Cheng Yuan, Luo Yahong, Hu Yue, Zhang Zhaohe, Wang Xingling, Yu Qing, Liu Guanyu, Cui Enuo, Yu Tao, Jiang Xiran
Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, 110122, People's Republic of China.
Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China.
Abdom Radiol (NY). 2021 Nov;46(11):5072-5085. doi: 10.1007/s00261-021-03219-0. Epub 2021 Jul 24.
PURPOSE: To investigate the value of multiparametric MRI-based radiomics on predicting response to nCRT in patients with rectal cancer. METHODS: This study enrolled 193 patients with pathologically confirmed LARC who received nCRT treatment between Apr. 2014 and Jun. 2018. All patients underwent baseline T1-weighted (T1W), T2-weighted (T2W) and T2-weighted fat-suppression (T2FS) MRI scans before neoadjuvant chemoradiotherapy. Radiomics features were extracted and selected from the MRI data to establish the radiomics signature. Important clinical predictors were identified by Mann-Whitney U test and Chi-square test. The nomogram integrating the radiomics signature and important clinical predictors was constructed using multivariate logistic regression. Prediction capabilities of each model were assessed with receiver operating characteristic (ROC) curve analysis. Performance of the nomogram was evaluated by its calibration and potential clinical usefulness. RESULTS: For the prediction of good response (GR) and pathologic complete response (pCR), the developed radiomics signature comprising 10 and 7 features, respectively, were significantly associated with the therapeutic response to nCRT. The nomogram incorporating the radiomics signature and important clinical predictors (CEA and CA19-9 for predicting GR; CEA, posttreatment length and posttreatment thickness for predicting pCR) achieved favorable prediction efficacy, with AUCs of 0.918 (95% confidence interval [CI]: 0.867-0.971, Sen = 0.972, Spe = 0.828) and 0.944 (95% CI: 0.891-0.997, Sen = 0.943, Spe = 0.828) in the training and validation cohort for predicting GR, respectively; with AUCs of 0.959 (95% CI: 0.927-0.991, Sen = 1.000, Spe = 0.833) and 0.912 (95% CI: 0.843-0.982, Sen = 1.000, Spe = 0.815) in the training and validation cohort for predicting pCR, respectively. Decision curve analysis confirmed potential clinical usefulness of our nomogram. CONCLUSIONS: This study demonstrated that the MRI-based radiomics nomogram is predictive of response to nCRT and can be considered as a promising tool for facilitating treatment decision-making for patients with LARC.
目的：探讨基于多参数磁共振成像（MRI）的影像组学在预测直肠癌患者新辅助放化疗（nCRT）疗效中的价值。 方法：本研究纳入了193例经病理确诊的局部晚期直肠癌（LARC）患者，这些患者于2014年4月至2018年6月期间接受了nCRT治疗。所有患者在新辅助放化疗前均接受了基线T1加权（T1W）、T2加权（T2W）和T2加权脂肪抑制（T2FS）MRI扫描。从MRI数据中提取并选择影像组学特征以建立影像组学特征模型。通过曼-惠特尼U检验和卡方检验确定重要的临床预测因素。使用多变量逻辑回归构建整合影像组学特征和重要临床预测因素的列线图。通过受试者操作特征（ROC）曲线分析评估每个模型的预测能力。通过校准和潜在临床实用性评估列线图的性能。 结果：对于预测良好反应（GR）和病理完全缓解（pCR），所建立的分别包含10个和7个特征的影像组学特征模型与nCRT的治疗反应显著相关。纳入影像组学特征和重要临床预测因素（预测GR时为癌胚抗原[CEA]和糖类抗原19-9[CA19-9]；预测pCR时为CEA、治疗后长度和治疗后厚度）构建的列线图具有良好的预测效能，在预测GR的训练队列和验证队列中，曲线下面积（AUC）分别为0.918（95%置信区间[CI]：0.867-0.971，灵敏度[Sen]=0.972，特异度[Spe]=0.828）和0.944（95%CI：0.891-0.997，Sen=0.943，Spe=0.828）；在预测pCR的训练队列和验证队列中，AUC分别为0.959（95%CI：0.927-0.991，Sen=1.000，Spe=0.833）和0.912（95%CI：0.843-0.982，Sen=1.000，Spe=0.815）。决策曲线分析证实了我们列线图的潜在临床实用性。 结论：本研究表明，基于MRI的影像组学列线图可预测nCRT的疗效，可被视为一种有前景的工具，有助于为LARC患者制定治疗决策。
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