Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma.
Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma.
复制标题
基于多相对比增强 CT 的机器学习模型预测透明细胞肾细胞癌的 Fuhrman 核分级
DOI：
10.2147/cmar.s290327
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发表时间：
2021
影响因子：
3.3
通讯作者：
Zhen X
中科院分区：
文献类型：
--
作者：
Lai S;Sun L;Wu J;Wei R;Luo S;Ding W;Liu X;Yang R;Zhen X
目的探讨不同机器学习模型对基于多期计算机断层扫描(CT)的肾透明细胞癌(CCRCC)高、低度核分级的预测性能。材料与方法选择2011年1月至2019年1月经手术病理证实的肾小细胞癌患者137例，其中低级别(1~2级)96例，高度(3~4级)41例。在四期(平扫、皮髓期、肾造影期和排泄期)CT图像上，对肿瘤最大断面的代表性切片进行目标感兴趣区(ROI)的勾画和纹理提取。将4个阶段特征的15个级联输入176个分类模型(用8个分类器和22种特征选择方法构建)，比较了2640个结果判别模型的分类性能，并对排名靠前的特征进行了分析。结果从平扫CT图像中提取的图像特征在分类性能上明显优于其他三个阶段的特征。“套袋+CMIM”判别模型的分类AUC最高，为0.75。由高到低的特征包括1个基于形状的特征和5个一阶统计特征。结论基于机器学习分类模型的UP图像特征在区分低、高核级肾细胞癌方面较其他CT相更有效。
Objective To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features. Materials and Methods A total of 137 consecutive patients with pathologically proven ccRCC (including 96 low-grade [grade 1 or 2] and 41 high-grade [grade 3 or 4] ccRCC) from January 2011 to January 2019 were enrolled in this retrospective study. Target region of interest (ROI) delineation followed by texture extraction was performed on a representative slice with the largest section of the tumor on the four-phase (unenhanced phase [UP], corticomedullary phase [CMP], nephrographic phase [NP] and excretory phase [EP]) CT images. Fifteen concatenations of the four-phase features were fed into 176 classification models (built with 8 classifiers and 22 feature selection methods), the classification performances of the 2640 resultant discriminative models were compared, and the top-ranked features were analyzed. Results Image features extracted from the unenhanced phase (UP) CT images demonstrated a dominant classification performance over features from the other three phases. The discriminative model “Bagging + CMIM” achieved the highest classification AUC of 0.75. The top-ranked features from the UP included one shape-based feature and five first-order statistical features. Conclusion Image features extracted from the UP are more effective than other CT phases in differentiating low and high nuclear grade ccRCC based on machine learning–based classification modeling.