Radiomics for predicting hematoma expansion in patients with hypertensive intraparenchymal hematomas
| Autor: | Jianan Liu, Tuerdialimu Niyazi, Guo Guocai, Peng Yan, Fei Liang, Shikai Liang, Chao Ma, Kun Wang, Jian Wei, Chuhan Jiang, Yupeng Zhang |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male medicine.medical_specialty Enhanced ct Computed tomography 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Hematoma Radiomics medicine Humans Radiology Nuclear Medicine and imaging In patient Aged Cerebral Hemorrhage medicine.diagnostic_test business.industry General Medicine Middle Aged medicine.disease Feature (computer vision) 030220 oncology & carcinogenesis Hypertension Female Radiology business Epidemiologic Methods Tomography X-Ray Computed Selection operator |
| Zdroj: | European journal of radiology. 115 |
| ISSN: | 1872-7727 |
| Popis: | Purpose To explore the feasibility of predicting hematoma expansion at acute phase via a radiomics approach. Methods 254 cases with hypertensive intraparenchymal hematomas were retrospectively reviewed. Baseline non-contrast enhanced CT scan (NECT) were obtained on admission and compared to follow up CT to confirm the occurrence of hematoma expansion. Cases were split into training dataset with 149 cases and a test dataset with 105 cases. Radiomics features were extracted and informative features were selected by least absolute shrinkage and selection operator (LASSO) with 3-fold-cross validation. A radiomics score was then constructed with the selected features to discriminate enlarged hematomas from those that remained stable. Discriminative performance of the score was evaluated on the training and test dataset with area under the curve (AUC) and confusion matrix related metrics. Results A total of 576 radiomics features were extracted from 6 feature groups on NECT, of which 484 were stable. 5 features were selected by LASSO and based on which a radiomics score were constructed. The radiomics score achieved high discrimination ability between hematoma expansion and no-expansion with AUC of 0.892 (95% CI: 0.824–0.959) and accuracy of 0.852 in the training dataset. In the test dataset, predicting sensitivity, specificity, PPV, NPV and accuracy were 0.808, 0.835, 0.618, 0.930 and 0.820, respectively. Conclusions Radiomics features were effective in the prediction of hematoma expansion for patients with hypertensive intraparenchymal hematomas. Our radiomics score may provide a fast and quantitative risk assessment for these patients. |
| Databáze: | OpenAIRE |
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