Exploring the in silico adaptation of the Nephroblastoma Oncosimulator to MRI scans, treatment data and histological profiles of patients from different risk groups

Marcel Meyerheim ^1* Foteini Panagiotidou ² Eleni Georgiadi ² Dimitrios Soudris ² Georgios S. Stamatakos ² Norbert Graf ¹

• ¹ Pediatric Oncology and Hematology, Faculty of Medicine, Saarland University, Homburg, Germany
• ² Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

Nephroblastoma, or Wilms' Tumor, is the most prevalent renal tumor in pediatric oncology. Although the overall survival rate is excellent today (~90%), there has been no significant improvement over the last two decades. In silico models aim to simulate tumor progression and response to treatment over time. They hold immense potential to enhance predictive accuracy and optimize treatment protocols, inspired by the digital twin paradigm. This study used T2-weighted MRI scans, chemotherapy treatment plans, and post-surgical histological profiles from three patients enrolled in the SIOP 2001/GPOH clinical trial. Each patient represents a distinct clinically assessed risk group. We investigated the clinical adaptation of the Nephroblastoma Oncosimulator to these datasets. The goal was to derive appropriate value distributions of the model input parameter that enable accurate prediction of tumor volume reduction in response to preoperative chemotherapy. Our primary focus was on the total cell kill ratio, a parameter that reflects treatment effectiveness. We derived distributions of this parameter for one patient of each risk group: low (Mdn = 0.875, IQR [0.750, 0.875], n = 178), intermediate (Mdn = 0.875, IQR [0.750, 0.875] , n = 175), and high risk (Mdn = 0.485, IQR [0.438, 0.532] , n = 103). Statistically significant differences were observed between the high-risk group and both the low-and intermediate-risk groups (p < .001). This work establishes a foundation for further studies using available retrospective datasets with additional patients per risk group. These efforts will help to validate the findings, advance model development, and extend this mechanistic multiscale discretized cancer model. Ultimately, clinical validation is required to assess the model's potential for use in clinical decision-support systems.