T1 Stage Clear Cell Renal Cell Carcinoma: A CT-Based Radiomics Nomogram to Estimate the Risk of Recurrence and Metastasis

Bing Kang; Cong Sun; Hui Gu; Shifeng Yang; Xianshun Yuan; Congshan Ji; Zhaoqin Huang; Xinxin Yu; Shaofeng Duan; Ximing Wang

doi:10.3389/fonc.2020.579619

T1 Stage Clear Cell Renal Cell Carcinoma: A CT-Based Radiomics Nomogram to Estimate the Risk of Recurrence and Metastasis

Front Oncol. 2020 Nov 4:10:579619. doi: 10.3389/fonc.2020.579619. eCollection 2020.

Authors

Bing Kang^{1

2}, Cong Sun³, Hui Gu², Shifeng Yang², Xianshun Yuan², Congshan Ji², Zhaoqin Huang², Xinxin Yu^{1

2}, Shaofeng Duan⁴, Ximing Wang^{1

2}

Affiliations

¹ School of Medicine, Shandong University, Jinan, China.
² Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China.
³ Department of Radiology, Shandong Medical Imaging Research Institute, Jinan, China.
⁴ GE Healthcare, Shanghai, China.

Abstract

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 and 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 developed based on reproducible features by means of 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 develop clinical factors model. Integrating radiomics signature and independent clinical factors, a radiomics nomogram was developed. Nomogram performance was determined by calibration, discrimination, and clinical usefulness.

Results: Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) of 0.86 in the training cohort and 0.85 in the validation cohort. By incorporating the sex, maximum diameter, neutrophil count, albumin count, and radiomics score, a radiomics nomogram was developed. Radiomics nomogram (AUC: training, 0.91; validation, 0.92) had higher performance than clinical factors model (AUC: training, 0.86; validation, 0.90) or radiomics signature as a means of identifying patients at high risk for recurrence and metastasis. The radiomics nomogram had higher sensitivity than clinical factors mode (McNemar's chi-squared = 4.1667, p = 0.04) and a little lower specificity than clinical factors model (McNemar's chi-squared = 3.2, p = 0.07). The nomogram showed good calibration. Decision curve analysis demonstrated the superiority of the nomogram compared with 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 provide assistance for clinicians in tailoring precise therapy.

Keywords: clear cell renal cell carcinoma; computed tomography; neoplasm metastasis; prediction model; recurrence.