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

  • Diagnostic Neuroradiology
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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.

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No funding was received for this study.

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Author notes

  1. LU and RC contributed equally to this work.

Authors and Affiliations

  1. Department of Advanced Biomedical Sciences, University of Naples “Federico II”, via Sergio Pansini 5, 80131, Naples, Italy

    Lorenzo Ugga, Renato Cuocolo, Alessandra D’Amico & Arturo Brunetti

  2. Department of Neurosciences, Reproductive and Odontostomatological Sciences, Division of Neurosurgery, University of Naples “Federico II”, Naples, Italy

    Domenico Solari, Teresa Somma, Paolo Cappabianca & Luigi Maria Cavallo

  3. Department of Advanced Biomedical Sciences, Pathology Section, University of Naples “Federico II”, Naples, Italy

    Elia Guadagno & Maria Laura del Basso de Caro

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  1. Lorenzo Ugga
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  2. Renato Cuocolo
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  3. Domenico Solari
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  4. Elia Guadagno
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  5. Alessandra D’Amico
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  7. Paolo Cappabianca
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  8. Maria Laura del Basso de Caro
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  9. Luigi Maria Cavallo
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  10. Arturo Brunetti
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Correspondence to Renato Cuocolo.

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Ugga, L., Cuocolo, R., Solari, D. et al. Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning. Neuroradiology 61, 1365–1373 (2019). https://doi.org/10.1007/s00234-019-02266-1

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