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Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice
Medical science in general and oncology in particular are dynamic, rapidly evolving subjects. Brain and spine tumors, whether primary or secondary, constitute a significant number of cases in any oncological practice. With the rapid influx of data in all aspects of neuro-oncological care, it is almo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202358/ https://www.ncbi.nlm.nih.gov/pubmed/34211861 http://dx.doi.org/10.4103/ajns.AJNS_265_20 |
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author | Hussain, Mustafa Mushtaq Shabbir, Ainsia Bakhshi, Saqib Kamran Shamim, Muhammad Shahzad |
author_facet | Hussain, Mustafa Mushtaq Shabbir, Ainsia Bakhshi, Saqib Kamran Shamim, Muhammad Shahzad |
author_sort | Hussain, Mustafa Mushtaq |
collection | PubMed |
description | Medical science in general and oncology in particular are dynamic, rapidly evolving subjects. Brain and spine tumors, whether primary or secondary, constitute a significant number of cases in any oncological practice. With the rapid influx of data in all aspects of neuro-oncological care, it is almost impossible for practicing clinicians to remain abreast with the current trends, or to synthesize the available data for it to be maximally beneficial for their patients. Machine-learning (ML) tools are fast gaining acceptance as an alternative to conventional reliance on online data. ML uses artificial intelligence to provide a computer algorithm-based information to clinicians. Different ML models have been proposed in the literature with a variable degree of precision and database requirements. ML can potentially solve the aforementioned problems for practicing clinicians by not just extracting and analyzing useful data, by minimizing or eliminating certain potential areas of human error, by creating patient-specific treatment plans, and also by predicting outcomes with reasonable accuracy. Current information on ML in neuro-oncology is scattered, and this literature review is an attempt to consolidate it and provide recent updates. |
format | Online Article Text |
id | pubmed-8202358 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-82023582021-06-30 Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice Hussain, Mustafa Mushtaq Shabbir, Ainsia Bakhshi, Saqib Kamran Shamim, Muhammad Shahzad Asian J Neurosurg Narrative Review Article Medical science in general and oncology in particular are dynamic, rapidly evolving subjects. Brain and spine tumors, whether primary or secondary, constitute a significant number of cases in any oncological practice. With the rapid influx of data in all aspects of neuro-oncological care, it is almost impossible for practicing clinicians to remain abreast with the current trends, or to synthesize the available data for it to be maximally beneficial for their patients. Machine-learning (ML) tools are fast gaining acceptance as an alternative to conventional reliance on online data. ML uses artificial intelligence to provide a computer algorithm-based information to clinicians. Different ML models have been proposed in the literature with a variable degree of precision and database requirements. ML can potentially solve the aforementioned problems for practicing clinicians by not just extracting and analyzing useful data, by minimizing or eliminating certain potential areas of human error, by creating patient-specific treatment plans, and also by predicting outcomes with reasonable accuracy. Current information on ML in neuro-oncology is scattered, and this literature review is an attempt to consolidate it and provide recent updates. Wolters Kluwer - Medknow 2021-02-23 /pmc/articles/PMC8202358/ /pubmed/34211861 http://dx.doi.org/10.4103/ajns.AJNS_265_20 Text en Copyright: © 2021 Asian Journal of Neurosurgery https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Narrative Review Article Hussain, Mustafa Mushtaq Shabbir, Ainsia Bakhshi, Saqib Kamran Shamim, Muhammad Shahzad Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice |
title | Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice |
title_full | Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice |
title_fullStr | Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice |
title_full_unstemmed | Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice |
title_short | Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice |
title_sort | are thinking machines breaking new frontiers in neuro-oncology? a narrative review on the emerging role of machine learning in neuro-oncological practice |
topic | Narrative Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202358/ https://www.ncbi.nlm.nih.gov/pubmed/34211861 http://dx.doi.org/10.4103/ajns.AJNS_265_20 |
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