Journal of neuro-oncology · 2026
Pathology-validated structural and physiologic habitat imaging for differentiating radiation necrosis from tumor recurrence in brain metastases.
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Park Ji Eun, Shao Guowen, Baisiwala Shivani, Kim Nakyoung, Tan Amelia, Sanvito Francesco, Liang Andrea, Wang Zexi, Nocera Gianluca, Raymond Catalina, Le Viên Lam, Kim Ho Sung, Salamon Noriko, Pope Whitney B, Kim Won, Ellingson Benjamin M, Yao Jingwen
Аннотация
PURPOSE: Differentiating tumor recurrence from radiation necrosis (RN) after stereotactic radiosurgery (SRS) remains a major diagnostic challenge in brain metastasis. We aimed to validate established MRI-based tumor habitat analysis for distinguishing tumor from RN in an independent cohort with histopathological ground truth.
MATERIALS AND METHODS: This retrospective study included 104 patients (104 lesions) with pathologically confirmed recurrent metastatic tumors (n = 68) or RN (n = 36) who underwent structural and physiologic MRI. Tumor habitats were generated using an established unsupervised clustering model applied to normalized T1-weighted enhanced, T2-weighted, apparent diffusion coefficient, and cerebral blood volume maps. Structural habitats (enhancing tissue, solid low-enhancing, nonviable) and physiologic habitats (hypervascular, hypovascular cellular, nonviable) were quantified as absolute volumes and volume fractions. Logistic regression and receiver operating characteristics analysis evaluated the ability to differentiate tumor and RN. Composite habitat scores integrating structural and physiologic habitats were also developed.
RESULTS: Recurrent metastatic tumors showed higher contrast-enhancing volume (P = .006), higher solid low-enhancing habitat volume (P = .029) and fraction (P = .04), higher hypervascular habitat volume (P = .02) and fraction (P = .03), and lower nonviable tissue habitat fractions on structural (P = .003) and physiologic MRI (P = .015), compared with RN. The combined structural and physiologic MRI habitat score showed the highest diagnostic performance (AUC, 0.80; 95% CI: 0.71-0.87; sensitivity, 89.7%; specificity, 58.3%).
CONCLUSION: MRI-based tumor habitat analysis provides a pathology-validated approach to distinguish tumor recurrence from radiation necrosis in patients with prior radiation therapy.
MATERIALS AND METHODS: This retrospective study included 104 patients (104 lesions) with pathologically confirmed recurrent metastatic tumors (n = 68) or RN (n = 36) who underwent structural and physiologic MRI. Tumor habitats were generated using an established unsupervised clustering model applied to normalized T1-weighted enhanced, T2-weighted, apparent diffusion coefficient, and cerebral blood volume maps. Structural habitats (enhancing tissue, solid low-enhancing, nonviable) and physiologic habitats (hypervascular, hypovascular cellular, nonviable) were quantified as absolute volumes and volume fractions. Logistic regression and receiver operating characteristics analysis evaluated the ability to differentiate tumor and RN. Composite habitat scores integrating structural and physiologic habitats were also developed.
RESULTS: Recurrent metastatic tumors showed higher contrast-enhancing volume (P = .006), higher solid low-enhancing habitat volume (P = .029) and fraction (P = .04), higher hypervascular habitat volume (P = .02) and fraction (P = .03), and lower nonviable tissue habitat fractions on structural (P = .003) and physiologic MRI (P = .015), compared with RN. The combined structural and physiologic MRI habitat score showed the highest diagnostic performance (AUC, 0.80; 95% CI: 0.71-0.87; sensitivity, 89.7%; specificity, 58.3%).
CONCLUSION: MRI-based tumor habitat analysis provides a pathology-validated approach to distinguish tumor recurrence from radiation necrosis in patients with prior radiation therapy.