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Neuroradiology

, Volume 61, Issue 12, pp 1365–1373 | Cite as

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning

  • Lorenzo Ugga
  • Renato CuocoloEmail author
  • Domenico Solari
  • Elia Guadagno
  • Alessandra D’Amico
  • Teresa Somma
  • Paolo Cappabianca
  • Maria Laura del Basso de Caro
  • Luigi Maria Cavallo
  • Arturo Brunetti
Diagnostic Neuroradiology

Abstract

Purpose

Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker which correlates with pituitary adenoma aggressiveness. Aim of our study was to assess the accuracy of machine learning analysis of texture-derived parameters from pituitary adenomas preoperative MRI for the prediction of ki-67 proliferation index class.

Methods

A total of 89 patients who underwent an endoscopic endonasal procedure for pituitary adenoma removal with available ki-67 labeling index were included. From T2w MR images, 1128 quantitative imaging features were extracted. To select the most informative features, different supervised feature selection methods were employed. Subsequently, a k-nearest neighbors (k-NN) classifier was employed to predict macroadenoma high or low proliferation index. Algorithm validation was performed with a train-test approach.

Results

Of the 12 subsets derived from feature selection, the best performing one was constituted by the 4 highest correlating parameters at Pearson’s test. These all showed very good (ICC ≥ 0.85) inter-observer reproducibility. The overall accuracy of the k-NN in the test group was of 91.67% (33/36) of correctly classified patients.

Conclusions

Machine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.

Keywords

Machine learning Magnetic resonance imaging Pituitary adenoma 

Notes

Funding

No funding was received for this study.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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.

Informed consent

Requirement for informed consent was waived by the local IRB.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lorenzo Ugga
    • 1
  • Renato Cuocolo
    • 1
    Email author
  • Domenico Solari
    • 2
  • Elia Guadagno
    • 3
  • Alessandra D’Amico
    • 1
  • Teresa Somma
    • 2
  • Paolo Cappabianca
    • 2
  • Maria Laura del Basso de Caro
    • 3
  • Luigi Maria Cavallo
    • 2
  • Arturo Brunetti
    • 1
  1. 1.Department of Advanced Biomedical SciencesUniversity of Naples “Federico II”NaplesItaly
  2. 2.Department of Neurosciences, Reproductive and Odontostomatological Sciences, Division of NeurosurgeryUniversity of Naples “Federico II”NaplesItaly
  3. 3.Department of Advanced Biomedical Sciences, Pathology SectionUniversity of Naples “Federico II”NaplesItaly

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