Evaluation of 18F-FDG PET and DWI Datasets for Predicting Therapy Response of Soft-Tissue Sarcomas Under Neoadjuvant Isolated Limb Perfusion Article Swipe
Michal Chodyla
,
Aydın Demircioğlu
,
Benedikt M. Schaarschmidt
,
Stefanie Bertram
,
Nils Martin Bruckmann
,
Jennifer Haferkamp
,
Yan Li
,
Sebastian Bauer
,
Lars Erik Podleska
,
Christoph Rischpler
,
Michael Forsting
,
Ken Herrmann
,
Lale Umutlu
,
Johannes Grueneisen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.2967/jnumed.120.248260
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.2967/jnumed.120.248260
Our purpose was to evaluate and compare the clinical utility of simultaneously obtained quantitative 18F-FDG PET and diffusion-weighted MRI datasets for predicting the histopathologic response of soft-tissue sarcoma (STS) to neoadjuvant isolated limb perfusion (ILP). Methods: In total, 37 patients with a confirmed STS of the extremities underwent 18F-FDG PET/MRI before and after ILP with melphalan and tumor necrosis factor-α. For each patient, the maximum tumor size, metabolic activity (SUV), and diffusion restriction (apparent diffusion coefficient, ADC) were determined in pre- and posttherapeutic examinations, and percentage changes during treatment were calculated. Mann-Whitney U testing and receiver-operating-characteristic analysis were used to compare the results of the different quantitative parameters to predict the histopathologic response to therapy. Results from histopathologic analysis after tumor resection served as the reference standard, and patients were defined as responders or nonresponders based on the grading scale by Salzer-Kuntschik. Results: Histopathologic analysis categorized 22 (59%) patients as responders (grades I-III) and 15 (41%) as nonresponders (grades IV-VI). Under treatment, tumors in responders showed a mean reduction in size (-9.7%) and metabolic activity (SUVpeak, -51.9%; SUVmean, -43.8%), as well as an increase of the ADC values (ADCmin, +29.4%; ADCmean, +32.8%). The percentage changes in nonresponders were -6.2% in tumor size, -17.3% in SUVpeak, -13.9% in SUVmean, +15.3% in ADCmin, and +14.6% in ADCmean Changes in SUV and ADCmean significantly differed between responders and nonresponders (<0.01), whereas differences in tumor size and ADCmin did not (>0.05). The corresponding AUCs were 0.63 for tumor size, 0.87 for SUVpeak, 0.82 for SUVmean, 0.63 for ADCmin, 0.84 for ADCmean, and 0.89 for ratio of ADCmean to SUVpeakConclusion: PET- and MRI-derived quantitative parameters (SUV and ADCmean) and their combination performed well in predicting the histopathologic therapy response of STS to neoadjuvant ILP. Therefore, integrated PET/MRI could serve as a valuable tool for pretherapeutic assessment as well as monitoring of neoadjuvant treatment strategies of STS.
Related Topics
Concepts
Medicine
Nuclear medicine
Effective diffusion coefficient
Soft tissue sarcoma
Soft tissue
Sarcoma
Grading (engineering)
Perfusion
Magnetic resonance imaging
Standardized uptake value
Diffusion MRI
Melphalan
Positron emission tomography
Radiology
Chemotherapy
Pathology
Internal medicine
Engineering
Civil engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2967/jnumed.120.248260
- https://jnm.snmjournals.org/content/jnumed/62/3/348.full.pdf
- OA Status
- bronze
- Cited By
- 12
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3046410703
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3046410703Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2967/jnumed.120.248260Digital Object Identifier
- Title
-
Evaluation of 18F-FDG PET and DWI Datasets for Predicting Therapy Response of Soft-Tissue Sarcomas Under Neoadjuvant Isolated Limb PerfusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-31Full publication date if available
- Authors
-
Michal Chodyla, Aydın Demircioğlu, Benedikt M. Schaarschmidt, Stefanie Bertram, Nils Martin Bruckmann, Jennifer Haferkamp, Yan Li, Sebastian Bauer, Lars Erik Podleska, Christoph Rischpler, Michael Forsting, Ken Herrmann, Lale Umutlu, Johannes GrueneisenList of authors in order
- Landing page
-
