Improving the robustness of radiomic features to patient size variations in CBCT imaging for radiotherapy.

BackgroundRadiomic feature extraction from cone-beam computed tomography (CBCT) images in radiotherapy has potential for predicting tumor control and treatment-related toxicity. However, the reliability of CBCT-based radiomics is limited by variations in scatter intensity associated with differences in patient size.ObjectiveTo evaluate the impact of patient size on CBCT-derived radiomic features and investigate whether a novel quantitative CBCT technique can reduce patient size-induced radiomic feature variability.MethodsPhantoms representing small and large body habitus were scanned using a linac-mounted CBCT incorporating a two-dimensional antiscatter grid (CBCT-2DASG) and standard clinical protocols. Ninety-three intensity and texture features were extracted from CBCT and multidetector CT (MDCT) images, and robust features were identified by analyzing the concordance correlation between small and large phantom images.ResultsCompared with MDCT, 53% of CBCT-2DASG features were robust to patient size variation, versus 8 features from standard CBCT. The CBCT-2DASG reduced size-dependent feature variation, approximately fourfold on average for intensity-based features, with improvement varying across feature types.ConclusionsCBCT-2DASG with effective scatter suppression reduces patient size-dependent radiomic feature variability in phantom experiments, improving the robustness of CBCT-derived features to patient size variation. This represents a promising step toward more reliable CBCT-based radiomics in radiotherapy.