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Journals / Sakarya Tıp Dergisi / 2019 / Cilt: 9 - Sayı: 2

A new method for discriminating cerebellar tumors from each other: algorithmic approach

Serebellar Tümörleri Birbirinden Ayırt Etmek İçin Yeni Bir Yöntem: Algoritmik Yaklaşım

Osman Melih TOPCUOĞLU
Pages
281–288
Publication Date
2019-06-28
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conclusion: CT in adults might be differentiated from each other by using some MR imaging clues and patient characteristics.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: To evaluate the MR imaging findings of the cerebellar tumors (CT) in adults and to make an algorithm to discriminate these tumors from each other.Materials and Methods: Adult patients with CT who were treated surgically were included.
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Key Findings & Data:Brain MRs and patient records were evaluated for pathological diagnosis, imaging findings and Ki-67 labeling index.
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Conclusion & Impact:Conclusion: CT in adults might be differentiated from each other by using some MR imaging clues and patient characteristics.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Görünür difüzyon katsayısı · serebellum · serebellar tümörler

Abstract

Objective: To evaluate the MR imaging findings of the cerebellar tumors (CT) in adults and to make an algorithm to discriminate these tumors from each other.Materials and Methods: Adult patients with CT who were treated surgically were included. Brain MRs and patient records were evaluated for pathological diagnosis, imaging findings and Ki-67 labeling index. The apparent diffusion coefficient (ADC) values of the tumors and centrum semiovale (CS) and the ratio of each other were noted. Kruskal-Wallis test was used for comparison.Results: 100 adults (45 women, 55 men with a mean age of 46.6±15.6 years) with pathologically proven CT were included. Tumors were divided into five groups as: a. primitive neuro-ectodermal tumor (PNET) + medulloblastoma (MB), b. grade 3+4 gliomas, c. grade 1+2 gliomas, d. metastasis (MET) and e. lymphoma. The ratio of the mean ADC (x10-3 mm2/s) of the tumor to those of CS was 1.21±0.22, 1.38±0.93, 1.98±0.69, 1.48±0.45 and 1.25±0.11 for PNET&MBs, grade 3+4 gliomas, grade 1+2 gliomas, METs and lymphomas, respectively. Significant difference was detected between grade 1+2 gliomas and PNET&MB (P=0.018), MET (P=0.004) and lymphoma (P=0.002). Conclusion: CT in adults might be differentiated from each other by using some MR imaging clues and patient characteristics.

Özet

Amaç: Erişkinlerde serebellar tümörlerin (ST) MR görüntüleme bulgularını inceleyip birbirlerinden ayrım için algoritmik yaklaşım oluşturabilmek. Gereç ve Yöntemler: Çalışmaya, cerrahi olarak tedavi edilmiş ST’li erişkin hastalar dahil edildi. Beyin MR’ları ve hasta kayıtları patolojik tanı, görüntüleme bulguları ve Ki-67 indeksi açısından tarandı. Tümöre ve sentrum semiovale (SS)’ye ait görünür difüzyon katsayıları (GDK) ile birbirlerine oranları hesaplandı. Karşılaştırma için Kruskal-Wallis testi kullanıldı. Bulgular: Çalışmaya yaş ortalaması 46.6±15.6 yıl olan 100 (45 kadın, 55 erkek) ST’li hasta dahil edildi. Tümörler 5 gruba ayrıldı: a. primitif nöroektodermal tumor (PNET)+medulloblastom (MB) b. evre 3+4 gliomlar, c. evre 1+2 gliomlar, d. metastaz (MET) ve e. lenfoma. PNET&MB, evre 3+4 gliomlar, evre1+2 gliomlar, MET and lenfomalara ait ortalama ST/SS GDK (x10-3 mm2/s) değerleri sıraıyla1.21±0.22, 1.38±0.93, 1.98±0.69, 1.48±0.45 and 1.25±0.11. Evre 1+2 gliomlar ve PNET&MB’ler (P=0.018), MET (P=0.004) ve lenfomalar (P=0.002) arasında anlamlı fark vardı. Sonuç: Erişkinde ST’ler bazı MR ipuçları ve hasta özellikleri ile birbirlerinden ayırt edilebilir.

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Cite This Article

TOPCUOĞLU, O. M. (2019). A new method for discriminating cerebellar tumors from each other: algorithmic approach. Sakarya Tıp Dergisi, Cilt: 9 - Sayı: 2, 281–288. https://ajindex.com/articles/261799

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