Abstract：
OBJECTIVE: This study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs).
METHODS: This retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model.
RESULTS: There were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists\' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001).
CONCLUSIONS: The CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies.
METHODS: This retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model.
RESULTS: There were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists\' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001).
CONCLUSIONS: The CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies.
摘要：
目的：本研究旨在建立一种基于临床和CT特征的诊断模型，以识别肾脏小肿块（SRM）中的透明细胞肾细胞癌（ccRCC）。
方法：这项回顾性多中心研究纳入了病理证实为SRM的患者。来自三个中心的数据被用作训练集（n=229），来自一个中心的数据作为独立的测试集(n=81)。采用单因素和多因素logistic回归分析筛选ccRCC的独立危险因素,建立分类回归树(CART)诊断模型。曲线下面积(AUC)用于评估模型的性能。为了证明该模型的临床实用性，3名放射科医师被要求根据专业经验诊断测试集中的SRM,并借助CART模型进行重新评估.
结果：309例患者中有310例SRM，71％（220/310）为ccRCC。在测试队列中，CART模型的AUC为0.90(95%CI:0.81,0.97)。对于放射科医生的评估，根据临床经验，三名放射科医生的AUC为0.78(95%CI:0.66,0.89)，0.65（95%CI：0.53,0.76），和0.68（95%CI：0.57,0.79）。随着CART模型的支持，三位放射科医生的AUC为0.93(95%CI:0.86,0.97)，0.87（95%CI：0.78,0.95）和0.87（95%CI：0.78,0.95）。在CART模型辅助下，观察者间的一致性得到改善（0.323vs0.654，P<0.001）。
结论：CART模型可以比经验丰富的放射科医生更好地识别ccRCC，并提高诊断性能，有可能减少不必要的活检数量。
方法：这项回顾性多中心研究纳入了病理证实为SRM的患者。来自三个中心的数据被用作训练集（n=229），来自一个中心的数据作为独立的测试集(n=81)。采用单因素和多因素logistic回归分析筛选ccRCC的独立危险因素,建立分类回归树(CART)诊断模型。曲线下面积(AUC)用于评估模型的性能。为了证明该模型的临床实用性，3名放射科医师被要求根据专业经验诊断测试集中的SRM,并借助CART模型进行重新评估.
结果：309例患者中有310例SRM，71％（220/310）为ccRCC。在测试队列中，CART模型的AUC为0.90(95%CI:0.81,0.97)。对于放射科医生的评估，根据临床经验，三名放射科医生的AUC为0.78(95%CI:0.66,0.89)，0.65（95%CI：0.53,0.76），和0.68（95%CI：0.57,0.79）。随着CART模型的支持，三位放射科医生的AUC为0.93(95%CI:0.86,0.97)，0.87（95%CI：0.78,0.95）和0.87（95%CI：0.78,0.95）。在CART模型辅助下，观察者间的一致性得到改善（0.323vs0.654，P<0.001）。
结论：CART模型可以比经验丰富的放射科医生更好地识别ccRCC，并提高诊断性能，有可能减少不必要的活检数量。
