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Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer

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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer.

Methods

The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient’s mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis.

Results

The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001).

Conclusion

The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.

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Acknowledgements

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Funding

This work was supported by the Medical Science and Technology Project of Zhejiang Province (2022KY122, 2024KY052); Traditional Chinese Medicine Science and Technology Project of Zhejiang Province (2024ZL040).

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Authors and Affiliations

  1. Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China

    Sikai Wu, Neng Wang & Wenjie Xu

  2. Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China

    Weiqun Ao & Guoqun Mao

  3. Department of Radiology, Putuo People’s Hospital, School of Medicine, Tongji University, Shanghai, China

    Jinwen Hu

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  1. Sikai Wu
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Contributions

SW, WA, and GM contributed to the study conception and design. Material preparation, data collection, and analysis were performed by NW, JH, and WX. The first draft of the manuscript was written by SW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Guoqun Mao.

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Wu, S., Wang, N., Ao, W. et al. Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04232-9

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  • DOI: https://doi.org/10.1007/s00261-024-04232-9

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