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Risk stratification by nomogram of deep learning radiomics based on multiparametric magnetic resonance imaging in knee meniscus injury

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Abstract

Purpose

To construct and validate a nomogram model that integrated deep learning radiomic features based on multiparametric MRI and clinical features for risk stratification of meniscus injury.

Methods

A total of 167 knee MR images were collected from two institutions. All patients were classified into two groups based on the MR diagnostic criteria proposed by Stoller et al. The automatic meniscus segmentation model was constructed through V-net. LASSO regression was performed to extract the optimal features correlated to risk stratification. A nomogram model was constructed by combining the Radscore and clinical features. The performance of the models was evaluated by ROC analysis and calibration curve. Subsequently, the model was simulated by junior doctors in order to test its practical application effect.

Results

The Dice similarity coefficients of automatic meniscus segmentation models were all over 0.8. Eight optimal features, identified by LASSO regression, were employed to calculate the Radscore. The combined model showed a better performance in both the training cohort (AUC = 0.90, 95%CI: 0.84–0.95) and the validation cohort (AUC = 0.84, 95%CI: 0.72–0.93). The calibration curve indicated a better accuracy of the combined model than either the Radscore or clinical model alone. The simulation results showed that the diagnostic accuracy of junior doctors increased from 74.9 to 86.2% after using the model.

Conclusion

Deep learning V-net demonstrated great performance in automatic meniscus segmentation of the knee joint. It was reliable for stratifying the risk of meniscus injury of the knee by nomogram which integrated the Radscores and clinical features.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

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Funding

This study has received funding by the Medical Health Science and Technology Commission of Zhejiang Province, China (No. 2021KY240, 2023KY162, 2023KY953), the Traditional Chinese Medicine Science and Technology Commission of Zhejiang Province, China (No. 2023ZL571), and the Hangzhou Biological Medicine and Health Industry Development Support Science and Technology Project (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, Zhejiang, 310006, Hangzhou, China

    Tao Zhen, Dacheng Hu, Mei Ruan, Luoyu Wang, Sandra Fan & Qijun Shen

  2. Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, 310006, China

    Jing Fang

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  1. Tao Zhen
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Contributions

All authors contributed to either the conception, design, data collection, or analysis. Material preparation and data collection were performed by Tao Zhen, Jing Fang, Mei Ruan, and Dacheng Hu. The first draft of the manuscript was written by Tao Zhen, and all authors commented on the previous versions of the manuscript. Tao Zhen and Luoyu Wang contributed to data analysis. Qijun Shen contributed to the final manuscript and supervised all the data. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qijun Shen.

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Ethics approval

This study was approved by the local institutional review board of the Hangzhou First People’s Hospital, Sichuan, China.

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Written informed consent was waived by the Institutional Review Board.

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The authors declare no competing interests.

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Zhen, T., Fang, J., Hu, D. et al. Risk stratification by nomogram of deep learning radiomics based on multiparametric magnetic resonance imaging in knee meniscus injury. International Orthopaedics (SICOT) 47, 2497–2505 (2023). https://doi.org/10.1007/s00264-023-05875-x

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