School of Medicine, Xiamen University, Xiamen, Fujian Province, China.
Radiology, Xiang'an Hospital of Xiamen University, Xiamen, China.
BMC Urol. 2022 Sep 12;22(1):147. doi: 10.1186/s12894-022-01099-0.
To investigate the value of computed tomography (CT)-based radiomics model analysis in differentiating renal oncocytoma (RO) from renal cell carcinoma subtypes (chromophobe renal cell carcinoma, clear cell carcinoma) and predicting the expression of Cytokeratin 7 (CK7).
In this retrospective study, radiomics was applied for patients with RO, chRCC and ccRCC who underwent surgery between January 2013 and December 2019 comprised the training cohort, and the testing cohort was collected between January and October 2020. The corticomedullary (CMP) and nephrographic phases (NP) were manually segmented, and radiomics texture parameters were extracted. Support vector machine was generated from CMP and NP after feature selection. Shapley additive explanations were applied to interpret the radiomics features. A radiomics signature was built using the selected features from the two phases, and the radiomics nomogram was constructed by incorporating the radiomics features and clinical factors. Receiver operating characteristic curve was calculated to evaluate the above models in the two sets. Furthermore, Rad-score was used for correlation analysis with CK7.
A total of 123 patients with RO, chRCC and ccRCC were analyzed in the training cohort and 57 patients in the testing cohort. Subsequently, 396 radiomics features were selected from each phase. The radiomics features combining two phases yielded the highest area under the curve values of 0.941 and 0.935 in the training and testing sets, respectively. The Pearson's correlation coefficient was statistically significant between Rad-score and CK7.
We proposed a non-invasive and individualized CT-based radiomics nomogram to differentiation among RO, chRCC and ccRCC preoperatively and predict the immunohistochemical protein expression for accurate clinical diagnosis and treatment decision.
为了探究基于计算机断层扫描(CT)的放射组学模型分析在鉴别肾嗜酸细胞瘤(RO)与肾细胞癌亚型(嫌色细胞癌、透明细胞癌)以及预测细胞角蛋白 7(CK7)表达中的价值。
本回顾性研究纳入了 2013 年 1 月至 2019 年 12 月期间接受手术治疗的 RO、chRCC 和 ccRCC 患者,这些患者的数据构成了训练集,而测试集则是在 2020 年 1 月至 10 月期间收集的。手动对皮质期(CMP)和肾实质期(NP)进行分割,并提取放射组学纹理参数。在特征选择后,使用支持向量机从 CMP 和 NP 中生成模型。应用 Shapley 加法解释来解释放射组学特征。使用来自两个阶段的选定特征构建放射组学特征,并将放射组学特征和临床因素结合起来构建放射组学列线图。计算接收者操作特征曲线以评估两个队列中的上述模型。此外,使用 Rad-score 进行与 CK7 的相关性分析。
在训练集中,共对 123 例 RO、chRCC 和 ccRCC 患者进行了分析,在测试集中,共对 57 例患者进行了分析。随后,从每个阶段中选择了 396 个放射组学特征。来自两个阶段的放射组学特征组合得出的曲线下面积最高,在训练组和测试组中分别为 0.941 和 0.935。Rad-score 与 CK7 之间的 Pearson 相关系数具有统计学意义。
我们提出了一种非侵入性的个体化 CT 基于放射组学列线图,用于术前鉴别 RO、chRCC 和 ccRCC,并预测免疫组织化学蛋白表达,以进行准确的临床诊断和治疗决策。