Diffusion tensor imaging can discriminate the primary cell type of intracranial metastases for patients with lung cancer

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dc.authorid0000-0003-1795-2596
dc.contributor.authorBilgin, Sabriye Şennur
dc.contributor.authorGültekin, Mehmet Ali
dc.contributor.authorYurtsever, İsmail
dc.contributor.authorYılmaz, Temel Fatih
dc.contributor.authorÇesme, Dilek Hacer
dc.contributor.authorBilgin, Melike
dc.contributor.authorTopcu, Atakan
dc.contributor.authorBeşiroğlu, Mehmet
dc.contributor.authorTürk, Hacı Mehmet
dc.contributor.authorAlkan, Alpay
dc.contributor.authorBilgin, Mehmet
dc.date.accessioned2022-07-25T13:57:22Z
dc.date.available2022-07-25T13:57:22Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Radyoloji Ana Bilim Dalı
dc.description.abstractPurpose: Histopathological differentiation of primary lung cancer is clinically important. We aimed to investigate whether diffusion tensor imaging (DTI) parameters of metastatic brain lesions could predict the histopathological types of the primary lung cancer. Methods: In total, 53 patients with 98 solid metastatic brain lesions of lung cancer were included. Lung tumors were subgrouped as non-small cell carcinoma (NSCLC) (n = 34) and small cell carcinoma (SCLC) (n = 19). Apparent diffusion coefficient (ADC) and Fractional anisotropy (FA) values were calculated from solid enhanced part of the brain metastases. The association between FA and ADC values and histopathological subtype of the primary tumor was investigated. Results: The mean ADC and FA values obtained from the solid part of the brain metastases of SCLC were significantly lower than the NSCLC metastases (P < 0.001 and P = 0.003, respectively). ROC curve analysis showed diagnostic performance for mean ADC values (AUC=0.889, P = < 0.001) and FA values (AUC = 0.677, P = 0.002). Cut-off value of > 0.909 × 10-3 mm2/s for mean ADC (Sensitivity = 80.3, Specificity = 83.8, PPV = 89.1, NPV = 72.1) and > 0.139 for FA values (Sensitivity = 80.3, Specificity = 54.1, PPV = 74.2, NPV= 62.5) revealed in differentiating NSCLC from NSCLC. Conclusion: DTI parameters of brain metastasis can discriminate SCLC and NSCLC. ADC and FA values of metastatic brain lesions due to the lung cancer may be an important tool to differentiate histopathological subgroups. DTI may guide clinicians for the management of intracranial metastatic lesions of lung cancer.
dc.identifier.citationBilgin, S. S., Gültekin, M. A., Yurtsever, İ., Yılmaz, T. F., Çesme, D. H., Bilgin, M. ... Bilgin, M. (2022). Diffusion tensor imaging can discriminate the primary cell type of intracranial metastases for patients with lung cancer. Magnetic Resonance in Medical Sciences, 21(3), 425-431. https://doi.org/10.2463/mrms.mp.2020-0183
dc.identifier.doi10.2463/mrms.mp.2020-0183
dc.identifier.endpage431
dc.identifier.issn1347-3182
dc.identifier.issn1880-2206
dc.identifier.issue3
dc.identifier.pmid33658441
dc.identifier.scopus2-s2.0-85133692354
dc.identifier.scopusqualityQ2
dc.identifier.startpage425
dc.identifier.urihttps://doi.org/10.2463/mrms.mp.2020-0183
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9607
dc.identifier.volume21
dc.identifier.wos000828297600001en_US
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorBilgin, Sabriye Şennur
dc.language.isoen
dc.publisherJapanese Society for Magnetic Resonance in Medicine
dc.relation.ispartofMagnetic Resonance in Medical Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectApparent Diffusion Coefficient
dc.subjectBrain Metastases
dc.subjectDiffusion Tensor Imaging
dc.subjectFractional Anisotropy
dc.subjectLung Cancer
dc.titleDiffusion tensor imaging can discriminate the primary cell type of intracranial metastases for patients with lung cancer
dc.typeArticle

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