直近一年間の累計
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ID 118065
タイトル別表記
Prediction of recurrence after chemoradiotherapy
著者
Ando, Ken Gunma Prefectural Cancer Center|Gunma University
Kato, Shingo Saitama International Medical Center
Kaneyasu, Yuko National Hospital Organization Fukuyama Medical Center
Uno, Takashi Chiba University
Okonogi, Noriyuki QST Hospital
Yoshida, Kenji Kobe University
Ariga, Takuro University of the Ryukyus
Isohashi, Fumiaki Osaka University
Harima, Yoko Kansai Medical University
Kanemoto, Ayae Niigata Cancer Center Hospital
Ii, Noriko Ise Red Cross Hospital
Wakatsuki, Masaru QST Hospital|Jichi Medical University
Ohno, Tatsuya Gunma University
キーワード
cervical cancer
chemoradiotherapy
MRI
out-of-field recurrence (OFR)
prediction
radiomics
資料タイプ
学術雑誌論文
抄録
We retrospectively assessed whether magnetic resonance imaging (MRI) radiomics combined with clinical parameters can improve the predictability of out-of-field recurrence (OFR) of cervical cancer after chemoradiotherapy. The data set was collected from 204 patients with stage IIB (FIGO: International Federation of Gynecology and Obstetrics 2008) cervical cancer who underwent chemoradiotherapy at 14 Japanese institutes. Of these, 180 patients were finally included for analysis. OFR-free survival was calculated using the Kaplan–Meier method, and the statistical significance of clinicopathological parameters for the OFR-free survival was evaluated using the log-rank test and Cox proportional-hazards model. Prediction of OFR from the analysis of diffusion-weighted images (DWI) and T2-weighted images of pretreatment MRI was done using the least absolute shrinkage and selection operator (LASSO) model for engineering image feature extraction. The accuracy of prediction was evaluated by 5-fold cross-validation of the receiver operating characteristic (ROC) analysis. Para-aortic lymph node metastasis (p = 0.003) was a significant prognostic factor in univariate and multivariate analyses. ROC analysis showed an area under the curve (AUC) of 0.709 in predicting OFR using the pretreatment status of para-aortic lymph node metastasis, 0.667 using the LASSO model for DWIs and 0.602 using T2 weighted images. The AUC improved to 0.734 upon combining the pretreatment status of para-aortic lymph node metastasis with that from the LASSO model for DWIs. Combining MRI radiomics with clinical parameters improved the accuracy of predicting OFR after chemoradiotherapy for locally advanced cervical cancer.
掲載誌名
Journal of Radiation Research
ISSN
13499157
cat書誌ID
AA00705792
出版者
Oxford University Press|The Japanese Radiation Research Society|Japanese Society for Radiation Oncology
63
1
開始ページ
98
終了ページ
106
発行日
2021-12-03
権利情報
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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出版社版DOI
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フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
医学系