# Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models?

## TL;DR

This paper investigates how treatment planning system differences affect predictive models for tumor recurrence in radiation oncology.

## Contribution

The study introduces the importance of accounting for treatment planning system parameters to reduce domain dependency in predictive models.

## Key findings

- Dose grid size differences had a greater impact on RayStation than Pinnacle.
- CT density curve variations introduced domain discrepancies in treatment plans.
- Standardized treatment planning parameters can improve model reliability and prediction accuracy.

## Abstract

Background: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly in medical diagnostics and radiation oncology. Predictive models designed to assess tumor recurrence rely on comprehensive and high-quality datasets, encompassing treatment planning parameters, imaging protocols, and patient-specific data. However, domain dependency, arising from variations in dose calculation algorithms, computed tomography (CT) density conversion curves, imaging modalities, and institutional protocols, can significantly undermine model reliability and clinical utility. Methods: This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. Variations in the dose grid size and CT density conversion curves were assessed for their impact on domain dependency. Results: Results showed that dose grid size differences had a more significant influence within RayStation than Pinnacle, while CT curve variations introduced potential domain discrepancies. The findings underscore the critical role of precise and standardized treatment planning in enhancing the reliability of predictive modeling for tumor recurrence assessment. Conclusions: Incorporating treatment planning parameters, such as dose distribution and target volumes, as explicit features in model training can mitigate the impact of domain dependency and enhance prediction accuracy. Solutions such as multi-institutional data harmonization and domain adaptation techniques are essential to improve model generalizability and robustness. These strategies support the better integration of predictive modeling into clinical workflows, ultimately optimizing patient outcomes and personalized treatment strategies.