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このページは機械翻訳されています。他のページは英語で表示される場合があります。View in English
  1. ホーム
  3. 研究分野
  5. 生物医学と臨床科学
  7. 腫瘍学とがん発生
  9. 分子標的
  11. 現実データとaiを用いた腫瘍学試験の適格性基準の評価
  1. ホーム
  3. 研究分野
  5. 生物医学と臨床科学
  7. 腫瘍学とがん発生
  9. 分子標的
  11. 現実データとaiを用いた腫瘍学試験の適格性基準の評価

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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現実データとAIを用いた腫瘍学試験の適格性基準の評価

Ruishan Liu1, Shemra Rizzo2, Samuel Whipple2

  • 1Department of Electrical Engineering, Stanford University, Stanford, CA, USA.

Nature
|April 8, 2021

PubMed で要約を見る

まとめ
この要約は機械生成です。

癌の臨床試験における受験資格の制限基準を拡大することで,患者の受験資格を倍増させ,生存率を向上させる可能性があります. このデータベースのアプローチは 臨床試験の包括性を高め 患者の安全性を確保します

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科学分野:

  • 腫瘍学
  • 臨床試験の設計
  • 医療情報学

背景:

  • 臨床試験では 患者への参加を制限する基準が設けられているため 患者への参加が困難です
  • 包摂的ながん試験の設計は,より広範な患者代表性と一般化可能な結果のために不可欠です.

研究 の 目的:

  • がん試験の集団と結果に対する適格性基準の影響を体系的に評価する.
  • 試験の危険性比に共通する検査値の除外が与える影響を評価する.
  • 患者の適格性を高めるために制限基準を拡大するためのデータベースのアプローチを探求する.

主な方法:

  • Trial Pathfinderのコンピューティングフレームワークを利用して,完成した先進的な非小細胞肺がん試験をエミュレートしました.
  • 全国の電子医療記録データベース (61,094人の患者) の実際のデータを分析した.
  • 制限基準を拡大し,適格な患者集団と生存結果の変化を評価するためにデータ主導の方法論を使用しました.

主要な成果:

  • 実験用値の除外を含む多くの一般的な適格性基準は,試験の危険性比率に最小限の影響を及ぼしました.
  • 制限基準を広げることで 対象となる患者の数は平均2倍以上になりました
  • 基準の拡大により,全生存に対する危険比率は平均0. 05減少した.

結論:

  • 限定的な適格性基準は,がん治療の恩恵を受ける可能性のある患者を排除する可能性があります.
  • 評価基準を拡大する際のデータベースのアプローチは 臨床試験の包括性を大幅に高めることができます
  • この方法論により,患者の安全性を損なうことなく,より包括的な臨床試験の設計が容易になります.