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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

A Commentary to this article was published on 22 January 2024

Abstract

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.

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Abbreviations

ACC:

Accuracy

AHA/ASA:

American Heart Association/American Stroke Association

AUC:

Area under the ROC curve

CT:

Computed tomography

CTA :

CT angiography

FOV :

Field of view

FPN :

Feature Pyramid Networks

GCS:

Glasgow coma scale

GLCM:

Gray level cooccurrence matrix

GLRLM:

Gray level run length matrix

GLSZM :

Gray level size zone matrix

HE:

Hematoma expansion

ICC:

Intraclass correlation coefficient

IoU:

Intersection over Union

LASSO:

Least Absolute Shrinkage and Selection Operator

NCCT:

Noncontrast computed tomography

PACS:

Picture Archiving and Communication System

ROC:

Receiver operating characteristic curve

ROI:

Region of interest

sICH:

Spontaneous intracerebral hemorrhage

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Acknowledgements

This study was supported in part by Hangzhou Jianpei Technology Company Ltd. We would like to thank Yuzhen Xi from 903rd Hospital of PLA and Luoyu Wang from Hangzhou First People’s Hospital for the data collection and interpretation.

Qijun Shen contributed to the final manuscript and supervising all the data.

Funding

This study has received funding from the medical and health research project of Zhejiang province, China (No. 2023KY953).

This study has received funding from the medical and health research project of Zhejiang province, China (No. 2021KY240).

This study has received funding from the Science and Technology Project for the development of Hangzhou Biomedicine and Health, China (No. 2021WJCY028).

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Authors and Affiliations

  1. Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China

    Changfeng Feng, Zhongxiang Ding, Tao Zhen, Mei Ruan & Qijun Shen

  2. Department of Radiology, Hangzhou Children’s Hospital, Hangzhou, Zhejiang, China

    Changfeng Feng & Qun Lao

  3. Department of Radiology, Zhejiang Kangjing Hospital, Hangzhou, Zhejiang, China

    Jing Han

  4. Hangzhou Jianpei Technology Company Ltd, Xiaoshan District, Hangzhou, Zhejiang, China

    Linyang He

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  1. Changfeng Feng
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Correspondence to Qijun Shen.

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Guarantor

The scientific guarantor of this publication is Qijun Shen.

Conflict of interest

One of the authors of this manuscript (Linyang He) is an employee of Hangzhou Jianpei Technology Company Ltd. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Informed consent was waived by IRB. Written informed consent was not required for this study because of the retrospective nature of the study.

Ethical approval

Institutional Review Board approval was obtained. This study was approved by the Institutional Review Board (Medical Ethics Committee) of Hangzhou First People's Hospital and was conducted in accordance with relevant guidelines.

Study subjects or cohorts overlap

Study subjects or cohorts have not been previously reported.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Feng, C., Ding, Z., Lao, Q. et al. Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography. Eur Radiol 34, 2908–2920 (2024). https://doi.org/10.1007/s00330-023-10410-y

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