Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework Article Swipe
Duen-Pang Kuo
,
Yung-Chieh Chen
,
Yi-Tien Li
,
Sho‐Jen Cheng
,
Kevin Li‐Chun Hsieh
,
Po‐Chih Kuo
,
Chen-Yin Ou
,
Cheng‐Yu Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s41747-024-00455-z
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s41747-024-00455-z
Background This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability. Results In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 ( p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. Conclusions Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. Relevance statement The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. Key points • Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction. Graphical Abstract
Related Topics
Concepts
Diffusion MRI
Pearson product-moment correlation coefficient
Nuclear medicine
Volume (thermodynamics)
Penumbra
Correlation coefficient
Limits of agreement
Artificial intelligence
Mathematics
Computer science
Statistics
Medicine
Physics
Magnetic resonance imaging
Ischemia
Radiology
Quantum mechanics
Cardiology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s41747-024-00455-z
- https://eurradiolexp.springeropen.com/counter/pdf/10.1186/s41747-024-00455-z
- OA Status
- gold
- Cited By
- 2
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396936963
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396936963Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s41747-024-00455-zDigital Object Identifier
- Title
-
Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning frameworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-15Full publication date if available
- Authors
-
Duen-Pang Kuo, Yung-Chieh Chen, Yi-Tien Li, Sho‐Jen Cheng, Kevin Li‐Chun Hsieh, Po‐Chih Kuo, Chen-Yin Ou, Cheng‐Yu ChenList of authors in order
- Landing page
-
https://doi.org/10.1186/s41747-024-00455-zPublisher landing page
- PDF URL
-
https://eurradiolexp.springeropen.com/counter/pdf/10.1186/s41747-024-00455-zDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://eurradiolexp.springeropen.com/counter/pdf/10.1186/s41747-024-00455-zDirect OA link when available
- Concepts
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Diffusion MRI, Pearson product-moment correlation coefficient, Nuclear medicine, Volume (thermodynamics), Penumbra, Correlation coefficient, Limits of agreement, Artificial intelligence, Mathematics, Computer science, Statistics, Medicine, Physics, Magnetic resonance imaging, Ischemia, Radiology, Quantum mechanics, CardiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
64Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ML | 73, 211, 244 |
| abstract_inverted_index.PV | 119, 131, 185, 200, 248 |
| abstract_inverted_index.We | 60 |
| abstract_inverted_index.an | 270 |
| abstract_inverted_index.as | 269 |
| abstract_inverted_index.at | 53 |
| abstract_inverted_index.be | 202, 287, 314 |
| abstract_inverted_index.by | 220, 289 |
| abstract_inverted_index.if | 272 |
| abstract_inverted_index.in | 12, 277 |
| abstract_inverted_index.mL | 122, 134, 162 |
| abstract_inverted_index.of | 7, 143, 150, 160, 164, 172 |
| abstract_inverted_index.to | 18, 42, 69, 90, 177, 216, 235 |
| abstract_inverted_index.2.5 | 161 |
| abstract_inverted_index.95% | 170 |
| abstract_inverted_index.DTI | 205, 290 |
| abstract_inverted_index.Key | 281 |
| abstract_inverted_index.ML. | 295 |
| abstract_inverted_index.Our | 196 |
| abstract_inverted_index.PDM | 52 |
| abstract_inverted_index.PVs | 87, 138 |
| abstract_inverted_index.The | 47, 75, 224, 241, 310 |
| abstract_inverted_index.and | 55, 65, 85, 102, 156 |
| abstract_inverted_index.are | 233 |
| abstract_inverted_index.can | 201, 246, 267, 286, 313 |
