Dosimetric influence of deformable image registration uncertainties on propagated structures for online daily adaptive proton therapy of lung cancer patients

Nenoff, Lena; Matter, Michael; Amaya, E.; Knopf, Antje; Lomax, Anthony; Persson, G.; Ribeiro, Cássia O.; Visser, Sabine; Walser, M.; Weber, Damien Charles; Zhang, Ye; Albertini, Francesca

doi:10.1016/j.radonc.2021.03.021

Cited by 20 publications

(17 citation statements)

References 67 publications

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“…However, with the good image contrast of these abdominal MRI images and HN CTs, adaptation on uncorrected propagated structures improved plan quality compared with no adaptation at all. The acceptable results of optimization on uncorrected deformed structures with a single-DIR algorithm agreed with previous studies, for example, those by Elmahy et al and Qiao et al [ 22 , 23 ] for prostate cancer, or that by Nenoff et al [ 24 ] for lung cancer.…”

Section: Discussionsupporting

confidence: 89%

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans

Nenoff

Buti

Bobić

et al. 2022

Cancers

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Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1–5 repeated images during treatment: ‘reference adaptation’, with structures drawn by a physician; ‘single-DIR adaptation’, using a single set of deformably propagated structures; ‘multi-DIR adaptation’, using robust planning with multiple deformed structure sets; ‘conservative adaptation’, using the intersection and union of all deformed structures; ‘probabilistic adaptation’, using the probability of a voxel belonging to the structure in the optimization weight; and ‘no adaptation’. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast ‘plausibility check’, and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable.

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Section: Discussionsupporting

confidence: 89%

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans

Nenoff

Buti

Bobić

et al. 2022

Cancers

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“…The contours obtained by deformable image registration were almost identical for all the protocols studied (Dice score above 0.98). The choice of the DIR algorithm is likely the source of much larger geometric uncertainty [ 33 , 34 ]. For the dose calculation, the protocol associated with the lowest imaging dose resulted in 2%/2 mm gamma pass rates over 99.95% for three beams and all ten patients, using the standard-protocol CT volume as reference.…”

Section: Discussionmentioning

confidence: 99%

Low-Dose Computed Tomography Scanning Protocols for Online Adaptive Proton Therapy of Head-and-Neck Cancers

Nesteruk

Bobić

Sharp

et al. 2022

Cancers

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Purpose: To evaluate the suitability of low-dose CT protocols for online plan adaptation of head-and-neck patients. Methods: We acquired CT scans of a head phantom with protocols corresponding to CT dose index volume CTDIvol in the range of 4.2–165.9 mGy. The highest value corresponds to the standard protocol used for CT simulations of 10 head-and-neck patients included in the study. The minimum value corresponds to the lowest achievable tube current of the GE Discovery RT scanner used for the study. For each patient and each low-dose protocol, the noise relative to the standard protocol, derived from phantom images, was applied to a virtual CT (vCT). The vCT was obtained from a daily CBCT scan corresponding to the fraction with the largest anatomical changes. We ran an established adaptive workflow twice for each low-dose protocol using a high-quality daily vCT and the corresponding low-dose synthetic vCT. For a relative comparison of the adaptation efficacy, two adapted plans were recalculated in the high-quality vCT and evaluated with the contours obtained through deformable registration of the planning CT. We also evaluated the accuracy of dose calculation in low-dose CT volumes using the standard CT protocol as reference. Results: The maximum differences in D98 between low-dose protocols and the standard protocol for the high-risk and low-risk CTV were found to be 0.6% and 0.3%, respectively. The difference in OAR sparing was up to 3%. The Dice similarity coefficient between propagated contours obtained with low-dose and standard protocols was above 0.982. The mean 2%/2 mm gamma pass rate for the lowest-dose image, using the standard protocol as reference, was found to be 99.99%. Conclusion: The differences between low-dose protocols and the standard scanning protocol were marginal. Thus, low-dose CT protocols are suitable for online adaptive proton therapy of head-and-neck cancers. As such, considering scanning protocols used in our clinic, the imaging dose associated with online adaption of head-and-neck cancers treated with protons can be reduced by a factor of 40.

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“…Rhee et al investigated deep-learning auto-contouring for the target and OAR structures in cervix cancer patients and found a DICE of 0.91 for the nodal CTV [9] . DIR-based target contour propagation was also investigated for five lung cancer patients by Nenoff et al [12] . They used six different DIR algorithms, including the ANACONDA algorithm used in this study.…”

Section: Discussionmentioning

confidence: 99%

Clinical implementation and validation of an automated adaptive workflow for proton therapy

Taasti

Hazelaar

Vaassen

et al. 2022

Physics and Imaging in Radiation Oncology

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Dosimetric influence of deformable image registration uncertainties on propagated structures for online daily adaptive proton therapy of lung cancer patients

Abstract: influence of deformable image registration uncertainties on propagated structures for online daily adaptive proton therapy of lung cancer patients, Radiotherapy and Oncology (2021), doi:

Cited by 20 publications

References 67 publications

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans

Low-Dose Computed Tomography Scanning Protocols for Online Adaptive Proton Therapy of Head-and-Neck Cancers

Clinical implementation and validation of an automated adaptive workflow for proton therapy

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