Evidence map›Paper›PMID 42135677›Full record

ArticleBMC medical imaging2026

Perilesional radiomics enhances 90-day outcome prediction in intracerebral hemorrhage: development and validation of a combined model with hematoma radiomics and clinical features.

Kangwei Zhang, Baoqing Yang, Tianzhi Yan, Kaixuan Wang, Mingyu Tan, Peijun Wang, Zhongling Wang

Abstract readValidation StudyMulticenter Study
In one paragraph

Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Kangwei Zhang *Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Baoqing Yang *Department of Cardiopulmonary Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Shanghai, 201600, China.
Tianzhi YanDepartment of Radiology, First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, 314000, Zhejiang Province, China.
Kaixuan WangDepartment of Radiology, First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, 314000, Zhejiang Province, China.
Mingyu TanDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China.
Peijun WangDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China. superlative_kw@sina.com.
Zhongling WangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China. zlwang138136@126.com.

Funding

Shanghai Clinical Research Ward SHDC2023CRW018BShanghai Municipal Science and Technology Commission's Project for High-Quality Development of Science and Technology Industry 25SF1908900the National Key Clinical Specialty Discipline Construction Project of China Z155080000004the National Natural Science Foundation of China 82227807the National Natural Science Foundation of China 82272057the Oriental Talents Top-Tier Program BJWS2025061
6 · The paper itself

Abstract

objectivesThis study aimed to develop and validate a prognostic model integrating hematoma (R1), perilesional (R2), and clinical features to predict 90-day outcomes.

methodsA total of 759 ICH patients from two centers were enrolled and allocated to training, internal validation, and external test sets. The primary endpoint was a poor 90-day outcome, defined as a modified Rankin Scale (mRS) score > 3. Independent clinical risk factors were identified via univariate and multivariate logistic regression analyses. Subsequently, seven prognostic models were constructed using R1, R2, clinical features, and their combinations. Model discrimination was compared using the DeLong test for the Area Under the Curve (AUC). Calibration and clinical utility were evaluated using calibration curves and Decision Curve Analysis (DCA).

resultsMultivariate analysis identified four independent risk factors for poor outcome: hematoma volume (OR 1.042; 95% CI 1.025-1.058; p < 0.001), mean hematoma density (OR 0.916; 95% CI 0.863-0.973; p = 0.004), age (OR 1.078; 95% CI 1.054-1.103; p < 0.001), and admission Glasgow Coma Scale (GCS) score (OR 0.777; 95% CI 0.708-0.853; p < 0.001). Among the seven models constructed, the tri-combined model (R1 + R2+Clinical) demonstrated the most stable and relatively better performance across all datasets, with an AUC of 0.791 (95% CI: 0.716-0.867) in the external test set. This model exhibited good calibration and favorable statistical net benefit on DCA.

conclusionThe integrated prognostic model combining hematoma and perilesional radiomic features with clinical data provides stable and incremental prognostic value for 90-day functional outcomes in patients with ICH.

Indexed as

Cerebral HemorrhageHematomaAgedFemaleHumansMaleMiddle AgedPrognosisRadiomicsRisk FactorsTomography, X-Ray ComputedIntracerebral hemorrhage (ICH)Logistic regressionModified Rankin Scale (mRS)Prognostic predictionRadiomics

Identifiers

PMID42135677
PMCPMC13352648

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