Abstract：
BACKGROUND: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC).
METHODS: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor\'s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification.
RESULTS: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17.
CONCLUSIONS: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.
METHODS: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor\'s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification.
RESULTS: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17.
CONCLUSIONS: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.
摘要：
背景：良性肾肿瘤，如肾嗜酸细胞瘤（RO），可能会被错误地诊断为恶性肾细胞癌（RCC），因为它们相似的成像特征。利用放射学特征的计算机辅助系统可用于更好地区分良性肾肿瘤和恶性肿瘤。这项工作的目的是建立一个机器学习模型来区分RO和透明细胞RCC（ccRCC）。
方法：我们收集了77例患者的CT图像，RO30例(39%)，ccRCC47例(61%)。从临床医生确定的肿瘤体积和肿瘤过渡区（ZOT）中提取放射学特征。我们使用遗传算法来执行特征选择，确定肿瘤分类最具描述性的特征集。我们构建了一个决策树分类器来区分RO和ccRCC。我们提出了管道的两个版本：在第一个版本中，特征选择是在数据分裂之前执行的，而在第二个，特征选择是在之后进行的，即，仅在训练数据上。我们评估了两种管道在癌症分类中的效率。
结果：通过遗传算法发现ZOT特征最具预测性。对整个数据集进行特征选择的流水线获得0.87±0.09的平均ROCAUC得分。第二条管道，其中仅对训练数据执行特征选择，获得的平均ROCAUC评分为0.62±0.17。
结论：所获得的结果证实了ZOT影像特征在捕获肾肿瘤特征方面的有效性。我们表明，两条拟建管道的性能存在显著差异，强调一些已经发表的放射学分析可能对模型的实际泛化能力过于乐观。
方法：我们收集了77例患者的CT图像，RO30例(39%)，ccRCC47例(61%)。从临床医生确定的肿瘤体积和肿瘤过渡区（ZOT）中提取放射学特征。我们使用遗传算法来执行特征选择，确定肿瘤分类最具描述性的特征集。我们构建了一个决策树分类器来区分RO和ccRCC。我们提出了管道的两个版本：在第一个版本中，特征选择是在数据分裂之前执行的，而在第二个，特征选择是在之后进行的，即，仅在训练数据上。我们评估了两种管道在癌症分类中的效率。
结果：通过遗传算法发现ZOT特征最具预测性。对整个数据集进行特征选择的流水线获得0.87±0.09的平均ROCAUC得分。第二条管道，其中仅对训练数据执行特征选择，获得的平均ROCAUC评分为0.62±0.17。
结论：所获得的结果证实了ZOT影像特征在捕获肾肿瘤特征方面的有效性。我们表明，两条拟建管道的性能存在显著差异，强调一些已经发表的放射学分析可能对模型的实际泛化能力过于乐观。
