Using Radiomics and Convolutional Neural Networks for the Prediction of Hematoma Expansion After Intracerebral Hemorrhage

Abstract

Ruting Bo,1,2,* Zhi Xiong,3,* Ting Huang,4 Lingling Liu,4 Zhiqiang Chen2,4 1Department of Ultrasound Tianjin Hospital, Tianjin, 300200, People’s Republic of China; 2Department of Radiology, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102, People’s Republic of China; 3Department of Radiology, Xianning Central Hospital, Xianning, 437100, People’s Republic of China; 4Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhiqiang Chen, Department of Radiology, The First Affiliated Hospital of Hainan Medical University, No. 31, Longhua Road, Haikou, Hainan Province, 570102, People’s Republic of China, Email [email protected]: Hematoma enlargement (HE) is a common complication following acute intracerebral hemorrhage (ICH) and is associated with early deterioration and unfavorable clinical outcomes. This study aimed to evaluate the predictive performance of a computed tomography (CT) based model that utilizes deep learning features in identifying HE.Methods: A total of 408 patients were retrospectively enrolled between January 2015 and December 2020 from our institution. We designed an automatic model that could mask the hematoma area and fusion features of radiomics, clinical data, and convolutional neural network (CNN) in a hybrid model. We assessed the model’s performance by using confusion matrix metrics (CM), the area under the receiver operating characteristics curve (AUC), and other statistical indicators.Results: After automated masking, 408 patients were randomly divided into two cohorts with 204 patients in the training set and 204 patients in the validation set. The first cohort trained the CNN model, from which we then extracted radiomics, clinical data, and CNN features for the second validation cohort. After feature selection by K-highest score, a support vector machines (SVM) model classification was used to predict HE. Our hybrid model exhibited a high AUC of 0.949, and 0.95 of precision, 0.83 of recall, and 0.94 of average precision (AP). The CM found that only 5 cases were misidentified by the model.Conclusion: The automatic hybrid model we developed is an end-to-end method and can assist in clinical decision-making, thereby facilitating personalized treatment for patients with ICH.Keywords: radiomics, hematoma expansion, prediction, convolutional neural networks, intracerebral hemorrhage

Keywords

• radiomics
• hematoma expansion
• prediction
• convolutional neural networks
• intracerebral hemorrhage.