Comparison of Two Contemporary Quantitative Atherosclerotic Plaque Assessment Tools for Coronary Computed Tomography Angiography: Single-Center Analysis and Multi-Center Patient Cohort Validation Article Swipe
Loris Weichsel
,
Alexander Giesen
,
Florian André
,
Matthias Renker
,
Stefan Baumann
,
Philipp Breitbart
,
Meinrad Beer
,
Pál Maurovitch-Horvat
,
Bálint Szilveszter
,
Borbála Vattay
,
Sebastian J. Buss
,
Mohamed Marwan
,
Andreas A. Giannopoulos
,
Sebastian Kelle
,
Norbert Frey
,
Grigorios Korosoglou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14020154
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14020154
Background: Coronary computed tomography angiography (CCTA) provides non-invasive quantitative assessments of plaque burden and composition. The quantitative assessment of plaque components requires the use of analysis software that provides reproducible semi-automated plaque detection and analysis. However, commercially available plaque analysis software can vary widely in the degree of automation, resulting in differences in terms of reproducibility and time spent. Aim: To compare the reproducibility and time spent of two CCTA analysis software tools using different algorithms for the quantitative assessment of coronary plaque volumes and composition in two independent patient cohorts. Methods: The study population included 100 patients from two different cohorts: 50 patients from a single-center (Siemens Healthineers, SOMATOM Force (DSCT)) and another 50 patients from a multi-center study (5 different > 64 slice CT scanner types). Quantitative measurements of total calcified and non-calcified plaque volume of the right coronary artery (RCA), left anterior descending (LAD), and left circumflex coronary artery (LCX) were performed on a total of 300 coronaries by two independent readers, using two different CCTA analysis software tools (Tool #1: Siemens Healthineers, syngo.via Frontier CT Coronary Plaque Analysis and Tool #2: Siemens Healthineers, successor CT Coronary Plaque Analysis prototype). In addition, the total time spent for the analysis was recorded with both programs. Results: The patients in cohorts 1 and 2 were 62.8 ± 10.2 and 70.9 ± 11.7 years old, respectively, 10 (20.0%) and 35 (70.0%) were female and 34 (68.0%) and 20 (40.0%), respectively, had hyperlipidemia. In Cohort #1, the inter- and intra-observer variabilities for the assessment of plaque volumes per patient for Tool #1 versus Tool #2 were 22.8%, 22.0%, and 26.0% versus 2.3%, 3.9%, and 2.5% and 19.7%, 21.4%, and 22.1% versus 0.2%, 0.1%, and 0.3%, respectively, for total, noncalcified, and calcified lesions (p < 0.001 for all between Tools #1 and 2 both for inter- and intra-observer). The inter- and intra-observer variabilities using Tool #2 remained low at 2.9%, 2.7%, and 3.0% and 3.8%, 3.7%, and 4.0%, respectively, for total, non-calcified, and calcified lesions in Cohort #2. For each dataset, the median processing time was higher for Tool #1 versus Tool #2 (459.5 s IQR = 348.0–627.0 versus 208.5 s; IQR = 198.0–216.0) (p < 0.001). Conclusion: The plaque analysis Tool #2 (CT-guided PCI) encompassing a higher degree of automated support required less manual editing, was more time-efficient, and showed a higher intra- and inter-observer reproducibility for the quantitative assessment of plaque volumes both in a representative single-center and in a multi-center validation cohort.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14020154
- https://www.mdpi.com/2075-4418/14/2/154/pdf?version=1704805419
- OA Status
- gold
- Cited By
- 7
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390775581
