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Abstract：
In our recent study, we explored the efficacy of three-dimensional (3D) measurement of tumor volume in predicting the improvement of quality of life (QoL) in patients suffering from renal cell cancer (RCC), who were treated with axitinib and anti-PD-L1 antibodies. This study encompassed 18 RCC patients, including 10 men and 8 women, with an average age of 56.83 ± 9.94 years. By utilizing 3D Slicer software, we analyzed pre- and post-treatment CT scans to assess changes in tumor volume. Patients\' QoL was evaluated through the FKSI-DRS questionnaire. Our findings revealed that 3D models for all patients were successfully created, and there was a moderate agreement between treatment response classifications based on RECIST 1.1 criteria and volumetric analysis (kappa = 0.556, p = 0.001). Notably, nine patients reported a clinically meaningful improvement in QoL following the treatment. Interestingly, the change in tumor volume as indicated by the 3D model showed a higher area under the curve in predicting QoL improvement compared to the change in diameter measured by CT, although this difference was not statistically significant (z = 0.593, p = 0.553). Furthermore, a multivariable analysis identified the change in tumor volume based on the 3D model as an independent predictor of QoL improvement (odds ratio = 1.073, 95% CI 1.002-1.149, p = 0.045).In conclusion, our study suggests that the change in tumor volume measured by a 3D model may be a more effective predictor of symptom improvement in RCC patients than traditional CT-based diameter measurements. This offers a novel approach for assessing treatment response and patient well-being, presenting a significant advancement in the field of RCC treatment.
摘要：
在我们最近的研究中,我们探讨了三维（3D）测量肿瘤体积在预测肾细胞癌（RCC）患者生活质量（QoL）改善中的功效，接受阿西替尼和抗PD-L1抗体治疗的患者。这项研究包括18例RCC患者，包括10名男性和8名女性,平均年龄56.83±9.94岁。通过利用3D切片器软件，我们分析了治疗前后的CT扫描,以评估肿瘤体积的变化.通过FKSI-DRS问卷评估患者的QoL。我们的研究结果表明，所有患者的3D模型都被成功创建，基于RECIST1.1标准的治疗应答分类与体积分析之间存在中度一致性(kappa=0.556,p=0.001).值得注意的是,9例患者报告治疗后QoL有临床意义的改善.有趣的是,与CT测量的直径变化相比，3D模型显示的肿瘤体积变化在预测QoL改善方面显示出更高的曲线下面积，尽管这种差异没有统计学意义(z=0.593,p=0.553).此外，一项多变量分析将基于3D模型的肿瘤体积变化确定为QoL改善的独立预测因子(比值比=1.073,95%CI1.002-1.149,p=0.045).总之,我们的研究表明,与传统的基于CT的直径测量相比,通过3D模型测量的肿瘤体积变化可能更有效地预测RCC患者的症状改善.这提供了一种评估治疗反应和患者健康状况的新方法，在RCC治疗领域取得了重大进展。
