Using auto-segmentation to reduce contouring and dose inconsistency in clinical trials: the simulated impact on RTOG 0617
Abstract
Purpose: Contouring inconsistencies are known yet under studied clinical radiotherapy trials. We applied auto-contouring to the RTOG 0617 dose escalation trial data. We hypothesized that the trial heart doses were higher than reported due to inconsistent and insufficient heart segmentation. We test our hypothesis by comparing doses between deep-learning (DL) segmented hearts and trial hearts. Materials/Methods: The RTOG 0617 data were downloaded from TCIA; the 442 patients with trial hearts and dose distributions were included. All hearts were re-segmented using our DL pipeline and quality assured to meet the requirements for clinical implementation. Dose (V5%, V30% and mean heart dose (MHD)) was compared between the two sets of hearts (Wilcoxon signed-rank test). Each dose metric was associated with overall survival (OS; Cox Proportional Hazards). Lastly, 18 volume similarity metrics were assessed for the hearts and correlated with |DoseDL- DoseRTOG0617| (linear regression; significance: p≤0.0028; corrected for 18 tests). Results: Dose metrics were significantly higher for DL hearts compared to trial hearts (e.g. MHD: 15Gy vs. 12Gy; p=5.8E-16). All three DL heart dose metrics were stronger OS predictors than those of the trial hearts (median: p=2.8E-5 vs. 2.0E-4). Thirteen similarity metrics explained |DoseDL-DoseRTOG0617|; the axial distance between the two centers of mass was the strongest predictor (CENTAxial; median R2=0.47; p=6.1E-62). CENTAxial agreed with the…
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