Kanserli dokuların mikroskop görüntülerinde kanser kök hücre oranın otomatik olarak belirlenmesi ve klinikte kullanılacak yazılım geliştirilmesi

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2016

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Demir, Gündüz Çiğdem
Güzelcan, Akhan Ece
Arslan, Tunç Musa
Yorulmaz, Onur
Fayetörbay, Rümeysa
Erşahin, Tülin
Mohammadvand, Navid
Örsçelik, Gökçe Simge
Üner, Ayşegül
Çetin, Enis Ahmet
Oğuz, Oğuzhan
Doğan, Deniz
Koyuncu, Fahrettin Can
Badawı, Diaa
Atalay, Rengül

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Kanser anormal hücrelerin kontrolsüz çoğalması ve yayılması olarak tanımlanan karmaşık bir hastalıktır. Bu nedenle kanser dokusu farklı özellikleri olan-ek popülasyon denilen-hücre gruplarını içerir. Bu hücrelerden olan kanser kök hücreleri normal kök hücreleri gibi kendini yenileme ve farklılaşma özelliklerini taşırken normal kök hücrelerinin aksine homeostatik kontrolleri olmayan, yani farklılaşma özelliklerini dengeleyip, çevresel sinyallere göre programlama özellikleri olmayan hücrelerdir. Bu özellikleri dolayısıyla da son yıllarda kanser kök hücre popülasyonu kanserin kötü prognozundan sorumlu hücreler olarak tanımlanmaktadırlar. Bu bağlamda kanser tedavisinde kanser kök hücre oran bilgisi, hastaya verilecek tedavinin planlanması amacıyla kullanılabilir. Hekimler kanserli dokunun kanser kök hücre popülasyon onarına göre henüz kanser metastaz yapmamış bile olsa bireye özgü tedavi yaklaşımı izlenebilir. Gerçekleştirilen bu proje kapsamında, kanserli parafin bloklara gömülü doku biyopsi örneklerinde, kanser kök hücre tanımlama öngörü aracını geliştirdik. Kovaryans matrisine dayalı sınıflandırıcılara ve Ana Bileşenler Analizi (PCA) algoritması sonucunda üç sınıf H&E boyanmış karaciğer kanseri dokularının sınıflandırma probleminde 76.0% imge sınıflandırma başarısı elde edilmiştir. Ana Bileşenler Analizi kanser hücresi belirleme probleminde %90’nın üstünde başarı ve kovaryans matrisine dayalı sınıflandırıcılar ile üç sınıf ayrıştırma probleminde %90’a yakın başarı sağlanmıştır. Kanserli hastalarda bireye ve hedefe yönelik tedavi yol haritasının belirlenmesine destek olabilecek ve hastalığın tedavisinde ve prognozunda iyileştirme sağlayabilecek yazılımı hekimlerin kullanıma sunduk. CANSTEM yazılıma http://users.metu.edu.tr/rengul/canstem.html adresinden ulaşılabilir.

Subject Keywords

Kanser kök hücre, Makine ile öğrenme, İmmunohistokimya, Tedavi, Prognoz

URI

https://app.trdizin.gov.tr/publication/project/detail/TVRVM01EVTE
https://hdl.handle.net/11511/49402

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Graduate School of Informatics, Project and Design

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Citation Formats

G. Ç. Demir et al., “Kanserli dokuların mikroskop görüntülerinde kanser kök hücre oranın otomatik olarak belirlenmesi ve klinikte kullanılacak yazılım geliştirilmesi,” 2016. Accessed: 00, 2020. [Online]. Available: https://app.trdizin.gov.tr/publication/project/detail/TVRVM01EVTE.