https://doi.org/10.2967/jnumed.120.248260Publisher landing page
- PDF URL
-
https://jnm.snmjournals.org/content/jnumed/62/3/348.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://jnm.snmjournals.org/content/jnumed/62/3/348.full.pdfDirect OA link when available
- Concepts
-
Medicine, Nuclear medicine, Effective diffusion coefficient, Soft tissue sarcoma, Soft tissue, Sarcoma, Grading (engineering), Perfusion, Magnetic resonance imaging, Standardized uptake value, Diffusion MRI, Melphalan, Positron emission tomography, Radiology, Chemotherapy, Pathology, Internal medicine, Engineering, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 2, 2022: 2, 2021: 5Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 5, 16, 51, 56, 70, 81, 84, 94, 127, 153, 172, 211, 218, 224, 232, 257, 266, 271, 273 |
| abstract_inverted_index.did | 234 |
| abstract_inverted_index.for | 20, 242, 246, 249, 252, 255, 259, 298 |
| abstract_inverted_index.not | 235 |
| abstract_inverted_index.the | 7, 22, 45, 63, 101, 104, 110, 124, 137, 185, 280 |
| abstract_inverted_index.was | 2 |
| abstract_inverted_index.(SUV | 270 |
| abstract_inverted_index.0.63 | 241, 251 |
| abstract_inverted_index.0.82 | 248 |
| abstract_inverted_index.0.84 | 254 |
| abstract_inverted_index.0.87 | 245 |
| abstract_inverted_index.0.89 | 258 |
| abstract_inverted_index.ADC) | 76 |
| abstract_inverted_index.AUCs | 239 |
| abstract_inverted_index.ILP. | 288 |
| abstract_inverted_index.PET- | 265 |
| abstract_inverted_index.STS. | 310 |
| abstract_inverted_index.each | 61 |
| abstract_inverted_index.from | 116 |
| abstract_inverted_index.limb | 32 |
| abstract_inverted_index.mean | 167 |
| abstract_inverted_index.pre- | 80 |
| abstract_inverted_index.size | 170, 231 |
| abstract_inverted_index.tool | 297 |
| abstract_inverted_index.used | 98 |
| abstract_inverted_index.well | 180, 277, 302 |
| abstract_inverted_index.were | 77, 89, 97, 129, 197, 240 |
| abstract_inverted_index.with | 40, 54 |
| abstract_inverted_index.(41%) | 155 |
| abstract_inverted_index.(59%) | 147 |
| abstract_inverted_index.(STS) | 28 |
| abstract_inverted_index.-6.2% | 198 |
| abstract_inverted_index.Under | 160 |
| abstract_inverted_index.after | 52, 119 |
| abstract_inverted_index.based | 135 |
| abstract_inverted_index.could | 292 |
| abstract_inverted_index.ratio | 260 |
| abstract_inverted_index.scale | 139 |
| abstract_inverted_index.serve | 293 |
| abstract_inverted_index.size, | 66, 201, 244 |
| abstract_inverted_index.their | 274 |
| abstract_inverted_index.tumor | 57, 65, 120, 200, 230, 243 |
| abstract_inverted_index.(ILP). | 34 |
| abstract_inverted_index.(SUV), | 69 |
| abstract_inverted_index.+14.6% | 212 |
| abstract_inverted_index.+15.3% | 208 |
| abstract_inverted_index.-13.9% | 205 |
| abstract_inverted_index.-17.3% | 202 |
| abstract_inverted_index.I-III) | 152 |
| abstract_inverted_index.before | 50 |
| abstract_inverted_index.during | 87 |
| abstract_inverted_index.served | 122 |
| abstract_inverted_index.showed | 165 |
| abstract_inverted_index.total, | 37 |
| abstract_inverted_index.tumors | 162 |
| abstract_inverted_index.values | 187 |
| abstract_inverted_index.(-9.7%) | 171 |
| abstract_inverted_index.(grades | 151, 158 |
| abstract_inverted_index.+29.4%; | 189 |
| abstract_inverted_index.-51.9%; | 176 |
| abstract_inverted_index.Changes | 215 |
| abstract_inverted_index.IV-VI). | 159 |
| abstract_inverted_index.PET/MRI | 49, 291 |
| abstract_inverted_index.Results | 115 |
| abstract_inverted_index.between | 222 |
| abstract_inverted_index.changes | 86, 194 |
| abstract_inverted_index.compare | 6, 100 |
| abstract_inverted_index.defined | 130 |
| abstract_inverted_index.grading | 138 |
| abstract_inverted_index.maximum | 64 |
| abstract_inverted_index.predict | 109 |
| abstract_inverted_index.purpose | 1 |
| abstract_inverted_index.results | 102 |
| abstract_inverted_index.sarcoma | 27 |
| abstract_inverted_index.testing | 93 |
| abstract_inverted_index.therapy | 282 |
| abstract_inverted_index.utility | 9 |
| abstract_inverted_index.whereas | 227 |
| abstract_inverted_index.(<0.01), | 226 |
| abstract_inverted_index.(>0.05). | 236 |
