Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.

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dc.citation.volumeEarly View
dc.contributor.authorZeng H
dc.contributor.authorChen Q
dc.contributor.authorE X
dc.contributor.authorFeng Y
dc.contributor.authorLv M
dc.contributor.authorZeng S
dc.contributor.authorShen W
dc.contributor.authorGuan W
dc.contributor.authorZhang Y
dc.contributor.authorZhao R
dc.contributor.authorWang S
dc.contributor.authorYu J
dc.coverage.spatialUnited States
dc.date.accessioned2024-12-10T19:06:02Z
dc.date.available2024-12-10T19:06:02Z
dc.date.issued2024-11-29
dc.description.abstractBackground Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high clinical feasibility for lung cancer. Methods This study enrolled 102 lung cancer patients, who underwent kV iterative cone-beam computed tomography (iCBCT). Data collection included iCBCT and planning CT (pCT) scans. Among these, data from 70 patients were employed for training the UNet++ based deep learning model, while 15 patients were allocated for testing the model. The model transformed iCBCT into adaptive CT (aCT). Clinical radiotherapy feasibility was verified in 17 patients. The dosimetric evaluation encompassed GTV, organs at risk (OARs), and monitor units (MU), while delivery accuracy was validated using ArcCHECK and thermoluminescent dosimeter (TLD) detectors. Results The UNet++ based deep learning model substantially improved image quality, reducing mean absolute error (MAE) by 70.05%, increasing peak signal-to-noise ratio (PSNR) by 17.97%, structural similarity (SSIM) by 7.41%, and the Hounsfield Units (HU) of aCT approaching a closer proximity to pCT compared to kV iCBCT. There were no significant differences observed in the dosimetric parameters of GTV and OARs between the aCT and pCT plans, confirming the accuracy of the dose maps in ART plans. Similarly, MU manifested no notable disparities, underscoring the consistency in treatment efficiency. Gamma passing rates for intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) plans derived from aCT and pCT exceeded 98%, while the deviations in TLD measurements (within 2% to 7%) also exhibited no significant differences, thus corroborating the precision of dose delivery. Conclusion An offline ART regimen utilizing kV iCBCT and UNet++ based deep learning model is clinically feasible for lung cancer treatment. This approach provides enhanced image quality, comparable treatment plans to pCT, and precise dose delivery.
dc.description.confidentialfalse
dc.format.paginatione14582-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/39611881
dc.identifier.citationZeng H, Chen Q, E X, Feng Y, Lv M, Zeng S, Shen W, Guan W, Zhang Y, Zhao R, Wang S, Yu J. (2024). Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.. J Appl Clin Med Phys. Early View. (pp. e14582-).
dc.identifier.doi10.1002/acm2.14582
dc.identifier.eissn1526-9914
dc.identifier.elements-typejournal-article
dc.identifier.issn1526-9914
dc.identifier.numbere14582
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72247
dc.languageeng
dc.publisherWiley Periodicals LLC on behalf of American Association of Physicists in Medicine
dc.publisher.urihttps://aapm.onlinelibrary.wiley.com/doi/10.1002/acm2.14582
dc.relation.isPartOfJ Appl Clin Med Phys
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.subjectUNet++ model
dc.subjectdose verification
dc.subjectkV iCBCT
dc.subjectlung cancer
dc.subjectoffline adaptive radiotherapy
dc.titleAssessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.
dc.typeJournal article
pubs.elements-id492521
pubs.organisational-groupOther
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