Evaluating the robustness of dosiomics features over treatment planning parameters: a phantom-based study.

Dosimetric biomarkers, in terms of dosiomics features, play a crucial role in modeling radiotherapy and should be analyze d for their robustness and stability. This study aims to investigate how these dosiomics features will change over variations in treatment planning parameters. Different treatment plans were created by varying such parameters as field number, dose calculation algorithm, dose grid resolution, energy, monitor units, fraction, dose, field size, multileaf collimator, collimator angle, table angle, source-to-surface distance and source-to-axis distance, and wedge for a hypothetical tumor in the CIRS phantom CT scan. Dosiomics features were extracted with different segment sizes. The coefficient of variation (COV) was used to evaluate dosiomics feature changes with consider COV ≤ 5% as robust features. Our findings showed that many of the dosiomics features had significant variations due to changes in treatment parameters. First-order and gray-level co-occurrence matrix (GLCM) features were more stable (COV ≤ 5%) compared to others. Field and wedge changes had the most significant impact on features, while the dose calculation algorithm, dose, and MU changes had the lesser effects. Dosiomics features were vulnerable over changing treatment parameters and should always be reported. The GLCM features set was the most robust. Further studies are needed to identify robust dosiomics features for future biomarker discovery.