Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography

• Published in: European radiology Vol. 34; no. 5; pp. 2908 - 2920
• Main Authors: Feng, Changfeng, Ding, Zhongxiang, Lao, Qun, Zhen, Tao, Ruan, Mei, Han, Jing, He, Linyang, Shen, Qijun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024; Springer Nature B.V
• Subjects: Accuracy; Aged; Algorithms; Cerebral Hemorrhage - diagnostic imaging; Classification; Computed tomography; Deep Learning; Diagnostic Radiology; Efficiency; Female; Hematoma; Hematoma - diagnostic imaging; Hemorrhage; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Machine learning; Male; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Nomograms; Prediction models; Predictive Value of Tests; Radiology; Radiomics; Retrospective Studies; Segmentation; Semantics; Stroke; Tomography; Tomography, X-Ray Computed - methods; Training; Ultrasound; Deep learning; Tomography (X-ray computed); Cerebral hemorrhage; Machine learning
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-10410-y

Objectives Aimed to develop a nomogram model based on deep learning features and radiomics features for the prediction of early hematoma expansion. Methods A total of 561 cases of spontaneous intracerebral hemorrhage (sICH) with baseline Noncontrast Computed Tomography (NCCT) were included. The metrics of hematoma detection were evaluated by Intersection over Union (IoU), Dice coefficient (Dice), and accuracy (ACC). The semantic features of sICH were judged by EfficientNet-B0 classification model. Radiomics analysis was performed based on the region of interest which was automatically segmented by deep learning. A combined model was constructed in order to predict the early expansion of hematoma using multivariate binary logistic regression, and a nomogram and calibration curve were drawn to verify its predictive efficacy by ROC analysis. Results The accuracy of hematoma detection by segmentation model was 98.2% for IoU greater than 0.6 and 76.5% for IoU greater than 0.8 in the training cohort. In the validation cohort, the accuracy was 86.6% for IoU greater than 0.6 and 70.0% for IoU greater than 0.8. The AUCs of the deep learning model to judge semantic features were 0.95 to 0.99 in the training cohort, while in the validation cohort, the values were 0.71 to 0.83. The deep learning radiomics model showed a better performance with higher AUC in training cohort (0.87), internal validation cohort (0.83), and external validation cohort (0.82) than either semantic features or Radscore. Conclusion The combined model based on deep learning features and radiomics features has certain efficiency for judging the risk grade of hematoma. Clinical relevance statement Our study revealed that the deep learning model can significantly improve the work efficiency of segmentation and semantic feature classification of spontaneous intracerebral hemorrhage. The combined model has a good prediction efficiency for early hematoma expansion. Key Points • We employ a deep learning algorithm to perform segmentation and semantic feature classification of spontaneous intracerebral hemorrhage and construct a prediction model for early hematoma expansion. • The deep learning radiomics model shows a favorable performance for the prediction of early hematoma expansion. • The combined model holds the potential to be used as a tool in judging the risk grade of hematoma.