Machine learning model to identify prognostic factors in glioblastoma: a SEER-based analysis

Year 2023, , 338 - 348, 05.04.2023

Batuhan Bakırarar , Emrah Egemen , Ümit Akın Dere , Fatih Yakar

https://doi.org/10.31362/patd.1179139

Abstract

Purpose: Analyzing and interpreting large amounts of complex health care data are becoming more insufficient
by traditional statistical approaches. However, analyzing Big Data (BD) by machine learning (ML) supports the
storage, classification of patient information. Therefore, improves disease identification, treatment evaluation,
surgical planning, and outcome prediction. The current study aims to create a competing risk model to identify
prognostic factors in glioblastoma (GB).
Materials and methods: The study included 31663 patients diagnosed with GB between 2007 and 2018. The
data in the study were taken from the Surveillance, Epidemiology, and End Results (SEER) database. Overall
survivals (OS), age, race, gender, primary site, laterality, surgery and tumor size at the time of diagnosis, vital
status, and follow-up time (months) were selected for the analyzes.
Results: The median OS of the patients was found to be 9.00±0.09 months. In addition, all variables in the
table were statistically significant risk factors for survival except gender. Therefore, surgery, age, laterality,
primary site, tumor size, race, gender variables were used as independent risk factors, and vital status was
used as a dependent variable for ML analysis. Looking at the ML results, hybrid model gave the best results
according to Accuracy, F-measure, and MCC performance criteria. According to hybrid model, which has the
best performance, the diagnosis of alive/dead in 84 and 74 out of 100 patients can be interpreted as correct for
1- and 2-year, respectively.
Conclusions: The model created by ML was 84.9% and 74.1% successful in predicting 1- and 2-year survival
in GB patients, respectively. Recognition of the fundamental ideas will allow neurosurgeons to understand BD
and help evaluate the extraordinary amount of data within the associated healthcare field.

Keywords

Machine learning, big data, glioblastoma, SEER

Project Number

-

Glioblastomda prognostik faktörleri tanımlamak için makine öğrenmesi modeli: SEER tabanlı analiz

Abstract

Amaç: Büyük miktarlardaki karmaşık sağlık hizmeti verilerinin analiz edilmesi ve yorumlanmasında geleneksel
istatistiksel yaklaşımlar giderek yetersiz kalmaktadır. Bununla birlikte, Büyük Verinin makine öğrenmesi ile analiz
edilmesi, hasta bilgilerinin depolanmasını, sınıflandırılmasını destekler. Bu nedenle hastalık tanımlamasını,
tedavi değerlendirmesini, cerrahi planlamayı ve sonuç tahminini geliştirir. Mevcut çalışma, glioblastomda (GB)
prognostik faktörleri tanımlamak için bir risk modeli oluşturmayı amaçlamaktadır.
Gereç ve yöntem: Çalışmaya 2007-2018 yılları arasında GB tanısı konan 31663 hasta dahil edilmiştir.
Çalışmadaki veriler Surveillance, Epidemiology, and End Results (SEER) veri tabanından alınmıştır. Analizler
için genel sağ kalımlar, yaş, ırk, cinsiyet, primer bölge, lateralite, cerrahi ve tanı anındaki tümör boyutu, vital
durum ve takip süresi (ay) seçildi.
Bulgular: Hastaların ortanca sağ kalımı 9,00±0,09 ay olarak bulundu. Ayrıca tablodaki tüm değişkenler cinsiyet
dışında sağ kalım için istatistiksel olarak anlamlı risk faktörleriydi. Bu nedenle, makine öğrenmesi analizi için
bağımsız risk faktörleri olarak cerrahi, yaş, lateralite, primer bölge, tümör boyutu, ırk, cinsiyet değişkenleri ve
vital durum bağımlı değişken olarak kullanıldı. Makine öğrenmesi sonuçlarına bakıldığında, doğruluk, F-ölçümü
ve MCC performans kriterlerine göre Hibrit Model en iyi sonuçları vermiştir. En iyi performansa sahip olan hibrit
modele göre 100 hastanın 84'ünde canlı/ölü tanısı sırasıyla 1 ve 2 yıl için doğru olarak yorumlanabilmektedir.
Sonuç: Makine öğrenmesi ile oluşturulan model GB hastalarında 1 ve 2 yıllık sağ kalımı öngörmede sırasıyla
%84,9 ve %74,1 başarılıydı. Temel fikirlerin tanınması, beyin cerrahlarının Büyük Veriyi anlamalarına ve ilgili
sağlık hizmetleri alanındaki olağanüstü miktarda veriyi değerlendirmelerine yardımcı olacaktır.

Keywords

Makine öğrenmesi, büyük veri, glioblastoma, SEER

Supporting Institution

Yok

Project Number

-

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