Literature DB >> 31350588

Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement.

Ji Eun Park1, Donghyun Kim2, Ho Sung Kim3, Seo Young Park4, Jung Youn Kim5, Se Jin Cho1, Jae Ho Shin6, Jeong Hoon Kim7.   

Abstract

OBJECTIVES: To evaluate radiomics studies according to radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to provide objective measurement of radiomics research.
MATERIALS AND METHODS: PubMed and Embase were searched for studies published in high clinical imaging journals until December 2018 using the terms "radiomics" and "radiogenomics." Studies were scored against the items in the RQS and TRIPOD guidelines. Subgroup analyses were performed for journal type (clinical vs. imaging), intended use (diagnostic vs. prognostic), and imaging modality (CT vs. MRI), and articles were compared using Fisher's exact test and Mann-Whitney analysis.
RESULTS: Seventy-seven articles were included. The mean RQS score was 26.1% of the maximum (9.4 out of 36). The RQS was low in demonstration of clinical utility (19.5%), test-retest analysis (6.5%), prospective study (3.9%), and open science (3.9%). None of the studies conducted a phantom or cost-effectiveness analysis. The adherence rate for TRIPOD was 57.8% (mean) and was particularly low in reporting title (2.6%), stating study objective in abstract and introduction (7.8% and 16.9%), blind assessment of outcome (14.3%), sample size (6.5%), and missing data (11.7%) categories. Studies in clinical journals scored higher and more frequently adopted external validation than imaging journals.
CONCLUSIONS: The overall scientific quality and reporting of radiomics studies is insufficient. Scientific improvements need to be made to feature reproducibility, analysis of clinical utility, and open science categories. Reporting of study objectives, blind assessment, sample size, and missing data is deemed to be necessary. KEY POINTS: • The overall scientific quality and reporting of radiomics studies is insufficient. • The RQS was low in demonstration of clinical utility, test-retest analysis, prospective study, and open science. • Room for improvement was shown in TRIPOD in stating study objective in abstract and introduction, blind assessment of outcome, sample size, and missing data categories.

Entities:  

Keywords:  Computed tomography; Machine learning; Magnetic resonance imaging; Neoplasm; Quality improvement

Mesh:

Year:  2019        PMID: 31350588     DOI: 10.1007/s00330-019-06360-z

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  91 in total

1.  Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.

Authors:  Philipp Kickingereder; David Bonekamp; Martha Nowosielski; Annekathrin Kratz; Martin Sill; Sina Burth; Antje Wick; Oliver Eidel; Heinz-Peter Schlemmer; Alexander Radbruch; Jürgen Debus; Christel Herold-Mende; Andreas Unterberg; David Jones; Stefan Pfister; Wolfgang Wick; Andreas von Deimling; Martin Bendszus; David Capper
Journal:  Radiology       Date:  2016-09-16       Impact factor: 11.105

2.  Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively.

Authors:  Tao Chen; Zhenyuan Ning; Lili Xu; Xingyu Feng; Shuai Han; Holger R Roth; Wei Xiong; Xixi Zhao; Yanfeng Hu; Hao Liu; Jiang Yu; Yu Zhang; Yong Li; Yikai Xu; Kensaku Mori; Guoxin Li
Journal:  Eur Radiol       Date:  2018-08-16       Impact factor: 5.315

3.  MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer.

Authors:  Huanhuan Liu; Caiyuan Zhang; Lijun Wang; Ran Luo; Jinning Li; Hui Zheng; Qiufeng Yin; Zhongyang Zhang; Shaofeng Duan; Xin Li; Dengbin Wang
Journal:  Eur Radiol       Date:  2018-11-09       Impact factor: 5.315

4.  Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour.

Authors:  Changliang Su; Jingjing Jiang; Shun Zhang; Jingjing Shi; Kaibin Xu; Nanxi Shen; Jiaxuan Zhang; Li Li; Lingyun Zhao; Ju Zhang; Yuanyuan Qin; Yong Liu; Wenzhen Zhu
Journal:  Eur Radiol       Date:  2018-10-12       Impact factor: 5.315

5.  Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival.

Authors:  Mei Yuan; Yu-Dong Zhang; Xue-Hui Pu; Yan Zhong; Hai Li; Jiang-Fen Wu; Tong-Fu Yu
Journal:  Eur Radiol       Date:  2017-05-18       Impact factor: 5.315

6.  Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes.

Authors:  Gu-Wei Ji; Yu-Dong Zhang; Hui Zhang; Fei-Peng Zhu; Ke Wang; Yong-Xiang Xia; Yao-Dong Zhang; Wang-Jie Jiang; Xiang-Cheng Li; Xue-Hao Wang
Journal:  Radiology       Date:  2018-10-16       Impact factor: 11.105

7.  Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.

