Using auto-segmentation to reduce contouring and dose inconsistency in clinical trials: The simulated impact on RTOG 0617 Journal Article
| Authors: | Thor, M.; Apte, A.; Haq, R.; Iyer, A.; LoCastro, E.; Deasy, J. O. |
| Article Title: | Using auto-segmentation to reduce contouring and dose inconsistency in clinical trials: The simulated impact on RTOG 0617 |
| Abstract: | Purpose: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (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 tested our hypothesis by comparing doses between deep-learning (DL) segmented hearts and trial hearts. Methods and Materials: The RTOG 0617 data were downloaded from The Cancer Imaging Archive; the 442 patients with trial hearts and dose distributions were included. All hearts were resegmented using our DL pipeline and quality assured to meet the requirements for clinical implementation. Dose (V5%, V30%, and mean heart dose) was compared between the 2 sets of hearts (Wilcoxon signed-rank test). Each dose metric was associated with overall survival (Cox proportional hazards). Lastly, 18 volume similarity metrics were assessed for the hearts and correlated with |DoseDL – DoseRTOG0617| (linear regression; significance: P ≤ .0028; corrected for 18 tests). Results: Dose metrics were significantly higher for DL hearts compared with trial hearts (eg, mean heart dose: 15 Gy vs 12 Gy; P = 5.8E-16). All 3 DL heart dose metrics were stronger overall survival 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 2 centers of mass was the strongest predictor (CENTAxial; median, R2 = 0.47; P = 6.1E-62). CENTAxial agreed with the qualitatively identified inconsistencies in the superior direction. The trial's qualitative heart contouring score was not correlated with |DoseDL – DoseRTOG0617| (median, R2 = 0.01; P = .02) or with any of the similarity metrics (median, Rs = 0.13 [range, –0.22 to 0.31]). Conclusions: Using a coherent heart definition, as enabled through our open-source DL algorithm, the trial heart doses in RTOG 0617 were found to be significantly higher than previously reported, which may have led to an even more rapid mortality accumulation. Auto-segmentation is likely to reduce contouring and dose inconsistencies and increase the quality of clinical RT trials. © 2020 Elsevier Inc. |
| Keywords: | adult; controlled study; major clinical study; overall survival; mortality; radiotherapy; simulation; algorithm; medical imaging; radiation dose distribution; heart; radiation therapy oncology groups; linear regression analysis; dose distributions; proportional hazards; wilcoxon signed ranks test; wilcoxon signed rank test; methods and materials; pipeline; human; male; female; article; deep learning; auto segmentation; heart segmentation; similarity metrics |
| Journal Title: | International Journal of Radiation Oncology, Biology, Physics |
| Volume: | 109 |
| Issue: | 5 |
| ISSN: | 0360-3016 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2021-04-01 |
| Start Page: | 1619 |
| End Page: | 1626 |
| Language: | English |
| DOI: | 10.1016/j.ijrobp.2020.11.011 |
| PUBMED: | 33197531 |
| PROVIDER: | scopus |
| PMCID: | PMC8729313 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 April 2021 -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work