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

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

Makale detayları

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

Yazarlar:

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

Yayıncı:

Pamukkale University

Ana Başlıklar:

Cerrahi

Dergi:

Pamukkale Tıp Dergisi Vol.16 Issue 2, pp.338-348

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281298

pISSN:

1308-0865

Anahtar kelimeler:

Özet:

 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 9.000.09 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 alivedead 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.

 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 9.000.09 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 alivedead 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.

 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 9.000.09 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 alivedead 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.

 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 9,000,09 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 84ünde canlıölü 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.

Makale detayları

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

Yazarlar:

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

Yayıncı:

Pamukkale University

Ana Başlıklar:

Cerrahi

Dergi:

Pamukkale Tıp Dergisi Vol.16 Issue 2, pp.338-348

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281298

pISSN:

1308-0865

Machine learning

big data

glioblastoma

SEER

Makine öğrenmesi

büyük veri

glioblastoma

SEER

Sayı

Makale detayları

Yazarlar: Batuhan Bakırarar | Emrah Egemen | Ümit Akın Dere | Fatih Yakar

Yayıncı: Pamukkale University

Ana Başlıklar: Cerrahi

Dergi: Pamukkale Tıp Dergisi Vol.16 Issue 2, pp.338-348

Belge Tipi: Makale

Yayın Tarihi: 5/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001281298

pISSN: 1308-0865

Sayı

Yazarlar:

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

Yayıncı:

Pamukkale University

Ana Başlıklar:

Cerrahi

Dergi:

Pamukkale Tıp Dergisi Vol.16 Issue 2, pp.338-348

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001281298

pISSN:

1308-0865