Zine-Eddine Khene1,2,3, Romain Kokorian4, Romain Mathieu5, Anis Gasmi5, Rioux-Leclercq Nathalie6, Kammerer-Jacquet Solène-Florence6, Shahrokh Shariat7, Renaud de Crevoisier4,8, Brigitte Laguerre4, Karim Bensalah5,8. 1. Department of Urology, Rennes University Hospital, Rennes, France. zineddine.khene@gmail.com. 2. Department of Medical Oncology, Centre Eugene Marquis, Rennes, France. zineddine.khene@gmail.com. 3. LTSI, Inserm U1099, Université de Rennes 1, Rennes, France. zineddine.khene@gmail.com. 4. Department of Medical Oncology, Centre Eugene Marquis, Rennes, France. 5. Department of Urology, Rennes University Hospital, Rennes, France. 6. Department of Pathology, Rennes University Hospital, Rennes, France. 7. Department of Urology, Medical University Vienna, General Hospital, Vienna, Austria. 8. LTSI, Inserm U1099, Université de Rennes 1, Rennes, France.
Abstract
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.
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.
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