{"title":"Diagnostic Performance of Imaging Methods in Predicting Lung Cancer Metastases.","authors":"Murat Aşık, Zeynep Nihal Kazci","doi":"10.1097/RCT.0000000000001706","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the possibility of distant organ metastasis using an algorithm developed to evaluate the morphology and localization of lung masses.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Assisted Tomography","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/RCT.0000000000001706","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: 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.
期刊介绍:
The mission of Journal of Computer Assisted Tomography is to showcase the latest clinical and research developments in CT, MR, and closely related diagnostic techniques. We encourage submission of both original research and review articles that have immediate or promissory clinical applications. Topics of special interest include: 1) functional MR and CT of the brain and body; 2) advanced/innovative MRI techniques (diffusion, perfusion, rapid scanning); and 3) advanced/innovative CT techniques (perfusion, multi-energy, dose-reduction, and processing).