Differentiating radiation necrosis from recurrent brain metastases using magnetic resonance elastography

Abstract Background Conventional MRI cannot reliably distinguish radiation necrosis (RN) from recurrent metastasis after cranial radiotherapy, as both can show similar enhancement despite different biology. We tested whether these entities are mechanically non-equivalent in vivo and separable by MRE-derived viscoelastic metrics and perilesional interface-instability features. Methods In a prospective, histopathology-anchored cohort, 11 post-radiotherapy enhancing lesions were classified as RN (n=3) or recurrent/progressive tumor (n=8). MRE was acquired at 3.0 T with single-frequency 60-Hz excitation to derive storage modulus (G′), loss modulus (G″), and complex shear modulus magnitude (|G*|). Co-primary endpoints were median tumor G′ and |G*|, each tested one-sided (RN > tumor) with Holm correction across the two co-primary tests. Median tumor G″ was tested two-sided. A prespecified secondary 6-endpoint family (absolute and tumor/NAWM-normalized G′, G″, and |G*|) was analyzed with Benjamini–Hochberg FDR control. Exploratory instability mapping in a 0–6 mm peritumoral shell generated interface-topology metrics, including convexity. Results Absolute tumor-core medians were higher in RN than tumor for |G*| (1.79 vs 1.32 kPa; Cliff’s δ=0.67; q=0.10), G′ (1.62 vs 1.09 kPa; δ=0.50; q=0.14), and G″ (0.81 vs 0.46 kPa; δ=0.75; q=0.10). NAWM normalization improved separation: tumor/NAWM |G*| (2.26 vs 1.41; δ=0.92; q=0.04) and tumor/NAWM G″ (2.67 vs 0.87; δ=1.00; q=0.04) were FDR-significant. Convexity also differentiated RN from tumor (0.49 vs 0.36; δ=1.00; MWU p=0.01). Conclusions Tumor/NAWM G″, tumor/NAWM |G*|, convexity, and tumor G″ emerged as the strongest candidate features, indicating that RN is mechanically harder and more dissipative than recurrent metastasis. Signal strength was high (Cliff’s δ up to 1.00) but should be interpreted cautiously given sample size. Exploratory analyses further suggest that instability mapping captures biologically relevant interface behavior. These findings support a mechanics-based RN-versus-recurrence framework and justify prespecified, preregistered external validation.