Diagnostic Performance of Imaging Methods in Predicting Lung Cancer Metastases

dc.authorid0000-0002-5267-1602
dc.contributor.authorAsik, Murat
dc.contributor.authorKazci, Zeynep Nihal
dc.date.accessioned2025-11-16T19:34:10Z
dc.date.issued2025
dc.departmentİstanbul Medeniyet Üniversitesi
dc.description.abstractObjective:This study aimed to investigate the possibility of distant organ metastasis using an algorithm developed to evaluate the morphology and localization of lung masses.Methods:Patients diagnosed with lung cancer between 2016 and 2023 were included. The lesion's morphological characteristics, proximity to important structures, and maximum standardized uptake value were recorded. Six common metastatic sites were identified: the contralateral lung, liver, brain, adrenal glands, bone, and other regions. The relationship between the characteristics of the mass and the metastatic location was investigated.Results:A total of 383 patients (260 men, 68%) with malignant lung lesions with a mean +/- SD age of 65.50 +/- 12.34 years (range: 36-74 years) were included in the study. Among them, 242 were diagnosed with primary lung cancer, and 106 (43.8%) exhibited metastases to other organs with primary lung tumors. Distant organ metastases were most frequently detected in the bones (n = 45, 42.5%) and were more frequent in male patients and lesions adjacent to the ribs and bronchi, those involving mediastinal lymph nodes, irregular contours, and maximum standardized uptake values above 11.15 +/- 5.67 (mean +/- SD).Conclusions:Evaluating radiological imaging of malignant lesions in patients with lung cancer using an algorithm that considers morphological and neighborhood characteristics can provide predictive information regarding the possibility of metastasis of malignant lung lesions and the metastatic location.
dc.identifier.doi10.1097/RCT.0000000000001706
dc.identifier.endpage470
dc.identifier.issn0363-8715
dc.identifier.issn1532-3145
dc.identifier.issue3
dc.identifier.pmid39663659
dc.identifier.startpage462
dc.identifier.urihttps://doi.org/10.1097/RCT.0000000000001706
dc.identifier.urihttps://hdl.handle.net/20.500.14730/15257
dc.identifier.volume49
dc.identifier.wosWOS:001487911300008
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofJournal of Computer Assisted Tomography
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250302
dc.subjectlung cancer
dc.subjectmetastases
dc.subjectcomputed tomography
dc.subjectmagnetic resonance imaging
dc.subjectpositron emission tomography
dc.titleDiagnostic Performance of Imaging Methods in Predicting Lung Cancer Metastases
dc.typeArticle

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