Integration of radiomics with MRI clear cell likelihood score for classification of clear cell renal cell carcinoma

Ahmet Kasım Karabulut; İlker Özgür Koska; Ali Çağlar Turgut; Banu Sarsik Kumbaraci; Fuat Kızılay; Ezgi Güler

doi:10.19161/etd.1882022

EN TR

Integration of radiomics with MRI clear cell likelihood score for classification of clear cell renal cell carcinoma

Abstract

Aim: To compare the diagnostic performance of the MRI-based clear cell likelihood score (ccLS), radiomics-based machine learning models, and their combination for differentiating clear cell renal cell carcinoma (ccRCC) from other renal tumor subtypes. Materials and Methods: This single-center retrospective study included patients with solid renal masses who underwent multiparametric MRI and had histopathologic confirmation. Lesions were evaluated using ccLS by two independent readers. Radiomic features were extracted from T1-weighted and T2-weighted images following standardized preprocessing. Multiple machine learning pipelines combining different feature selection methods and classifiers were evaluated using stratified 10-fold cross-validation with four repetitions. Using multivariable logistic regression, the radiomics score together with clinical factors and semantic imaging features were evaluated, and a nomogram was constructed based on the selected variables. Results: For differentiating ccRCC from other renal tumor subtypes, the ccLS model achieved an area under the receiver operating characteristic curve (AUC) of 0.832 (0.759–0.899). The best-performing radiomics-based machine learning model achieved a mean AUC of 0.89 ± 0.10. The combined model demonstrated higher diagnostic performance, with a mean AUC of 0.96 ± 0.01. Conclusions: Combining radiomics-based machine learning with established semantic MRI assessments was associated with improved differentiation of ccRCC from other renal tumor subtypes compared with individual approaches. Further validation in larger, multicenter cohorts is warranted before broader clinical application.

Keywords

Şeffaf hücreli renal hücreli karsinomun sınıflandırılması için manyetik rezonans görüntüleme temelli şeffaf hücreli olabilirlik skoru ile radyomik analizin entegrasyonu

Abstract

Amaç: Manyetik rezonans görüntüleme (MRG) tabanlı şeffaf hücreli olabilirlik skoru, radyomik tabanlı makine öğrenmesi modelleri ve bu yaklaşımların kombinasyonunun, şeffaf hücreli renal hücreli karsinomu diğer renal tümör alt tiplerinden ayırt etmedeki tanısal performanslarını karşılaştırmaktır. Gereç ve Yöntem: Tek merkezli retrospektif çalışmaya, multiparametrik MRG incelemesi olan ve histopatolojik doğrulaması mevcut solid böbrek kitlesi olan hastalar dahil edildi. Lezyonlar iki bağımsız okuyucu tarafından şeffaf hücreli olabilirlik skoru kullanılarak değerlendirildi. Radyomik öznitelikler, standart ön işleme adımlarını takiben T1-ağırlıklı ve T2-ağırlıklı görüntülerden çıkarıldı. Farklı öznitelik seçimi yöntemleri ve sınıflandırıcıları içeren modeller, dört tekrar içeren katmanlı 10-katlı çapraz doğrulama ile değerlendirildi. Çok değişkenli lojistik regresyon kullanılarak, radyomik skoru yanı sıra klinik faktörler ile semantik görüntüleme özellikleri değerlendirilerek, seçilen değişkenlerle nomogram görselleştirmesi yapıldı. Bulgular: Şeffaf hücreli olabilirlik skor modelinin eğri altında kalan alan değeri 0,832 (0,759–0,899) olarak bulundu. En iyi performans gösteren radyomik tabanlı makine öğrenmesi modelinde bu değer ortalama 0,89 ± 0,10 idi. Kombine model ise 0,96 ± 0,01 değeri ile en yüksek ayırt edici performansı göstermiştir. Sonuç: Radyomik tabanlı makine öğrenmesinin semantik MRG ölçümleriyle birleştirilmesi, şeffaf hücreli renal hücreli karsinomun diğer alt tiplerden ayrımında tekil yaklaşımlara kıyasla daha yüksek tanısal performans göstermiştir. Bu bulguların klinik uygulamaya geçebilmesi için daha geniş ve çok merkezli çalışmalarla doğrulanması gerekmektedir.

