Feng Quanzhi, Xiang Wang, Fan Yuhan, Li Jing, Jing Xiyue, Ji Xiaodong, Han Tong, Xia Shuang
Department of Radiology, The First Central Clinical School, Tianjin Medical University, Tianjin, China.
Department of Radiology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China.
Quant Imaging Med Surg. 2025 Jan 2;15(1):299-313. doi: 10.21037/qims-24-1319. Epub 2024 Dec 24.
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.
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.
The SLNTE had a stronger spatial relationship with SLLR than did SLTE (χ=4.77; P=0.029). In data set 3, both the SLNTE and SLTE were associated with the SLLR (r=0.70, P<0.001; r=0.34, P=0.005). In data set 4, the SLLR was correlated with SLNTE but not with SLTE (r=0.59, P=0.029; r=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.
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.
尽管胶质母细胞瘤（GBM）的空间异质性可通过磁共振成像（MRI）生成的图像清晰显示，但准确预测局部复发的空间位置（SLLR）仍具有重大挑战。本研究旨在识别GBM不同程度的强化，包括非肿瘤成分和肿瘤成分，并确定它们与SLLR的关系。
对三个三级医疗中心进行回顾性分析，共有109例鼻咽癌（NPC）放疗后颞叶坏死（TLN）患者和619例GBM患者。以TLN作为非肿瘤成分参考，通过聚类分析确定GBM患者术前图像中非肿瘤成分强化的空间位置（SLNTE）和肿瘤成分强化的空间位置（SLTE），然后分析它们与SLLR的关系。基于SLNTE和SLTE构建10折交叉验证的决策树模型以预测SLLR。采用曲线下面积（AUC）评估这些模型的预测效能。
SLNTE与SLLR的空间关系比SLTE更强（χ=4.77；P=0.029）。在数据集3中，SLNTE和SLTE均与SLLR相关（r=0.70，P<0.001；r=0.34，P=0.005）。在数据集4中，SLLR与SLNTE相关，但与SLTE无关（r=0.59，P=0.029；r=0.20，P=0.50）。在数据集3和4中，基于SLNTE的决策树模型预测SLLR的准确率分别为81%和79%，AUC值分别大于0.80和0.75。同时，基于SLTE的决策树模型预测SLLR的准确率分别为72%和50%，AUC值分别为0.70和0.60。
NPC放疗后TLN是检测非肿瘤成分的高效参考指标。GBM非肿瘤成分强化相邻的肿瘤周边可能与局部复发风险较高有关，这可能为进行超全切除提供更准确的影像学依据。
