Enhancement of the nontumor component in newly diagnosed glioblastoma as a more accurate predictor of local recurrence location: a multicenter study

胶质母细胞瘤 医学 磁共振成像 鼻咽癌 组分(热力学) 核医学 数据集 内科学 肿瘤科 放射科 人工智能 放射治疗 计算机科学 癌症研究 物理 热力学
作者
Quanzhi Feng,Xiang Wang,Yuhan Fan,Jing Li,Xiyue Jing,Xiaodong Ji,Tong HAN,Shuang Xia
出处
期刊:Quantitative imaging in medicine and surgery [AME Publishing Company]
卷期号:15 (1): 299-313
标识
DOI:10.21037/qims-24-1319
摘要

Background: Although the spatial heterogeneity of glioblastoma (GBM) can be clearly mapped by the habitats generated by magnetic resonance imaging (MRI), the means to accurately predicting the spatial location of local recurrence (SLLR) remains a significant challenge. The aim of this study was to identify the different degrees enhancement of GBM, including the nontumor component and tumor component, and determine their relationship with SLLR. Methods: A retrospective analysis was performed from three tertiary medical centers, totaling 728 patients with 109 radiation-induced temporal lobe necrosis (TLN) of nasopharyngeal carcinoma (NPC) and 619 with GBM. The spatial location of nontumor component enhancement (SLNTE) and the spatial location of tumor component enhancement (SLTE) for the preoperative images of patients with GBM were identified using TLN as the nontumor component reference by clustering analysis, and then their relationship with the SLLR was analyzed. Decision tree models of 10-fold cross-validation based on SLNTE and SLTE built to predict the SLLR. The area under the curve (AUC) was used to evaluate the predictive efficacy of these models. Results: The SLNTE had a stronger spatial relationship with SLLR than did SLTE (χ2=4.77; P=0.029). In data set 3, both the SLNTE and SLTE were associated with the SLLR (rSLNTE=0.70, P<0.001; rSLTE=0.34, P=0.005). In data set 4, the SLLR was correlated with SLNTE but not with SLTE (rSLNTE=0.59, P=0.029; rSLTE=0.20, P=0.50). In data sets 3 and 4, the SLNTE-based decision tree models predicted the SLLR with 81% and 79% accuracy, respectively, and the AUC values were greater than 0.80 and 0.75, respectively. Meanwhile, the SLTE-based decision tree models predicted the SLLR with 72% and 50% accuracy, respectively, with AUC values of 0.70 and 0.60, respectively. Conclusions: Radiation-induced TLN of NPC is a highly effective reference indicator for detecting nontumor components. The tumor periphery adjacent to the nontumor component enhancement of GBM may be associated with a higher risk of local recurrence, which may provide a more accurate imaging basis for performing supertotal resection.

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