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390775581Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics14020154Digital Object Identifier
- Title
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Comparison of Two Contemporary Quantitative Atherosclerotic Plaque Assessment Tools for Coronary Computed Tomography Angiography: Single-Center Analysis and Multi-Center Patient Cohort ValidationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-09Full publication date if available
- Authors
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Loris Weichsel, Alexander Giesen, Florian André, Matthias Renker, Stefan Baumann, Philipp Breitbart, Meinrad Beer, Pál Maurovitch-Horvat, Bálint Szilveszter, Borbála Vattay, Sebastian J. Buss, Mohamed Marwan, Andreas A. Giannopoulos, Sebastian Kelle, Norbert Frey, Grigorios KorosoglouList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics14020154Publisher landing page
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https://www.mdpi.com/2075-4418/14/2/154/pdf?version=1704805419Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2075-4418/14/2/154/pdf?version=1704805419Direct OA link when available
- Concepts
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Medicine, Reproducibility, Nuclear medicine, Computed tomography angiography, Radiology, Coronary arteries, Angiography, Artery, Internal medicine, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 5, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.CT | 125, 178, 188 |
| abstract_inverted_index.In | 193, 242 |
| abstract_inverted_index.To | 60 |
| abstract_inverted_index.at | 316 |
| abstract_inverted_index.by | 161 |
| abstract_inverted_index.in | 44, 50, 52, 86, 210, 333, 403, 408 |
| abstract_inverted_index.of | 10, 18, 24, 47, 54, 67, 80, 130, 137, 158, 253, 377, 399 |
| abstract_inverted_index.on | 155 |
| abstract_inverted_index.s; | 358 |
| abstract_inverted_index.± | 217, 221 |
| abstract_inverted_index.#1, | 244 |
| abstract_inverted_index.#1: | 173 |
| abstract_inverted_index.#2. | 335 |
| abstract_inverted_index.#2: | 184 |
| abstract_inverted_index.100 | 96 |
| abstract_inverted_index.300 | 159 |
| abstract_inverted_index.For | 336 |
| abstract_inverted_index.IQR | 353, 359 |
| abstract_inverted_index.The | 15, 92, 208, 306, 366 |
| abstract_inverted_index.all | 295 |
| abstract_inverted_index.and | 13, 33, 56, 64, 84, 112, 133, 147, 182, 213, 219, 228, 233, 236, 247, 267, 272, 274, 277, 282, 288, 299, 304, 308, 319, 321, 324, 330, 387, 392, 407 |
| abstract_inverted_index.can | 41 |
| abstract_inverted_index.for | 76, 199, 250, 258, 285, 294, 302, 327, 345, 395 |
| abstract_inverted_index.had | 240 |
| abstract_inverted_index.low | 315 |
| abstract_inverted_index.per | 256 |
| abstract_inverted_index.the | 22, 45, 62, 77, 138, 195, 200, 245, 251, 339, 396 |
| abstract_inverted_index.two | 68, 87, 99, 162, 166 |
| abstract_inverted_index.use | 23 |
| abstract_inverted_index.was | 202, 343, 384 |
| abstract_inverted_index.> | 122 |
| abstract_inverted_index.< | 292, 363 |
| abstract_inverted_index.10.2 | 218 |
| abstract_inverted_index.11.7 | 222 |
| abstract_inverted_index.2.5% | 273 |
| abstract_inverted_index.3.0% | 320 |
| abstract_inverted_index.62.8 | 216 |
| abstract_inverted_index.70.9 | 220 |
| abstract_inverted_index.Aim: | 59 |
| abstract_inverted_index.CCTA | 69, 168 |
| abstract_inverted_index.PCI) | 372 |
| abstract_inverted_index.Tool | 183, 259, 262, 312, 346, 349, 369 |
| abstract_inverted_index.both | 205, 301, 402 |
| abstract_inverted_index.each | 337 |
| abstract_inverted_index.from | 98, 104, 116 |
| abstract_inverted_index.left | 143, 148 |
| abstract_inverted_index.less | 381 |
| abstract_inverted_index.more | 385 |
| abstract_inverted_index.old, | 224 |
