Beyond Conventional Magnetic Resonance Imaging (MRI): Advanced Neuroimaging for Early Detection and Precision Characterization of Brain Metastases in Lung Cancer: Narrative Review

Abstract

Brain metastases are a frequent manifestation of lung cancer and a substantial proportion of patients require accurate intracranial assessment at diagnosis, during staging or when neurological symptoms emerge. Conventional Magnetic Resonance Imaging (MRI), which relies on T1 weighted post contrast imaging together with T2 weighted and FLAIR sequences and in which three-dimensional T1 weighted imaging is the reference standard for detection, remains central to diagnosis and longitudinal monitoring, but its specificity is limited in the post treatment setting, where imaging features may not reliably distinguish viable tumor from radiation necrosis. The objective of this narrative review was to appraise the role of advanced and quantitative MRI in four clinically distinct questions, namely detection of small or early lesions, characterization and differential diagnosis, post treatment assessment and quantitative or artificial intelligence-based applications, in patients with lung cancer. Literature was identified through PubMed and included systematic reviews, meta-analyses, narrative reviews, original clinical studies and imaging-based investigations, with evidence derived from lung cancer specific cohorts distinguished throughout from evidence derived from mixed brain metastasis populations. The strongest evidence supports characterization and post treatment assessment. Perfusion derived relative cerebral blood volume and fractional tumor burden were higher in progression than in radiation necrosis and diffusion metrics, permeability parameters, MR spectroscopy and delayed contrast enhanced imaging contributed additional information, although these findings derive largely from mixed brain metastasis cohorts, group level differences overlap substantially at the level of the individual patient and no validated diagnostic threshold exists for any single biomarker. Evidence that advanced sequences improve detection of small or occult lesions beyond optimized three-dimensional T1 weighted imaging is considerably weaker and rests largely on mechanistic reasoning rather than on comparative diagnostic accuracy studies. Radiomics and machine learning have reported promising discrimination for post treatment differential diagnosis and for prediction of molecular and histological subtype, but reported performance is vulnerable to overfitting, data leakage, segmentation variability and incomplete reporting, calibration is seldom assessed and external validation remains uncommon. Advanced and quantitative MRI should presently be regarded as complementary to conventional MRI and tissue based molecular testing rather than as clinically validated substitutes and prospective multicenter studies using standardized acquisition and reporting are required before routine adoption.