Melanomanın yüzeyde zenginleştirilmiş Raman spektroskopisi ile tespitinde ortama eklenen serumun sınıflandırma başarısı üzerine etkisinin incelenmesi

Yıl 2025, Cilt: 14 Sayı: 1, 1 - 1

Fatma Uysal Çiloğlu

https://doi.org/10.28948/ngumuh.1493656

Öz

Dünya genelinde ölüm oranları açısından ilk sıralarda yer alan melanomanın tespitinde kullanılan yöntemlerin dezavantajlarından dolayı yeni yöntemlerin geliştirilmesine ihtiyaç duyulmaktadır. Bu doğrultuda yüzeyde zenginleştirilmiş Raman spektroskopisi (YZRS) güçlü ve alternatif bir teknik olarak karşımıza çıkmaktadır. Bu çalışmada deri fibroblast, tümör ilişkili fibroblast ve melanoma hücrelerinin kültür aşamasında hücre kültür ortamına eklenen serumun hücrelerden toplanan YZRS spektrumlarının sınıflandırma başarısı üzerine etkisi araştırılmıştır. Serum eklenen ve eklenmeyen ortamda kültürlenen hücrelerden elde edilen YZRS spektrumlarının; temel bileşen analizi (TBA), yeniden yapılanma bağımsız bileşen analizi (YYBBA) ve seyrek filtreleme (SF) yöntemleri kullanılarak öz nitelik çıkarımı gerçekleştirilmiştir. Çıkarılan öz nitelikleri sınıflandırmak için destek vektör makinası (DVM) ve k en yakın komşu (KEYK) algoritması kullanılmıştır. Serum eklenmeyen hücre kültür ortamlarında kültürlenen hücrelerden toplanan spektrumların makine öğrenme teknikleri ile sınıflandırma işlemi gerçekleştirildiğinde sınıflandırma başarılarının serum eklenen gruba göre daha yüksek olduğu tespit edilmiştir. Serum eklenmeyen hücre grubunun sınıflandırma başarısı SF ile DVM kullanıldığında %96.4±0.4, TBA ile KEYK algoritması kullanıldığında %96.8±0.29 olarak bulunmuş olup serum eklenen ve eklenmeyen gruplara ait sınıflandırma başarısı arasında istatistiksel açıdan anlamlı bir fark bulunmuştur.

Anahtar Kelimeler

melanoma, Yüzeyde zenginleştirilmiş Raman spektroskopi, temel bileşen analizi, yeniden yapılandırma bağımsız bileşen analizi, seyrek filtreleme

Investigation of the effect of serum added to the medium on the classification success in the detection of melanoma using surface-enhanced Raman spectroscopy

Öz

Due to the disadvantages of the methods used for the detection of melanoma, which ranks among the leading causes of death worldwide, there is a need for the development of new methods. In this context, surface-enhanced Raman spectroscopy (SERS) emerges as a powerful and alternative technique. In this study, the effect of serum added to the cell culture medium during the culture of skin fibroblasts, tumor-associated fibroblasts, and melanoma cells on the classification success of SERS spectra collected from the cells was investigated. Feature extraction was performed using principal component analysis (PCA), reconstruction-independent component analysis (RICA), and sparse filtering (SF) methods on SERS spectra obtained from cells cultured in media with and without serum. Support vector machines (SVM) and k-nearest neighbors (KNN) algorithms were used to classify the extracted features. It was found that the classification accuracy of spectra collected from cells cultured in serum-free media was higher compared to those from the serum-added group when classified using machine learning techniques. The classification accuracy of the serum-free cell group was found to be 96.4±0.4% using SF with SVM and 96.8±0.29% using PCA with the KNN algorithm, with a statistically significant difference in classification accuracy between the serum-added and serum-free groups.

Anahtar Kelimeler

melanoma, Surface enhanced Raman spectroscopy, Principal component analysis, Reconstruction independent component analysis, sparse filtering

Kaynakça

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