Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer.
Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer.
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DOI：
10.1088/1361-6560/aacd22
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发表时间：
2018-07-19
影响因子：
3.5
通讯作者：
Lu W
中科院分区：
文献类型：
--
作者：
Riyahi S;Choi W;Liu CJ;Zhong H;Wu AJ;Mechalakos JG;Lu W
我们提出了一种利用雅可比图检测和量化局部化疗后肿瘤形态学变化的框架，并从雅可比图中提取定量放射学特征来预测局部晚期食管癌患者的病理肿瘤反应。在20例接受CRT的患者中，进行了多分辨率BSpline可变形配准，将随访（CRT后）CT与基线CT图像进行了配准。计算雅可比映射(J)作为变形向量场梯度的行列式。雅可比图测量肿瘤局部体积变化的比值，其中J < 1表示肿瘤缩小，J > 1表示肿瘤扩大。人工圈定肿瘤，并在基线和随访图像上生成相应的解剖标志。然后从肿瘤的雅可比图中提取强度、纹理和几何特征来量化肿瘤的形态变化。通过单变量和多变量分析评估每个雅可比特征在预测病理肿瘤反应中的重要性。我们使用支持向量机（SVM）分类器和最小绝对收缩和选择算子（LASSO）进行特征选择，构建了多元预测模型。采用10次重复10次交叉验证（10×10-fold CV）对SVM-LASSO模型进行评价。配准后的平均目标配准误差为4.30±1.09mm (LR:1.63mm AP:1.59mm SI:3.05mm)，表明配准误差在2个体素以内，接近4mm切片厚度。视觉上，雅可比图显示肿瘤局部收缩和扩张区域的平滑变化。在数量上，应答肿瘤和无应答肿瘤的平均中位雅可比值分别为0.80±0.10和1.05±0.15。这些结果表明，平均而言，有反应的肿瘤有20%的中位体积缩小，而无反应的肿瘤有5%的中位体积扩大。在单因素分析中，最小雅可比矩阵（p=0.009， AUC=0.98）和中位数雅可比矩阵（p=0.004， AUC=0.95）是最显著的预测因子。当选择这两个特征时，SVM-LASSO模型的准确率最高（Sensitivity=94.4%, Specificity=91.8%， AUC=0.94）。从雅可比图中提取的新特征仅使用基线肿瘤轮廓来量化局部肿瘤形态变化，而无需对治疗后的肿瘤进行分割。采用中位雅可比矩阵和最小雅可比矩阵的SVM-LASSO模型在预测肿瘤病理反应方面具有较高的准确性。雅可比图显示了纵向评价肿瘤反应的巨大潜力。
We proposed a framework to detect and quantify local tumor morphological changes due to chemo-radiotherapy (CRT) using Jacobian map and to extract quantitative radiomic features from the Jacobian map to predict the pathologic tumor response in locally advanced esophageal cancer patients. In 20 patients who underwent CRT, a multi-resolution BSpline deformable registration was performed to register the follow-up (post-CRT) CT to the baseline CT image. Jacobian map (J) was computed as the determinant of the gradient of the Deformation Vector Field. Jacobian map measured the ratio of local tumor volume change where J < 1 indicated tumor shrinkage and J > 1 denoted expansion. The tumor was manually delineated and corresponding anatomical landmarks were generated on the baseline and follow-up images. Intensity, texture and geometry features were then extracted from the Jacobian map of the tumor to quantify tumor morphological changes. The importance of each Jacobian feature in predicting pathologic tumor response was evaluated by both univariate and multivariate analysis. We constructed a multivariate prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO) for feature selection. The SVM-LASSO model was evaluated using ten-times repeated 10-fold cross-validation (10×10-fold CV). After registration, the average Target Registration Error was 4.30±1.09mm (LR:1.63mm AP:1.59mm SI:3.05mm) indicating registration error was within two voxels and close to 4mm slice thickness. Visually, Jacobian map showed smoothly-varying local shrinkage and expansion regions in a tumor. Quantitatively, the average Median Jacobian was 0.80±0.10 and 1.05±0.15 for responder and non-responder tumors, respectively. These indicated that on average responder tumors had 20% median volume shrinkage while non-responder tumors had 5% median volume expansion. In univariate analysis, Minimum Jacobian (p=0.009, AUC=0.98) and Median Jacobian (p=0.004, AUC=0.95) were the most significant predictors. The SVM-LASSO model achieved the highest accuracy when these two features were selected (Sensitivity=94.4%, Specificity=91.8%, AUC=0.94). Novel features extracted from the Jacobian map quantified local tumor morphological changes using only baseline tumor contour without post-treatment tumor segmentation. The SVM-LASSO model using Median Jacobian and Minimum Jacobian achieved high accuracy in predicting pathologic tumor response. Jacobian map showed great potential for longitudinal evaluation of tumor response.
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影响因子：
5.5
作者：
Duan XF;Tang P;Yu ZT
通讯作者：
Yu ZT
影响因子：
8.4
作者：
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作者：
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影响因子：
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作者：
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通讯作者：
Lammertsma, Adriaan A
影响因子：
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作者：
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通讯作者：
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