Using three-dimensional model-based tumour volume change to predict the symptom improvement in patients with renal cell cancer

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.

Keywords: Axitinib; PD-1; Quality of life; Renal cell cancer; Response Evaluation Criteria in Solid Tumours 1.1; Volumetric analysis.

Fig. 1

Enhanced Visualization of RCC Treatment Response in Case 1 Using CT and 3D Modeling. This figure presents a comparative view of case 1's renal cell carcinoma (RCC) before and after treatment. It includes CT images alongside a 3D virtual model. The RECIST 1.1 criteria classify the treatment response as stable disease (SD) with a slight decrease in tumor diameter. However, the volumetric criteria show an increase in tumor volume, also indicating SD. The color coding in the 3D model is as follows: artery in red, venous structures in dark blue, tumor in brown, excretory system in yellow, and normal kidney tissue in purple

Fig. 2

Comparative Analysis of RCC Treatment Response in Case 3 Using CT and 3D Modeling. This figure illustrates the RCC treatment response for case 3, featuring CT images and a corresponding 3D virtual model. According to the RECIST 1.1 criteria, the case is classified as SD, with a minor decrease in tumor diameter. In contrast, volumetric criteria classify the response as partial response (PR), with a significant decrease in tumor volume. The 3D model’s color scheme is as follows: artery in red, venous structures in dark blue, tumor in brown, excretory system in green, and normal kidney tissue in purple

Fig. 3

Waterfall Plot Demonstrating Changes in Tumor Diameter and Volume for 18 RCC Patients. This waterfall plot displays the changes in tumor diameter (Panel A) and tumor volume (Panel B) in 18 patients with RCC after treatment. Each bar represents an individual patient, showing the percentage change in tumor size or volume. The plot visually compares the extent of tumor shrinkage or growth post-treatment, based on RECIST 1.1 criteria for diameter and volumetric criteria for volume

Fig. 4

ROC Curve Analysis Comparing Tumor Diameter and Volume Changes in Predicting QoL Improvement. This figure showcases ROC (Receiver Operating Characteristic) curves evaluating the effectiveness of changes in tumor diameter versus tumor volume in predicting meaningful improvements in quality of life (QoL). The analysis compares the sensitivity and specificity of both measurement methods, with the area under the curve (AUC) indicating the predictive accuracy for each method