| abstract_inverted_index.for | 109, 184, 252, 260, 305, 316 |
| abstract_inverted_index.had | 139 |
| abstract_inverted_index.its | 228 |
| abstract_inverted_index.mL. | 179 |
| abstract_inverted_index.map | 274 |
| abstract_inverted_index.min | 57 |
| abstract_inverted_index.our | 237 |
| abstract_inverted_index.the | 5, 19, 98, 113, 116, 128, 165, 181, 187, 191, 217, 221, 250, 278, 300 |
| abstract_inverted_index.was | 49, 107, 120, 132, 190, 299 |
| abstract_inverted_index.• | 283, 296, 309 |
| abstract_inverted_index.< | 154 |
| abstract_inverted_index.(ML) | 31 |
| abstract_inverted_index.(PV) | 16 |
| abstract_inverted_index.0.88 | 144 |
| abstract_inverted_index.0.93 | 151 |
| abstract_inverted_index.49.9 | 178 |
| abstract_inverted_index.Mean | 297 |
| abstract_inverted_index.PDM. | 223 |
| abstract_inverted_index.PV), | 168 |
| abstract_inverted_index.This | 2 |
| abstract_inverted_index.also | 266 |
| abstract_inverted_index.bias | 159 |
| abstract_inverted_index.five | 71 |
| abstract_inverted_index.from | 175 |
| abstract_inverted_index.mL), | 126 |
| abstract_inverted_index.mL). | 136 |
| abstract_inverted_index.male | 38 |
| abstract_inverted_index.mean | 166, 188 |
| abstract_inverted_index.most | 192, 301 |
| abstract_inverted_index.need | 251 |
| abstract_inverted_index.rats | 39 |
| abstract_inverted_index.test | 114 |
| abstract_inverted_index.that | 199 |
| abstract_inverted_index.used | 61, 183, 304 |
| abstract_inverted_index.were | 40, 78, 88 |
| abstract_inverted_index.with | 33, 169, 207, 262, 292, 318 |
| abstract_inverted_index.(2.4% | 163 |
| abstract_inverted_index.(DTI) | 11 |
| abstract_inverted_index.-44.9 | 176 |
| abstract_inverted_index.102.0 | 133 |
| abstract_inverted_index.106.4 | 121 |
| abstract_inverted_index.Among | 180 |
| abstract_inverted_index.Human | 231 |
| abstract_inverted_index.These | 137 |
| abstract_inverted_index.after | 58 |
| abstract_inverted_index.agent | 254 |
| abstract_inverted_index.fails | 276 |
| abstract_inverted_index.model | 76, 92, 225, 245 |
| abstract_inverted_index.range | 124 |
| abstract_inverted_index.rats, | 115 |
| abstract_inverted_index.serve | 268 |
| abstract_inverted_index.study | 3, 197 |
| abstract_inverted_index.train | 70 |
| abstract_inverted_index.using | 51, 80, 204 |
| abstract_inverted_index.(PDM), | 24 |
| abstract_inverted_index.artery | 45 |
| abstract_inverted_index.kidney | 263, 319 |
| abstract_inverted_index.limits | 171 |
| abstract_inverted_index.median | 118, 130 |
| abstract_inverted_index.middle | 43 |
| abstract_inverted_index.option | 259 |
| abstract_inverted_index.points | 282 |
| abstract_inverted_index.tensor | 9 |
| abstract_inverted_index.volume | 15, 95, 141, 218, 285 |
| abstract_inverted_index.0.001), | 155 |
| abstract_inverted_index.Feature | 105 |
| abstract_inverted_index.Methods | 36 |
| abstract_inverted_index.Pearson | 99, 147 |
| abstract_inverted_index.Results | 111 |
| abstract_inverted_index.Sixteen | 37 |
| abstract_inverted_index.defined | 219 |
| abstract_inverted_index.feature | 303 |
| abstract_inverted_index.imaging | 10 |
| abstract_inverted_index.machine | 29 |
| abstract_inverted_index.metrics | 64, 206 |
| abstract_inverted_index.models. | 74 |
| abstract_inverted_index.ranging | 174 |
| abstract_inverted_index.results | 214 |
| abstract_inverted_index.studies | 232 |
| abstract_inverted_index.through | 94 |
| abstract_inverted_index.volume. | 308 |
| abstract_inverted_index.whereas | 127 |
| abstract_inverted_index.without | 249 |
| abstract_inverted_index.Abstract | 0, 322 |
| abstract_inverted_index.approach | 32, 312 |
| abstract_inverted_index.cerebral | 44 |
| abstract_inverted_index.clinical | 229, 279 |
| abstract_inverted_index.combined | 291 |
| abstract_inverted_index.compared | 17, 89 |
