T1 Stage Clear Cell Renal Cell Carcinoma: A CT-Based Radiomics Nomogram to Estimate the Risk of Recurrence and Metastasis - Kang, Bing; Sun, Cong; Gu, Hui; Yang, Shifeng; Yuan, Xianshun; Ji, Congshan; Huang, Zhaoqin; Yu, Xinxin; Duan, Shaofeng; Wang, Ximing (Frontiers in Oncology, 2020)
Objectives To develop and validate a radiomics nomogram to improve prediction of recurrence and metastasis risk in T1 stage clear cell renal cell carcinoma (ccRCC). Methods This retrospective study recruited 168 consecutive patients (mean age,53.9 years; range, 28-76 years; 43 women) with T1 ccRCC between January 2012 to June 2019, including 50 aggressive ccRCC based on synchronous metastasis or recurrence after surgery. The patients were divided into two cohorts (training and validation) at a 7:3 ratio. Radiomics features were extracted from contrast enhanced CT images. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator method. Demographics, laboratory variables (including sex, age, Fuhrman grade, hemoglobin, platelet, neutrophils, albumin and calcium) and CT findings were combined to build clinical factors model. Integrated radiomics signature and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was determined by calibration, discrimination, and clinical usefulness. Results Ten features were used to build radiomics signature, which yielded area under the curve (AUC) of 0.86 in the training cohort and 0.85 in the validation cohort. Radiomics nomogram (AUC: training, 0.91; validation, 0.92) had higher performance than clinical factor model (AUC: training, 0.86 (p= 0.051); validation, 0.90 (p= 0.401)) or radiomics signature as a means of identifying patients at high risk for recurrence and metastasis. The nomogram showed good calibration. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model in terms of clinical usefulness. Conclusion The CT-based radiomics nomogram could help in predicting recurrence and metastasis risk in T1 ccRCC, which might assist clinicians in tailoring precise therapy.