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Routine brain MRI in suspected lung cancer: clinical justification and diagnostic yield
Camilla Hviid1, Christian B Laursen1, Pia Iben Pietersen2
1Department of Respiratory Medicine, Odense University Hospital, Odense, Denmark; Odense Respiratory Research Unit (ODIN), Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Background:
Lung cancer is the leading cause of cancer-related death worldwide. Brain metastases are common and associated with poor prognosis. While MRI is highly sensitive for detecting brain metastases, the clinical value of routinely adding up-front MRI to PET/CT during initial staging remains debated. This study focuses on suspected lung cancer patients, integrating workflow data (MRI waiting time, MDT availability) to evaluate up-front brain MRI and its impact on clinical decision-making.
Methods:
This retrospective cohort study included 183 patients with suspected lung cancer referred for up-front brain MRI at Odense University Hospital between March-August 2021. All patients underwent pre-diagnostic PET/CT, and brain MRI was performed in cases with suspected stage II-IV disease (TNM 8th edition).
Results:
Brain metastases were detected by MRI in 21/183 patients (11.5%), predominantly from primary lung cancer (n = 19, 90.4%). Among patients with confirmed lung cancer (n = 134), the prevalence was 14.2% (19/134), corresponding to a number needed to scan (NNS) of 7.1; across the full cohort, the NNS was 8.7. MRI led to upstaging to stage IV disease in 3/134 lung cancer patients (2.2%), resulting in a NNS of 44.7. PET/CT had a positive predictive value of 57.1% and a negative predictive value of 90.3%, indicating limited reliability detecting brain metastases.
Conclusion:
Routine up-front brain MRI identified brain metastases in a small but clinically relevant subset of patients, primarily those with signs of dissemination on initial PET/CT. A risk-stratified approach targeting high-risk groups may reduce MRI use without compromising detection, improving staging efficiency and resource allocation.
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