Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features
Issued Date
2024-01-01
Resource Type
ISSN
00283940
eISSN
14321920
Scopus ID
2-s2.0-85205684348
Journal Title
Neuroradiology
Rights Holder(s)
SCOPUS
Bibliographic Citation
Neuroradiology (2024)
Suggested Citation
Thabarsa P., Inkeaw P., Madla C., Vuthiwong W., Unsrisong K., Jitmahawong N., Sudsang T., Angkurawaranon C., Angkurawaranon S. Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features. Neuroradiology (2024). doi:10.1007/s00234-024-03481-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101589
Title
Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Purpose: To assess the efficacy of radiomics features extracted from non-contrast computed tomography (NCCT) scans in differentiating multiple etiologies of spontaneous intracerebral hemorrhage (ICH). Methods: CT images and clinical data from 141 ICH patients from 2010 to 2022 were collected. The cohort comprised primary (n = 57), tumorous (n = 46), and vascular malformation-related ICH (n = 38). Radiomics features were extracted from the initial brain NCCT scans and identified potential features using mutual information. A hierarchical classification with AdaBoost classifiers was employed to classify the multiple etiologies of ICH. Age of the patient and ICH’s location were examined alongside radiomics features. The accuracy, area under the curve (AUC), sensitivity, and specificity were used to evaluate classification performance. Results: The proposed method achieved an accuracy of 0.79. For identifying primary ICH, the model achieved a sensitivity of 0.86 and specificity of 0.87. Meanwhile, the sensitivity and specificity for identifying tumoral causes were 0.78 and 0.93, respectively. For vascular malformation, the model reached a sensitivity and specificity of 0.72 and 0.89, respectively. The AUCs for primary, tumorous, and vascular malformation were 0.86, 0.85, and 0.82, respectively. The findings further highlight the importance of texture-based variables in ICH classification. The age and location of the ICH can enhance the classification performance. Conclusion: The use of a machine learning model with radiomics features has the potential in classifying the three types of non-traumatic ICH. It may help the radiologist decide on an appropriate further examination plan to arrive at a correct diagnosis.