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.

中文翻译：

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转移性透明细胞肾细胞癌：计算机断层扫描纹理分析作为纳武单抗治疗患者生存的预测生物标志物

介绍

评估基于图像的纹理分析对预测接受纳武单抗治疗的转移性透明细胞肾癌 (cCCR) 患者的无进展生存期 (PFS) 和总生存期 (OS) 的价值。

方法

这项回顾性研究包括 48 名接受纳武单抗治疗的转移性 cCCR 患者。Nivolumab 被用作二线或三线单一疗法。在治疗前 1 个月内获得的 CT 扫描仪上进行转移病灶的纹理分析。提取了与灰度直方图、灰度共生、游程矩阵特征、自回归模型特征和Haar小波特征相关的纹理特征。进行套索惩罚 Cox 回归分析以确定 PFS 和 OS 的独立预测因子。

结果

中位 PFS 和 OS 分别为 5.7 和 13.8 个月。39 名患者出现进展，27 名患者死亡。Lasso 惩罚 Cox 回归分析确定了三个纹理参数作为 PFS 的潜在预测因子：偏度，S.2.2。Correlat 和 S.1.1。SumVarnc。多变量 Cox 回归分析证实偏度（HR (95% CI) 1.49 [1.21–1.85]，p  < 0.001）是 PFS 的独立预测因子。关于 OS，Lasso 惩罚 Cox 回归分析确定了三个纹理参数作为 OS 的潜在预测因子：S20SumVarnc、S22Contrast 和 S22Entropy。多变量 Cox 回归分析证实 S22Entropy（HR (95% CI) 1.68 (1.31–2.14), p  < 0.001）是 OS 的独立预测因子。

结论

这项初步研究的结果表明，CT 纹理分析可能是一种有前景的定量成像工具，可预测开始纳武单抗治疗后的肿瘤学结果。