Application of machine learning techniques for survival prediction in pediatric malignant non-seminomatous germ cell testicular tumors: a SEER database study

Year 2025, Volume: 18 Issue: 3, 1 - 1

İncinur Genişol , Batuhan Bakırarar

https://doi.org/10.31362/patd.1528303

Abstract

Purpose: Childhood testicular cancers constitute 1-2% of all childhood tumors. According to the Surveillance, Epidemiology, and End Results (SEER) database, based on data from 2013 to 2019, the 5-year survival rate is 95.2%. The second most common type of testicular tumor is malignant non-seminomatous germ cell tumor. In recent years, various statistical techniques and extensive databases have been used to obtain information on disease prognosis and survival. In this study, we aimed to develop software using artificial intelligence and machine learning techniques to accurately predict the overall survival of patients with malignant nonseminomatous germ cell testicular tumors.
Materials and methods: Our study included data from 788 patients aged 0-18 diagnosed with malignant nonseminomatous germ cell testicular cancer between January 1975 and December 2019. The main hypothesis of the study was to provide overall survival (OS) in years from the date of diagnosis to the date of death or the last follow-up date for surviving patients. In addition to survival analysis, we also analyzed patient age at diagnosis, race, laterality, year of diagnosis, tumor histological type, T stage, N stage, M stage, tumor size, mortality, and follow-up duration.
Results: The OS was found to be 41.29±0.43 years. The median survival time was 43.21±0.62 years for patients <15 and 40.34±0.52 years for patients aged ≥15. We developed software that enabled the provision of patient-specific survival in addition to OS for all patients.
Conclusion: Recently, artificial intelligence techniques, such as machine learning, have shown remarkable advancements compared to other statistical methods. As a result, in this study, we found that the survival rate in pediatric NSCGT was higher if the tumor was diagnosed after 2000, was less than 2 cm in size, and had a T1M0N0 stage yolk sac tumor. We created a 10-year survival prediction model with the results and thought that this model would contribute to the advancement of artificial intelligence studies in prognosis, recurrence and survival analysis.

Keywords

Non-seminomatous germ cell tumors, tescicular tumors, SEER, machine learning, survival

Pediatrik malign non-seminamatöz germ hücreli testis tümörlerinde sağkalım tahmini için makine öğrenme tekniklerinin uygulanması: bir SEER veritabanı çalışması

Abstract

Amaç: Çocukluk çağı testis kanserleri, tüm çocukluk çağı tümörlerinin %1-2'sini oluşturur. 2013-2019 yılları arasındaki verilere dayanan Surveillance, Epidemiology, and End Results (SEER) veritabanına göre, 5 yıllık sağkalım oranı %95,2 olarak görülmüştür. İkinci en yaygın testis tümörü türü ise malign non-seminomatöz germ hücreli tümördür. Son yıllarda, hastalığın prognozu ve sağkalımı hakkında bilgi elde etmek için çeşitli istatistiksel teknikler ve geniş veritabanları kullanılmaktadır. Bu çalışmada, malign non-seminomatous germ hücreli testis tümörü olan hastaların genel sağkalımını doğru bir şekilde tahmin etmek için yapay zeka ve makine öğrenimi tekniklerini kullanarak yazılım geliştirmeyi amaçladık.
Gereç ve yöntem: Çalışmamız, Ocak 1975 ile Aralık 2019 tarihleri arasında malign non-seminomatous germ hücreli testis kanseri tanısı konmuş 0-18 yaş arası 788 hastanın verilerini içermektedir. Çalışmanın temel hipotezi, tanı tarihinden ölüm tarihi veya sağ kalan hastalar için son takip tarihi itibarıyla genel sağkalımı yıllar olarak gösterebilmekti. Sağkalım yanı sıra, tanı anındaki hasta yaşı, ırk, tümörün yerleşim yeri, tanı yılı, tümör histolojik tipi, T evresi, N evresi, M evresi, tümör büyüklüğü, mortalite ve takip süresi gibi faktörleri de analiz ettik.
Bulgular: Genel sağkalım 41,29±0,43 yıl olarak bulundu. Medyan sağkalım süresi, 15 yaşından küçük hastalar için 43,21±0,62 yıl ve 15 yaş ve üzeri hastalar için 40,34±0,52 yıl olarak bulundu. Tüm hastalar için genel sağkalıma ek olarak, hastaya özgü sağkalım sağlayan bir yazılım geliştirdik.
Sonuç: Son zamanlarda, makine öğrenimi gibi yapay zeka teknikleri, diğer istatistiksel yöntemlere kıyasla dikkate değer ilerlemeler göstermiştir. Sonuç olarak bu çalışmamızda pediatrik NSCGT’de tümörün 2000 yılından sonra teşhis edilmesi, boyutunun 2 cm. den küçük olması, T1M0N0 evre yolk sak tümör olmasının hayatta kalma oranının daha yüksek olduğunu saptadık. Sonuçlarla 10 yıllık sağ kalma tahmin modeli oluşturduk ve bu modelin prognoz, nüks ve hayatta kalma analizinde yapay zeka çalışmalarının ilerlemesine katkı sağlayacagını düşündük.

Keywords

Non-seminomatöz germ hücreli tümör, testis tümörü, SEER, machine learning, sağkalım

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