Performance of Conventional Approaches Compared to Novel Blood Volume-, Angiogenesis-, and Tumor Hypoxia-Based Approaches to Diagnosing Tumor Recurrence on MRI After Radiation Therapy of Brain Metastases

Background/Objectives: Differentiating tumor recurrence (TR) from radiation necrosis (RN) remains a significant challenge in the post-radiation management of brain metastases due to their overlapping clinical and radiographic features. Existing imaging techniques often lack the sensitivity and specificity necessary for accurate differentiation. This study evaluates the performance of a novel suite of imaging biomarkers characterizing microvasculature, blood flow dynamics, and tumor oxygen utilization in distinguishing TR from RN, compared with established cerebral blood volume (CBV) measures.

Methods

This retrospective study included 119 symptomatic patients with metastatic brain lesions, 7–15 months post-radiotherapy. The training cohort (n = 96, 182 lesions) spanned June 2013 to May 2018, while a separate test cohort (n = 23, 23 lesions) was collected from January 2023 to December 2024. Lesions were classified as TR or RN based on expert consensus, pathology, and long-term follow-up. Eleven imaging biomarkers were assessed in the historical cohort and nine shared biomarkers were evaluated across cohorts, including both standard and novel biomarkers. Standard biomarkers included the Apparent Diffusion Coefficient (ADC), corrected and non-corrected Cerebral Blood Volume (cCBV and ncCBV), and the Relative Amplitude of the Peak (rRHP). Novel biomarkers included Arterial Input Function Delay (Delay), Capillary Transit Time Heterogeneity (CTH and CTH Max), Oxygen Extraction Fraction (OEF), Relative Extravasation Correction (rLeakage), Relative Cerebral Metabolic Rate of Oxygen (rCMRO2), and Coefficient of Variance (COV). For each biomarker, a region of interest was selected from the lesion parenchyma, and a lesion-to-control parenchyma ratio was calculated. Biomarkers were evaluated for their ability to differentiate TR from RN, using the area under the receiver operating characteristic curve (AUC) for univariate analyses and random forest modeling to identify optimal biomarker combinations. Bivariate analyses modeled lesion and control parenchyma as independent variables for each biomarker and compared AUCs for models in both cohorts.