| abstract_inverted_index.contrast | 253 |
| abstract_inverted_index.enhanced | 34, 227 |
| abstract_inverted_index.ensemble | 28, 82, 210, 294 |
| abstract_inverted_index.estimate | 247 |
| abstract_inverted_index.evaluate | 91 |
| abstract_inverted_index.feature. | 194 |
| abstract_inverted_index.features | 68, 182 |
| abstract_inverted_index.learning | 30 |
| abstract_inverted_index.mismatch | 23 |
| abstract_inverted_index.offering | 256 |
| abstract_inverted_index.patients | 261, 317 |
| abstract_inverted_index.penumbra | 48 |
| abstract_inverted_index.proposed | 242, 311 |
| abstract_inverted_index.setting. | 280 |
| abstract_inverted_index.standard | 20, 222 |
| abstract_inverted_index.validate | 236 |
| abstract_inverted_index.valuable | 258 |
| abstract_inverted_index.yielding | 213 |
| abstract_inverted_index.DTI-based | 243 |
| abstract_inverted_index.Graphical | 321 |
| abstract_inverted_index.Penumbral | 284 |
| abstract_inverted_index.Relevance | 239 |
| abstract_inverted_index.agreement | 173 |
| abstract_inverted_index.analysis, | 101 |
| abstract_inverted_index.analysis. | 104 |
| abstract_inverted_index.approach, | 212 |
| abstract_inverted_index.confirmed | 198 |
| abstract_inverted_index.diffusion | 8 |
| abstract_inverted_index.estimated | 203, 288 |
| abstract_inverted_index.findings. | 238 |
| abstract_inverted_index.important | 193, 302 |
| abstract_inverted_index.penumbral | 14, 307 |
| abstract_inverted_index.perfusion | 273 |
| abstract_inverted_index.potential | 6 |
| abstract_inverted_index.statement | 240 |
| abstract_inverted_index.subjected | 41 |
| abstract_inverted_index.utilizing | 25 |
| abstract_inverted_index.warranted | 234 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.beneficial | 315 |
| abstract_inverted_index.comparable | 215 |
| abstract_inverted_index.determined | 108 |
| abstract_inverted_index.identified | 50 |
| abstract_inverted_index.importance | 106 |
| abstract_inverted_index.integrated | 79 |
| abstract_inverted_index.occlusion. | 46, 59 |
| abstract_inverted_index.predicting | 306 |
| abstract_inverted_index.relevance. | 230 |
| abstract_inverted_index.similarity | 96, 142 |
| abstract_inverted_index.voxel-wise | 72 |
| abstract_inverted_index.Conclusions | 195 |
| abstract_inverted_index.DTI-derived | 63 |
| abstract_inverted_index.PDM-defined | 86, 129, 167 |
| abstract_inverted_index.alternative | 271 |
| abstract_inverted_index.assessment, | 97 |
| abstract_inverted_index.coefficient | 149 |
| abstract_inverted_index.correlation | 100, 148 |
| abstract_inverted_index.diffusivity | 189, 298 |
| abstract_inverted_index.identifying | 13 |
| abstract_inverted_index.performance | 93 |
| abstract_inverted_index.prediction, | 186 |
| abstract_inverted_index.predictions | 77 |
| abstract_inverted_index.stack-based | 27, 81, 209, 293 |
| abstract_inverted_index.techniques. | 83 |
| abstract_inverted_index.44.6–157.3 | 125 |
| abstract_inverted_index.ML-estimated | 84, 117 |
| abstract_inverted_index.dysfunction. | 264, 320 |
| abstract_inverted_index.investigates | 4 |
| abstract_inverted_index.(52.1–144.9 | 135 |
| abstract_inverted_index.(0.79–0.96), | 145 |
| abstract_inverted_index.(interquartile | 123 |
| abstract_inverted_index.Bland–Altman | 103, 158 |
| abstract_inverted_index.distance-based | 67 |
| abstract_inverted_index.explainability | 226 |
| abstract_inverted_index.interpretation | 275 |
| abstract_inverted_index.administration, | 255 |
| abstract_inverted_index.explainability. | 35, 110 |
| abstract_inverted_index.gadolinium-required | 21 |
| abstract_inverted_index.perfusion–diffusion | 22 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.76368839 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |