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  • Title: Tumor Growth Rate in Spinal Giant Cell Tumors of Bone and Association With the Immune Microenvironment and Denosumab Treatment Responsiveness: A Multicenter Study.
    Author: Zheng BW, Zheng BY, Niu HQ, Yang YF, Zhu GQ, Li J, Zhang TL, Zou MX.
    Journal: Neurosurgery; 2023 Mar 01; 92(3):524-537. PubMed ID: 36409294.
    Abstract:
    BACKGROUND: Currently, little is known about the prognostic value of tumor growth rate (TGR) in spinal giant cell tumors of bone (GCTB). OBJECTIVE: To investigate the correlation of TGR with clinicopathological features, immune microenvironment, prognosis, and response to denosumab treatment of spinal GCTB. METHODS: A total of 128 patients with spinal GCTB treated at 5 centers from 2011 to 2021 were included. TGR was assessed by 2 independent neuroradiologists using at least 2 preoperative thin-section magnetic resonance imaging scans at a minimum interval of 2 months. Immunohistochemistry was used to assess tumor-infiltrating lymphocyte subtypes for CD3, CD4, CD8, CD20, PD-1, PD-L1, and Foxp3. Then, these parameters were analyzed for their associations with patient outcomes (progression-free survival and overall survival), clinicopathological features, and denosumab treatment responsiveness. RESULTS: High TGR predicted both poor progression-free survival and overall survival (both P < .001). In addition, TGR was associated with postoperative neurological dysfunction ( P < .001), Enneking staging ( P = .016), denosumab treatment responsiveness ( P = .035), and the number of CD3 + ( P < .001), PD-1 + ( P = .009), PD-L1 + ( P < .001), and FoxP3 + tumor-infiltrating lymphocyte ( P = .02). Importantly, TGR outperformed the traditional Enneking, Campanacci, and American Joint Committee on Cancer staging systems in predicting the clinical outcomes of spinal GCTB. CONCLUSION: These data support the use of TGR as a reliable predictive tool for clinically relevant outcomes and response to denosumab therapy of spinal GCTB, which may be helpful in guiding prognostic risk stratification and therapeutic optimization of patients.
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