Keywords

Ethical Statement

This single-center retrospective study was conducted following approval from the Ege University Medical Research Ethics Committee (approval date: November 30, 2023; decision no: 23-11.2T/8).

References

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Details

Primary Language

English

Subjects

Radiology and Organ Imaging

Journal Section

Research Article

Authors

Ahmet Kasım Karabulut ^*
0000-0002-1665-0716
Türkiye

İlker Özgür Koska
0000-0003-0971-3827
Türkiye

Ali Çağlar Turgut
0000-0003-4159-2201
Türkiye

Banu Sarsik Kumbaraci
0000-0003-4775-3942
Türkiye

Fuat Kızılay
0000-0003-1856-0404
Türkiye

Ezgi Güler
0000-0002-8028-0350
Türkiye

Publication Date

June 10, 2026

Submission Date

February 4, 2026

Acceptance Date

March 2, 2026

Published in Issue

Year 2026 Volume: 65 Number: 2

DOI

https://doi.org/10.19161/etd.1882022

IZ

https://izlik.org/JA29RR25WE

Cite

RIS / Bibtex

APA

Karabulut, A. K., Koska, İ. Ö., Turgut, A. Ç., Sarsik Kumbaraci, B., Kızılay, F., & Güler, E. (2026). Integration of radiomics with MRI clear cell likelihood score for classification of clear cell renal cell carcinoma. Ege Tıp Dergisi, 65(2), 313-320. https://doi.org/10.19161/etd.1882022

AMA

1.Karabulut AK, Koska İÖ, Turgut AÇ, Sarsik Kumbaraci B, Kızılay F, Güler E. Integration of radiomics with MRI clear cell likelihood score for classification of clear cell renal cell carcinoma. EJM. 2026;65(2):313-320. doi:10.19161/etd.1882022

Chicago

Karabulut, Ahmet Kasım, İlker Özgür Koska, Ali Çağlar Turgut, Banu Sarsik Kumbaraci, Fuat Kızılay, and Ezgi Güler. 2026. “Integration of Radiomics With MRI Clear Cell Likelihood Score for Classification of Clear Cell Renal Cell Carcinoma”. Ege Tıp Dergisi 65 (2): 313-20. https://doi.org/10.19161/etd.1882022.

EndNote

Karabulut AK, Koska İÖ, Turgut AÇ, Sarsik Kumbaraci B, Kızılay F, Güler E (June 1, 2026) Integration of radiomics with MRI clear cell likelihood score for classification of clear cell renal cell carcinoma. Ege Tıp Dergisi 65 2 313–320.

IEEE

[1]A. K. Karabulut, İ. Ö. Koska, A. Ç. Turgut, B. Sarsik Kumbaraci, F. Kızılay, and E. Güler, “Integration of radiomics with MRI clear cell likelihood score for classification of clear cell renal cell carcinoma”, EJM, vol. 65, no. 2, pp. 313–320, June 2026, doi: 10.19161/etd.1882022.

ISNAD

Karabulut, Ahmet Kasım - Koska, İlker Özgür - Turgut, Ali Çağlar - Sarsik Kumbaraci, Banu - Kızılay, Fuat - Güler, Ezgi. “Integration of Radiomics With MRI Clear Cell Likelihood Score for Classification of Clear Cell Renal Cell Carcinoma”. Ege Tıp Dergisi 65/2 (June 1, 2026): 313-320. https://doi.org/10.19161/etd.1882022.

JAMA

1.Karabulut AK, Koska İÖ, Turgut AÇ, Sarsik Kumbaraci B, Kızılay F, Güler E. Integration of radiomics with MRI clear cell likelihood score for classification of clear cell renal cell carcinoma. EJM. 2026;65:313–320.

MLA

Karabulut, Ahmet Kasım, et al. “Integration of Radiomics With MRI Clear Cell Likelihood Score for Classification of Clear Cell Renal Cell Carcinoma”. Ege Tıp Dergisi, vol. 65, no. 2, June 2026, pp. 313-20, doi:10.19161/etd.1882022.

Vancouver

1.Ahmet Kasım Karabulut, İlker Özgür Koska, Ali Çağlar Turgut, Banu Sarsik Kumbaraci, Fuat Kızılay, Ezgi Güler. Integration of radiomics with MRI clear cell likelihood score for classification of clear cell renal cell carcinoma. EJM. 2026 Jun. 1;65(2):313-20. doi:10.19161/etd.1882022