| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-11-18 |
| タイトル |
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タイトル |
Elucidating Celecoxib's Preventive Effect in Capecitabine-Induced Hand-Foot Syndrome Using Medical Natural Language Processing |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Tsuchiya, Masami
Kawazoe, Yoshimasa
Shimamoto, Kiminori
Seki, Tomohisa
Imai, Shungo
Kizaki, Hayato
Shinohara, Emiko
矢田, 竣太郎
若宮, 翔子
荒牧, 英治
Hori, Satoko
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
PURPOSE Capecitabine, an oral anticancer agent, frequently causes hand-foot syndrome (HFS), affecting patients' quality of life and treatment adherence. However, such symptomatic toxicities are often difficult to detect in structured electronic health record (EHR) data. This study primarily aimed to validate a natural language processing (NLP) approach to identifying capecitabine-induced HFS from unstructured clinical text and demonstrate its application in evaluating medication-associated adverse event trends in real-world settings. METHODS We conducted a retrospective cohort study using EHRs from the University of Tokyo Hospital (2004-2021). HFS cases were identified using the MedNERN-CR-JA NLP model. After propensity score matching, we compared capecitabine users with and without celecoxib and assessed time to HFS onset using Cox proportional hazards models. NLP-based HFS detection was validated through manual annotation of aggregated clinical notes. Negative control and sensitivity analyses ensured robustness. RESULTS Among 44,502 patients with cancer, 669 capecitabine users were analyzed. HFS incidence was significantly higher among capecitabine users (hazard ratio [HR], 1.93 [95% CI, 1.48 to 2.52]; P < .001) compared with nonusers. Celecoxib use showed a suggestive association with a reduced HFS risk (HR, 0.51 [95% CI, 0.24 to 1.07]; P = .073). The NLP model demonstrated high accuracy in identifying HFS, achieving a precision of 0.875, recall of 1.000, and F1 score of 0.933, based on manual annotation of patient-level clinical notes. Outcome trends remained consistent when using manually annotated HFS case labels instead of NLP-detected events, supporting the method's robustness.CONCLUSION These findings demonstrate the effectiveness of NLP in detecting HFS from real-world clinical records. The application to celecoxib-HFS detection illustrates the potential utility of this approach for retrospective safety analysis. Further work is needed to evaluate generalizability across diverse clinical settings. |
| 書誌情報 |
en : JCO Clinical Cancer Informatics
巻 9,
ページ数 11,
発行日 2025-08-12
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| 出版者 |
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出版者 |
American Society of Clinical Oncology |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2473-4276 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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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-25-00096 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://ascopubs.org/doi/full/10.1200/CCI-25-00096 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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権利情報 |
Copyright © 2025 American Society of Clinical Oncology. All rights reserved. Creative Commons Attribution Non-Commercial No Derivatives 4.0 License. |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
Japan Science and Technology Agency(JST) |
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研究課題番号 |
JPMJCR22N1 |
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研究課題番号URI |
https://projectdb.jst.go.jp/grant/JST-PROJECT-22717060/ |
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研究課題名 |
リアルワールドテキスト処理の深化によるデータ駆動型探薬 |
| 助成情報 |
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助成機関名 |
Cabinet Office, Government of Japan |
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研究課題番号 |
JPJ012425 |
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研究課題名 |
Cross-ministerial Strategic Innovation Promotion Program (SIP) on “Integrated Health Care System” |
