Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma

• Published in: Abdominal imaging Vol. 49; no. 4; pp. 1185 - 1193
• Main Authors: Zhang, Hongyu, Li, Fukai, Jing, Mengyuan, Xi, Huaze, Zheng, Yali, Liu, Jianli
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Bladder; Calibration; Carcinoma, Renal Cell - diagnostic imaging; Carcinoma, Renal Cell - pathology; Carcinoma, Renal Cell - surgery; Clear cell-type renal cell carcinoma; Computed tomography; Confidence intervals; Gastroenterology; Grading; Hepatology; Humans; Imaging; Kidney cancer; Kidney Neoplasms - diagnostic imaging; Kidney Neoplasms - pathology; Kidney Neoplasms - surgery; Kidneys; Medicine; Medicine & Public Health; Multivariate analysis; Neuroimaging; Nomograms; Parameters; Performance prediction; Radiology; Regression analysis; Regression models; Retroperitoneum; Retrospective Studies; Statistical analysis; Tomography, X-Ray Computed - methods; Ureters; World Health Organization; The systemic immune-inflammatory index; Spectral CT; WHO/ISUP grading; Clear cell renal cell carcinoma; Nomogram
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-024-04199-7

Purpose To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC). Methods Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n  = 52 and WHO/ISUP 3/4, n  = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model. Results Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878–0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer–Lemeshow test showed that the model had a good fitness (χ 2  = 5.574, p  = 0.695). Conclusion The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.