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Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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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.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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分割型ダイオファンチン模糊ミュアヘッド平均操作者によるTOPSIS方法に基づく抗癌治療法の分析

Abbas Qadir1, Saleem Abdullah1, Ariana Abdul Rahimzai2

  • 1Department of Mathematics, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.

Scientific reports
|August 27, 2025
PubMed
まとめ
この要約は機械生成です。

この研究では,断片型ダイオファントの模糊集合 (FDFS) と分析階層プロセス (AHP) を用いて,最適な,手頃な価格のがん治療法を選択し,医療意思決定を改善する新しい方法が紹介されています.

キーワード:
分析的階層的プロセスフラクチャル・ディオファントイン・フージ・セットミュアヘッド 平均操作者トプシス法

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科学分野:

  • 意思決定科学
  • 医療情報学
  • 応用数学

背景:

  • 多くの要因と不確実性のために最適の癌治療法を選択することは複雑です.
  • 既存の意思決定方法では,基準と専門家の隠された重量情報に適切に対処できない可能性があります.

研究 の 目的:

  • 最適で手頃な価格の 癌治療法を開発する
  • 分割されたダイオファントの fuzzy セット (FDFS) と分析階層プロセス (AHP) を統合し,医療意思決定のサポートを強化する.

主な方法:

  • 不確実性を扱うために,分数ダイオファンチン模糊セット (FDFS) とミュアヘッド平均演算子を導入した.
  • 基準と意思決定者の重さを決定するために,分析的階層プロセス (AHP) を利用しました.
  • 治療の評価のために分断型ディオファントイン・フージー-TOPSIS (FDF-TOPSIS) テクニックを提案し,適用した.

主要な成果:

  • FDF-TOPSIS技術は現実世界の癌治療選択の問題に成功裏に適用されました.
  • 提案された方法は,既存のアプローチと比較して信頼性,正確性,実行可能性を示した.

結論:

  • 開発されたFDF-TOPSISメソッドは,がん治療の選択のための堅固な枠組みを提供します.
  • このアプローチは,腫瘍学における医療従事者の意思決定プロセスを大幅に改善する可能性があります.