Nazofarenks karsinomu olan hastalarda metastatik lenf nodlarının saptanmasında manyetik rezonans görüntüleme doku analizi

Year 2023, , 461 - 465, 31.08.2023

Halil Özer , Abdussamet Batur , Nurullah Özdemir , Mehmet Sedat Durmaz , Abidin Kılınçer

https://doi.org/10.54005/geneltip.1311577

Abstract

Özet

Amaç: Nazofarenks karsinomu (NK) olan hastalarda metastatik lenf nodlarının saptanmasında manyetik rezonans görüntüleme (MRG) doku analizinin (DA) rolünü araştırmak.

Gereç ve yöntemler: Ocak 2020-Ekim 2021 tarihleri arasında 32 metastatik lenf nodu olan 15 NK hastası ve benign lenf nodu olan 30 sağlıklı birey çalışmaya dahil edildi. Doku özellikleri, metastatik ve benign lenf nodları arasında karşılaştırıldı. Çok değişkenli regresyon analizi kullanılarak metastatik lenf nodlarını tahmin için bağımsız değişkenler belirlendi. Regresyon modellerinin tanısal performansını değerlendirmek için receiver operator characteristics (ROC) analizi kullanıldı.

Bulgular: First order doku özellikleri gruplar arasında anlamlı farklılık göstermedi (p>0,05). Metastatik lenf nodlarındaki correlation dışında tüm gray-level co-occurrence matrix (GLCM) ve gray-level run length matrix (GLRLM) özellikleri anlamlı olarak farklıydı (p<0,05). GLCM özelliklerinden joint entropy, joint energy ve maximum probability; GLRLM özelliklerinden gray level non uniformity ve low gray level run emphasis metastatik lenf nodlarının tahmininde bağımsız değişkenlerdi. GLCM regresyon modeli ve GLRLM regresyon modeli için eğri altındaki alan (AUC) değerleri sırasıyla 0,975 ve 0,928 idi.

Sonuç: MRG doku analizi, doku heterojenitesi ve hücresel kompozisyon hakkında kantitatif bilgi sağlayarak NK’li hastalarda metastatik lenf nodlarını saptamada yararlı olabilir.

Keywords

Manyetik rezonans görüntüleme, doku analizi, nazofarenks karsinomu, metastatik lenf nodları

Magnetic Resonance Imaging Texture Analysis in the Detection of Metastatic Lymph Nodes in Patients with Nasopharyngeal Carcinoma

Abstract

Abstract

Aims: To investigate the role of magnetic resonance imaging (MRI) texture analysis (TA) in the detection of metastatic lymph nodes in patients with nasopharyngeal carcinoma (NPC).

Material and methods: Between January 2020 and October 2021, 15 NPC patients with 32 metastatic lymph nodes and 30 healthy subjects with benign lymph nodes were included in the study. The texture features compared between metastatic and benign lymph nodes. The independent predictor parameters of metastatic lymph nodes were determined using multivariate regression analysis. Receiver operator characteristics (ROC) analysis was used to evaluate the diagnostic performance of the regression models.

Results: The first order texture features did not differ significantly between groups (p>0.05). Except for correlation in metastatic lymph nodes, all gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) features were significantly different (p<0.05). The GLCM features of joint entropy, joint energy, and maximum probability; and the GLRLM features of gray level non uniformity and low gray level run emphasis were independent predictors of metastatic lymph nodes. The area under the curve (AUC) values for the GLCM regression model and GLRLM regression model were 0.975 and 0.928, respectively.

Conclusion: MRI texture analysis may be useful to detect metastatic lymph nodes in patients with NPC by providing quantitative information on tissue heterogeneity and cellular composition.

Keywords

Magnetic resonance imaging, texture analysis, nasopharyngeal carcinoma, metastatic lymph nodes

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