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Utility of FDG PET/CT for assessment of lung nodules identified during low dose computed tomography screening

Sarah Hadique, Pranav Jain, Yousaf Hadi, Aneeqah Baig, John E. Parker

2020BMC Medical Imaging16 citationsDOIOpen Access PDF

Abstract

BACKGROUND: Many clinical guidelines recommend FDG PET/CT for the evaluation of pulmonary nodules ≥8 mm detected during low dose computed tomography (LDCT) lung cancer screening. However, its added value in this setting requires confirmation. We evaluated the clinical utility of FDG PET/CT, including incidental findings, during the evaluation of lung nodules detected on LDCT screening. METHODS: A retrospective cohort study was performed among 75 patients who completed FDG PET/CT between January 2010 and December 2017, after lung nodules > 8 mm had been detected on LDCT lung cancer screening. We report demographic variables, characteristics of the initial nodules on LDCT and FDG PET/CT, incidental findings on FDG PET/CT, as well as further work up performed and the influence of FDG PET/CT findings on management. RESULTS: Nodules were reported to be benign on FDG PET/CT in 38/75 (50.6%) patients. Physicians chose either radiological follow-up or no further work up in all 38. FDG PET/CT was indeterminate or suggested malignancy in 37 (49.3%) patients. Biopsy was performed in 32 (86%) of these patients. Incidental findings on FDG PET/CT were reported in 37/75 (49%) patients. Further work-up of incidental findings was performed in 21/75 (28%) of patients. CONCLUSIONS: In this study, for majority of individuals with lung nodules identified during LDCT lung cancer screening, FDG PET/CT results were able to guide physicians in choosing between routine follow up or invasive biopsies. Conversely, 28% of these patients required additional investigations to address incidental findings.

Topics & Concepts

MedicineRadiologyLung cancerLung cancer screeningMalignancyRetrospective cohort studyLungBiopsyRadiological weaponComputed tomographyNuclear medicineSurgeryPathologyInternal medicineLung Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingRadiation Dose and Imaging
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