Salome Patrick, Sforazzini Francesco, Grugnara Gianluca, Kudak Andreas, Dostal Matthias, Herold-Mende Christel, Heiland Sabine, Debus Jürgen, Abdollahi Amir, Knoll Maximilian
Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany.
Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany.
Cancers (Basel). 2023 Feb 2;15(3):965. doi: 10.3390/cancers15030965.
This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG).
MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods' performance.
Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman's rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences.
The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.
本研究调查不同强度归一化（IN）方法对原发性高级别胶质瘤（pHGG）和复发性高级别胶质瘤（rHGG）中磁共振成像（MR）序列的总生存（OS）影像组学模型性能的影响。
从多个扫描仪的两个独立队列（rHGG C1：197例，pHGG C2：141例）中检索放疗前获取的MR扫描图像（15台、14台）。序列包括T1加权（w）、对比增强T1w（T1wce）、T2w和T2w液体衰减反转恢复序列（FLAIR）。在应用15种不同的IN方法后，从肿瘤体积中提取与OS相关的序列特异性显著特征（SF）。使用Cox比例风险（CPH）模型和泊松回归（POI）模型进行生存分析。基于10倍交叉验证（CV）一致性指数（C-I）、均方误差（MSE）和赤池信息准则（AIC）分配一个排序分数，以评估这些方法的性能。
10次CV的C-I和MSE相对于AIC的散点图显示，IN方法和MR序列之间对生存预测有影响（C1/C2 C-I范围：0.62 - 0.71/0.61 - 0.72，MSE范围：0.20 - 0.42/0.13 - 0.22）。白条纹方法对T1wce显示出稳定的结果（C1/C2 C-I：0.71/0.65，MSE：0.21/0.14）。Combat方法（0.68/0.62，0.22/0.15）和直方图匹配（HM，0.67/0.64，0.22/0.15）对T2w模型显示出一致的预测结果。它们也是C2中T1w的最佳表现方法（Combat：0.67，0.13；HM：0.67，0.13）；然而，只有HM在C1中实现了高预测（0.66，0.22）。使用斯皮尔曼等级相关系数消除受IN影响的SF后，在所有四个序列中观察到C-I和MSE平均分别下降0.05和0.03。
IN方法影响生存模型的预测能力；因此，性能取决于序列。
