Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT) Article Swipe
Fiona Dierksen
,
J Sommer
,
Tran Anh Tuan
,
Huang Lin
,
Stefan P. Haider
,
Ilko Maier
,
Sanjay Aneja
,
Pina C. Sanelli
,
Ajay Malhotra
,
Adnan I. Qureshi
,
Jan Claassen
,
Soojin Park
,
Santosh B. Murthy
,
Guido J. Falcone
,
Kevin N. Sheth
,
Seyedmehdi Payabvash
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14242827
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14242827
Background: Intracerebral hemorrhages (ICH) and perihematomal edema (PHE) are respective imaging markers of primary and secondary brain injury in hemorrhagic stroke. In this study, we explored the potential added value of PHE radiomic features for prognostication in ICH patients. Methods: Using a multicentric trial cohort of acute supratentorial ICH (n = 852) patients, we extracted radiomic features from ICH and PHE lesions on admission non-contrast head CTs. We trained and tested combinations of different machine learning classifiers and feature selection methods for prediction of poor outcome—defined by 4-to-6 modified Rankin Scale scores at 3-month follow-up—using five different input strategies: (a) ICH radiomics, (b) ICH and PHE radiomics, (c) admission clinical predictors of poor outcomes, (d) ICH radiomics and clinical variables, and (e) ICH and PHE radiomics with clinical variables. Models were trained on 500 patients, tested, and compared in 352 using the receiver operating characteristics Area Under the Curve (AUC), Integrated Discrimination Index (IDI), and Net Reclassification Index (NRI). Results: Comparing the best performing models in the independent test cohort, both IDI and NRI demonstrated better individual-level risk assessment by addition of PHE radiomics as input to ICH radiomics (both p < 0.001), but with insignificant improvement in outcome prediction (AUC of 0.74 versus 0.71, p = 0.157). The addition of ICH and PHE radiomics to clinical variables also improved IDI and NRI risk-classification (both p < 0.001), but with a insignificant increase in AUC of 0.85 versus 0.83 (p = 0.118), respectively. All machine learning models had greater or equal accuracy in outcome prediction compared to the widely used ICH score. Conclusions: The addition of PHE radiomics to hemorrhage lesion radiomics, as well as radiomics to clinical risk factors, can improve individual-level risk assessment, albeit with an insignificant increase in prognostic accuracy. Machine learning models offer quantitative and immediate risk stratification—on par with or more accurate than the ICH score—which can potentially guide patients’ selection for interventions such as hematoma evacuation.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14242827
- https://www.mdpi.com/2075-4418/14/24/2827/pdf?version=1734334365
- OA Status
- gold
- Cited By
- 3
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405445652
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405445652Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics14242827Digital Object Identifier
- Title
-
Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-16Full publication date if available
- Authors
-
Fiona Dierksen, J Sommer, Tran Anh Tuan, Huang Lin, Stefan P. Haider, Ilko Maier, Sanjay Aneja, Pina C. Sanelli, Ajay Malhotra, Adnan I. Qureshi, Jan Claassen, Soojin Park, Santosh B. Murthy, Guido J. Falcone, Kevin N. Sheth, Seyedmehdi PayabvashList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics14242827Publisher landing page
- PDF URL
-
https://www.mdpi.com/2075-4418/14/24/2827/pdf?version=1734334365Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2075-4418/14/24/2827/pdf?version=1734334365Direct OA link when available
- Concepts
-
Medicine, Intracerebral hemorrhage, Radiomics, Modified Rankin Scale, Receiver operating characteristic, Cohort, Radiology, Internal medicine, Subarachnoid hemorrhage, Ischemic stroke, IschemiaTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4296157897, https://openalex.org/W3000213001, https://openalex.org/W2296329450, https://openalex.org/W2615735913, https://openalex.org/W3177271099, https://openalex.org/W4385994551, https://openalex.org/W4376598185, https://openalex.org/W4292451764, https://openalex.org/W1947113872, https://openalex.org/W2891753019, https://openalex.org/W2409181187, https://openalex.org/W4391438399, https://openalex.org/W1901965673, https://openalex.org/W2161703502, https://openalex.org/W2767128594, https://openalex.org/W4211013386, https://openalex.org/W4306850819, https://openalex.org/W2140931881, https://openalex.org/W2071505659, https://openalex.org/W2103798426, https://openalex.org/W1970998115, https://openalex.org/W2027083677, https://openalex.org/W2102470119, https://openalex.org/W2897292178, https://openalex.org/W2966601466, https://openalex.org/W2932580657, https://openalex.org/W2594356997, https://openalex.org/W4224044929, https://openalex.org/W3206246604, https://openalex.org/W2322994866, https://openalex.org/W4316466582, https://openalex.org/W4226105371, https://openalex.org/W4390614288, https://openalex.org/W4225851356 |
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