MRI 中鼻咽癌影像组学特征的预后和预测价值
Prognostic and predictive value of radiomics features at MRI in nasopharyngeal carcinoma.
作者信息
Bao Dan, Zhao Yanfeng, Liu Zhou, Zhong Hongxia, Geng Yayuan, Lin Meng, Li Lin, Zhao Xinming, Luo Dehong
机构信息
Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China.
Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116 China.
出版信息
Discov Oncol. 2021;12(1):63. doi: 10.1007/s12672-021-00460-3. Epub 2021 Dec 17.
PURPOSE
To explore the value of MRI-based radiomics features in predicting risk in disease progression for nasopharyngeal carcinoma (NPC).
METHODS
199 patients confirmed with NPC were retrospectively included and then divided into training and validation set using a hold-out validation (159: 40). Discriminative radiomic features were selected with a Wilcoxon signed-rank test from tumors and normal masticatory muscles of 37 NPC patients. LASSO Cox regression and Pearson correlation analysis were applied to further confirm the differential expression of the radiomic features in the training set. Using the multiple Cox regression model, we built a radiomic feature-based classifier, Rad-Score. The prognostic and predictive performance of Rad-Score was validated in the validation cohort and illustrated in all included 199 patients.
RESULTS
We identified 1832 differentially expressed radiomic features between tumors and normal tissue. Rad-Score was built based on one radiomic feature: CET1-w_wavelet.LLH_GLDM_Dependence-Entropy. Rad-Score showed a satisfactory performance to predict disease progression in NPC with an area under the curve (AUC) of 0.604, 0.732, 0.626 in the training, validation, and the combined cohort (all 199 patients included) respectively. Rad-Score improved risk stratification, and disease progression-free survival was significantly different between these groups in every cohort of patients (p = 0.044 or p < 0.01). Combining radiomics and clinical features, higher AUC was achieved of the prediction of 3-year disease progression-free survival (PFS) (AUC, 0.78) and 5-year disease PFS (AUC, 0.73), although there was no statistical difference.
CONCLUSION
The radiomics classifier, Rad-Score, was proven useful for pretreatment prognosis prediction and showed potential in risk stratification for NPC.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s12672-021-00460-3.
目的
探讨基于MRI的放射组学特征在预测鼻咽癌（NPC）疾病进展风险中的价值。
方法
回顾性纳入199例确诊为NPC的患者，然后采用留出法验证（159:40）将其分为训练集和验证集。通过Wilcoxon符号秩检验从37例NPC患者的肿瘤和正常咀嚼肌中选择具有鉴别性的放射组学特征。应用LASSO Cox回归和Pearson相关分析进一步确认训练集中放射组学特征的差异表达。使用多元Cox回归模型，我们构建了基于放射组学特征的分类器Rad-Score。Rad-Score的预后和预测性能在验证队列中得到验证，并在所有纳入的199例患者中进行了说明。
结果
我们在肿瘤和正常组织之间鉴定出1832个差异表达的放射组学特征。Rad-Score基于一个放射组学特征构建：CET1-w_wavelet.LLH_GLDM_Dependence-Entropy。Rad-Score在预测NPC疾病进展方面表现出令人满意的性能，在训练集、验证集和合并队列（包括所有199例患者）中的曲线下面积（AUC）分别为0.604、0.732、0.626。Rad-Score改善了风险分层，并且在每个患者队列中，这些组之间的无疾病进展生存期有显著差异（p = 0.044或p < 0.01）。结合放射组学和临床特征，在预测3年无疾病进展生存期（PFS）（AUC，0.78）和5年疾病PFS（AUC，0.73）方面实现了更高的AUC，尽管没有统计学差异。
结论
放射组学分类器Rad-Score被证明可用于治疗前预后预测，并在NPC风险分层中显示出潜力。
补充信息
在线版本包含可在10.1007/s12672-021-00460-3获取的补充材料。
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