| abstract_inverted_index.+32.8%). | 191 |
| abstract_inverted_index.-43.8%), | 178 |
| abstract_inverted_index.<i>U</i> | 92 |
| abstract_inverted_index.activity | 68, 174 |
| abstract_inverted_index.analysis | 96, 118, 144 |
| abstract_inverted_index.clinical | 8 |
| abstract_inverted_index.datasets | 19 |
| abstract_inverted_index.differed | 221 |
| abstract_inverted_index.evaluate | 4 |
| abstract_inverted_index.increase | 183 |
| abstract_inverted_index.isolated | 31 |
| abstract_inverted_index.necrosis | 58 |
| abstract_inverted_index.obtained | 12 |
| abstract_inverted_index.patient, | 62 |
| abstract_inverted_index.patients | 39, 128, 148 |
| abstract_inverted_index.response | 24, 112, 283 |
| abstract_inverted_index.therapy. | 114 |
| abstract_inverted_index.valuable | 296 |
| abstract_inverted_index.(apparent | 73 |
| abstract_inverted_index.confirmed | 42 |
| abstract_inverted_index.different | 105 |
| abstract_inverted_index.diffusion | 71, 74 |
| abstract_inverted_index.melphalan | 55 |
| abstract_inverted_index.metabolic | 67, 173 |
| abstract_inverted_index.performed | 276 |
| abstract_inverted_index.perfusion | 33 |
| abstract_inverted_index.reduction | 168 |
| abstract_inverted_index.reference | 125 |
| abstract_inverted_index.resection | 121 |
| abstract_inverted_index.standard, | 126 |
| abstract_inverted_index.treatment | 88, 307 |
| abstract_inverted_index.underwent | 47 |
| abstract_inverted_index.Therefore, | 289 |
| abstract_inverted_index.assessment | 300 |
| abstract_inverted_index.determined | 78 |
| abstract_inverted_index.factor-α. | 59 |
| abstract_inverted_index.integrated | 290 |
| abstract_inverted_index.monitoring | 304 |
| abstract_inverted_index.parameters | 107, 269 |
| abstract_inverted_index.percentage | 85, 193 |
| abstract_inverted_index.predicting | 21, 279 |
| abstract_inverted_index.responders | 132, 150, 164, 223 |
| abstract_inverted_index.strategies | 308 |
| abstract_inverted_index.treatment, | 161 |
| abstract_inverted_index.MRI-derived | 267 |
| abstract_inverted_index.calculated. | 90 |
| abstract_inverted_index.categorized | 145 |
| abstract_inverted_index.combination | 275 |
| abstract_inverted_index.differences | 228 |
| abstract_inverted_index.extremities | 46 |
| abstract_inverted_index.neoadjuvant | 30, 287, 306 |
| abstract_inverted_index.restriction | 72 |
| abstract_inverted_index.soft-tissue | 26 |
| abstract_inverted_index.Mann-Whitney | 91 |
| abstract_inverted_index.coefficient, | 75 |
| abstract_inverted_index.quantitative | 13, 106, 268 |
| abstract_inverted_index.corresponding | 238 |
| abstract_inverted_index.examinations, | 83 |
| abstract_inverted_index.nonresponders | 134, 157, 196, 225 |
| abstract_inverted_index.significantly | 220 |
| abstract_inverted_index.pretherapeutic | 299 |
| abstract_inverted_index.simultaneously | 11 |
| abstract_inverted_index.<b>Methods:</b> | 35 |
| abstract_inverted_index.<b>Results:</b> | 142 |
| abstract_inverted_index.Histopathologic | 143 |
| abstract_inverted_index.histopathologic | 23, 111, 117, 281 |
| abstract_inverted_index.posttherapeutic | 82 |
| abstract_inverted_index.ADC<sub>min</sub> | 233 |
| abstract_inverted_index.Salzer-Kuntschik. | 141 |
| abstract_inverted_index.<sup>18</sup>F-FDG | 14, 48 |
| abstract_inverted_index.ADC<sub>mean</sub> | 214, 219, 262 |
| abstract_inverted_index.ADC<sub>min</sub>, | 210, 253 |
| abstract_inverted_index.diffusion-weighted | 17 |
| abstract_inverted_index.(ADC<sub>min</sub>, | 188 |
| abstract_inverted_index.ADC<sub>mean</sub>) | 272 |
| abstract_inverted_index.ADC<sub>mean</sub>, | 190, 256 |
| abstract_inverted_index.SUV<sub>mean</sub>, | 177, 207, 250 |
| abstract_inverted_index.SUV<sub>peak</sub>, | 204, 247 |
| abstract_inverted_index.(SUV<sub>peak</sub>, | 175 |
| abstract_inverted_index.receiver-operating-characteristic | 95 |
| abstract_inverted_index.SUV<sub>peak</sub><b>Conclusion:</b> | 264 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 14 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.82044292 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |