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Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions

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Abstract

Purpose

To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients.

Method

We presented an overview of the fundamental concepts in ML, its various stages used in healthcare, and highlighted the key components typically present in an imaging-based tumor analysis pipeline. A search was conducted across four databases (PubMed, Ovid, Embase, and Google Scholar) to gather research articles from the past 10 years (2009-2019) involving imaging related to pituitary tumor and ML. We grouped the studies by imaging modalities and analyzed the ML tasks in terms of the data inputs, reference standards, methodologies, and limitations.

Results

Of the 16 studies included in our analysis, 10 appeared in 2018–2019. Most of the studies utilized retrospective data and followed a semi-automatic ML pipeline. The studies included use of magnetic resonance imaging (MRI), facial photographs, surgical microscopic video, spectrometry, and spectroscopy imaging. The objectives of the studies covered 14 distinct applications and majority of the studies addressed a binary classification problem. Only five of the 11 MRI-based studies had an external validation or a holdout set to test the performance of a final trained model.

Conclusion

Through our concise evaluation and comparison of the studies using the concepts presented, we highlight future directions so that potential ML applications using different imaging modalities can be developed to benefit the clinical care of pituitary tumor patients.

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Abbreviations

ML:

Machine learning

AI:

Artificial intelligence

NLP:

Natural language processing

GBM:

Glioblastoma multiforme

PA:

Pituitary adenoma

ACTH:

Adrenocorticotrophic hormone

TSH:

Thyrotropin

OCT:

Optical coherence tomography

CT:

Computed tomography

MRI:

Magnetic resonance imaging

CV:

Cross-validation

LOOCV:

Leave-one-out cross validation

DNN:

Deep neural network

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Funding

The financial support provided through the Canadian Institute for Military and Veteran Health Research (CIMVHR) and patient donations (BrainMatters Foundation, the Dodig foundation) and Ryerson University are gratefully acknowledged. The opinions expressed in this work are authors’ and not of those the supporting agencies that provided the funding.

Author information

Author notes

  1. Ashirbani Saha and Samantha Tso have contributed equally to the development of the manuscript.

Authors and Affiliations

  1. Division of Neurosurgery, St. Michael’s Hospital, Toronto, ON, Canada

    Ashirbani Saha, Samantha Tso, Jessica Rabski & Michael D. Cusimano

  2. Department of Surgery, University of Toronto, Toronto, ON, Canada

    Michael D. Cusimano

  3. Department of Computer Science, Ryerson University, Toronto, Canada

    Alireza Sadeghian

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  1. Ashirbani Saha
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Correspondence to Ashirbani Saha.

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Saha, A., Tso, S., Rabski, J. et al. Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions. Pituitary 23, 273–293 (2020). https://doi.org/10.1007/s11102-019-01026-x

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