エピテリア卵巣がんの再発および治療パラダイムシフトによる生存における変化 治療パラダイムシフト
Jeongyun Kim1, Dong Hoon Suh1,2, Kidong Kim1,2
1Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, South Korea.
The journal of obstetrics and gynaecology research
|February 13, 2026
まとめ
この研究は,上皮性卵巣がん (EOC) の進行性のない生存率 (PFS) が変わっていない一方で,外科技術と標的治療の進歩により,5年間の全生存率 (OS) が著しく改善したことを示しています.
科学分野:
- 婦人科腫瘍学 婦人科腫瘍学
- 外科腫瘍学外科腫瘍学
- 医療腫瘍学 医療腫瘍学
背景:
- 進化する外科および全身療法パラダイムは,上皮性卵巣がん (EOC) のアウトカムに影響を及ぼします.
- 長期的な腫瘍学的結果を理解することは,患者の管理に極めて重要です.
研究 の 目的:
- エピテリア性卵巣がん (EOC) の患者の腫瘍学的アウトカムを評価するために.
- 進化する外科療法と全身療法が生存に与える影響を評価する.
主な方法:
- 763人のEOC患者 (2003年−2020年) の遡及コホート研究.
- 治療の変更を反映するために,診断期間によってグループ化された患者.
- カプラン・マイヤーとコックス回帰を用いた総合生存率 (OS) と無進行生存率 (PFS) を分析した.
主要な成果:
- 完全な細胞縮小率は,時間とともに増加し,特にプライマリデブルッキング手術 (PDS) で増加しました.
- インターバルデブルキング手術 (IDS) の使用は増加したが,IDS後の残留疾患 (<1cm) は,より悪い結果と相関していた.
- PFSが変化しないにもかかわらず,5年経過したOSは,最新の期間に進行段階のEOC患者で有意に改善した (64.0%から82.5%).
結論:
- EOC治療では,特にPDSによる完全な細胞縮小がますます達成されています.
- IDSの使用は増加したが,残留疾患は依然として陰性予後要因である.
- 標的型療法と外科戦略の進歩により,先進段階のEOC患者の長期的な生存率が向上しています.
キーワード:
ベバシズマブ (bevacizumab) が使用されています.細胞縮小手術は,細胞縮小手術である.卵巣がん 卵巣がん 卵巣がんポリ (ADP-リボース) ポリメラーゼ阻害剤治療の有効性 治療の有効性さらに関連する動画
04:04Endobronchial Ultrasound-guided Intratumoral Injection of Cisplatin for the Treatment of Isolated Mediastinal Recurrence of Lung Cancer
Published on: February 12, 2017
11.1K
07:46In vivo Imaging and Therapeutic Treatments in an Orthotopic Mouse Model of Ovarian Cancer
Published on: August 17, 2010
34.6K
関連する概念動画
Cancer Survival Analysis
774
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...
774
Treatment Resistant Cancers
3.8K
Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
3.8K
Survival Curves
745
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
745
Survival Tree
437
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
437
Introduction To Survival Analysis
840
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
840
Comparing the Survival Analysis of Two or More Groups
618
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
618
