Comparison of MR‐soft tissue based versus biliary stent based alignment for image guidance in pancreatic SBRT Article Swipe
Zhaohui Han
,
Atchar Sudhyadhom
,
Shu‐Hui Hsu
,
Yue‐Houng Hu
,
Raymond H. Mak
,
Mai Anh Huynh
,
Ritchell R. van Dams
,
Shyam Tanguturi
,
Veena Venkatachalam
,
Joseph D. Mancias
,
Harvey J. Mamon
,
Neil E. Martin
,
Miranda B. Lam
,
Jonathan E. Leeman
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/acm2.13965
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/acm2.13965
Purpose The role of biliary stents in image‐guided localization for pancreatic cancer has been inconclusive. To date, stent accuracy has been largely evaluated against implanted fiducials on cone beam computed tomography. We aim to use magnetic resonance (MR) soft tissue as a direct reference to examine the geometric and dosimetric impacts of stent‐based localization on the newly available MR linear accelerator. Methods Thirty pancreatic cancer patients (132 fractions) treated on our MR linear accelerator were identified to have a biliary stent. In our standard adaptive workflow, patients were set up to the target using soft tissue for image registration and structures were re‐contoured on daily MR images. The original plan was then projected on treatment anatomy and dose predicted, followed by plan re‐optimization and treatment delivery. These online predicted plans were soft tissue‐based and served as reference plans. Retrospective image registration to the stent was performed offline to simulate stent‐based localization and the magnitude of shifts was taken as the geometric accuracy of stent localization. New predicted plans were generated based on stent‐alignment for dosimetric comparison. Results Shifts were within 3 mm for 90% of the cases (mean = 1.5 mm); however, larger shifts up to 7.2 mm were observed. Average PTV coverage dropped by 1.1% with a maximum drop of 26.8%. The mean increase in V35Gy was 0.15, 0.05, 0.02, and 0.02 cc for duodenum, stomach, small bowel and large bowel, respectively. Stent alignment was significantly worse for all metrics except for small bowel ( p = 0.07). Conclusions Overall discrepancy between stent‐ and soft tissue‐alignment was modest; however, large discrepancies were observed for select cases. While PTV coverage loss may be compensated for by using a larger margin, the increase in dose to gastrointestinal organs at risk may limit the role of biliary stents in image‐guided localization.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/acm2.13965
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/acm2.13965
- OA Status
- gold
- Cited By
- 1
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4327622652
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4327622652Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/acm2.13965Digital Object Identifier
- Title
-
Comparison of MR‐soft tissue based versus biliary stent based alignment for image guidance in pancreatic SBRTWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-16Full publication date if available
- Authors
-
Zhaohui Han, Atchar Sudhyadhom, Shu‐Hui Hsu, Yue‐Houng Hu, Raymond H. Mak, Mai Anh Huynh, Ritchell R. van Dams, Shyam Tanguturi, Veena Venkatachalam, Joseph D. Mancias, Harvey J. Mamon, Neil E. Martin, Miranda B. Lam, Jonathan E. LeemanList of authors in order
- Landing page
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https://doi.org/10.1002/acm2.13965Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/acm2.13965Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/acm2.13965Direct OA link when available
- Concepts
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Fiducial marker, Stent, Medicine, Biliary stent, Soft tissue, Pancreatic cancer, Nuclear medicine, Image registration, Radiology, Pancreatic carcinoma, Cancer, Computer science, Artificial intelligence, Image (mathematics), Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.were | 75, 88, 102, 131, 169, 179, 199, 263 |
| abstract_inverted_index.with | 207 |
| abstract_inverted_index.(mean | 188 |
| abstract_inverted_index.0.02, | 221 |
| abstract_inverted_index.0.05, | 220 |
| abstract_inverted_index.0.15, | 219 |
| abstract_inverted_index.Stent | 234 |
| abstract_inverted_index.These | 127 |
| abstract_inverted_index.V35Gy | 217 |
| abstract_inverted_index.While | 268 |
| abstract_inverted_index.based | 171 |
| abstract_inverted_index.bowel | 229, 245 |
| abstract_inverted_index.cases | 187 |
| abstract_inverted_index.daily | 105 |
| abstract_inverted_index.date, | 17 |
| abstract_inverted_index.image | 98, 140 |
| abstract_inverted_index.large | 231, 261 |
| abstract_inverted_index.limit | 291 |
| abstract_inverted_index.newly | 57 |
| abstract_inverted_index.plans | 130, 168 |
| abstract_inverted_index.small | 228, 244 |
| abstract_inverted_index.stent | 18, 144, 164 |
| abstract_inverted_index.taken | 158 |
| abstract_inverted_index.using | 94, 277 |
| abstract_inverted_index.worse | 238 |
