Zhou Qing, Zhang Bin, Xue Caiqiang, Ren Jialiang, Zhang Peng, Ke Xiaoai, Man Jiangwei, Zhou Junlin
Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, 730030, Lanzhou, Gansu, China.
Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China.
Strahlenther Onkol. 2024 Sep 13. doi: 10.1007/s00066-024-02289-5.
Tumor-associated macrophages (TAMs) are important biomarkers of tumor invasion and prognosis in patients with glioblastoma. We combined the imaging and radiomics features of preoperative MRI to predict CD68+ macrophage infiltration.
Clinical, MRI image, and pathology data of 188 patients with glioblastoma were analyzed. Overall, 143 patients were included in the training (n = 101) and validation (n = 42) sets, whereas 45 patients were included in an independent test set. The optimal cut-off value (14.8%) was based on the minimum p-value formed by the Kaplan-Meier survival analysis and log-rank tests which divided patients into groups with high CD68+ TAMs (≥ 14.8%) and low CD68+ TAMs (< 14.8%). Regions of interest and radiomics features extraction were based on contrast-enhanced T1-weighted images (CE-T1WI) and T2WI. Multi-parameter stepwise regression was used to create the clinical, radiomics, and combined models, each evaluated using the receiver operating characteristic curve. Decision curve analysis was used to assess the clinical applicability of the nomogram.
A clinical model based on the minimum apparent diffusion coefficient (ADCmin) revealed an area under the curve (AUC) of 0.768, 0.764, and 0.624 for the training set, validation set, and test set, respectively. The 2D radiomics model, based on two features, revealed an AUC of 0.783, 0.724, and 0.789 for the training, validation, and test sets, respectively. The 3D radiomics model, based on three features, revealed AUCs of 0.823, 0.811, and 0.787 for the training, validation, and test sets, respectively. The combined model, with ADCmin and radiomics features, showed the best performance, with AUCs of 0.865, 0.822, and 0.776 for the training, validation, and test sets, respectively. The calibration curve of the combined model nomogram showed good agreement between the estimated and actual probabilities.
The combined model constructed using ADCmin, a quantitative imaging parameter, combined with five key radiomics features can be used to evaluate the extent of CD68+ macrophages before surgery.
肿瘤相关巨噬细胞（TAM）是胶质母细胞瘤患者肿瘤侵袭和预后的重要生物标志物。我们结合术前MRI的影像学和放射组学特征来预测CD68 +巨噬细胞浸润。
分析188例胶质母细胞瘤患者的临床、MRI图像和病理数据。总体而言，143例患者被纳入训练集（n = 101）和验证集（n = 42），而45例患者被纳入独立测试集。最佳截断值（14.8%）基于Kaplan-Meier生存分析和对数秩检验形成的最小p值，该值将患者分为高CD68 + TAM组（≥14.8%）和低CD68 + TAM组（<14.8%）。感兴趣区域和放射组学特征提取基于对比增强T1加权图像（CE-T1WI）和T2WI。使用多参数逐步回归创建临床、放射组学和联合模型，每个模型均使用受试者操作特征曲线进行评估。决策曲线分析用于评估列线图的临床适用性。
基于最小表观扩散系数（ADCmin）的临床模型在训练集、验证集和测试集上的曲线下面积（AUC）分别为0.768、0.764和0.624。基于两个特征的二维放射组学模型在训练集、验证集和测试集上的AUC分别为0.783、0.724和0.789。基于三个特征的三维放射组学模型在训练集、验证集和测试集上的AUC分别为0.823、0.811和0.787。结合ADCmin和放射组学特征的联合模型表现最佳，在训练集、验证集和测试集上的AUC分别为0.865、0.822和0.776。联合模型列线图的校准曲线显示估计概率与实际概率之间具有良好的一致性。
使用定量成像参数ADCmin与五个关键放射组学特征构建的联合模型可用于术前评估CD68 +巨噬细胞的浸润程度。
