TY  - JOUR
T1  - Prediction of prognosis in brain metastasis with artificial-intelligence-driven methods for whole brain radiotherapy
TT  - Beyin metastazında tüm beyin radyoterapisi sonrası yapay zeka destekli prognoz tahmini
AU  - Özkan, Emine Elif
AU  - Serel, Tekin Ahmet
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.17826/cumj.1661241
JF  - Cukurova Medical Journal
JO  - Cukurova Med J
PB  - Cukurova University
WT  - DergiPark
SN  - 2602-3032
SP  - 661
EP  - 672
VL  - 50
IS  - 3
LA  - en
AB  - Purpose: Inferentially, 24%–45% of cancer patients develop brain metastases in their course. Individual survival estimation for these patients is crucial to identify the subset that may not benefit from whole-brain irradiation (WBI) due to a short survival time. This study aimed to identify variables and evaluate an artificial intelligence algorithm to determine which patients would benefit from WBI.  Materials and Methods:  The data of 345 patients with brain metastasis who were treated with 30 Gy in 10 fractions of WBI were retrospectively analyzed. In this cohort, a total of 15 clinical / laboratory factors are evaluated with 15 models of machine learning algorithms using Python 2.3, Pycaret library. Results: The Gradient Boosting Regressor was found to be the most accurate model, with a 0.68 R2 an R² value of 0.68, and a mean absolute error (MAE) of 12.90.The prediction error for the gradient Boosting Regressor was calculated as R2: 0.841. When the importance of features was investigated, time from diagnosis to metastasis was found to be the most important predictive variable for survival.Conclusion: The results of this study enable us to identify patients who may have an early death and provide a consequential decision guide in terms of whole-brain radiotherapy or additional labor-intensive techniques.
KW  - brain metastases
KW  - machine learning
KW  - prognosis
KW  - radiotherapy
N2  - Amaç: Çıkarımsal olarak kanser hastalarının %24-45'i, seyirleri sırasında beyin metastazları geliştirir. Bu hastalar için bireysel sağkalım tahmini, kısa sağkalım süresi nedeniyle tüm beyin ışınlamasından (WBI) fayda görmeyebilecek hasta alt grubunu ayırt etmek için önemlidir. Bu çalışma, değişkenler üzerinde arama yapmayı ve WBI'dan fayda görecek hasta alt grubunu belirlemek için bir yapay zeka algoritmasını değerlendirmeyi amaçlamaktadır.Gereç ve Yöntem: 10 fraksiyonda 30 Gy WBI ile tedavi edilen beyin metastazı olan 345 hastanın verileri retrospektif olarak analiz edildi. Bu kohortta toplam 15 klinik/laboratuvar faktörü, Python 2.3, Pycaret kütüphanesi kullanılarak 15 makine öğrenme algoritması modeli ile değerlendirildi.Bulgular: Gradient Boosting Regressor'un 0,68 R2 değeri ve 12,90 ortalama mutlak değer (MAE) ile doğru modelleme olduğu bulundu. Gradient Boosting Regressor için tahmin hatası R2: 0,841 olarak hesaplandı. Özelliklerin önemi incelendiğinde, tanıdan metastaza kadar geçen sürenin sağ kalım için en önemli öngörücü değişken olduğu bulundu.Sonuç: Bu çalışmanın sonuçları erken ölüm riski olan hastaları belirlemeyi mümkün kılıyor ve tüm beyin radyoterapisi veya ek emek yoğun teknikler açısından sonuç odaklı bir karar kılavuzu sağlıyor.
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UR  - https://doi.org/10.17826/cumj.1661241
L1  - https://dergipark.org.tr/en/download/article-file/4705964
ER  -