術前の高リスク前立腺がんの予測のための多パラメータMRIからの解釈可能な生息地と周周周放射線学:多機関研究
Mengxuan Yuan1,2,3, Di Chang4, Wanjun Lu5
1Department of Radiology, Jiangdu People's Hospital Affiliated to Yangzhou University, 100 Jiangzhou Road, Jiangdu district, Yangzhou, Jiangsu, 225200, PR China. 15895711125@yzu.edu.cn.
Journal of translational medicine
|February 14, 2026
まとめ
この研究は,手術前前立腺がんのリスク評価を正確にするために,高度な放射線学フレームワークを導入しています. ハビタットと周周腫瘍分析を利用した統合モデルは,高リスク前立腺がんの予測を大幅に改善します.
科学分野:
- 放射線学 放射線学
- 腫瘍学 腫瘍学
- 人工知能 (AI) とは,人工知能 (AI) のことです.
背景:
- 現在進行中の前立腺がんの術前評価は,主観的なPI-RADSスコアと診断の不確実性を含む制限があります.
- 高リスクの前立腺がんと良性または低リスクの病変の区別は依然として困難です.
研究 の 目的:
- 術前高リスク前立腺がんの予測のための解釈可能なアンサンブル学習フレームワークを開発する.
- マルチパラメトリックMRIから生息地ベースの放射学と周周分析を統合する.
主な方法:
- 立体病理学的に確証された前立腺病変の896人の患者を対象とした多院間の遡及的研究.
- K-meansクラスタリングを用いた腫瘍内生息地分析と1〜5mmの膨張リングを用いた腫瘍周分析.
- mRMRおよびLASSO回帰,外部検証,および解釈可能性のためのSHAP分析による特徴選択.
主要な成果:
- 統合された生息地および周周腫瘍放射線学モデルは,外部検証で優れた性能 (AUC: 0.860-0.876) を達成しました.
- 生息地シグネチャーは,腫瘍内および臨床シグネチャーを大幅に上回った.
- ADCから派生した特徴,特に生息地領域H3から派生した特徴は,主要な予測因子であった.
結論:
- 統合された生息地と周周腫瘍放射線学は,前立腺がんのための強力な術前リスク分層化を提供します.
- ADCから派生した生息地特性は,高リスク前立腺がんを予測する上で優れたパフォーマンスを示しています.
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