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

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.

Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal Cell Carcinoma Patients

“…Risk signature has been broadly used in prognosis prediction in tumors. Several risk signatures, including lncRNAs based risk signatures, have been developed for ccRCC and appeared satisfactory predictive effectiveness (45)(46)(47)(48). However, the roles of GID-lncRNAs were rarely mentioned in ccRCC.…”

Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I–III Renal Cell Carcinoma

“…Our study has filled a gap in the literature on PFS risk of stage I-III RCC in the setting of radiomics. In the recent literature, Radiomics nomogram has demonstrated excellent efficacy in differential diagnosis, nuclear grading, prognosis, and gene expression of RCC (38)(39)(40)(41)(42)(43). Among the 6 RFs selected in this study, there were 3 features from the corticomedullary phase, suggesting that the corticomedullary phase may contain more abundant information to predict PFS.…”

CT-Based Radiomics Nomogram for Prediction of Progression-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma

“…Prior studies on tumor prognosis have confirmed the potential of CT-based radiomics as a reliable biomarker for predicting prognosis. [17][18][19][20] In head and neck cancer research, a study showed an association between the radiomics signature of CT images with survival and control after radiotherapy for locoregionally advanced head and neck cancer. 21 Similarly, Chen et al found that CT-based imaging histological signatures and nomogram showed good overall survival prediction accuracy in patients with laryngeal squamous cell carcinoma.…”