| abstract_inverted_index.0.07). | 249 |
| abstract_inverted_index.26.8%. | 212 |
| abstract_inverted_index.Shifts | 178 |
| abstract_inverted_index.Thirty | 63 |
| abstract_inverted_index.bowel, | 232 |
| abstract_inverted_index.cancer | 12, 65 |
| abstract_inverted_index.cases. | 267 |
| abstract_inverted_index.direct | 43 |
| abstract_inverted_index.except | 242 |
| abstract_inverted_index.larger | 193, 279 |
| abstract_inverted_index.linear | 60, 73 |
| abstract_inverted_index.online | 128 |
| abstract_inverted_index.organs | 287 |
| abstract_inverted_index.plans. | 138 |
| abstract_inverted_index.select | 266 |
| abstract_inverted_index.served | 135 |
| abstract_inverted_index.shifts | 156, 194 |
| abstract_inverted_index.stent. | 81 |
| abstract_inverted_index.stents | 6, 296 |
| abstract_inverted_index.target | 93 |
| abstract_inverted_index.tissue | 40, 96 |
| abstract_inverted_index.within | 180 |
| abstract_inverted_index.Average | 201 |
| abstract_inverted_index.Methods | 62 |
| abstract_inverted_index.Overall | 251 |
| abstract_inverted_index.Purpose | 1 |
| abstract_inverted_index.Results | 177 |
| abstract_inverted_index.against | 24 |
| abstract_inverted_index.anatomy | 116 |
| abstract_inverted_index.between | 253 |
| abstract_inverted_index.biliary | 5, 80, 295 |
| abstract_inverted_index.dropped | 204 |
| abstract_inverted_index.examine | 46 |
| abstract_inverted_index.images. | 107 |
| abstract_inverted_index.impacts | 51 |
| abstract_inverted_index.largely | 22 |
| abstract_inverted_index.margin, | 280 |
| abstract_inverted_index.maximum | 209 |
| abstract_inverted_index.metrics | 241 |
| abstract_inverted_index.modest; | 259 |
| abstract_inverted_index.offline | 147 |
| abstract_inverted_index.treated | 69 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accuracy | 19, 162 |
| abstract_inverted_index.adaptive | 85 |
| abstract_inverted_index.computed | 30 |
| abstract_inverted_index.coverage | 203, 270 |
| abstract_inverted_index.followed | 120 |
| abstract_inverted_index.however, | 192, 260 |
| abstract_inverted_index.increase | 215, 282 |
| abstract_inverted_index.magnetic | 36 |
| abstract_inverted_index.observed | 264 |
| abstract_inverted_index.original | 109 |
| abstract_inverted_index.patients | 66, 87 |
| abstract_inverted_index.simulate | 149 |
| abstract_inverted_index.standard | 84 |
| abstract_inverted_index.stent‐ | 254 |
| abstract_inverted_index.stomach, | 227 |
| abstract_inverted_index.alignment | 235 |
| abstract_inverted_index.available | 58 |
| abstract_inverted_index.delivery. | 126 |
| abstract_inverted_index.duodenum, | 226 |
| abstract_inverted_index.evaluated | 23 |
| abstract_inverted_index.fiducials | 26 |
| abstract_inverted_index.generated | 170 |
| abstract_inverted_index.geometric | 48, 161 |
| abstract_inverted_index.implanted | 25 |
| abstract_inverted_index.magnitude | 154 |
| abstract_inverted_index.observed. | 200 |
| abstract_inverted_index.performed | 146 |
| abstract_inverted_index.predicted | 129, 167 |
| abstract_inverted_index.projected | 113 |
| abstract_inverted_index.reference | 44, 137 |
| abstract_inverted_index.resonance | 37 |
| abstract_inverted_index.treatment | 115, 125 |
| abstract_inverted_index.workflow, | 86 |
| abstract_inverted_index.dosimetric | 50, 175 |
| abstract_inverted_index.fractions) | 68 |
| abstract_inverted_index.identified | 76 |
| abstract_inverted_index.pancreatic | 11, 64 |
| abstract_inverted_index.predicted, | 119 |
| abstract_inverted_index.structures | 101 |
| abstract_inverted_index.Conclusions | 250 |
| abstract_inverted_index.accelerator | 74 |
| abstract_inverted_index.comparison. | 176 |
| abstract_inverted_index.compensated | 274 |
| abstract_inverted_index.discrepancy | 252 |
| abstract_inverted_index.tomography. | 31 |
| abstract_inverted_index.accelerator. | 61 |
| abstract_inverted_index.localization | 9, 54, 151 |
| abstract_inverted_index.registration | 99, 141 |
| abstract_inverted_index.Retrospective | 139 |
| abstract_inverted_index.discrepancies | 262 |
| abstract_inverted_index.inconclusive. | 15 |
| abstract_inverted_index.localization. | 165, 299 |
| abstract_inverted_index.respectively. | 233 |
| abstract_inverted_index.significantly | 237 |
| abstract_inverted_index.stent‐based | 53, 150 |
| abstract_inverted_index.image‐guided | 8, 298 |
| abstract_inverted_index.re‐contoured | 103 |
| abstract_inverted_index.tissue‐based | 133 |
| abstract_inverted_index.gastrointestinal | 286 |
| abstract_inverted_index.re‐optimization | 123 |
| abstract_inverted_index.stent‐alignment | 173 |
| abstract_inverted_index.tissue‐alignment | 257 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5102805378 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I1283280774, https://openalex.org/I136199984, https://openalex.org/I4210117453, https://openalex.org/I4210161994 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.66331407 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |