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ラジオミクスおよびラジオゲノミクス：卵巣癌の診断および予後予測のために医療画像からより多くの情報を抽出する

  • role： First author第一作者
  • Affiliation：

    Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang 110004, China

Song Zeng1,  
  • Affiliation：

    Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang 110004, China

Xin-Lu Wang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang 110004, China

  • Email：yangh1@sj-hospital.org
  • Introduction：* Hua Yang yangh1@sj-hospital.org
Hua Yang1*

サマリー

卵巣癌（OC）は世界的に最も致命的な婦人科悪性腫瘍の一つであり続けています。OCのスクリーニングのために様々な医療画像アプローチが実施されているにもかかわらず、画像の性能のばらつきにより卵巣腫瘍の正確な鑑別診断は依然として大きな課題であり、医療専門家の経験に大きく依存する客観性の欠如を招いています。この課題は、従来の医療画像から価値のある情報を高速で抽出できるラジオミクスの登場と進歩によって解決できます。さらに、ラジオミクスはゲノミクスと統合でき、新しいアプローチであるラジオゲノミクスと呼ばれ、腫瘍の生物学的特徴をより包括的かつ正確、個別化して評価することが可能です。本レビューでは、卵巣腫瘍の診断と予測におけるラジオミクスおよびラジオゲノミクスの応用に関する広範な概観を示します。結果は、画像に基づく人工知能手法が良性および悪性の卵巣腫瘍を正確に区別し、そのサブタイプを分類できることを示しています。さらに、これらの手法はOC患者の生存率、治療結果、転移リスク、再発を予測するのに効果的です。これらの進歩は、OC管理の意思決定支援ツールとして機能し、精密医療の発展に寄与すると予想されます。

キーワード

ラジオミクス;ラジオゲノミクス;機械学習;深層学習;卵巣癌

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