Yapay zeka algoritmalarının epitelyal over kanseri hastalarında progresyonsuz sağkalımı tahmin etmedeki etkinliği

Year 2025, Volume: 7 Issue: 5, 687 - 694, 15.09.2025

Aysun Alcı , Necim Yalçın , Mustafa Gökkaya , Gülsüm Ekin Sarı , Fatih İkiz , Işın Üreyen , Tayfun Toptaş

https://doi.org/10.38053/acmj.1751000

Abstract

Amaç: Bu çalışmanın amacı, epitelyal over kanseri tanısı alan hastalarda progresyonsuz sağkalımı (PFS) tahmin etmede yapay zeka tabanlı modellerin öngörü performansını değerlendirmek ve yorumlanabilir makine öğrenimi yaklaşımlarının karşılaştırmalı analizini yapmaktır.
Gereç ve Yöntem: Ocak 2015 ile Aralık 2020 tarihleri arasında Antalya Eğitim ve Araştırma Hastanesi Jinekolojik Onkoloji Anabilim Dalı'nda cerrahi müdahaleye alınan toplam 159 hasta retrospektif olarak çalışmaya dahil edildi. Sonuç analizi için klinik veriler kullanılmış ve kohort rastgele bir eğitim grubu (n = 117; %75) ve bir doğrulama grubu (n = 42; %25) olarak sınıflandırılmıştır. Çalışmamız için en popüler ve uygun olan sekiz algoritma modeli kullanılarak bir makine öğrenimi (ML) çalışması yapılmıştır. Modelin oluşturulması için gereken süre, ortalama mutlak hata, kök ortalama kare hata ve korelasyon katsayısı belirlendi.
Sonuçlar: Rastgele Orman algoritması en başarılı algoritma olarak ortaya çıktı. Bu algoritma, sonraki araştırmaların odak noktası oldu. Rastgele Orman algoritmasının korelasyon katsayısı 0,5731, ortalama mutlak hatası 16,45 ve kök ortalama kare hatası 20,98 idi. Modeli oluşturmak için gereken süre 0,03 saniyeydi. Kalan algoritmalar Doğrusal Regresyon, Bootstrap Aggregating (bagging), Additive Regression, Random Committee ve Regression by Discretization (Korelasyon Katsayıları: 0,5326, 0,4915, 0,4491, 0,4077, 0,3817) idi. En iyi performans gösteren Rastgele Orman algoritması için gerçek ve tahmin edilen PFS değerleri arasında orta düzeyde bir korelasyon gözlemlenmiştir (Korelasyon Katsayısı: 0,4-0,6), bu da tahminlerde orta düzeyde bir başarı oranına işaret etmektedir.

Keywords

Yapay zeka , Jinekolojik onkolojide derin öğrenme , Epiteliyal over neoplazmları

Effectiveness of artificial intelligence algorithms in predicting progression-free survival in epithelial ovarian cancer patients

Abstract

Aims: This study aimed to assess the predictive performance of artificial intelligence–based models in estimating progressionfree survival (PFS) in patients with epithelial ovarian cancer and to compare various interpretable machine learning approaches.
Methods: Between January 2015 and December 2020, a total of 167 patients who underwent surgical intervention at the Gynaecological Oncology Department of Antalya Training and Research Hospital were retrospectively included in the study if their data were complete. Clinical data were analysed, and the dataset was randomly divided into a training group (n=117; 75%) and a validation group (n=42; 25%). A machine learning (ML) analysis was conducted using the eight most relevant and widely applied algorithmic models for this study design. Model development time, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (CC) were evaluated.
Results: Random Forest demonstrated the highest accuracy (MAE=16.45, CC=0.571, RMSE=20.98, time=0.03) and thus became the focus of subsequent analyses. Other algorithms included Linear Regression, Bootstrap Aggregating, Additive Regression, Random Committee, and Regression by Discretization (CC=0.533, 0.492, 0.449, 0.408, and 0.382, respectively). For Random Forest, a moderate correlation was observed between actual and predicted PFS values (CC=0.4–0.6), indicating moderate predictive performance.
Conclusion: The findings of this study demonstrate that machine learning models, particularly Random Forest, can achieve moderate yet clinically relevant prognostic performance based on routinely collected clinical data. In particular, Random Forest demonstrates potential clinical value in guiding patient follow-up strategies and supporting individualized management in ovarian cancer, although further research is required to enhance its clinical validity and applicability.

Keywords

Artificial intelligence , deep learning in gynecologic oncology , epithelial ovarian neoplasms

Ethical Statement

Ethics Committee of Antalya Education and Research Hospital in Turkey (Approval No. 17/2; 7 November 2024)

Supporting Institution

NONE

Thanks

NONE

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