A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma
A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma
复制标题
基于 CT 的放射组学列线图用于区分无可见脂肪的肾血管平滑肌脂肪瘤与同质透明细胞肾细胞癌
DOI：
10.1007/s00330-019-06427-x
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发表时间：
2020-02-01
影响因子：
5.9
通讯作者：
Niu, Haitao
中科院分区：
文献类型：
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作者：
Nie, Pei;Yang, Guangjie;Niu, Haitao
目的建立并验证无可见脂肪肾血管平滑肌脂肪瘤（AML.wovf）与均质透明细胞肾细胞癌（hm-ccRCC）术前鉴别的放射组学特征图。方法99例急性髓性白血病患者。wovf （n = 36）和hm-ccRCC （n = 63）分为训练集（n = 80）和验证集（n = 19）。从皮质髓质期和肾图期CT图像中提取放射组学特征。构建放射组学特征并计算放射组学评分（Rad-score）。对人口统计学和CT结果进行评估，建立临床因素模型。结合rad评分和独立临床因素，构建放射组学线图。从校准、鉴别和临床有用性方面评估Nomogram性能。结果利用14个特征构建放射组学特征。放射组学特征在训练集（AUC[曲线下面积]，0.879;95%；置信区间[CI]， 0.793-0.966）和验证集（AUC, 0.846; 95% CI, 0.643-1.000）中具有良好的辨别能力。放射组学模式图在训练集（AUC, 0.896, 95% CI, 0.810-0.983）和验证集（AUC, 0.949, 95% CI, 0.856-1.000）中具有良好的校准和鉴别能力，与临床因素模型（AUC, 0.788, 95% CI, 0.683-0.893）相比，具有更好的鉴别能力（p < 0.05）。决策曲线分析表明，就临床有用性而言，nomogram （nomogram）优于临床因素模型和放射组学特征。结论基于ct的放射组学影像学检查是一种无创的术前预测工具，结合了rad评分和临床因素，对AML的鉴别具有良好的预测效果。从hm-ccRCC，这可能有助于临床医生定制精确的治疗。
Objectives To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC). Methods Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness. Results Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793-0.966) and the validation set (AUC, 0.846; 95% CI, 0.643-1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810-0.983) and the validation set (AUC, 0.949; 95% CI, 0.856-1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness. Conclusions The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy.