Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer
Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer
复制标题
扩散加权成像的定量分析预测局部晚期直肠癌新辅助放化疗的病理良好反应
DOI：
10.1016/j.radonc.2018.11.007
复制
发表时间：
2019-03-01
影响因子：
5.7
通讯作者：
Tian, Jie
中科院分区：
文献类型：
--
作者：
Tang, Zhenchao;Zhang, Xiao-Yan;Tian, Jie
背景和目的：局部晚期直肠癌（LARC）患者在新辅助放化疗（nCRT）后表现出病理学良好反应（pGR），分期下调至ypT 0 - 1 N 0，可接受器官保留治疗，而不是全直肠系膜切除术（TME）。材料与方法：222例接受nCRT和TME治疗的LARC患者，随机分为训练集（152例）和验证集（70例）。在训练集中构建了三种pGR预测模型，包括基于定量DWI特征的DWI预测模型、基于临床特征的临床预测模型以及融合DWI和临床预测因子的组合预测模型。结果：DWI模型（AUC = 0.866，ACC = 91.43%）和组合模型（AUC = 0.890，ACC = 90%）在独立验证集上均获得了较好的预测效果。然而，临床预测模型的表现比其他两个模型差（在验证集中AUC = 0.631，ACC = 75.71%）。校正分析表明，组合预测模型预测的pGR概率接近完美预测。结论：DWI定量分析与临床特征相结合，可有效识别pGR患者，为nCRT治疗后的器官保护策略提供决策支持。(C)2018爱思唯尔B. V.保留所有权利。
Background and purpose: Locally advanced rectal cancer (LARC) patients showing pathological good response (pGR) of down-staging to ypT0-1N0 after neoadjuvant chemoradiotherapy (nCRT) may receive organ-preserving treatment instead of total mesorectal excision (TME). In the current study, quantitative analysis of diffusion weighted imaging (DWI) is conducted to predict pGR patients in order to provide decision support for organ-preserving strategies.Materials and methods: 222 LARC patients receiving nCRT and TME are enrolled from Beijing Cancer Hospital and allocated into training (152) and validation (70) set. Three pGR prediction models are constructed in the training set, including DWI prediction model based on quantitative DWI features, clinical prediction model based on clinical characteristics, and combined prediction model integrating DWI and clinical predictors. Prediction performances are assessed by area under receiver operating characteristic curve (AUC), classification accuracy (ACC), positive and negative predictive values (PPV and NPV).Results: The DWI (AUC = 0.866, ACC = 91.43%) and combined (AUC = 0.890, ACC = 90%) prediction model obtains good prediction performance in the independent validation set. Nevertheless, the clinical prediction model performs worse than the other two models (AUC = 0.631, ACC = 75.71% in validation set). Calibration analysis indicates that the pGR probability predicted by the combined prediction model is close to perfect prediction. Decision curve analysis reveals that the LARC patients will acquire clinical benefit if receiving organ-preserving strategy according to combined prediction model.Conclusion: Combination of quantitative DWI analysis and clinical characteristics holds great potential in identifying the pGR patients and providing decision support for organ-preserving strategies after nCRT treatment. (C) 2018 Elsevier B.V. All rights reserved.