 寛
Aoki, Shinichi
Suzuki, Hidekazu
Sano, Naoki
Nonaka, Hotaka,
渡邊, 裕陽
WEKO
18089
| ja |
渡邊, 裕陽
kakenhi 山梨大学
13501
|
| ja-Kana |
ワタナベ, ヒロアキ
|
| en |
Watanabe, Hiroaki
University of Yamanashi
|
Search repository
Funayama, Satoshi
大西, 洋
|
| 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 |
|
|
言語 |
en |
|
権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
Creative Commons Attribution 4.0 International (CC BY 4.0) |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
© 2024 The Authors |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
lung cancer |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
machine learning |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
PET imaging |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
radiomics |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
SBRT |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Purpose: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strategy based on the risk of recurrence. In this study, we evaluated the performance of both machine learning models using positron emission tomography (PET) and computed tomography (CT) radiomic features for predicting recurrence after SBRT.
Methods: Planning CT and PET images of 82 non-small cell lung cancer patients who performed SBRT at our hospital were used. First, tumors were delineated on each CT and PET of each patient, and 111 unique radiomic features were extracted, respectively. Next, the 10 features were selected using three different feature selection algorithms, respectively. Recurrence prediction models based on the selected features and four different machine learning algorithms were developed, respectively. Finally, we compared the predictive performance of each model for each recurrence pattern using the mean area under the curve (AUC) calculated following the 0.632+ bootstrap method.
Results: The highest performance for local recurrence, regional lymph node metastasis, and distant metastasis were observed in models using Support vector machine with PET features (mean AUC = 0.646), Naive Bayes with PET features (mean AUC = 0.611), and Support vector machine with CT features (mean AUC = 0.645), respectively.
Conclusions: We comprehensively evaluated the performance of prediction model developed for recurrence following SBRT. The model in this study would provide information to predict the recurrence pattern and assist in making treatment strategies. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Wiley |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
The American Association of Physicists in Medicine |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子（シンプル） |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ（シンプル） |
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 |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1002/acm2.14322 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
1526-9914 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
1526-9914 |
| 書誌情報 |
en : Journal of Applied Clinical Medical Physics
巻 25,
号 7,
発行日 2024-07-12
|
Journal of Applied Clinical Medical Physics25_7_e14322.pdf (1.6 MB)
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