Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI.
发表日期： 2016.11.01 来源： Clinical Cancer Research, 2016, 22(21):5256-5264.
Nie K1, Shi L2, Chen Q2, Hu X3, Jabbour SK1, Yue N1, Niu T4,3,5, Sun X4.
1. Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Rutgers-The State University of New Jersey, New Brunswick, New Jersey.
2. Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
3. Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
4. Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. email@example.com firstname.lastname@example.org.
5. Institute of Translational Medicine, Zhejiang University, Hangzhou, China.
To evaluate multiparametric MRI features in predicting pathologic response after preoperative chemoradiation therapy (CRT) for locally advanced rectal cancer (LARC).
对接受新辅助CRT的连续48例（2012年1月 - 2014年11月）进行登记。在CRT前，全部经历了解剖T1 / T2，扩散加权MRI（DWI）和动态造影增强（DCE）MRI。从每个患者提取总共103个成像特征，使用体积平均和体素化方法进行分析。进行单变量分析，以评估每个参数预测基于肿瘤回归分级评估的病理完全反应（pCR）或良好反应（GR）的能力。进一步利用具有4倍验证技术的人造神经网络来选择最佳预测值集合来对不同的响应组进行分类，并使用接收器工作特性曲线（ROC）计算预测性能。
Forty-eight consecutive patients (January 2012–November 2014) receiving neoadjuvant CRT were enrolled. All underwent anatomical T1/T2, diffusion-weighted MRI (DWI) and dynamic contrast-enhanced (DCE) MRI before CRT. A total of 103 imaging features, analyzed using both volume-averaged and voxelized methods, were extracted for each patient. Univariate analyses were performed to evaluate the capability of each individual parameter in predicting pathologic complete response (pCR) or good response (GR) evaluated based on tumor regression grade. Artificial neural network with 4-fold validation technique was further utilized to select the best predictor sets to classify different response groups and the predictive performance was calculated using receiver operating characteristic (ROC) curves.
The conventional volume-averaged analysis could provide an area under ROC curve (AUC) ranging from 0.54 to 0.73 in predicting pCR. While if the models were replaced by voxelized heterogeneity analysis, the prediction accuracy measured by AUC could be improved to 0.71–0.79. Similar results were found for GR prediction. In addition, each subcategory images could generate moderate power in predicting the response, which if combining all information together, the AUC could be further improved to 0.84 for pCR and 0.89 for GR prediction, respectively.
Through a systematic analysis of multiparametric MR imaging features, we are able to build models with improved predictive value over conventional imaging metrics. The results are encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailoring the treatment into the era of personalized medicine.
阅读原文：PMID: 27185368 DOI:10.1158/1078-0432.CCR-15-2997