Differential Diagnosis of Type 1 and Type 2 Papillary Renal Cell Carcinoma Based on Enhanced CT Radiomics Nomogram

Frontiers in Oncology

Jianying Li¹  Guoquan Huang²  Yan Jiang³  Yingying Miao⁴  Chao Zhu⁴  Yankun Gao⁴  Xingwang Wu⁴  Xiaoying Zhao⁴  Cuiping Li⁴  Xingwei Wang⁴  Shihui Wang⁵ 

[1] CT Research Center, GE Healthcare China, Shanghai, China;Department of Imaging, Wuhu Second People’s Hospital, Wuhu, China;Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, China;Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China;Department of Radiology, The First Affiliated Hospital of Wannan Medical college, Wuhu, China;

关键词: radiomics nomogram;    papillary renal cell carcinoma;    differential diagnosis;    computed tomography;    tumour subtypes;   

DOI  :  10.3389/fonc.2022.854979

来源: DOAJ

【 摘 要 】

ObjectivesTo construct a contrast-enhanced CT-based radiomics nomogram that combines clinical factors and a radiomics signature to distinguish papillary renal cell carcinoma (pRCC) type 1 from pRCC type 2 tumours.MethodsA total of 131 patients with 60 in pRCC type 1 and 71 in pRCC type 2 were enrolled and divided into training set (n=91) and testing set (n=40). Patient demographics and enhanced CT imaging characteristics were evaluated to set up a clinical factors model. A radiomics signature was constructed and radiomics score (Rad-score) was calculated by extracting radiomics features from contrast-enhanced CT images in corticomedullary phase (CMP) and nephrographic phase (NP). A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to multivariate logistic regression analysis. The diagnostic performance of the clinical factors model, radiomics signature and radiomics nomogram was evaluated on both the training and testing sets.ResultsThree validated features were extracted from the CT images and used to construct the radiomics signature. Boundary blurring as an independent risk factor for tumours was used to build clinical factors model. The AUC value of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.855 and 0.831 in the training and testing sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the training set indicated an overall net benefit over the clinical factors model.ConclusionRadiomics nomogram combining clinical factors and radiomics signature is a non-invasive prediction method with a good prediction for pRCC type 1 tumours and type 2 tumours preoperatively and has some significance in guiding clinicians selecting subsequent treatment plans.

【 授权许可】

Unknown