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An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma

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Abstract

The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features. A total of 318 cases were enrolled in the study. The clinical, radiomics, and DTL features were selected to construct models. The calculation of radiomics and DTL score was completed by using selected features and correlation coefficient. The deep transfer learning radiomics (DTLR) nomogram was constructed by selected clinical features, radiomics score, and DTL score. The area under the receiver operator characteristic curve (AUC) was calculated. The models were compared by Delong test of AUCs and decision curve analysis (DCA). The features of sex, size, and peritumoral edema were selected to construct clinical model. Seven radiomics features and 15 DTL features were selected. The AUCs of clinical, radiomics, DTL model, and DTLR nomogram were 0.746, 0.75, 0.717, and 0.779 respectively. DTLR nomogram had the highest AUC of 0.779 (95% CI 0.6643–0.8943) with an accuracy rate of 0.734, a sensitivity value of 0.719, and a specificity value of 0.75 in test set. There was no significant difference in AUCs among four models in Delong test. The DTLR nomogram had a larger net benefit than other models across all the threshold probability. The DTLR nomogram had a satisfactory performance in Ki-67 prediction and could be a new evaluation method of meningioma which would be useful in the clinical decision-making.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the receiver operating characteristic curve

CI:

Confidence interval

CNN:

Convolutional neural networks

DCA:

Decision curve analysis

DL:

Deep learning

DTL:

Deep transfer learning

DTLR:

Deep transfer learning radiomics

ICC:

Intraclass correlation efficient

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

MRI:

Magnetic resonance imaging

ROC:

Receiver operating characteristic

ROI:

Region of interest

WHO:

World Health Organization

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Authors and Affiliations

  1. Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China

    Chongfeng Duan, Dapeng Hao, Jiufa Cui, Gang Wang, Wenjian Xu & Xuejun Liu

  2. Department of Information Management, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China

    Nan Li

Authors
  1. Chongfeng Duan
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Chongfeng Duan, Nan Li, and Xuejun Liu. The first draft of the manuscript was written by Chongfeng Duan, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xuejun Liu.

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Ethics Approval

This is an observational study. The institutional review board of the Affiliated Hospital of Qingdao University has confirmed that no ethical approval is required.

Consent to Participate

Informed consent was not necessary to obtain because the study used MRI images which did not include identifying information.

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The authors declare no competing interests.

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Key Points

• The DTLR nomogram had an AUC of 0.779 (95% CI 0.6643–0.8943) with an accuracy rate of 0.734, a sensitivity value of 0.719, and a specificity value of 0.75.

• The DTLR nomogram had a satisfactory performance in Ki-67 prediction and could be a new evaluation method of meningioma which would be useful in the clinical decision-making.

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Duan, C., Hao, D., Cui, J. et al. An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma. J Digit Imaging. Inform. med. 37, 510–519 (2024). https://doi.org/10.1007/s10278-023-00937-3

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