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胡斯娴, 杨康, 汪昕蓉, 等. 磁共振影像组学模型在直肠癌肝转移评估中的临床价值[J]. 四川大学学报(医学版), 2021, 52(2): 311-318. DOI: 10.12182/20210360202
引用本文: 胡斯娴, 杨康, 汪昕蓉, 等. 磁共振影像组学模型在直肠癌肝转移评估中的临床价值[J]. 四川大学学报(医学版), 2021, 52(2): 311-318. DOI: 10.12182/20210360202
HU Si-xian, YANG Kang, WANG Xin-rong, et al. Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer[J]. Journal of Sichuan University (Medical Sciences), 2021, 52(2): 311-318. DOI: 10.12182/20210360202
Citation: HU Si-xian, YANG Kang, WANG Xin-rong, et al. Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer[J]. Journal of Sichuan University (Medical Sciences), 2021, 52(2): 311-318. DOI: 10.12182/20210360202

磁共振影像组学模型在直肠癌肝转移评估中的临床价值

Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer

  • 摘要:
      目的  探究基于不同磁共振序列构建的影像组学模型在直肠癌肝转移评估中的临床应用价值。
      方法  回顾性纳入2015年4月−2018年5月经病理证实为直肠癌并在我院行治疗前基线磁共振检查的患者140例。根据肝脏穿刺活检、手术病理和影像结果分为肝转移组和未转移组。通过ITK-SNAP软件在T2加权图像(T2 weighted image,T2WI)、弥散加权图像(diffusion weighted image,DWI)和表观弥散系数(apparent diffusion coefficient,ADC)图像上对原发灶逐层勾画感兴趣区(region of interest,ROI)。3D ROI导入Artificial Intelligent Kit软件平台提取影像组学特征,每个序列图像提取396个特征。基于Python平台对特征数据进行预处理,使用支持向量机-合成少数类过采样法(Support Vector Machine-Synthetic Minority Over-Sampling Technique,SVM-SMOTE)对样本进行过采样,使截止随访时发生肝转移组和未发生肝转移组样本数平衡,之后按2∶1比例分为训练集和测试集。对影像组学特征进行筛选后,使用R软件构建logistic回归模型,用受试者工作特征(ROC)曲线和校准曲线对模型效果进行评价。
      结果  纳入的患者中发生肝转移的有52例,截止随访时未发生肝转移的有88例,癌胚抗原(CEA)水平、MRI的T分期和N分期在肝转移组和未转移组的差异有统计学意义(P<0.05)。在对特征进行预处理和筛选后,最终,除去非影像组学特征17个,多序列联合数据集(T2WI+DWI+ADC)共筛选出32个特征,T2WI独立数据集10个特征,DWI独立数据集30个特征,ADC独立数据集15个特征。多序列联合数据集、T2WI独立数据集以及ADC独立数据集构建的模型能准确评估肝转移,训练集的ROC曲线下面积(AUC)为93.5%、89.2%、90.6%,测试集的AUC分别为80.8%、80.5%、81.4%,多序列联合数据集并未表现出高于独立数据集的AUC。DWI独立数据集表现稍差,训练集和测试集的AUC为90.3%、75.1%。校准曲线显示,联合数据集模型的波动最小,最接近参考线;3个独立数据集模型的波动范围相接近;4种模型的校准曲线均显示随着风险升高,模型预测从对风险的低估转为对风险的高估。总体而言,多序列联合数据集与独立T2WI数据集、独立ADC数据集都具有较高的AUC,而多序列联合数据集校准曲线偏离对角参考线最近,模型效果最好。独立T2WI和ADC数据集总体效果次之,独立DWI数据集效果欠佳。
      结论  磁共振影像组学模型能够对直肠癌肝转移进行有效评估,为临床分期和诊治提供信息。

     

    Abstract:
      Obejective   To explore the clinical value of using radiomics models based on different MRI sequences in the assessment of hepatic metastasis of rectal cancer.
      Methods  140 patients with pathologically confirm edrectal cancer were included in the study. They underwent baseline magnetic resonance imaging (MRI) between April 2015 and May 2018 before receiving any treatment. According to the results of liver biopsy, surgical pathology, and imaging, patients were put into two groups, the patients with hepatic metastasis and those without. T2 weighted images (T2WI), diffusion weighted images (DWI) and apparent diffusion coefficient (ADC) images were used to draw the region of interest (ROI) of primary lesions on consecutive slices on ITK-SNAP. 3-D ROIs were generated and loaded into Artificial Intelligent Kit for extraction of radiomics features and 396 features were extracted for each sequence. The feature data were preprocessed on Python and the samples were oversampled, using Support Vector Machine-Synthetic Minority Over-Sampling Technique (SVM-SMOTE) to balance the number of samples in the group with liver metastasis and the group with no liver metastasis at the end of the follow-up. Then, the samples were divided into the training cohort and the test cohort at a ratio of 2∶1. The logistic regression models were developed with selected radionomic features on R software. The receiver operating characteristics (ROC) curves and calibration curves were used to evaluate the performance of the models.
      Results  In total, 52 patients with liver metastasis and 88 patients without liver metastasis at the end of follow-up were enrolled. Carcinoembryonic antigen (CEA) and T stage and N stage evaluated on the MRI images showed statistically significant difference between the two groups (P<0.05). After data preprocessing and selecting, except for 17 non-radiomic features, the model combining T2WI, DWI and ADC features, the model of T2WI features alone, the model of DWI features alone and the model of ADC features alone were developed with 32 features, 10 features, 30 features and 15 features, respectively. The combined model (T2WI+DWI+ADC), the T2WI model, and the ADC model can assess hepatic metastasis accurately, with the area under curve (AUC) on the train set reaching 93.5%, 89.2%, 90.6% and that of the test set reaching 80.8%, 80.5%, 81.4%, respectively. The combined model did not show a higher AUC than those of the T2WI and ADC alone models. Model based on DWI features has a slightly insufficient AUC of 90.3% in the train set and 75.1% in the test set. The calibration curve showed the smallest fluctuation in the combined model, which is closest fit to the diagonal reference line. The fluctuation in the three independent data set models were similar. The calibration curves of all the four models showed that as the risk increased, the prediction of the models turned from an underestimation to an overestimating the risk. In brief, the combined model showed the best performance, with the best fit to the diagonal reference line in calibration curve and high AUC comparable to the AUC of the T2WI model and ADC model. The performance of T2WI and ADC alone models were second to that of the combined model, while the DWI alone model showed relatively poor performance.
      Conclusion  Radiomics models based on MRI could be effectively used in assessing liver metastasis in rectal cancer, which may help determine clinical staging and treatment.

     

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