PDF(Pubmed)
Abstract：
Widespread clinical use of MRI radiomic tumor profiling for prognostication and treatment planning in cancers faces major obstacles due to limitations in standardization of radiomic features. The purpose of the current work was to assess the impact of different MRI scanning- and normalization protocols for the statistical analyses of tumor radiomic data in two patient cohorts with uterine endometrial-(EC) (n = 136) and cervical (CC) (n = 132) cancer. 1.5 T and 3 T, T1-weighted MRI 2 min post-contrast injection, T2-weighted turbo spin echo imaging, and diffusion-weighted imaging were acquired. Radiomic features were extracted from within manually segmented tumors in 3D and normalized either using z-score normalization or a linear regression model (LRM) accounting for linear dependencies with MRI acquisition parameters. Patients were clustered into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication were analyzed using Kruskal-Wallis tests, Kaplan-Meier plots, log-rank test, random survival forests and LASSO Cox regression with time-dependent area under curve (tdAUC) (α = 0.05). A large proportion of the radiomic features was statistically associated with MRI scanning protocol in both cohorts (EC: 162/385 [42%]; CC: 180/292 [62%]). A substantial number of EC (49/136 [36%]) and CC (50/132 [38%]) patients changed cluster when clustering was performed after z-score-versus LRM normalization. Prognostic modeling based on cluster groups yielded similar outputs for the two normalization methods in the EC/CC cohorts (log-rank test; z-score: p = 0.02/0.33; LRM: p = 0.01/0.45). Mean tdAUC for prognostic modeling of disease-specific survival (DSS) by the radiomic features in EC/CC was similar for the two normalization methods (random survival forests; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75; LASSO Cox; z-score: mean tdAUC = 0.64/0.76; LRM: mean tdAUC = 0.76/0.75). Severe biases in tumor radiomics data due to MRI scanning parameters exist. Z-score normalization does not eliminate these biases, whereas LRM normalization effectively does. Still, radiomic cluster groups after z-score- and LRM normalization were similarly associated with DSS in EC and CC patients.
摘要：
由于影像组学特征标准化的局限性，MRI影像组学肿瘤谱分析在癌症预后和治疗计划中的广泛临床应用面临着主要障碍。当前工作的目的是评估不同的MRI扫描和标准化方案对两个患有子宫内膜子宫内膜癌（EC）（n=136）和子宫颈癌（CC）（n=132）的患者队列中肿瘤影像数据的统计分析的影响。1.5T和3T，造影剂注射后2分钟T1加权MRI,T2加权涡轮自旋回波成像，并获得弥散加权成像。从3D中手动分割的肿瘤中提取放射学特征，并使用z评分归一化或线性回归模型（LRM）进行归一化，以说明与MRI采集参数的线性依赖性。根据影像学资料将患者分为两组。使用Kruskal-Wallis测试分析MRI扫描参数对簇组成和预后的影响，卡普兰-迈耶地块,对数秩检验，随机生存森林和LASSOCox回归具有时间依赖性曲线下面积(tdAUC)(α=0.05)。在两个队列中，大部分影像组学特征与MRI扫描方案在统计学上相关(EC:162/385[42%];CC:180/292[62%])。当在z评分与LRM归一化后进行聚类时,相当数量的EC(49/136[36%])和CC(50/132[38%])患者改变聚类。基于聚类组的预后建模在EC/CC队列中对于两种归一化方法产生了相似的输出(对数秩检验;z分数:p=0.02/0.33;LRM:p=0.01/0.45)。根据EC/CC的影像组学特征对疾病特异性生存（DSS）进行预后建模的平均tdAUC与两种归一化方法相似（随机生存森林；z评分：平均tdAUC=0.77/0.78；LRM：平均tdAUC=0.80/0.75；LASSOCox；z评分：平均tdAUC=0.64/0.75；LRM：平均tAU76）。由于MRI扫描参数，肿瘤影像组学数据存在严重偏差。Z分数归一化并不能消除这些偏见，而LRM标准化有效地做到了。尽管如此，在EC和CC患者中,z-score-和LRM正常化后的影像组学簇群与DSS相似.
