Case Study Review of Diffusion Weighted MRI in Differentiating Recurrent Brain Tumors from Post Treatment Changes

Abstract

Diffusion Weighted MRI (DWI) deals with the major problems in neuroimaging distinguishing between recurrent brain tumors and post-therapeutic alterations such as radiation necrosis and MRI pseudoprogression. Traditional MRI is prone to fail because there are overlapping contrast enhancements and thus quantitative DWI measurements are required like ADC values to distinguish tumors. The literature review in this case study included prospective trials, meta-analyses, and histopathological correlations in aggregated sets of gliomas and metastases (more than 150 lesions). The systematic review concerned DWI imaging patterns, ADC thresholds (e.g., <1.22 × 10 -6 mm 2/s necrosis), and multi-parametric integrations, using the cost-effective global evidence evidence without restriction on primary data collection. Other important observations made included the better sensitivity (85-92%) and specificity (89) of DWI to detect the recurrence of brain tumors. In recurrent tumors, hypercellularity was associated with homogeneously low ADC, in comparison with heterogeneous facilitated diffusion in the effects of treatment. Necrosis was predicted by central restriction (AUC 0.85), whereas pseudoprogression demonstrated a transient high ADC longitudinal resolution. Multi-parametric Multi-parametric methods improved accuracy up to 94% but interobserver error (kappa 0.49) and low-grade overlaps remain. This literature review synthesis highlights the transformative capabilities of DWI in neuro-oncology, in steering biopsy avoidance, timing of therapy, and accuracy oncology processes, efficiently. New AI-advanced protocols in the future will be even more refined. .