Improved early outcome prediction by MRI-based 3D tumor volume assessment in patients with CNS lymphomas

• Published in: Neuro-oncology (Charlottesville, Va.) Va.), 2024-02, Vol.26 (2), p.374-386
• Main Authors: Lauer, Eliza M, Riegler, Ella, Mutter, Jurik A, Alig, Stefan K, Bleul, Sabine, Kuehn, Julia, Ranganathan, Lavanya, Klingler, Christian, Demerath, Theo, Würtemberger, Urs, Rau, Alexander, Weiß, Jakob, Eisenblaetter, Michel, Bamberg, Fabian, Prinz, Marco, Finke, Jürgen, Duyster, Justus, Illerhaus, Gerald, Diehn, Maximilian, Alizadeh, Ash A, Schorb, Elisabeth, Reinacher, Peter C, Scherer, Florian
• Format: Article
• Language: English
• Subjects: Central Nervous System Neoplasms - diagnostic imaging; Humans; Lymphoma - diagnostic imaging; Lymphoma, Non-Hodgkin; Magnetic Resonance Imaging; Neuroimaging; Prognosis; Tumor Burden
• ISSN: 1522-8517; 1523-5866; 1523-5866
• DOI: 10.1093/neuonc/noad177

Abstract Background Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment. Methods We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment. Results At diagnosis, patients with more than 3 lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL. Conclusions We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.