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The application of machine learning to imaging in hematological oncology: A scoping review

BACKGROUND: Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps. METHODS: The review was...

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Autores principales: Kotsyfakis, Stylianos, Iliaki-Giannakoudaki, Evangelia, Anagnostopoulos, Antonios, Papadokostaki, Eleni, Giannakoudakis, Konstantinos, Goumenakis, Michail, Kotsyfakis, Michail
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808781/
https://www.ncbi.nlm.nih.gov/pubmed/36605438
http://dx.doi.org/10.3389/fonc.2022.1080988
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author Kotsyfakis, Stylianos
Iliaki-Giannakoudaki, Evangelia
Anagnostopoulos, Antonios
Papadokostaki, Eleni
Giannakoudakis, Konstantinos
Goumenakis, Michail
Kotsyfakis, Michail
author_facet Kotsyfakis, Stylianos
Iliaki-Giannakoudaki, Evangelia
Anagnostopoulos, Antonios
Papadokostaki, Eleni
Giannakoudakis, Konstantinos
Goumenakis, Michail
Kotsyfakis, Michail
author_sort Kotsyfakis, Stylianos
collection PubMed
description BACKGROUND: Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps. METHODS: The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle–Ottawa scale was used to assess the quality of observational studies. RESULTS: Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case–control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation. CONCLUSION: To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.
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spelling pubmed-98087812023-01-04 The application of machine learning to imaging in hematological oncology: A scoping review Kotsyfakis, Stylianos Iliaki-Giannakoudaki, Evangelia Anagnostopoulos, Antonios Papadokostaki, Eleni Giannakoudakis, Konstantinos Goumenakis, Michail Kotsyfakis, Michail Front Oncol Oncology BACKGROUND: Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps. METHODS: The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle–Ottawa scale was used to assess the quality of observational studies. RESULTS: Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case–control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation. CONCLUSION: To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity. Frontiers Media S.A. 2022-12-19 /pmc/articles/PMC9808781/ /pubmed/36605438 http://dx.doi.org/10.3389/fonc.2022.1080988 Text en Copyright © 2022 Kotsyfakis, Iliaki-Giannakoudaki, Anagnostopoulos, Papadokostaki, Giannakoudakis, Goumenakis and Kotsyfakis https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Kotsyfakis, Stylianos
Iliaki-Giannakoudaki, Evangelia
Anagnostopoulos, Antonios
Papadokostaki, Eleni
Giannakoudakis, Konstantinos
Goumenakis, Michail
Kotsyfakis, Michail
The application of machine learning to imaging in hematological oncology: A scoping review
title The application of machine learning to imaging in hematological oncology: A scoping review
title_full The application of machine learning to imaging in hematological oncology: A scoping review
title_fullStr The application of machine learning to imaging in hematological oncology: A scoping review
title_full_unstemmed The application of machine learning to imaging in hematological oncology: A scoping review
title_short The application of machine learning to imaging in hematological oncology: A scoping review
title_sort application of machine learning to imaging in hematological oncology: a scoping review
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808781/
https://www.ncbi.nlm.nih.gov/pubmed/36605438
http://dx.doi.org/10.3389/fonc.2022.1080988
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