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Prediction of Hepatocellular Carcinoma After Hepatitis C Virus Sustained Virologic Response Using a Random Survival Forest Model

https://hiroshima.repo.nii.ac.jp/records/2040741
https://hiroshima.repo.nii.ac.jp/records/2040741
a1f4edf9-248d-477c-b6d2-40c1cdff099b
名前 / ファイル ライセンス アクション
JCOCCI_8.pdf JCOCCI_8.pdf (2 MB)
 Download is available from 2025/12/18.
Item type デフォルト(1)
公開日 2025-06-17
タイトル
タイトル Prediction of Hepatocellular Carcinoma After Hepatitis C Virus Sustained Virologic Response Using a Random Survival Forest Model
言語 en
作成者 Nakahara, Hikaru

× Nakahara, Hikaru

en Nakahara, Hikaru

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Ono, Atsushi

× Ono, Atsushi

en Ono, Atsushi

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Hayes, C. Nelson

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en Hayes, C. Nelson

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Shirane, Yuki

× Shirane, Yuki

en Shirane, Yuki

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Miura, Ryoichi

× Miura, Ryoichi

en Miura, Ryoichi

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Fujii, Yasutoshi

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en Fujii, Yasutoshi

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Murakami, Serami

× Murakami, Serami

en Murakami, Serami

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Yamaoka, Kenji

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en Yamaoka, Kenji

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Bao, Hauri

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en Bao, Hauri

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Uchikawa, Shinsuke

× Uchikawa, Shinsuke

en Uchikawa, Shinsuke

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Fujino, Hatsue

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en Fujino, Hatsue

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Murakami, Eisuke

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en Murakami, Eisuke

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Kawaoka, Tomokazu

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en Kawaoka, Tomokazu

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Miki, Daiki

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en Miki, Daiki

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Tsuge, Masataka

× Tsuge, Masataka

en Tsuge, Masataka

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Oka, Shiro

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en Oka, Shiro

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アクセス権
アクセス権 embargoed access
アクセス権URI http://purl.org/coar/access_right/c_f1cf
権利情報
言語 en
権利情報 This is not the published version. Please cite only the published version.
権利情報
言語 ja
権利情報 この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。
内容記述
内容記述タイプ Abstract
内容記述 Purpose
Postsustained virologic response (SVR) screening following clinical guidelines does not address individual risk of hepatocellular carcinoma (HCC). Our aim is to provide tailored screening for patients using machine learning to predict HCC incidence after SVR.
Methods
Using clinical data from 1,028 SVR patients, we developed an HCC prediction model using a random survival forest (RSF). Model performance was assessed using Harrel's c-index and validated in an independent cohort of 737 SVR patients. Shapley additive explanation (SHAP) facilitated feature quantification, whereas optimal cutoffs were determined using maximally selected rank statistics. We used Kaplan-Meier analysis to compare cumulative HCC incidence between risk groups.
Results
We achieved c-index scores and 95% CIs of 0.90 (0.85 to 0.94) and 0.80 (0.74 to 0.85) in the derivation and validation cohorts, respectively, in a model using platelet count, gamma-glutamyl transpeptidase, sex, age, and ALT. Stratification resulted in four risk groups: low, intermediate, high, and very high. The 5-year cumulative HCC incidence rates and 95% CIs for these groups were as follows: derivation: 0% (0 to 0), 3.8% (0.6 to 6.8), 26.2% (17.2 to 34.3), and 54.2% (20.2 to 73.7), respectively, and validation: 0.7% (0 to 1.6), 7.1% (2.7 to 11.3), 5.2% (0 to 10.8), and 28.6% (0 to 55.3), respectively.
Conclusion
The integration of RSF and SHAP enabled accurate HCC risk classification after SVR, which may facilitate individualized HCC screening strategies and more cost-effective care.
言語 en
出版者
出版者 American Society of Clinical Oncology
言語 en
日付
日付 2025-12-18
日付タイプ Available
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 AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
関連情報
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1200/CCI.24.00108
助成情報
助成機関識別子タイプ Crossref Funder
助成機関識別子タイプURI https://doi.org/10.13039/100009619
助成機関名 日本医療研究開発機構
言語 en
助成機関名 Japan Agency for Medical Research and Development (AMED)
言語 ja
研究課題番号 24tm0524006
研究課題名 肝疾患の課題解決にむけたゲノム情報の活用
言語 ja
書誌情報 en : JCO Clinical Cancer Informatics

巻 8, 発行日 2024-12-18
備考
言語 en
値 The full-text file will be made open to the public on 18 December 2025 in accordance with publisher's 'Terms and Conditions for Self-Archiving'
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