基于 MRI 的 delta 放射组学可预测局部晚期直肠癌新辅助放化疗后的病理完全缓解。
MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
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, #17 Panjiayuan nanli, Chaoyang district, Beijing, China, 100021.
Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan nanli, Chaoyang district, Beijing, China, 100021.
出版信息
Acad Radiol. 2021 Nov;28 Suppl 1:S95-S104. doi: 10.1016/j.acra.2020.10.026. Epub 2020 Nov 12.
RATIONALE AND OBJECTIVES
To investigate the capability of delta-radiomics to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).
MATERIALS AND METHODS
This retrospective study enrolled 165 consecutive patients with LARC (training set, n = 116; test set, n = 49) who received nCRT before surgery. All patients underwent pre- and post-nCRT MRI examination from which radiomics features were extracted. A delta-radiomics feature was defined as the percentage change in a radiomics feature from pre- to post-nCRT MRI. A data reduction and feature selection process including the least absolute shrinkage and selection operator algorithm was performed for building T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) delta-radiomics signature. Logistic regression was used to build a T2WI and DWI combined radiomics model. Receiver operating characteristic analysis was performed to assess diagnostic performance. Delong method was used to compare the performance of delta-radiomics model with that of magnetic resonance tumor regression grade (mrTRG).
RESULTS
Twenty-seven of 165 patients (16.4%) achieved pCR. T2WI and DWI delta-radiomics signature, and the combined model showed good predictive performance for pCR. The combined model achieved the highest areas under the receiver operating characteristic curves of 0.91 (95% confidence interval: 0.85-0.98) and 0.91 (95% confidence interval: 0.83-0.99) in the training and test sets, respectively (significantly greater than those for mrTRG; training set, p < 0.001; test set, p = 0.04).
CONCLUSION
MRI-based delta-radiomics can help predict pCR after nCRT in patients with LARC with better performance than mrTRG.
背景与目的
探究放射组学 delta 特征在预测接受新辅助放化疗（nCRT）的局部进展期直肠癌（LARC）患者病理完全缓解（pCR）中的作用。
材料与方法
本回顾性研究纳入了 165 例接受 nCRT 治疗的 LARC 患者（训练集，n=116；测试集，n=49），所有患者均在术前接受了 nCRT 前后的 MRI 检查，并从中提取了放射组学特征。放射组学 delta 特征定义为 nCRT 前后 MRI 上放射组学特征的百分比变化。采用最小绝对收缩和选择算子算法进行数据降维和特征选择，构建 T2 加权成像（T2WI）和弥散加权成像（DWI）的 delta 放射组学特征。采用逻辑回归构建 T2WI 和 DWI 联合放射组学模型。采用受试者工作特征曲线分析评估诊断效能。采用 Delong 方法比较 delta 放射组学模型与磁共振肿瘤退缩分级（mrTRG）的性能。
结果
165 例患者中有 27 例（16.4%）达到 pCR。T2WI 和 DWI 的 delta 放射组学特征和联合模型对 pCR 均具有较好的预测性能。联合模型在训练组和测试组中的受试者工作特征曲线下面积最高，分别为 0.91（95%置信区间：0.85-0.98）和 0.91（95%置信区间：0.83-0.99）（均显著大于 mrTRG；训练集，p<0.001；测试集，p=0.04）。
结论
基于 MRI 的 delta 放射组学有助于预测接受 nCRT 的 LARC 患者的 pCR，其性能优于 mrTRG。
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