Classification of acute intracerebral hemorrhage using radiomics on brain computed tomography images

Name: Phattanun Thabarsa

Organization : Chiang Mai University. Faculty of Engineering

Email : phattanun_th@cmu.ac.th

Name: Salita Angkurawaranon

Organization : Chiang Mai University. Faculty of Medicine

Email : salita.ang@cmu.ac.th

Name: Chakri Madla

Organization : Chiang Mai University. Faculty of Medicine

Email : chakri.madla@cmu.ac.th

Name: Withawat Vuthiwong

Organization : Chiang Mai University. Faculty of Medicine

Email : withawat.vuthiwong@cmu.ac.th

Name: Kittisak Unsrisong

Organization : Chiang Mai University. Faculty of Medicine

Email : kittisak.unsrisong@cmu.ac.th

Name: Papangkorn Inkeaw

Organization : Chiang Mai University. Faculty of Medicine

Email : papangkorn.i@cmu.ac.th

keyword: Acute intracerebral

LCSH: Hemorrhage

; Radiomics

LCSH: Machine Learning

Abstract: Acute intracerebral hemorrhage (ICH) entity accounts for 1 0 to 15% of all s trokes a nd is associated w ith a higher mortality rate ischemic stroke or subarachnoid hemorrhage. Causes of ICH are divided into primary, and secondary, including vascular malformation and tumorous. Primary ICH accounts for approximately 80% of all ICH cases. Vascular anomalies rank as the second most common cause of spontaneous ICH overall. Furthermore, hemorrhage resulting from brain t umors can o ccur i n u p t o 1 0% o f all primary o r metastatic tumors. Early recognizing of these three causes of bleeding is critical for clinicians in precise diagnosis, effective treatment management, and helps avoid delayed diagnosis. We proposed a radiomics approach for classifying multiple causes of acute ICH as vascular malformation, tumorous, and primaryrelated hematoma. Non-contrast brain computed tomography with c linical f eatures w as u sed a s i nput. T he r egions of b oth hematoma and perihematomal edema were delineated by using manual segmentation approach. Four feature selection methods were adopted. Also, three classification models were investigated in this study. The results showed that using the features selected by F-value applied with SVM classifier outperformed the other models, achieving weighted average accuracy (± SD) of 0.84 (± 0.07). Additionally, the model demonstrated average sensitivity and positive predictive value of 0.84 (± 0.06) and 0.86 (± 0.05), respectively. We also evaluate the overall performance of discriminating each class from the rest using AUC. The result suggested that our proposed model achieved the weighted average AUC of 0.90. Our proposed method highlights the potential in identifying multiple causes of acute and nontraumatic ICH, which has not been previously explored.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2023

Modified: 2024-12-23

Issued: 2024-12-23

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BibliograpyCitation : In IEEE Computational Intelligence Society Thailand Chapter, King Mongkut's Institute of Technology Ladkrabang. School of Information Technology and Universitas Gadjah Mada. Department of Electrical Engineering and Information Technology. The 15th International Conference on Information Technology and Electrical Engineering (ICITEE 2023) (pp.263-268) Nonthaburi : IEEE Computational Intelligence Society Thailand Chapter, 2023

eng

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