Authors:  Zhi-Cheng Li; Hongmin Bai; Qiuchang Sun; Qihua Li; Lei Liu; Yan Zou; Yinsheng Chen; Chaofeng Liang; Hairong Zheng
Journal:  Eur Radiol       Date:  2018-03-21       Impact factor: 5.315

8.  Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

Authors:  Yanqi Huang; Zaiyi Liu; Lan He; Xin Chen; Dan Pan; Zelan Ma; Cuishan Liang; Jie Tian; Changhong Liang
Journal:  Radiology       Date:  2016-06-27       Impact factor: 11.105

9.  In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature.

Authors:  Hamed Akbari; Spyridon Bakas; Jared M Pisapia; MacLean P Nasrallah; Martin Rozycki; Maria Martinez-Lage; Jennifer J D Morrissette; Nadia Dahmane; Donald M O'Rourke; Christos Davatzikos
Journal:  Neuro Oncol       Date:  2018-07-05       Impact factor: 12.300

10.  QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

Authors:  Penny F Whiting; Anne W S Rutjes; Marie E Westwood; Susan Mallett; Jonathan J Deeks; Johannes B Reitsma; Mariska M G Leeflang; Jonathan A C Sterne; Patrick M M Bossuyt
Journal:  Ann Intern Med       Date:  2011-10-18       Impact factor: 25.391

View more
  61 in total

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

Authors:  Jingyu Zhong; Yangfan Hu; Liping Si; Geng Jia; Yue Xing; Huan Zhang; Weiwu Yao
Journal:  Eur Radiol       Date:  2020-09-02       Impact factor: 5.315

2.  Current status and quality of radiomics studies in lymphoma: a systematic review.

Authors:  Hongxi Wang; Yi Zhou; Li Li; Wenxiu Hou; Xuelei Ma; Rong Tian
Journal:  Eur Radiol       Date:  2020-05-29       Impact factor: 5.315

3.  Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.

Authors:  Suyon Chang; Kyunghwa Han; Young Joo Suh; Byoung Wook Choi
Journal:  Eur Radiol       Date:  2022-03-01       Impact factor: 5.315

4.  Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell Lymphoma.

Authors:  Russell Frood; Matthew Clark; Cathy Burton; Charalampos Tsoumpas; Alejandro F Frangi; Fergus Gleeson; Chirag Patel; Andrew F Scarsbrook
Journal:  Cancers (Basel)       Date:  2022-03-28       Impact factor: 6.639

5.  Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment.

Authors:  G I Cassinelli Petersen; J Shatalov; T Verma; W R Brim; H Subramanian; A Brackett; R C Bahar; S Merkaj; T Zeevi; L H Staib; J Cui; A Omuro; R A Bronen; A Malhotra; M S Aboian
Journal:  AJNR Am J Neuroradiol       Date:  2022-03-31       Impact factor: 3.825

Review 6.  Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.

Authors:  Qiuying Chen; Lu Zhang; Xiaokai Mo; Jingjing You; Luyan Chen; Jin Fang; Fei Wang; Zhe Jin; Bin Zhang; Shuixing Zhang
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-08-17       Impact factor: 9.236

7.  Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy.

Authors:  Wei Mu; Ilke Tunali; Jhanelle E Gray; Jin Qi; Matthew B Schabath; Robert J Gillies
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12-05       Impact factor: 9.236

8.  Radiomics predicts risk of cachexia in advanced NSCLC patients treated with immune checkpoint inhibitors.

Authors:  Wei Mu; Evangelia Katsoulakis; Christopher J Whelan; Kenneth L Gage; Matthew B Schabath; Robert J Gillies
Journal:  Br J Cancer       Date:  2021-04-07       Impact factor: 7.640

Review 9.  The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up.

Authors:  Radouane El Ayachy; Nicolas Giraud; Paul Giraud; Catherine Durdux; Philippe Giraud; Anita Burgun; Jean Emmanuel Bibault
Journal:  Front Oncol       Date:  2021-05-05       Impact factor: 6.244

10.  Quality control of radiomic features using 3D-printed CT phantoms.

Authors:  Usman Mahmood; Aditya Apte; Christopher Kanan; David D B Bates; Giuseppe Corrias; Lorenzo Manneli; Jung Hun Oh; Yusuf Emre Erdi; John Nguyen; Joseph O'Deasy; Amita Shukla-Dave
Journal:  J Med Imaging (Bellingham)       Date:  2021-06-29
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.