軟組織肉腫のex-vivoラーマン光譜とAIベースの分類
Maede Boroji1, Vahid Danesh1, David Barrera2
1Department of Mechanical Engineering, Stony Brook University, Stony Brook, New York, United States of America.
PloS one
|September 2, 2025
まとめ
顕微鏡ラーマン光譜検査は,軟組織サーコマ (STS) を正常組織から区別するのに有望である. このテクニックは手術中に 異常な組織や限界を特定する際に 手術師を導くための 迅速で非侵襲的なツールとなるでしょう
科学分野:
- 腫瘍学
- 生物医学工学
- スペクトロスコーピー
背景:
- 軟組織肉腫 (STS) は,正確な診断と治療を必要とする希少で異質な悪性腫瘍です.
- STSの現在の手術内マージン評価方法には,効率と正確性が欠けている.
- 柔らかい組織サルコマの 手術のガイドラインを向上させるには 改善されたツールが必要です
研究 の 目的:
- STSサブタイプ,良性腫瘍,正常組織を区別するために,顕微鏡ラマンスペクトロスコーピーを調査する.
- ラマンスペクトルを手術の際に迅速な診断ツールとして使用する可能性を評価する.
- ラマンデータに基づいて組織を分類するための計算モデルを開発し,検証する.
主な方法:
- 7人の患者の286,672個の組織サンプルで,ex- vivoラーマン光譜検査を行った.
- 633 nmの刺激波長がスペクトル獲得に使用された.
- データの事前処理と分類のためにカスタム ResNet アーキテクチャが採用されました.
主要な成果:
- ResNetモデルは,組織タイプを分類する上で,全体的に97.1%の高精度を達成した.
- 臨床的警戒率は1. 46%と記録され,悪性組織の誤分類は最小限であった.
- ラマン光譜法では,様々な軟組織を区別する大きな可能性が示されました.
結論:
- 顕微鏡ラーマン光学では 軟組織肉腫,良性腫瘍,正常組織を正確に区別することができます.
- 単一のラマンスペクトルは 迅速かつ非侵襲的な手術指導のツールとして 潜在性を示しています
- このテクニックは,サルコマ切除における異常組織と外科的限界の特定の精度を向上させることができます.
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