An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics

Abstract

Objective To update the systematic review of radiomics in osteosarcoma. Methods PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched to identify articles on osteosarcoma radiomics until May 15, 2022. The studies were assessed by Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Checklist for Artificial Intelligence in Medical Imaging (CLAIM), and modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The evidence supporting radiomics application for osteosarcoma was rated according to meta-analysis results. Results Twenty-nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 29.2%, 59.2%, and 63.7%, respectively. RQS identified a radiomics-specific issue of phantom study. TRIPOD addressed deficiency in blindness of assessment. CLAIM and TRIPOD both pointed out shortness in missing data handling and sample size or power calculation. CLAIM identified extra disadvantages in data de-identification and failure analysis. External validation and open science were emphasized by all the above three tools. The risk of bias and applicability concerns were mainly related to the index test. The meta-analysis of radiomics predicting neoadjuvant chemotherapy response by MRI presented a diagnostic odds ratio (95% confidence interval) of 28.83 (10.27–80.95) on testing datasets and was rated as weak evidence. Conclusions The quality of osteosarcoma radiomics studies is insufficient. More investigation is needed before using radiomics to optimize osteosarcoma treatment. CLAIM is recommended to guide the design and reporting of radiomics research.

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

“…However, the RQS was far from perfect. With an increasing trend of deep learning application in radiomics, RQS could not well identify the advantages and disadvantages in radiomics studies applying as the CLAIM [ 72 ]. The TRIPOD checklist might further identify room for improvement in radiomics studies, but some items were not suitable for radiomics studies [ 76 ].…”

A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers

“…We used a data collection instrument to collect bibliographical information, study characteristics, radiomics considerations, and model metrics of included studies (Additional file 1 : Table S1) [ 18 , 19 ]. The included studies were comprehensively evaluated using RQS [ 31 ], TRIPOD [ 32 ], CLAIM [ 33 ], and QUADAS-2 tools [ 34 ] (Additional file 1 : Tables S2 to S5).…”

“…The TRIPOD statement provides a checklist consisting of twenty-two criteria with thirty-seven items, and is recommended for distinguishing shortness of model reporting of radiomics models [ 29 , 30 ]. The CLAIM tool includes seven topics with forty-two items, and is considered as a better tool to identify technical disadvantages in radiomics studies [ 18 ]. The QUADAS-2 tool was tailored to our review by modifying the signaling questions [ 18 , 19 ].…”

“…The CLAIM tool includes seven topics with forty-two items, and is considered as a better tool to identify technical disadvantages in radiomics studies [ 18 ]. The QUADAS-2 tool was tailored to our review by modifying the signaling questions [ 18 , 19 ]. The consensus reached during data extraction and quality assessment are shown in Additional file 1 : Note S3.…”

“…As a subset of artificial intelligence, many recently developed tools have been recommended to assess the quality and reporting of radiomics research [ 18 , 19 , 29 , 30 ], including radiomics quality score (RQS) [ 31 ], the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist [ 32 ], the checklist for artificial intelligence in medical imaging (CLAIM) [ 33 ], and the modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool [ 34 ]. Although these tools are useful in identifying the reporting disadvantages, methodological shortness, and potential risk of bias in radiomics studies, their rating are all at the level of study.…”