Item type |
デフォルト(1) |
公開日 |
2025-06-17 |
タイトル |
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タイトル |
Prediction of Hepatocellular Carcinoma After Hepatitis C Virus Sustained Virologic Response Using a Random Survival Forest Model |
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言語 |
en |
作成者 |
Nakahara, Hikaru
Ono, Atsushi
Hayes, C. Nelson
Shirane, Yuki
Miura, Ryoichi
Fujii, Yasutoshi
Murakami, Serami
Yamaoka, Kenji
Bao, Hauri
Uchikawa, Shinsuke
Fujino, Hatsue
Murakami, Eisuke
Kawaoka, Tomokazu
Miki, Daiki
Tsuge, Masataka
Oka, Shiro
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アクセス権 |
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アクセス権 |
embargoed access |
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アクセス権URI |
http://purl.org/coar/access_right/c_f1cf |
権利情報 |
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言語 |
en |
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権利情報 |
This is not the published version. Please cite only the published version. |
権利情報 |
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言語 |
ja |
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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 |
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内容記述タイプ |
Abstract |
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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. |
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言語 |
en |
出版者 |
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出版者 |
American Society of Clinical Oncology |
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言語 |
en |
日付 |
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日付 |
2025-12-18 |
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日付タイプ |
Available |
言語 |
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言語 |
eng |
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 |
出版タイプ |
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出版タイプ |
AM |
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出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
関連情報 |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1200/CCI.24.00108 |
助成情報 |
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助成機関識別子タイプ |
Crossref Funder |
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助成機関識別子タイプURI |
https://doi.org/10.13039/100009619 |
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助成機関名 |
日本医療研究開発機構 |
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言語 |
en |
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助成機関名 |
Japan Agency for Medical Research and Development (AMED) |
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言語 |
ja |
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研究課題番号 |
24tm0524006 |
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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 |
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言語 |
ja |
書誌情報 |
en : JCO Clinical Cancer Informatics
巻 8,
発行日 2024-12-18
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備考 |
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言語 |
en |
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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' |