基于 CT 纹理分析预测肾透明细胞癌的 Fuhrman 病理分级

IF 1.5 Q3 UROLOGY & NEPHROLOGY American journal of clinical and experimental urology Pub Date : 2024-02-15 eCollection Date: 2024-01-01
Zhuang Dong, Chao Guan, Xuezhen Yang
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引用次数: 0

摘要

目的研究基于CT平扫纹理分析的成像模型在区分肾透明细胞癌Fuhrman病理分级低(I级和II级)和高(III级和IV级)时的预测性能:回顾性分析了 TCGA-KIRC 公共数据库中 94 例接受 CT 扫描并经活检或手术确诊的 ccRCC 患者的临床数据。其中 32 例为低级别 ccRCC,62 例为高级别 ccRCC。通过分层抽样法,按照 7:3 的比例将患者随机分为训练集和验证集。计算普通 CT 图像中 ccRCC 的成像特征。采用拉索回归法降低训练集成像特征的维度,并采用二元逻辑回归法构建预测模型。使用 Bootstrap 方法验证了训练集模型和验证集模型,并分别计算了接收者操作特征曲线(ROC)下面积(AUC):二元逻辑回归结果显示,只有影像学特征是预测ccRCC Furhman分类的独立风险因素。预测模型为 y = 1/[1 + exp (-z)],z = 1.274 × 影像学风险评分 + 0.072。引导内部验证结果显示,训练组的 AUC 为 0.961(95% CI:0.900-0.913)。Hosmer-Lemeshow 拟合优度检验表明,预测模型在训练组具有良好的校准性(P = 0.416)。验证组预测模型的 AUC 为 0.731(95% CI:0.500-1.000)。Hosmer-Lemeshow拟合优度检验结果显示,预测模型在验证组具有良好的校准性(P = 0.592):结论:基于CT纹理分析的模型在区分低级别和高级别ccRCC方面具有良好的预测效果,可为患者的治疗和预后提供参考。
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Prediction of Fuhrman pathological grade of renal clear cell carcinoma based on CT texture analysis.

Objective: To study the predictive performance of the imaging model based on the texture analysis of CT plain scan in distinguishing between low (grade I and II) and high (grade III and IV) of Fuhrman pathological grade of renal clear cell carcinoma.

Methods: The clinical data of 94 patients with ccRCC who underwent CT scan and were confirmed by biopsy or surgery in TCGA-KIRC public database were retrospectively analyzed. There were 32 cases of low-grade ccRCC and 62 cases of high-grade ccRCC. The patients were randomly divided into training set and verification set according to the proportion of 7:3 by stratified sampling method. The imaging characteristics of ccRCC were calculated in the plain CT images. Lasso regression was used to reduce the dimensionality of the imaging characteristics of the training set, and binary logistic regression was used to construct the prediction model. Bootstrap method was used to verify the training set model and the validation set model, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated respectively.

Results: Binary logistic regression showed that only imaging features were independent risk factors for predicting the Furhman classification of ccRCC. The predictive model was y = 1/[1 + exp (-z)], z = 1.274 × imaging risk score + 0.072. The results of bootstrap internal validation showed that the AUC of the training group was 0.961 (95% CI: 0.900-0.913). The Hosmer-Lemeshow goodness of fit test showed that the prediction model had a good calibration in the training group (P = 0.416). The AUC of prediction model in validation group was 0.731 (95% CI: 0.500-1.000). The Hosmer-Lemeshow goodness of fit test results showed that the prediction model had a good calibration in the validation group (P = 0.592).

Conclusion: The model based on CT texture analysis has a good predictive effect in differentiating low-grade and high-grade ccRCC and can provide reference for the treatment and prognosis of patients.

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