• Yangzhi Qi, Yang Xu, Tengfeng Yan, Wei Xiong, Xinyi Zhu, Qian Sun, Gang Deng, Fanen Yuan, Liguo Ye, Ping Hu, Baohui Liu, Shenqi Zhang, Shuhang Deng, Yan He, Gao Wu, Qingsong Ye, and Qianxue Chen.
• 1Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
• J. Neurosurg. 2024 Sep 1; 141 (3): 673683673-683.

ObjectiveCirculating tumor cell (CTC) detection is a promising noninvasive technique that can be used to diagnose cancer, monitor progression, and predict prognosis. In this study, the authors aimed to investigate the clinical utility of CTCs in the management of diffuse glioma.MethodsSixty-three patients with newly diagnosed diffuse glioma were included in this multicenter clinical cohort. The authors used a platform based on isolation by size of epithelial tumor cells (ISET) to detect and analyze CTCs and circulating tumor microemboli (CTMs) in the peripheral blood of patients both before and after surgery. Least absolute shrinkage and selector operation (LASSO) and Cox regression analyses were used to verify whether CTCs and CTMs are independent prognostic factors for diffuse glioma.ResultsCTC levels were closely related to the degree of malignancy, WHO grade, and pathological subtypes. Receiver operating characteristic curve analysis revealed that a high CTC level was a predictor for glioblastoma. The results also showed that CTMs originate from the parental tumor rather than from the circulation and are an independent prognostic factor for diffuse glioma. The postoperative CTC level is related to the peripheral immune system and patient survival. Cox regression analysis showed that postoperative CTC levels and CTM status are independent prognostic factors for diffuse glioma, and CTC- and CTM-based survival models had high accuracy in internal validation.ConclusionsThe authors revealed a correlation between CTCs and clinical characteristics and demonstrated that CTCs and CTMs are independent predictors for the diagnosis and prognosis of diffuse glioma. Their CTC- and CTM-based survival models can enable clinicians to evaluate patients' response to surgery as well as their outcomes.

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