| アイテムタイプ |
文献 / Documents(1) |
| 公開日 |
2025-10-02 |
| アクセス権 |
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アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 出版社版DOI |
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関連識別子 |
https://doi.org/10.4103/jmp.jmp_39_24 |
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関連名称 |
10.4103/jmp.jmp_39_24 |
| 出版タイプ |
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出版タイプ |
VoR |
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出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
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タイトル |
Retrospective Comparison of Geometrical Accuracy among Atlas-based Auto-segmentation, Deep Learning Auto-segmentation, and Deformable Image Registration in the Treatment Replanning for Adaptive Radiotherapy of Head-and-Neck Cancer |
| タイトル別表記 |
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その他のタイトル |
Treatment replanning for head‑and‑neck cancer |
| 著者 |
Nagayasu, Yukari
Inui, Shoki
Ueda, Yoshihiro
Masaoka, Akira
富永, 正英
Miyazaki, Masayoshi
Konishi, Koji
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| 抄録 |
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内容記述 |
Aims: This study aimed to evaluate the geometrical accuracy of atlas-based auto-segmentation (ABAS), deformable image registration (DIR), and deep learning auto-segmentation (DLAS) in adaptive radiotherapy (ART) for head-and-neck cancer (HNC). Subjects and Methods: Seventeen patients who underwent replanning for ART were retrospectively studied, and delineated contours on their replanning computed tomography (CT2) images were delineated. For DIR, the planning CT image (CT1) of the evaluated patients was utilized. In contrast, ABAS was performed using an atlas dataset comprising 30 patients who were not part of the evaluated group. DLAS was trained with 143 patients from different patients from the evaluated patients. The ABAS model was improved, and a modified ABAS (mABAS) was created by adding the evaluated patients’ own CT1 to the atlas datasets of ABAS (number of patients of the atlas dataset, 31). The geometrical accuracy of DIR, DLAS, ABAS, and mABAS was evaluated. Results: The Dice similarity coefficient in DIR was the highest, at >0.8 at all organs at risk. The mABAS was delineated slightly more accurately than the standard ABAS. There was no significant difference between ABAS and DLAS in delineation accuracy. DIR had the lowest Hausdorff distance (HD) value (within 10 mm). The HD values in ABAS, mABAS, and DLAS were within 16 mm. Conclusions: DIR delineation is the most geometrically accurate ART for HNC. |
| キーワード |
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主題 |
Adaptive radiotherapy |
| キーワード |
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主題 |
atlas-based auto-segmentation |
| キーワード |
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主題 |
automatic segmentation |
| キーワード |
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主題 |
deep learning auto-segmentation |
| キーワード |
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主題 |
deformable image registration |
| キーワード |
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主題 |
head and neck |
| 書誌情報 |
Journal of Medical Physics
巻 49,
号 3,
p. 335-342,
発行日 2024-09-21
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| 収録物ID |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
19983913 |
| 収録物ID |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
09716203 |
| 出版者 |
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出版者 |
Wolters Kluwer |
| 出版者 |
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出版者 |
Medknow |
| 権利情報 |
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権利情報 |
This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution‑NonCommercial‑ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non‑commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
| EID |
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識別子 |
417907 |
| 言語 |
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言語 |
eng |
jmp_49_3_335.pdf (1.3 MB)
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