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肺がんにおけるEGFR予測のためのオープンソースAIモデルの祖先関連パフォーマンス変数

Mehrdad Rakaee1,2, Amin H Nassar1,3, Masoud Tafavvoghi2,4

  • 1Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.

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科学分野:

  • コンピューティング病理学
  • ゲノム解析ゲノム解析について
  • 腫瘍学における人工知能

背景:

  • 人工知能 (AI) モデルは,病理学スライドからゲノム変異の迅速かつ低コストの予測を提供し,肺がんの治療決定を加速させる可能性があります.
  • AIモデルの汎用性は,さまざまな患者集団と組織タイプにわたって,ほとんど不明のままです.

研究 の 目的:

  • 肺腺がん (LUAD) のEGFR変異状態を予測するための2つのオープンソースAI病理学モデルの性能と汎用性を評価する.
  • 独立したコホートと多様な祖先サブグループにおけるモデルのパフォーマンスを評価する.

主な方法:

  • コホート研究では,2つの独立したコホート (ダナ・ファーバーがん研究所とヨーロッパを拠点とする試験) のLUAD患者と,ペアリングされた次世代のシーケンシングと全スライド画像データを含む.
  • 遺伝的祖先は,DFCIコホートにおける生殖線遺伝子型データを用いて推論された.
  • モデルの性能は,受信機の動作特性曲線 (AUC) 下の面積によって測定され,全体的に評価され,祖先のサブグループによって,サンプルタイプによって評価されました.

主要な成果:

  • 1つのAIモデルは,DFCIコホートにおける他のモデル (0.68) と比較して,全体的により高いAUC (0.83) を達成した.
  • 性能は祖先のサブグループによって異なるが,より高い性能のモデルは,AUC 0.84 (ヨーロッパ),0.85 (アフリカ),0.68 (アジア) を示した.
  • モデル性能は,肺の標本 (AUC,0.86) と比較して,肺の標本 (AUC,0.66) で減少した. AI主導トリアージは,高感度 (0.84) と高特異性 (0.99) の急速EGFR検査の潜在的57%の減少を示唆しました.

結論:

  • AIベースの病理学ツールは,肺がんにおけるEGFR予測の予備補充として潜在性を示しています.
  • 祖先サブグループ間のパフォーマンス差異は,慎重に解釈し,さらに検証する必要があります.
  • AIのツールは,EGFR検査のワークフローを簡素化し,肺がん管理の効率性を向上させる可能性があります.