Öz
Amaç
Bilgisayarlı tomografi (BT) görüntüleri üzerinden klivusun radiomics verilerini, makine öğrenme algoritmaları ile kombine ederek osteoporozu (OP) tahmin etmeyi amaçladık.
Gereç ve Yöntem
Retrospektif çalışmamızda, kurumumuzda çift enerjili X-ışını absorpsiyometrisi (DEXA) ve bir yıl içerisinde kraniofasial bölgeye BT tetkiki yapılmış olan 140 olgu incelemeye alındı. Hastalar DEXA T-skorlarına göre, 30’u OP, 33’ü osteopeni ve 77’si normal üç gruba ayrıldı. Segmentasyon işlemi ve radiomics özelliklerin çıkarımı “3D slicer” programı ile tek hekim tarafından yapıldı. Klivusun manuel çizilerek segmente edildi. Radiomics çıktıları, orijinal, ince-kaba Laplacian of Gaussian ve wavelet transform filtreli görüntülerden oluşmaktadır. Toplam 1023 adet radiomics özellik elde edildi. Voksel yeniden örnekleme 1x1x1 mm³ olarak standardize edildi. Makine öğrenmesi (MÖ) için Orange Data Mining programı kullanıldı. Özellik azaltma için relieff ve fast correlation based filter metodları uygulandı. MÖ algoritmaları olarak k-nearest neighborhood, decision tree, random forest, logistic regression, support vector machine (SVM), Naive Bayes ve neural network sınıflandırmaları kullanıldı. Sınıflandırmaları karşılaştırmak için eğri altında kalan alan (EAA), duyarlılık (sensitivity, recall), özgüllük (spesifite), alıcı çalışma karakteristik eğri analizi, hata matriksi gibi parametreler kullanıldı. Tüm istatistiksel sonuçlar için p<0,05 değeri anlamlı kabul edildi.
Bulgular
OP ve OP olmayan (osteopeni + normal) ikili sınıflandırmada OP tahminin en yüksek başarıyı nöral network algoritması elde etti (EAA: 0,87). Düşük kemik mineral yoğunluğu (KMY) ile normal KMY’li olgulardan oluşan ikili sınıflandırmada, düşük KMY’yi en iyi tahmin eden SVM algoritması oldu (EAA: 0,82). Son olarak OP, osteopeni ve normal olmak üzere üçlü gruplandırma yapıldı. OP’yi ayırt etmede en yüksek performansı Naive Bayes algoritması elde etti (EAA: 0,9). Osteopeni grubunu tahmin etmede de Naive Bayes algoritması ön plana çıktı (EAA: 0,69). Düşük KMY ile normal KMY’li olgular arasında HU değerleri anlamlı olarak farklıydı (p<0,001).
Sonuç
Çalışmamızda klivusun BT tabanlı radiomics çıktılarını kullanarak elde edilen MÖ algoritmalarının OP tahmininde kullanılabileceğini ve KMY hakkında fikir verdiği gösterdi.
Anahtar Kelimeler: Anahtar kelimeler: Osteoporoz, klivus, makine öğrenmesi, radiomics
Kaynakça
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