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European Journal of Nuclear Medicine and Molecular Imaging

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Open Access 23-12-2021 | Computed Tomography | Review Article

Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy

Authors: Zhenwei Shi, Zhen Zhang, Zaiyi Liu, Lujun Zhao, Zhaoxiang Ye, Andre Dekker, Leonard Wee

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 8/2022

Abstract

Purpose

Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines.

Methods

A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality.

Results

Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 “good” item ratings.

Conclusion

A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.

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Title: Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy
Authors: Zhenwei Shi
Zhen Zhang
Zaiyi Liu
Lujun Zhao
Zhaoxiang Ye
Andre Dekker
Leonard Wee
Publication date: 23-12-2021
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Esophageal Cancer
Computed Tomography
Esophageal Cancer
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 8/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-021-05658-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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