Düşük evreli gliomların radiomic ve alan bilgisi temelli öznitelikler aracılığı ile manyetik rezonans görüntülerinden izositrat dehidrogenaz mutasyon durumunun otomatik tahmini

Year 2024, , 178 - 186, 29.05.2024

İlker Özgür Koska , Çağan Koska , Antonio Fernandes

https://doi.org/10.21673/anadoluklin.1378673

Abstract

Amaç: En yaygın ve en ölümcül birincil merkezi sinir tümörleri olan glial tümörler, heterojen hücre klonları barındırırlar. Glial tümörlerin genomik profillerinin invazif olmayan bir şekilde belirlenmesi, bu tümörlerin sınıflandırılması, yönetimi ve prognostikasyonu ile ilgili önemli etkilere sahip olacaktır. İzositrat dehidrogenaz mutasyonu varlığı bu tümörler için önemli bir genetik belirteç olup daha iyi prognoz göstergesidir. Radyomik yöntemler, lezyonların non invazif sınıflandırılması için umut verici bir araçtır. Bu çalışmada radyomik özelliklerin yanı sıra alan bilgisinden ilham alan özelliklerle, yapay zekâ ile manyetik rezonans görüntüleme (MRI), görüntülerinden İzositrat Dehidrogenaz (IDH) mutasyon tahmini yapacak bir model geliştirilmesi amaçlanmıştır.

Yöntemler: Radyomik öznitelik kümesi çıkarılmış buna ek olarak radyologların lezyon tariflemede kullandığı belirteçler kodlanarak otomatik olarak elde edilmeye çalışılmıştır. Her iki yöntem ile elde edilen öznitelikler ile sınıflayıcı modeler geliştirilmiştir.

Bulgular: Radyomik ve radyolog bilgisinden ilham alan özelliklerin kombinasyonundan oluşan en iyi modelimiz 0,93 f1 puanı (Standart Sapma (SD): 0,03), 0,93 doğruluk (SD:0,03) ve 0,98 eğri altındaki alan (EAA)'ya (SD:0,02) ulaştı.

Sonuç: Bu çalışmada kullanılan çok parametreli ve çok bölgeli yaklaşım hem radyomik hem de alan bilgisinden ilham alan özelliklerin entegrasyonu ile birleştiğinde, nihai sonuç için her bir stratejinin katkısını vurgulayan yüksek performanslı bir modelle sonuçlandı.

Keywords

Glial hücreli tümörler, mutasyon, yapay zeka

Automatic prediction of isocitrate dehydrogenase mutation status of low-grade gliomas using radiomics and domain knowledge inspired features in magnetic resonance imaging

Abstract

Aim: Most common and most deadly primary central nervous tumors, glial tumors harbor many heterogeneous clones of cells. Noninvasive determination of the genomic profiles of these tumors would have important implications regarding the classification, management, and prognostication of these tumors. Isocitrate dehydrogenase mutation is a key genomic signature that can downgrade the expected dismal course of these tumors. In this study we aimed to build a performant prediction model which can determine the Isocitrate Dehydrogenase (IDH) mutation status of glial tumors, using radiomics and leveraging automatic computation of domain knowledge-inspired features.

Methods: Radiomics methods based on high throughput feature extraction and application of data science principles to these extracted features are promising tools for the noninvasive classification of lesions. Domain knowledge-inspired features besides radiomics features can contribute positively to the performance of the models. Some efforts particularly a joint approach to standardize the magnetic resonance imaging (MRI), reporting of glial tumors are mainstay for domain knowledge-inspired features. However, this requires active involvement and reporting of the radiologist which hampers automatization efforts. Additionally, this feature set evaluates a small subset of all possible signal and spatial-based computations. In this study, we combined domain knowledge-inspired features with radiomics features along with a multiparametric multihabitat comprehensive lesion description strategy.

Results: Our best model which consisted of a combination of radiomics, and radiologist knowledge-inspired features reached a 0.93 f1 score (standard deviation (SD): 0.03), 0.93 accuracy (SD:0.03), and 0.98 area under curve (AUC), (SD:0.02).

Conclusion: The multiparametric and multiregional approach employed in this study coupled with the integration of both radiomics and domain knowledge-inspired features resulted in a high-performance model emphasizing the contribution of each strategy to the outcome.

Keywords

Glial cell tumors, mutation, radiomics

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