| abstract_inverted_index.that | 27 |
| abstract_inverted_index.time | 57, 65, 197, 342 |
| abstract_inverted_index.vary | 42 |
| abstract_inverted_index.were | 153, 215, 231, 264 |
| abstract_inverted_index.with | 204 |
| abstract_inverted_index.(LCX) | 152 |
| abstract_inverted_index.(Tool | 172 |
| abstract_inverted_index.0.001 | 293 |
| abstract_inverted_index.0.1%, | 281 |
| abstract_inverted_index.0.2%, | 280 |
| abstract_inverted_index.0.3%, | 283 |
| abstract_inverted_index.2.3%, | 270 |
| abstract_inverted_index.2.7%, | 318 |
| abstract_inverted_index.2.9%, | 317 |
| abstract_inverted_index.208.5 | 357 |
| abstract_inverted_index.22.1% | 278 |
| abstract_inverted_index.26.0% | 268 |
| abstract_inverted_index.3.7%, | 323 |
| abstract_inverted_index.3.8%, | 322 |
| abstract_inverted_index.3.9%, | 271 |
| abstract_inverted_index.4.0%, | 325 |
| abstract_inverted_index.Force | 110 |
| abstract_inverted_index.Tools | 297 |
| abstract_inverted_index.right | 139 |
| abstract_inverted_index.slice | 124 |
| abstract_inverted_index.spent | 66, 198 |
| abstract_inverted_index.study | 93, 119 |
| abstract_inverted_index.terms | 53 |
| abstract_inverted_index.tools | 72, 171 |
| abstract_inverted_index.total | 131, 157, 196 |
| abstract_inverted_index.using | 73, 165, 311 |
| abstract_inverted_index.years | 223 |
| abstract_inverted_index.(459.5 | 351 |
| abstract_inverted_index.(CCTA) | 5 |
| abstract_inverted_index.(LAD), | 146 |
| abstract_inverted_index.(RCA), | 142 |
| abstract_inverted_index.19.7%, | 275 |
| abstract_inverted_index.21.4%, | 276 |
| abstract_inverted_index.22.0%, | 266 |
| abstract_inverted_index.22.8%, | 265 |
| abstract_inverted_index.Cohort | 243, 334 |
| abstract_inverted_index.Plaque | 180, 190 |
| abstract_inverted_index.artery | 141, 151 |
| abstract_inverted_index.burden | 12 |
| abstract_inverted_index.degree | 46, 376 |
| abstract_inverted_index.female | 232 |
| abstract_inverted_index.higher | 344, 375, 390 |
| abstract_inverted_index.inter- | 246, 303, 307 |
| abstract_inverted_index.intra- | 391 |
| abstract_inverted_index.manual | 382 |
| abstract_inverted_index.median | 340 |
| abstract_inverted_index.plaque | 11, 19, 31, 38, 82, 135, 254, 367, 400 |
| abstract_inverted_index.showed | 388 |
| abstract_inverted_index.spent. | 58 |
| abstract_inverted_index.total, | 286, 328 |
| abstract_inverted_index.versus | 261, 269, 279, 348, 356 |
| abstract_inverted_index.volume | 136 |
| abstract_inverted_index.widely | 43 |
| abstract_inverted_index.(20.0%) | 227 |
| abstract_inverted_index.(68.0%) | 235 |
| abstract_inverted_index.(70.0%) | 230 |
| abstract_inverted_index.(DSCT)) | 111 |
| abstract_inverted_index.0.001). | 364 |
| abstract_inverted_index.SOMATOM | 109 |
| abstract_inverted_index.Siemens | 174, 185 |
| abstract_inverted_index.another | 113 |
| abstract_inverted_index.between | 296 |
| abstract_inverted_index.cohort. | 412 |
| abstract_inverted_index.cohorts | 211 |
| abstract_inverted_index.compare | 61 |
| abstract_inverted_index.lesions | 290, 332 |
| abstract_inverted_index.patient | 89, 257 |
| abstract_inverted_index.scanner | 126 |
| abstract_inverted_index.support | 379 |
| abstract_inverted_index.types). | 127 |
| abstract_inverted_index.volumes | 83, 255, 401 |
| abstract_inverted_index.(40.0%), | 238 |
| abstract_inverted_index.(Siemens | 107 |
| abstract_inverted_index.Analysis | 181, 191 |
| abstract_inverted_index.Coronary | 1, 179, 189 |
| abstract_inverted_index.Frontier | 177 |
| abstract_inverted_index.However, | 35 |
| abstract_inverted_index.Methods: | 91 |
| abstract_inverted_index.Results: | 207 |
| abstract_inverted_index.analysis | 25, 39, 70, 169, 201, 368 |
| abstract_inverted_index.anterior | 144 |
