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Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion

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Abstract

Purpose

The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.

Method

A total of 108 patients with intracerebral hemorrhage were retrospectively evaluated. Images of baseline non-contrast computed tomography (NCCT) and follow-up NCCT scan within 24 h were retrospectively reviewed. An HE was defined as a volume increase of more than 33% or an increase greater than 12.5 mL from the volume of the baseline NCCT. Texture parameters of the baseline NCCT images were selected by the least absolute shrinkage and selection operator (LASSO) regression. We used support vector machine (SVM), decision tree (DT), conditional inference trees (CIT), random forest (RF), k‑nearest neighbors (KNN), back-propagation neural network (BPNet) and Bayes to build models. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) was performed and compared among models.

Results

Every model had a relatively high AUC (all > 0.75), SVM and KNN had the highest AUC of 0.91. There were significant differences between SVM and CIT (Z > 2.266, p = 0.02345), KNN and CIT (Z = 2.4834, p = 0.01301), RF and CIT (Z = 2.6956, p = 0.007027), KNN and BPNet (Z = 2.0122, p = 0.0442), RF and BPNet (Z = 1.9793, p = 0.04778). There was no significant difference among SVM, DT, RF, KNN and Bayes (p > 0.05). The SVM obtained the largest net benefit when the threshold probability was less than 0.33, while KNN obtained the largest net benefit when the threshold probability was greater than 0.33. Combined with ROC and DCA, SVM and KNN performed better in all the models for predicting HE.

Conclusion

Radiomic models based on different machine learning methods can be used to predict HE and the models generated by SVM and KNN performed best.

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Abbreviations

AUC:

Area under the ROC curves

BPNet:

Back-propagation neural network

CIT:

Conditional inference trees CIT

CTA:

Computed tomography angiography

DCA:

Decision curve analysis

DT:

Decision tree

HE:

Hematoma expansion

ICC:

Intraclass correlation coefficient

ICH:

Intracerebral hemorrhage

KNN:

K‑nearest neighbors

LASSO:

Least absolute shrinkage and selection operator

NCCT:

Non-contrast computed tomography

RF:

Random forest

ROC:

Receiver operating characteristic

ROI:

Region of interest

SVM:

Support vector machine

U:

Uniformity

V:

Variance

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Authors and Affiliations

  1. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

    Chongfeng Duan, Fang Liu, Song Gao, Jiping Zhao, Lei Niu, Song Liu, Gang Wang, Xiaoming Zhou, Yande Ren, Wenjian Xu & Xuejun Liu

  2. Department of Information Management, The Affiliated Hospital of Qingdao University, Qingdao, China

    Nan Li

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  1. Chongfeng Duan
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Correspondence to Xuejun Liu.

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Conflict of interest

Chongfeng Duan, Fang Liu, Song Gao, Jiping Zhao, Lei Niu, Nan Li, Song Liu, Gang Wang, Xiaoming Zhou, Yande Ren, Wenjian Xu and Xuejun Liu declare that they have no competing interests.

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Duan, C., Liu, F., Gao, S. et al. Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion. Clin Neuroradiol 32, 215–223 (2022). https://doi.org/10.1007/s00062-021-01040-2

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