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Multi-center application of a convolutional neural network for preoperative detection of cavernous sinus invasion in pituitary adenomas

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Objective

Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers.

Methods

A total of 729 cases were retrospectively obtained in five medical centers with (n = 543) or without CSI (n = 186) from January 2011 to December 2021. The CNN model was trained using T1-enhanced MRI from two pituitary centers of excellence (n = 647). The other three municipal centers (n = 82) as the external testing set were imported to evaluate the model performance. The area-under-the-receiver-operating-characteristic-curve values (AUC-ROC) analyses were employed to evaluate predicted performance. Gradient-weighted class activation mapping (Grad-CAM) was used to determine models' regions of interest.

Results

The CNN model achieved high diagnostic accuracy (0.89) in identifying CSI in the external testing set, with an AUC-ROC value of 0.92 (95% CI, 0.88–0.97), better than CSI clinical predictor of diameter (AUC-ROC: 0.75), length (AUC-ROC: 0.80), and the three kinds of dichotomizations of the Knosp grading system (AUC-ROC: 0.70–0.82). In cases with Knosp grade 3A (n = 24, CSI rate, 0.35), the accuracy the model accounted for 0.78, with sensitivity and specificity values of 0.72 and 0.78, respectively. According to the Grad-CAM results, the views of the model were confirmed around the sellar region with CSI.

Conclusions

The deep learning model is capable of accurately identifying CSI and satisfactorily able to localize CSI in multicenters.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Code availability

No custom-made code or commercially available software code was used.

Abbreviations

AUC-ROC:

The area under the receiver operating characteristic

CNN:

Convolutional Neural Network

CSI:

Cavernous sinus invasion

DOR:

Diagnostic odds ratio

Grad-CAM:

Gradient-weighted class activation mapping

ICA:

Internal carotid artery

MRI:

Magnetic resonance imaging

NPV:

Negative predictive value

PA:

Pituitary adenoma

PPV:

Positive predictive value

ROI:

Regions of interest

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Acknowledgements

The authors would like to thank all investigators involved in this study, without whom the study would not have been possible

Funding

The present study was funded by the National Key R&D Program of China (2021YFE0114300) and the Fujian Provincial Key Project of Science and Technology Plan (grant no. 2018Y0067).

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Author notes

  1. Yi Fang, He Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, the Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China

    Yi Fang, He Wang, Wentai Zhang & Renzhi Wang

  2. Department of Neurosurgery, Fuzhou 900TH Hospital, Fuzong Clinical Medical College of Fujian Medical University, No. 156, Xi’erhuanbei Road, Fuzhou, Fujian, China

    Yi Fang, Ming Feng, Hongjie Chen, Liangfeng Wei, Shuwen Mu, Zhijie Pei, Jun Li & Shousen Wang

  3. Department of Neurosurgery, The Affiliated Hospital of Yangzhou University, Yangzhou University, Jiangsu, China

    Demao Cao

  4. Department of Neurosurgery, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, China

    Shengyu Cai

  5. Department of Neurosurgery, the Tongling People’s Hospital, Tongling, China

    Chengxing Qian

  6. Department of Neurosurgery, the Tiantan Hospital, Capital Medical University, Beijing, China

    Lei Cao

  7. Chinese University of Hong Kong (Shenzhen) School of Medicine, Shenzhen, Guangdong, People’s Republic of China

    Renzhi Wang

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Contributions

YF and HW had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: YF, SW, RW. Acquisition, analysis, or interpretation of data: YF, DC, SC, CQ, Zi. Critical revision of the manuscript for important intellectual content: SW, RW, HW, MF, LC. Statistical analysis: YF, HW, LW. Obtain funding: SW, YF. Administrative, technical, and material support: SW, YF, HW, HC, JL, SM. Supervision: SW, RW.

Corresponding authors

Correspondence to Renzhi Wang or Shousen Wang.

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We declare that we have no conflict of interest.

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All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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The requirement for informed consent was waived due to the retrospective study.

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Fang, Y., Wang, H., Cao, D. et al. Multi-center application of a convolutional neural network for preoperative detection of cavernous sinus invasion in pituitary adenomas. Neuroradiology 66, 353–360 (2024). https://doi.org/10.1007/s00234-024-03287-1

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