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Abstract：
UNASSIGNED: To investigate the diagnostic efficacy of the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model based on ultrasonographic radiomics for the differentiation of small clear cell Renal Cell Carcinoma (ccRCC) and Renal Angiomyolipoma (RAML).
UNASSIGNED: The clinical, ultrasound, and contrast-enhanced CT(CECT) imaging data of 302 small renal tumors (maximum diameter ≤ 4cm) patients in Tianjin Medical University Cancer Institute and Hospital from June 2018 to June 2022 were retrospectively analyzed, with 182 patients of ccRCC and 120 patients of RAML. The ultrasound images of the largest diameter of renal tumors were manually segmented by ITK-SNAP software, and Pyradiomics (v3.0.1) module in Python 3.8.7 was applied to extract ultrasonographic radiomics features from ROI segmented images. The patients were randomly divided into training and internal validation cohorts in the ratio of 7:3. The Random Forest algorithm of the Sklearn module was applied to construct the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model. The efficacy of the prediction models was verified in an independent external validation cohort consisting of 69 patients, from 230 small renal tumor patients in two different institutions. The Delong test compared the predictive ability of three models and CECT. Calibration Curve and clinical Decision Curve Analysis were applied to evaluate the model and determine the net benefit to patients.
UNASSIGNED: 491 ultrasonographic radiomics features were extracted from 302 small renal tumor patients, and 9 ultrasonographic radiomics features were finally retained for modeling after regression and dimensionality reduction. In the internal validation cohort, the area under the curve (AUC), sensitivity, specificity, and accuracy of the clinical ultrasound imaging model, ultrasonographic radiomics model, comprehensive model, and CECT were 0.75, 76.7%, 60.0%, 70.0%; 0.80, 85.6%, 61.7%, 76.0%; 0.88, 90.6%, 76.7%, 85.0% and 0.90, 92.6%, 88.9%, 91.1%, respectively. In the external validation cohort, AUC, sensitivity, specificity, and accuracy of the three models and CECT were 0.73, 67.5%, 69.1%, 68.3%; 0.89, 86.7%, 80.0%, 83.5%; 0.90, 85.0%, 85.5%, 85.2% and 0.91, 94.6%, 88.3%, 91.3%, respectively. The DeLong test showed no significant difference between the clinical ultrasound imaging model and the ultrasonographic radiomics model (Z=-1.287, P=0.198). The comprehensive model showed superior diagnostic performance than the ultrasonographic radiomics model (Z=4. 394, P<0.001) and the clinical ultrasound imaging model (Z=4. 732, P<0.001). Moreover, there was no significant difference in AUC between the comprehensive model and CECT (Z=-0.252, P=0.801). Both in the internal and external validation cohort, the Calibration Curve and Decision Curve Analysis showed a better performance of the comprehensive model.
UNASSIGNED: It is feasible to construct an ultrasonographic radiomics model for distinguishing small ccRCC and RAML based on ultrasound images, and the diagnostic performance of the comprehensive model is superior to the clinical ultrasound imaging model and ultrasonographic radiomics model, similar to that of CECT.
UNASSIGNED: The clinical, ultrasound, and contrast-enhanced CT(CECT) imaging data of 302 small renal tumors (maximum diameter ≤ 4cm) patients in Tianjin Medical University Cancer Institute and Hospital from June 2018 to June 2022 were retrospectively analyzed, with 182 patients of ccRCC and 120 patients of RAML. The ultrasound images of the largest diameter of renal tumors were manually segmented by ITK-SNAP software, and Pyradiomics (v3.0.1) module in Python 3.8.7 was applied to extract ultrasonographic radiomics features from ROI segmented images. The patients were randomly divided into training and internal validation cohorts in the ratio of 7:3. The Random Forest algorithm of the Sklearn module was applied to construct the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model. The efficacy of the prediction models was verified in an independent external validation cohort consisting of 69 patients, from 230 small renal tumor patients in two different institutions. The Delong test compared the predictive ability of three models and CECT. Calibration Curve and clinical Decision Curve Analysis were applied to evaluate the model and determine the net benefit to patients.
UNASSIGNED: 491 ultrasonographic radiomics features were extracted from 302 small renal tumor patients, and 9 ultrasonographic radiomics features were finally retained for modeling after regression and dimensionality reduction. In the internal validation cohort, the area under the curve (AUC), sensitivity, specificity, and accuracy of the clinical ultrasound imaging model, ultrasonographic radiomics model, comprehensive model, and CECT were 0.75, 76.7%, 60.0%, 70.0%; 0.80, 85.6%, 61.7%, 76.0%; 0.88, 90.6%, 76.7%, 85.0% and 0.90, 92.6%, 88.9%, 91.1%, respectively. In the external validation cohort, AUC, sensitivity, specificity, and accuracy of the three models and CECT were 0.73, 67.5%, 69.1%, 68.3%; 0.89, 86.7%, 80.0%, 83.5%; 0.90, 85.0%, 85.5%, 85.2% and 0.91, 94.6%, 88.3%, 91.3%, respectively. The DeLong test showed no significant difference between the clinical ultrasound imaging model and the ultrasonographic radiomics model (Z=-1.287, P=0.198). The comprehensive model showed superior diagnostic performance than the ultrasonographic radiomics model (Z=4. 394, P<0.001) and the clinical ultrasound imaging model (Z=4. 732, P<0.001). Moreover, there was no significant difference in AUC between the comprehensive model and CECT (Z=-0.252, P=0.801). Both in the internal and external validation cohort, the Calibration Curve and Decision Curve Analysis showed a better performance of the comprehensive model.
