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基于T2WI与RS-EPI DWI影像组学特征的自动化机器学习模型预测直肠癌术前T分期的价值

Application of Automated Machine Learning Based on Radiomics Features of T2WI and RS-EPI DWI to Predict Preoperative T Staging of Rectal Cancer

    摘要
  • 摘要:
      目的  探讨基于磁共振T2加权成像（T2 weighted image, T2WI）和分段读出平面回波成像（readout-segmented EPI, RS-EPI）与扩散加权成像（diffusion weighted image, DWI）的影像组学特征，通过开发和验证自动化机器学习模型，预测直肠癌术前病理T分期的价值。
      方法  回顾性分析2016年10月−2018年12月经手术病理结果证实为直肠癌且在我院行术前直肠磁共振的患者131例。采用ITK-SNAP软件从T2WI和RS-EPI DWI图像中手动分割出肿瘤区域。使用PyRadiomics包提取出200个特征〔100个特征来自于T2WI，100个特征来自RS-EPI DWI的表观弥散系数（apparent diffusion coefficient, ADC）图〕。使用mwmote与neater重采样均衡数据，加入13例T1-2期模拟数据。根据3∶1的比例将总体数据分割成训练集111例和测试集37例。在训练集上使用Tree-based Pipeline Optimization Tool（TPOT）最优化模型参数并选取最重要的组学特征建模，得到5个互相独立的T分期模型。使用准确率和受试者工作特征（ROC）曲线下面积（area under the curve, AUC）筛选出最优模型。在测试集和原数据集上预测直肠癌T分期。
      结果  自动化机器学习推荐的5个T分期模型，在训练集上的准确率为0.802～0.838，敏感度为0.762～0.825，特异度为0.833～0.896，AUC范围为0.841～0.893，average precision（AP）范围为0.870～0.901。经过对比后，最终选择的模型的敏感度、特异度、AUC，在训练集上为0.810、0.875、0.893，在测试集上为0.810、0.813、0.810，在原始数据集上为0.810、0.830、0.860。
      结论  基于T2WI和RS-EPI DWI的影像组学数据，通过自动化机器学习建立的模型在预测直肠癌T分期上有较高的准确率。

     

    Abstract:
      Objective  To explore the radiomics features of T2 weighted image (T2WI) and readout-segmented echo-planar imaging (RS-EPI) plus difusion-weighted imaging (DWI), to develop an automated mahchine-learning model based on the said radiomics features, and to test the value of this model in predicting preoperative T staging of rectal cancer.
      Methods  The study retrospectively reviewed 131 patients who were diagnosed with rectal cancer confirmed by the pathology results of their surgical specimens at West China Hospital of Sichuan University between October, 2017 and December, 2018. In addition, these patients had preoperative rectal MRI. Tumor regions from preoperative MRI were manually segmented by radiologists with the ITK-SNAP software from T2WI and RS-EPI DWI images. PyRadiomics was used to extract 200 features—100 from T2WI and 100 from the apparent diffusion coefficient (ADC) calculated from the RS-EPI DWI. MWMOTE and NEATER were used to resample and balance the dataset, and 13 cases of T1-2 stage simulation cases were added. The overall dataset was divided into a training set (111 cases) and a test set (37 cases) by a ratio of 3∶1. Tree-based Pipeline Optimization Tool (TPOT) was applied on the training set to optimize model parameters and to select the most important radiomics features for modeling. Five independent T stage models were developed accordingly. Accuracy and the area under the curve (AUC) of receiver operating characteristic (ROC) were used to pick out the optimal model, which was then applied on the training set and the original dataset to predict the T stage of rectal cancer.
      Results  The performance of the the five T staging models recommended by automated machine learning were as follows: The accuracy for the training set ranged from 0.802 to 0.838, sensitivity, from 0.762 to 0.825, specificity, from 0.833 to 0.896, AUC, from 0.841 to 0.893, and average precision (AP) from 0.870 to 0.901. After comparison, an optimal model was picked out, with sensitivity, specificity and AUC for the training set reaching 0.810, 0.875, and 0.893, respectively. The sensitivity, specificity and AUC for the test set were 0.810, 0.813, and 0.810, respectively. The sensitivity, specificity and AUC for the original dataset were 0.810, 0.830, and 0.860, respectively.
      Conclusion   Based on the radiomics data of T2WI and RS-EPI DWI, the model established by automated machine learning showed a fairly high accuracy in predicting rectal cancer T stage.

     

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