A CT-based radiomics nomogram for differentiation of small masses (< 4 cm) of renal oncocytoma from clear cell renal cell carcinoma

• Published in: Abdominal imaging 2021-11, Vol.46 (11), p.5240-5249
• Main Authors: Li, Xiaoli, Ma, Qianli, Tao, Cheng, Liu, Jinling, Nie, Pei, Dong, Cheng
• Format: Article
• Language: English
• Subjects: Adenoma, Oxyphilic; Bladder; Carcinoma, Renal Cell - diagnostic imaging; Cell Differentiation; Clear cell-type renal cell carcinoma; Computed tomography; Construction; Differentiation; Fascia; Feature extraction; Gastroenterology; Hepatology; Humans; Image contrast; Image enhancement; Imaging; Kidney cancer; Kidney Neoplasms - diagnostic imaging; Kidneys; Medical imaging; Medicine; Medicine & Public Health; Nomograms; Patients; Radiology; Radiomics; Regression analysis; Retroperitoneum; Thickening; Tomography, X-Ray Computed; Training; Ureters
• ISSN: 2366-004X; 2366-0058
• DOI: 10.1007/s00261-021-03213-6

Purpose Renal oncocytoma (RO) is the most commonly resected benign renal tumor because of misdiagnosis as renal cell carcinoma. This misdiagnosis is generally owing to overlapping imaging features. This study describes the building of a radiomics nomogram based on clinical data and radiomics signature for the preoperative differentiation of RO from clear cell renal cell carcinoma (ccRCC) on tri-phasic contrast-enhanced CT. Methods A total of 122 patients (85 in training set and 37 in external validation set) with ROs (n = 46) or ccRCCs (n = 76) were enrolled. Patient characteristics and tri-phasic contrast-enhanced CT imaging features were evaluated to build a clinical factors model. A radiomics signature was constructed by extracting radiomics features from tri-phasic contrast-enhanced CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to a multivariate logistic regression analysis. The diagnostic performance of the above three models was evaluated in training and validation sets. Results Central stellate area and perirenal fascia thickening were selected to build the clinical factors model. Eleven radiomics features were combined to construct the radiomics signature. The AUCs of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.960 and 0.898 in the training and validation sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the validation set indicated an overall net benefit over the clinical factors model. Conclusion Our radiomics nomogram can effectively predict the preoperative diagnosis of ROs and may therefore be of assistance in sparing unnecessary surgery and tailoring precise therapy. Graphic abstract The ROC curves of the clinical model, the radiomics signature and the radiomics nomogram for the validation set. RO = Renal oncocytoma; ccRCC = Clear cell renal cell carcinoma.