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Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Purpose

To make individualised preoperative prediction of non-functioning pituitary adenoma (NFPAs) subtypes between null cell adenomas (NCAs) and other subtypes using a radiomics approach.

Methods

We enrolled 112 patients (training set: n = 75; test set: n = 37) with complete T1-weighted magnetic resonance imaging (MRI) and contrast-enhanced T1-weighted MRI (CE-T1). A total of 1482 quantitative imaging features were extracted from T1 and CE-T1 images. Support vector machine trained a predictive model that was validated using a receiver operating characteristics (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction.

Results

T1 image features yielded area under the curve (AUC) values of 0.8314 and 0.8042 for the training and test sets, respectively, while CE-T1 image features provided no additional contribution to the predictive model. The nomogram incorporating sex and the T1 radiomics signature yielded good calibration in the training and test sets (concordance index (CI) = 0.854 and 0.857, respectively).

Conclusion

This study focused on the preoperative prediction of NFPA subtypes between NCAs and others using a radiomics approach. The developed model yielded good performance, indicating that radiomics had good potential for the preoperative diagnosis of NFPAs.

Key points

• MRI may help in the pre-operative diagnosis of NFPAs subtypes

• Retrospective study showed T1-weighted MRI more useful than CE-T1 in NCAs diagnosis

• Treatment decision making becomes more individualised

• Radiomics approach had potential for classification of NFPAs

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Abbreviations

NFPAs:

Non-functioning pituitary adenomas

NCAs:

Null cell adenomas

CE-T1:

Contrast-enhanced T1-weighted

SVM:

Support vector machine

AUC:

Area under the curve

ROC:

Receiver operating characteristic

ICCs:

Inter-observer correlation coefficients

mRMR:

minimum-Redundancy Maximum-Relevancy

BIC:

Bayesian information criterion

C-index:

Concordance index

NRI:

Net reclassification improvement

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Funding

This study has received funding by National Key Research and Development Program of China (2017YFA0205200, 2017YFC1308700, 2106YFC0103702, 2016YFA0201401, 2017YFC1308701, 2017YFC1309100, 2016CZYD0001), National Natural Science Foundation of China (81227901, 81527805, 61231004, 81501616, 81671851), Beijing Municipal Science & Technology Commission (Z161100002616022, Z171100000117023), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), the International Innovation Team of CAS (20140491524), the Instrument Developing Project of the Chinese Academy of Sciences (YZ201502) and National High Technology Research and Development Program of China (2015AA020504).

Author information

Author notes

  1. Shuaitong Zhang and Guidong Song contributed equally to this work.

Authors and Affiliations

  1. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Shuaitong Zhang, Yali Zang, Chao Wang, Jie Tian & Di Dong

  2. University of Chinese Academy of Sciences, Beijing, 100080, China

    Shuaitong Zhang, Yali Zang, Jie Tian & Di Dong

  3. Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100050, China

    Guidong Song, Jian Jia, Chuzhong Li & Yazhuo Zhang

  4. Electrical Engineering School, Harbin University of Science & Technology, Harbin, 150040, China

    Chao Wang

  5. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China

    Chuzhong Li & Yazhuo Zhang

  6. CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Jie Tian

Authors
  1. Shuaitong Zhang
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  2. Guidong Song
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  3. Yali Zang
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Corresponding authors

Correspondence to Yali Zang or Yazhuo Zhang.

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Guarantor

The scientific guarantor of this publication is Jie Tian.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr. Di Dong from the University of Chinese Academy of Sciences, who is one of the authors, has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board of Beijing Tiantan Hospital Affiliated to Capital Medical University.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Zhang, S., Song, G., Zang, Y. et al. Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery. Eur Radiol 28, 3692–3701 (2018). https://doi.org/10.1007/s00330-017-5180-6

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  • DOI: https://doi.org/10.1007/s00330-017-5180-6

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