UNASSIGNED: It is feasible to construct an ultrasonographic radiomics model for distinguishing small ccRCC and RAML based on ultrasound images, and the diagnostic performance of the comprehensive model is superior to the clinical ultrasound imaging model and ultrasonographic radiomics model, similar to that of CECT.
摘要：
■为了探讨临床超声成像模型的诊断效能，超声影像组学模型，和基于超声影像组学的综合模型，用于小透明细胞肾细胞癌（ccRCC）和肾血管平滑肌脂肪瘤（RAML）的分化。
■临床，超声，回顾性分析2018年6月至2022年6月天津医科大学肿瘤医院302例肾脏小肿瘤（最大直径≤4cm）患者的CT及增强扫描（CECT）影像资料，182例ccRCC患者和120例RAML患者。用ITK-SNAP软件对肾肿瘤最大直径的超声图像进行手工分割,Python3.8.7中的Pyradiomics（v3.0.1）模块用于从ROI分割图像中提取超声影像组学特征。患者以7:3的比例随机分为训练和内部验证队列。应用Sklearn模块的随机森林算法构建临床超声成像模型,超声影像组学模型，综合模型。在由69名患者组成的独立外部验证队列中验证了预测模型的有效性，来自两个不同机构的230名小肾肿瘤患者。Delong检验比较了三种模型和CECT的预测能力。校准曲线和临床决策曲线分析用于评估模型并确定对患者的净益处。
■从302例小肾肿瘤患者中提取了491例超声影像组学特征，在回归和降维后，最终保留了9个超声影像组学特征进行建模。在内部验证队列中，曲线下面积（AUC），灵敏度,特异性，和临床超声成像模型的准确性，超声影像组学模型，综合模型，CECT为0.75,76.7%,60.0%,70.0%;0.80,85.6%,61.7%,76.0%；0.88、90.6%，76.7%,85.0%和0.90、92.6%，88.9%,91.1%,分别。在外部验证队列中，AUC,灵敏度,特异性，三种模型和CECT的准确率分别为0.73、67.5%，69.1%,68.3%;0.89,86.7%,80.0%,83.5%;0.90,85.0%,85.5%,85.2%和0.91、94.6%，88.3%,91.3%,分别。DeLong检验显示，临床超声成像模型与超声影像组学模型之间没有显着差异（Z=-1.287，P=0.198）。综合模子显示出比超声放射组学模子优越的诊断机能(Z=4。394，P<0.001）和临床超声成像模子（Z=4。732，P<0.001）。此外，综合模型与CECT的AUC差异无统计学意义(Z=-0.252,P=0.801)。在内部和外部验证队列中，校准曲线和决策曲线分析显示了综合模型的较好性能。
■构建基于超声图像区分小ccRCC和RAML的超声影像组学模型是可行的，综合模型的诊断性能优于临床超声成像模型和超声影像组学模型，类似于CECT。
■临床，超声，回顾性分析2018年6月至2022年6月天津医科大学肿瘤医院302例肾脏小肿瘤（最大直径≤4cm）患者的CT及增强扫描（CECT）影像资料，182例ccRCC患者和120例RAML患者。用ITK-SNAP软件对肾肿瘤最大直径的超声图像进行手工分割,Python3.8.7中的Pyradiomics（v3.0.1）模块用于从ROI分割图像中提取超声影像组学特征。患者以7:3的比例随机分为训练和内部验证队列。应用Sklearn模块的随机森林算法构建临床超声成像模型,超声影像组学模型，综合模型。在由69名患者组成的独立外部验证队列中验证了预测模型的有效性，来自两个不同机构的230名小肾肿瘤患者。Delong检验比较了三种模型和CECT的预测能力。校准曲线和临床决策曲线分析用于评估模型并确定对患者的净益处。
■从302例小肾肿瘤患者中提取了491例超声影像组学特征，在回归和降维后，最终保留了9个超声影像组学特征进行建模。在内部验证队列中，曲线下面积（AUC），灵敏度,特异性，和临床超声成像模型的准确性，超声影像组学模型，综合模型，CECT为0.75,76.7%,60.0%,70.0%;0.80,85.6%,61.7%,76.0%；0.88、90.6%，76.7%,85.0%和0.90、92.6%，88.9%,91.1%,分别。在外部验证队列中，AUC,灵敏度,特异性，三种模型和CECT的准确率分别为0.73、67.5%，69.1%,68.3%;0.89,86.7%,80.0%,83.5%;0.90,85.0%,85.5%,85.2%和0.91、94.6%，88.3%,91.3%,分别。DeLong检验显示，临床超声成像模型与超声影像组学模型之间没有显着差异（Z=-1.287，P=0.198）。综合模子显示出比超声放射组学模子优越的诊断机能(Z=4。394，P<0.001）和临床超声成像模子（Z=4。732，P<0.001）。此外，综合模型与CECT的AUC差异无统计学意义(Z=-0.252,P=0.801)。在内部和外部验证队列中，校准曲线和决策曲线分析显示了综合模型的较好性能。
■构建基于超声图像区分小ccRCC和RAML的超声影像组学模型是可行的，综合模型的诊断性能优于临床超声成像模型和超声影像组学模型，类似于CECT。
