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関連する概念動画

Mouse Models of Cancer Study02:43

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
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精密腫瘍学のための視覚言語基盤モデル

Jinxi Xiang1, Xiyue Wang1, Xiaoming Zhang2

  • 1Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.

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|January 8, 2025
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まとめ

新しいAIモデルであるMUSKは 病理画像と臨床テキストを統合して 癌の診断と治療の予測を改善します このマルチモダルのアプローチは,大規模なデータセットを活用して 改善された臨床意思決定を行うことができます.

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科学分野:

  • 医療における人工知能
  • コンピューター病理学
  • 医療情報学

背景:

  • 臨床的意思決定は,臨床ノートや病理学などの多様なデータを統合することに依存しています.
  • 効果的な人工知能 (AI) モデルの開発は,よく注釈されたマルチモダルの臨床データセットが不足しているため,困難です.
  • 既存のAIアプローチは,イメージングとテキストの両方の臨床データに存在する補完的な情報を完全に活用するために苦労しています.

研究 の 目的:

  • 新しい視覚言語基盤モデルを開発し,統一されたマSKedモデリング (MUSK) を備えたマルチモダルトランスフォーマーを開発し,大規模で,ラベル付けされていない,ペアリングされていない病理画像とテキストデータを統合することができます.
  • 視覚と言語の機能の有効な調整を可能にするために,広範な病理画像とテキストデータセットでMUSKを訓練する.
  • MUSKの性能を評価する 臨床アプリケーションの幅広い範囲で 最小限の研修または研修なし

主な方法:

  • 5千万の病理画像と10億のテキストトークンを用いた 統一された仮面モデリングを用いた ビジョン言語基盤モデル"MUSK"を開発しました
  • 視覚と言語の仕様を調整するために 100万の画像とテキストのペアでマスクを訓練しました
  • 検索,視覚的な質問応答,分類,結果予測のタスクを含む23のパッチレベルとスライドレベルのベンチマークでMUSKをテストしました.

主要な成果:

  • マスクは23のベンチマークで優れたパフォーマンスを示し,画像からテキスト,テキストから画像の検索,視覚的な質問の回答,画像の分類に優れている.
  • メラノーマの再発,全身がんの予後,肺がんおよび胃食道がんにおける免疫療法反応を含む,分子バイオマーカーの予測と結果の予測において強力な結果を達成した.
  • 病理画像と臨床報告から得られた情報を効果的に統合し,がんの診断と治療の改善の可能性を示しました.

結論:

  • 先程訓練された視覚言語の基礎モデルであるMUSKは,高度な臨床応用のために多式病理学データ (画像とテキスト) を成功裏に統合しています.
  • このモデルは,がん治療の診断の正確性と精度を向上させ,改善された結果の予測を可能にします.
  • マスクが大規模で,ラベル付けされていない,ペアリングされていないデータを活用する能力は,臨床意思決定にAIを適用する上で重要な進歩を表しています.