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Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study

BACKGROUND: Machine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognos...

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Autores principales: Jiang, Tianxiang, Zhao, Zhou, Liu, Xueting, Shen, Chaoyong, Mu, Mingchun, Cai, Zhaolun, Zhang, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10507631/
https://www.ncbi.nlm.nih.gov/pubmed/37731636
http://dx.doi.org/10.3389/fonc.2023.1161237
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author Jiang, Tianxiang
Zhao, Zhou
Liu, Xueting
Shen, Chaoyong
Mu, Mingchun
Cai, Zhaolun
Zhang, Bo
author_facet Jiang, Tianxiang
Zhao, Zhou
Liu, Xueting
Shen, Chaoyong
Mu, Mingchun
Cai, Zhaolun
Zhang, Bo
author_sort Jiang, Tianxiang
collection PubMed
description BACKGROUND: Machine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance. METHODS: The PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared. RESULTS: Out of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias. CONCLUSION: The current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction. STUDY REGISTRATION: This protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b).
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spelling pubmed-105076312023-09-20 Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study Jiang, Tianxiang Zhao, Zhou Liu, Xueting Shen, Chaoyong Mu, Mingchun Cai, Zhaolun Zhang, Bo Front Oncol Oncology BACKGROUND: Machine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance. METHODS: The PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared. RESULTS: Out of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias. CONCLUSION: The current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction. STUDY REGISTRATION: This protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b). Frontiers Media S.A. 2023-09-04 /pmc/articles/PMC10507631/ /pubmed/37731636 http://dx.doi.org/10.3389/fonc.2023.1161237 Text en Copyright © 2023 Jiang, Zhao, Liu, Shen, Mu, Cai and Zhang 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
Jiang, Tianxiang
Zhao, Zhou
Liu, Xueting
Shen, Chaoyong
Mu, Mingchun
Cai, Zhaolun
Zhang, Bo
Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study
title Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study
title_full Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study
title_fullStr Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study
title_full_unstemmed Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study
title_short Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study
title_sort methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10507631/
https://www.ncbi.nlm.nih.gov/pubmed/37731636
http://dx.doi.org/10.3389/fonc.2023.1161237
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