The Role of Percentage Signal Recovery (PSR) in MRI Perfusion for the Diagnosis of Primary Central Nervous System Lymphoma.

Introduction Primary central nervous system lymphoma (PCNSL) is a rare type of malignancy confined to the central nervous system. While histopathological analysis remains the gold standard for diagnosis, it is invasive and not always feasible. Percentage signal recovery (PSR) derived from perfusion-weighted magnetic resonance imaging (MRI) has emerged as a promising non-invasive biomarker for PCNSL. Objective This study aims to evaluate the diagnostic accuracy of PSR and relative cerebral blood volume (rCBV) in distinguishing PCNSL from other CNS mass lesions using perfusion MRI. Methods We conducted a retrospective study using the neuro-oncology database of a national referral center in Mexico. Patients with histopathological confirmation and available perfusion MRI were included in the analysis. Results From a total of 16,198 patients, 700 met the inclusion criteria and were classified as follows: PCNSL (n = 86), high-grade gliomas (HGGs; n = 435), metastases (n = 80), neuroinfections (n = 80), and pseudotumoral demyelinating disorders (PDDs) (n = 19). Mean rCBV for PCNSL was 1.1, HGGs 3.9, metastases 3.0, neuroinfections 0.5, and PDDs 0.9, while mean PSR for PCNSL was 176%, HGGs 92%, metastases 81%, neuroinfections 95%, and PDDs 91%, with a statistically significant difference for PCNSL (p ≤ 0.0003). Receiver operating characteristic (ROC) curve analysis showed that PSR was the only parameter with robust diagnostic performance. A PSR threshold of >110 yielded 98% sensitivity (95% CI: 90.7-100) and 99% specificity (95% CI: 91.3-100) for the diagnosis of PCNSL. Conclusion PSR demonstrated high diagnostic accuracy in differentiating PCNSL from other enhancing brain lesions and may be particularly valuable in patients with atypical presentations, multiple comorbidities, or nondiagnostic or contraindicated biopsies. PSR is a promising non-invasive imaging biomarker that warrants further prospective validation.