Radiology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
PET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
Eur Radiol. 2020 Feb;30(2):1274-1284. doi: 10.1007/s00330-019-06427-x. Epub 2019 Sep 10.
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. KEY POINTS: • Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.
目的：建立并验证一种用于术前鉴别无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）的影像组学列线图。
方法：将 99 例 AML.wovf（n=36）和 hm-ccRCC（n=63）患者分为训练集（n=80）和验证集（n=19）。从皮质期和肾实质期 CT 图像中提取影像组学特征。构建影像组学特征并计算影像组学评分（Rad-score）。评估患者的临床资料和 CT 表现，以构建临床因素模型。将 Rad-score 与独立的临床因素相结合，构建影像组学列线图。通过校准、判别和临床实用性来评估列线图的性能。
结果：采用 14 个特征构建影像组学特征。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，该影像组学特征显示出良好的鉴别能力。影像组学列线图在训练集（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判别能力，且在训练集的判别能力优于临床因素模型（AUC，0.788；95%CI，0.683-0.893）（p<0.05）。决策曲线分析表明，该列线图在临床实用性方面优于临床因素模型和影像组学特征。
结论：基于 CT 的影像组学列线图是一种非侵入性的术前预测工具，整合了 Rad-score 和临床因素，对鉴别 AML.wovf 和 hm-ccRCC 具有良好的预测效果，有助于临床医生制定精确的治疗方案。
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