Zhao Xun, Yan Ye, Xie Wanfang, Qin Zijian, Zhao Litao, Liu Cheng, Zhang Shudong, Liu Jiangang, Ma Lulin
Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, P.R. China.
School of Engineering Medicine, Beihang University, Beijing, 100191, P.R. China.
BMC Cancer. 2024 Dec 6;24(1):1508. doi: 10.1186/s12885-024-13283-6.
The management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on CT imaging may provide additional useful information.
A total of 322 patients with Bosniak II-IV cysts were included in the study from January 2010 to December 2019. Contrast-enhanced CT scans were performed on all patients. ITK-snap was used for segmentation, and the PyRadiomics 3.0.1 package was used for feature extraction. The radiomics features were screened via the least absolute shrinkage and selection operator (LASSO) regression method. After feature selection, a logistic regression (LR) model, support vector machine (SVM) model and random forest (RF) model were constructed.
In the present study, 217 benign renal cysts (67.4%) and 105 cystic renal cell carcinomas (32.6%) were identified. According to the Bosniak classification, the sample included 179 (55.6%) Bosniak II cysts, 38 (11.8%) Bosniak IIF cysts, 44 (13.7%) Bosniak III cysts and 61 (18.9%) Bosniak IV cysts. A total of 1334 radiomics features were extracted from both unenhanced and cortical CT scans. After LASSO regression, all the models (LR, SVM and RF) showed satisfactory discrimination and reliability in both unenhanced and cortical CT scans (AUC > 0.950). In the Bosniak IIF-III subgroup analysis, the diagnostic accuracy of the LR model was very low for both the unenhanced and cortical scans. In contrast, the SVM model and RF model showed excellent and stable performance in classifying Bosniak IIF-III cysts. The AUCs of the models were all > 0.85, with a maximum of 0.941. The sensitivity, specificity, accuracy, and AUC of the RF model were 0.889, 0.913, 0.902, and 0.941, respectively.
Our data indicate that radiomics models can effectively distinguish between cystic renal cell carcinoma (cRCC) and complex renal cysts (Bosniak II-IV). Radiomics models may still have high diagnostic accuracy even for Bosniak IIF-III cysts that are clinically difficult to distinguish. However, external validation of these findings is still needed.
复杂肾囊肿的管理由博斯尼亚克分类系统指导,但该系统在对患者进行风险分层以确定适当干预措施时可能并不充分。基于CT成像的放射组学模型可能提供额外有用信息。
2010年1月至2019年12月,共有322例患有博斯尼亚克II-IV级囊肿的患者纳入本研究。对所有患者进行了增强CT扫描。使用ITK-snap进行分割,使用PyRadiomics 3.0.1软件包进行特征提取。通过最小绝对收缩和选择算子(LASSO)回归方法筛选放射组学特征。特征选择后,构建了逻辑回归(LR)模型、支持向量机(SVM)模型和随机森林(RF)模型。
在本研究中,共识别出217个良性肾囊肿(67.4%)和105个囊性肾细胞癌(32.6%)。根据博斯尼亚克分类,样本包括179个(55.6%)博斯尼亚克II级囊肿、38个(11.8%)博斯尼亚克IIF级囊肿、44个(13.7%)博斯尼亚克III级囊肿和61个(18.9%)博斯尼亚克IV级囊肿。从未增强和皮质期CT扫描中总共提取了1334个放射组学特征。经过LASSO回归后,所有模型(LR、SVM和RF)在未增强和皮质期CT扫描中均显示出令人满意的区分度和可靠性(AUC>0.950)。在博斯尼亚克IIF-III亚组分析中,LR模型在未增强和皮质期扫描中的诊断准确性都非常低。相比之下,SVM模型和RF模型在对博斯尼亚克IIF-III级囊肿进行分类时表现出优异且稳定的性能。这些模型的AUC均>0.85,最高为0.941。RF模型的敏感性、特异性、准确性和AUC分别为0.889、0.913、0.902和0.941。
我们的数据表明,放射组学模型可以有效区分囊性肾细胞癌(cRCC)和复杂肾囊肿(博斯尼亚克II-IV级)。即使对于临床上难以区分的博斯尼亚克IIF-III级囊肿,放射组学模型可能仍具有较高的诊断准确性。然而,这些发现仍需外部验证。