Spinal BT Taramasında Multipl Miyelom ve Osteolitik Kemik Metastazının Ayırt Edilmesi: Konvolüsyonel Sinir Ağı Kullanan Kapsamlı Bir Çalışma

Yıl 2025, Cilt: 22 Sayı: 1, 1 - 7

Muhammet Kürşat Şimşek , Yusuf Kenan Çetinoğlu , Resul Bircan , Ali Balcı

https://doi.org/10.35440/hutfd.1563046

Öz

Amaç: Spinal multipl miyelom (MM) ve osteolitik metastatik kemik tümörünün (OMKT) doğru bir şekilde ayırt edilmesi zorlayıcı olabilir, genellikle doğru tanı için görüntüleme yöntemleri, laboratuvar testleri ve biyopsinin kombinasyonu uygulanır. Bu çalışmada, MM ve OMKT hastalarından elde edilen BT görüntülerini CNN modelleri kullanarak ayırt etmeyi amaçladık.
Materyal ve Metod: Ocak 2015 ile Ocak 2023 arasında elde edilen 91 hastanın (1886 OMKT görüntüsü ve 1821 MM görüntüsü; 46 erkek ve 45 kadın; ortalama yaş: 61,2 yıl) 3707 BT görüntüsü incelendi. Eğitim seti için 2667, doğrulama seti için 740, test seti için 300 görüntü rastgele seçildi. DenseNet121, DenseNet169, EfficientNetB0, MobileNet, MobileNetV2, VGG16 ve Xception CNN mimarilerine dayanan bir transfer öğrenimi yaklaşımı kullanıldı. Modellerin performansı değerlendirildi.
Bulgular: Modellerin MM ve OMKT ayrımındaki duyarlılık, özgüllük, pozitif prediktif değer, negatif prediktif değer, doğruluk, F1 skoru ve kappa ölçümleri değerlendirildiğinde, en başarılı modeller sırasıyla %88, %86,33 ve %86 doğruluk oranları ile MobileNetV2, MobileNet ve VGG16 olmuştur.
Sonuç: Çalışmamızda CNN tabanlı yapay zekâ modellerinin BT görüntülerinde MM ve OMKT'yi ayırt edebileceğini gösterdik.

Anahtar Kelimeler

Yapay Zekâ, Multipl Miyelom, Spinal Metastaz

Differentiating Multiple Myeloma and Osteolytic Bone Metastasis on Spinal CT Scan: A Comprehensive Study Using Convolutional Neural Network

Öz

Background: Accurate differentiation of spinal multiple myeloma (MM) and osteolytic metastatic bone tumor (OMBT) can be challenging. Usually, imaging methods, laboratory tests, and biopsy are performed for the correct diagnosis. In this study, we aimed to differentiate CT images from patients with MM and OMBT using CNN models.
Materials and Methods: 3707 CT images of 91 patients (1886 OMBT images and 1821 MM images; 46 males and 45 females; mean age: 61.2 years) obtained between January 2015 and January 2023 were reviewed. 2667 images were randomly selected for the training set, 740 for the validation set, and 300 for the test set. A transfer learning approach was used based on DenseNet121, DenseNet169, EfficientNetB0, MobileNet, MobileNetV2, VGG16, and Xception CNN architectures. The performance of the models was evaluated.
Results: When the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, and kappa measurements of the models in the MM and OMBT differentiation are evaluated, the most successful ones are MobileNetV2, MobileNet, and VGG16, with accuracy of 88%, 86.33%, and 86%, respectively.
Conclusions: Our study showed that CNN-based artificial intelligence models can differentiate MM and OMBT on CT images.

Anahtar Kelimeler

Artificial Intelligence, Multiple Myeloma, Spinal Metastasis

Kaynakça

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