Metastatic clear cell renal cell carcinoma: computed tomography texture analysis as predictive biomarkers of survival in patients treated with nivolumab.

INTRODUCTION

To evaluate the value of image-based texture analysis for predicting progression-free survival (PFS) and overall survival (OS) in patients with metastatic clear cell renal carcinoma (cCCR) treated with nivolumab.

METHODS

This retrospective study included 48 patients with metastatic cCCR treated with nivolumab. Nivolumab was used as a second- or third-line monotherapy. Texture analysis of metastatic lesions was performed on CT scanners obtained within 1 month before treatment. Texture features related to the gray-level histogram, gray-level co-occurrence, run-length matrix features, autoregressive model features, and Haar wavelet feature were extracted. Lasso penalized Cox regression analyses were performed to identify independent predictors of PFS and OS.

RESULTS

Median PFS and OS were 5.7 and 13.8 months. 39 patients experienced progression and 27 died. The Lasso penalized Cox regression analysis identified three texture parameters as potential predictors of PFS: skewness, S.2.2. Correlat and S.1.1. SumVarnc. Multivariate Cox regression analysis confirmed skewness (HR (95% CI) 1.49 [1.21-1.85], p < 0.001) as an independent predictor of PFS. Regarding OS, the Lasso penalized Cox regression analysis identified three texture parameters as potential predictors of OS: S20SumVarnc, S22Contrast and S22Entropy. Multivariate Cox regression analysis confirmed S22Entropy (HR (95% CI) 1.68 (1.31-2.14), p < 0.001) as an independent predictor of OS.

CONCLUSIONS

Results from this preliminary study suggest that CT texture analysis might be a promising quantitative imaging tool that predicts oncological outcomes after starting nivolumab treatment.