| abstract_inverted_index.cohorts. | 90 |
| abstract_inverted_index.cohorts: | 101 |
| abstract_inverted_index.computed | 2 |
| abstract_inverted_index.coronary | 81, 140, 150 |
| abstract_inverted_index.dataset, | 338 |
| abstract_inverted_index.editing, | 383 |
| abstract_inverted_index.included | 95 |
| abstract_inverted_index.patients | 97, 103, 115, 209 |
| abstract_inverted_index.provides | 6, 28 |
| abstract_inverted_index.readers, | 164 |
| abstract_inverted_index.recorded | 203 |
| abstract_inverted_index.remained | 314 |
| abstract_inverted_index.required | 380 |
| abstract_inverted_index.requires | 21 |
| abstract_inverted_index.software | 26, 40, 71, 170 |
| abstract_inverted_index.addition, | 194 |
| abstract_inverted_index.analysis. | 34 |
| abstract_inverted_index.automated | 378 |
| abstract_inverted_index.available | 37 |
| abstract_inverted_index.calcified | 132, 289, 331 |
| abstract_inverted_index.detection | 32 |
| abstract_inverted_index.different | 74, 100, 121, 167 |
| abstract_inverted_index.performed | 154 |
| abstract_inverted_index.programs. | 206 |
| abstract_inverted_index.resulting | 49 |
| abstract_inverted_index.successor | 187 |
| abstract_inverted_index.syngo.via | 176 |
| abstract_inverted_index.(CT-guided | 371 |
| abstract_inverted_index.algorithms | 75 |
| abstract_inverted_index.assessment | 17, 79, 252, 398 |
| abstract_inverted_index.circumflex | 149 |
| abstract_inverted_index.components | 20 |
| abstract_inverted_index.coronaries | 160 |
| abstract_inverted_index.descending | 145 |
| abstract_inverted_index.population | 94 |
| abstract_inverted_index.processing | 341 |
| abstract_inverted_index.tomography | 3 |
| abstract_inverted_index.validation | 411 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Conclusion: | 365 |
| abstract_inverted_index.angiography | 4 |
| abstract_inverted_index.assessments | 9 |
| abstract_inverted_index.automation, | 48 |
| abstract_inverted_index.composition | 85 |
| abstract_inverted_index.differences | 51 |
| abstract_inverted_index.independent | 88, 163 |
| abstract_inverted_index.prototype). | 192 |
| abstract_inverted_index.Quantitative | 128 |
| abstract_inverted_index.commercially | 36 |
| abstract_inverted_index.composition. | 14 |
| abstract_inverted_index.encompassing | 373 |
| abstract_inverted_index.measurements | 129 |
| abstract_inverted_index.multi-center | 118, 410 |
| abstract_inverted_index.non-invasive | 7 |
| abstract_inverted_index.quantitative | 8, 16, 78, 397 |
| abstract_inverted_index.reproducible | 29 |
| abstract_inverted_index.348.0–627.0 | 355 |
| abstract_inverted_index.Healthineers, | 108, 175, 186 |
| abstract_inverted_index.non-calcified | 134 |
| abstract_inverted_index.noncalcified, | 287 |
| abstract_inverted_index.respectively, | 225, 239, 284, 326 |
| abstract_inverted_index.single-center | 106, 406 |
| abstract_inverted_index.variabilities | 249, 310 |
| abstract_inverted_index.198.0–216.0) | 361 |
| abstract_inverted_index.inter-observer | 393 |
| abstract_inverted_index.intra-observer | 248, 309 |
| abstract_inverted_index.non-calcified, | 329 |
| abstract_inverted_index.representative | 405 |
| abstract_inverted_index.semi-automated | 30 |
| abstract_inverted_index.hyperlipidemia. | 241 |
| abstract_inverted_index.reproducibility | 55, 63, 394 |
| abstract_inverted_index.time-efficient, | 386 |
| abstract_inverted_index.intra-observer). | 305 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5046215425 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 16 |
| corresponding_institution_ids | https://openalex.org/I4210130271 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.92829061 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |