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Feasibility of function‐guided lung treatment planning with parametric response mapping
dc.contributor.authorMatrosic, Charles K.
dc.contributor.authorOwen, D. Rocky
dc.contributor.authorPolan, Daniel
dc.contributor.authorSun, Yilun
dc.contributor.authorJolly, Shruti
dc.contributor.authorSchonewolf, Caitlin
dc.contributor.authorSchipper, Matthew
dc.contributor.authorHaken, Randall K. Ten
dc.contributor.authorGalban, Craig J.
dc.contributor.authorMatuszak, Martha
dc.date.accessioned2021-12-02T02:29:58Z
dc.date.available2022-12-01 21:29:57en
dc.date.available2021-12-02T02:29:58Z
dc.date.issued2021-11
dc.identifier.citationMatrosic, Charles K.; Owen, D. Rocky; Polan, Daniel; Sun, Yilun; Jolly, Shruti; Schonewolf, Caitlin; Schipper, Matthew; Haken, Randall K. Ten; Galban, Craig J.; Matuszak, Martha (2021). "Feasibility of function‐guided lung treatment planning with parametric response mapping." Journal of Applied Clinical Medical Physics 22(11): 80-89.
dc.identifier.issn1526-9914
dc.identifier.issn1526-9914
dc.identifier.urihttps://hdl.handle.net/2027.42/170991
dc.description.abstractPurposeRecent advancements in functional lung imaging have been developed to improve clinicians’ knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)–based functional lung imaging method that utilizes a voxel‐wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning.Methods and materialsA retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric‐modulated arc therapy and PRM‐guided treatment plans were designed for each patient.ResultsPRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM‐guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity.ConclusionsPRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM‐guided treatment planning.
dc.publisherWiley Periodicals, Inc.
dc.subject.otherlung cancer
dc.subject.othertreatment planning
dc.subject.otherfunctional imaging
dc.titleFeasibility of function‐guided lung treatment planning with parametric response mapping
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/170991/1/acm213436.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/170991/2/acm213436_am.pdf
dc.identifier.doi10.1002/acm2.13436
dc.identifier.sourceJournal of Applied Clinical Medical Physics
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dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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