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A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation

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机构: [1]Shanghai Jiao Tong Univ, Tongren Hosp, Dept Imaging, Sch Med, 1111 Xianxia Rd, Shanghai 200050, Peoples R China [2]Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Dept Radiol, 600 Yishan Rd, Shanghai 200233, Peoples R China [3]Shanghai Jiao Tong Univ, Ruijin Hosp, Dept Radiol, Sch Med, 197 Ruijin 2nd Rd, Shanghai 200025, Peoples R China
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关键词: Osteosarcoma Machine learning Quality improvement Neoadjuvant therapy Systematic review

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Objectives To assess the methodological quality and risk of bias in radiomics studies investigating diagnosis, therapy response, and survival of patients with osteosarcoma. Methods In this systematic review, literatures on radiomics in osteosarcoma were included and assessed for methodological quality through the radiomics quality score (RQS). The risk of bias and concern of application was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A meta-analysis of studies focusing on predicting osteosarcoma response to neoadjuvant chemotherapy was performed. Results Twelve radiomics studies exploring osteosarcoma were identified, and five were included in meta-analysis. The RQS reached an average of 20.4% (6.92 of 36) with good inter-rater agreement (ICC 0.95, 95% CI 0.85-0.99). Four studies validated results with an internal dataset, none of which used external dataset; one study was prospectively designed, and another one shared part of the dataset. The risk of bias and concern of application were mainly related to index test aspect. The meta-analysis showed a diagnostic odds ratio of 43.68 (95%CI 13.5-141.31) for predicting response to neoadjuvant chemotherapy with high heterogeneity and low methodological quality. Conclusions The overall scientific quality of included studies is insufficient; however, radiomics remains a promising technology for predicting treatment response, which might guide therapeutic decision-making and related to prognosis. Improvements in study design, validation, and open science needs to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application of RQS, pre-trained RQS scoring procedure, and modification of RQS in response to clinical needs are necessary.

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出版当年[2020]版:
大类 | 2 区 医学
小类 | 2 区 核医学
最新[2023]版:
大类 | 2 区 医学
小类 | 2 区 核医学
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出版当年[2019]版:
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

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第一作者机构: [1]Shanghai Jiao Tong Univ, Tongren Hosp, Dept Imaging, Sch Med, 1111 Xianxia Rd, Shanghai 200050, Peoples R China
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