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Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review

SIMPLE SUMMARY: We present a systematic review of published reports on machine learning (ML) applications for the differentiation of gliomas from brain metastases by summarizing study characteristics, strengths, and pitfalls. Based on these findings, we present recommendations for future research in...

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Autores principales: Jekel, Leon, Brim, Waverly R., von Reppert, Marc, Staib, Lawrence, Cassinelli Petersen, Gabriel, Merkaj, Sara, Subramanian, Harry, Zeevi, Tal, Payabvash, Seyedmehdi, Bousabarah, Khaled, Lin, MingDe, Cui, Jin, Brackett, Alexandria, Mahajan, Amit, Omuro, Antonio, Johnson, Michele H., Chiang, Veronica L., Malhotra, Ajay, Scheffler, Björn, Aboian, Mariam S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946855/
https://www.ncbi.nlm.nih.gov/pubmed/35326526
http://dx.doi.org/10.3390/cancers14061369
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author Jekel, Leon
Brim, Waverly R.
von Reppert, Marc
Staib, Lawrence
Cassinelli Petersen, Gabriel
Merkaj, Sara
Subramanian, Harry
Zeevi, Tal
Payabvash, Seyedmehdi
Bousabarah, Khaled
Lin, MingDe
Cui, Jin
Brackett, Alexandria
Mahajan, Amit
Omuro, Antonio
Johnson, Michele H.
Chiang, Veronica L.
Malhotra, Ajay
Scheffler, Björn
Aboian, Mariam S.
author_facet Jekel, Leon
Brim, Waverly R.
von Reppert, Marc
Staib, Lawrence
Cassinelli Petersen, Gabriel
Merkaj, Sara
Subramanian, Harry
Zeevi, Tal
Payabvash, Seyedmehdi
Bousabarah, Khaled
Lin, MingDe
Cui, Jin
Brackett, Alexandria
Mahajan, Amit
Omuro, Antonio
Johnson, Michele H.
Chiang, Veronica L.
Malhotra, Ajay
Scheffler, Björn
Aboian, Mariam S.
author_sort Jekel, Leon
collection PubMed
description SIMPLE SUMMARY: We present a systematic review of published reports on machine learning (ML) applications for the differentiation of gliomas from brain metastases by summarizing study characteristics, strengths, and pitfalls. Based on these findings, we present recommendations for future research in this field. ABSTRACT: Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.
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spelling pubmed-89468552022-03-25 Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review Jekel, Leon Brim, Waverly R. von Reppert, Marc Staib, Lawrence Cassinelli Petersen, Gabriel Merkaj, Sara Subramanian, Harry Zeevi, Tal Payabvash, Seyedmehdi Bousabarah, Khaled Lin, MingDe Cui, Jin Brackett, Alexandria Mahajan, Amit Omuro, Antonio Johnson, Michele H. Chiang, Veronica L. Malhotra, Ajay Scheffler, Björn Aboian, Mariam S. Cancers (Basel) Systematic Review SIMPLE SUMMARY: We present a systematic review of published reports on machine learning (ML) applications for the differentiation of gliomas from brain metastases by summarizing study characteristics, strengths, and pitfalls. Based on these findings, we present recommendations for future research in this field. ABSTRACT: Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis. MDPI 2022-03-08 /pmc/articles/PMC8946855/ /pubmed/35326526 http://dx.doi.org/10.3390/cancers14061369 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Systematic Review
Jekel, Leon
Brim, Waverly R.
von Reppert, Marc
Staib, Lawrence
Cassinelli Petersen, Gabriel
Merkaj, Sara
Subramanian, Harry
Zeevi, Tal
Payabvash, Seyedmehdi
Bousabarah, Khaled
Lin, MingDe
Cui, Jin
Brackett, Alexandria
Mahajan, Amit
Omuro, Antonio
Johnson, Michele H.
Chiang, Veronica L.
Malhotra, Ajay
Scheffler, Björn
Aboian, Mariam S.
Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
title Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
title_full Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
title_fullStr Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
title_full_unstemmed Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
title_short Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
title_sort machine learning applications for differentiation of glioma from brain metastasis—a systematic review
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946855/
https://www.ncbi.nlm.nih.gov/pubmed/35326526
http://dx.doi.org/10.3390/cancers14061369
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