Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review
by
, , , and
Paul Windisch
1,*
,
Carole Koechli
1,
Susanne Rogers
2,
Christina Schröder
1,
Robert Förster
1,
Daniel R. Zwahlen
1 and
Stephan Bodis
2
1
Department of Radiation Oncology, Kantonsspital Winterthur, 8400 Winterthur, Switzerland
2
Department of Radiation Oncology, Kantonsspital Aarau, 5001 Aarau, Switzerland
*
Author to whom correspondence should be addressed.
Cancers 2022, 14(11), 2676; https://doi.org/10.3390/cancers14112676
Submission received: 26 April 2022
/
Revised: 18 May 2022
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Accepted: 26 May 2022
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Published: 27 May 2022
(This article belongs to the Special Issue Deep Neural Networks for Cancer Screening and Classification)
Simple Summary
Machine learning in radiology of the central nervous system has seen many interesting publications in the past few years. Since the focus has largely been on malignant tumors such as brain metastases and high-grade gliomas, we conducted a systematic review on benign tumors to summarize what has been published and where there might be gaps in the research. We found several studies that report good results, but the descriptions of methodologies could be improved to enable better comparisons and assessment of biases.
Abstract
Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.
