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Published in: European Radiology 1/2020

01-01-2020 | Imaging Informatics and Artificial Intelligence

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

Authors: Ji Eun Park, Donghyun Kim, Ho Sung Kim, Seo Young Park, Jung Youn Kim, Se Jin Cho, Jae Ho Shin, Jeong Hoon Kim

Published in: European Radiology | Issue 1/2020

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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.
Appendix
Available only for authorised users
Literature
1.
go back to reference Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMed Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMed
2.
go back to reference Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762CrossRefPubMed Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762CrossRefPubMed
3.
go back to reference Sanduleanu S, Woodruff HC, de Jong EEC et al (2018) Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score. Radiother Oncol 127:349–360CrossRefPubMed Sanduleanu S, Woodruff HC, de Jong EEC et al (2018) Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score. Radiother Oncol 127:349–360CrossRefPubMed
4.
go back to reference O'Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186CrossRefPubMed O'Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186CrossRefPubMed
5.
go back to reference Sung NS, Crowley WF Jr, Genel M et al (2003) Central challenges facing the national clinical research enterprise. JAMA 289:1278–1287CrossRefPubMed Sung NS, Crowley WF Jr, Genel M et al (2003) Central challenges facing the national clinical research enterprise. JAMA 289:1278–1287CrossRefPubMed
6.
go back to reference Choi YJ, Chung MS, Koo HJ, Park JE, Yoon HM, Park SH (2016) Does the reporting quality of diagnostic test accuracy studies, as defined by STARD 2015, affect citation? Korean J Radiol 17:706–714CrossRefPubMedPubMedCentral Choi YJ, Chung MS, Koo HJ, Park JE, Yoon HM, Park SH (2016) Does the reporting quality of diagnostic test accuracy studies, as defined by STARD 2015, affect citation? Korean J Radiol 17:706–714CrossRefPubMedPubMedCentral
7.
go back to reference Waterton JC, Pylkkanen L (2012) Qualification of imaging biomarkers for oncology drug development. Eur J Cancer 48:409–415CrossRefPubMed Waterton JC, Pylkkanen L (2012) Qualification of imaging biomarkers for oncology drug development. Eur J Cancer 48:409–415CrossRefPubMed
8.
go back to reference Moons KG, Altman DG, Reitsma JB et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162:W1–W73 Moons KG, Altman DG, Reitsma JB et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162:W1–W73
9.
go back to reference Heus P, Damen JAAG, Pajouheshnia R et al (2018) Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med 16:120CrossRefPubMedPubMedCentral Heus P, Damen JAAG, Pajouheshnia R et al (2018) Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med 16:120CrossRefPubMedPubMedCentral
10.
go back to reference Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529–536CrossRefPubMed Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529–536CrossRefPubMed
11.
go back to reference Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefPubMed Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefPubMed
13.
go back to reference Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957CrossRefPubMed Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957CrossRefPubMed
14.
go back to reference Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRefPubMed Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRefPubMed
15.
go back to reference Kickingereder P, Bonekamp D, Nowosielski M et al (2016) Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology 281:907–918CrossRefPubMed Kickingereder P, Bonekamp D, Nowosielski M et al (2016) Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology 281:907–918CrossRefPubMed
16.
go back to reference Kickingereder P, Burth S, Wick A et al (2016) Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280:880–889CrossRefPubMed Kickingereder P, Burth S, Wick A et al (2016) Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280:880–889CrossRefPubMed
17.
go back to reference Kickingereder P, Gotz M, Muschelli J et al (2016) Large-scale Radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771CrossRefPubMedPubMedCentral Kickingereder P, Gotz M, Muschelli J et al (2016) Large-scale Radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771CrossRefPubMedPubMedCentral
18.
go back to reference Li H, Zhu Y, Burnside ES et al (2016) MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays. Radiology 281:382–391CrossRefPubMed Li H, Zhu Y, Burnside ES et al (2016) MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays. Radiology 281:382–391CrossRefPubMed
19.
go back to reference Nie K, Shi L, Chen Q et al (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 22:5256–5264CrossRefPubMed Nie K, Shi L, Chen Q et al (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 22:5256–5264CrossRefPubMed
20.
go back to reference Coroller TP, Agrawal V, Huynh E et al (2017) Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol 12:467–476CrossRefPubMed Coroller TP, Agrawal V, Huynh E et al (2017) Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol 12:467–476CrossRefPubMed
21.
go back to reference Grossmann P, Narayan V, Chang K et al (2017) Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol 19:1688–1697CrossRefPubMedPubMedCentral Grossmann P, Narayan V, Chang K et al (2017) Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol 19:1688–1697CrossRefPubMedPubMedCentral
22.
go back to reference Hu LS, Ning S, Eschbacher JM et al (2017) Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 19:128–137CrossRefPubMed Hu LS, Ning S, Eschbacher JM et al (2017) Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 19:128–137CrossRefPubMed
23.
go back to reference Liu TT, Achrol AS, Mitchell LA et al (2017) Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment. Neuro Oncol 19:997–1007PubMed Liu TT, Achrol AS, Mitchell LA et al (2017) Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment. Neuro Oncol 19:997–1007PubMed
24.
go back to reference Liu Z, Zhang XY, Shi YJ et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 23:7253–7262CrossRefPubMed Liu Z, Zhang XY, Shi YJ et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 23:7253–7262CrossRefPubMed
25.
go back to reference Lohmann P, Stoffels G, Ceccon G et al (2017) Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase (18)F-FET PET accuracy without dynamic scans. Eur Radiol 27:2916–2927CrossRefPubMed Lohmann P, Stoffels G, Ceccon G et al (2017) Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase (18)F-FET PET accuracy without dynamic scans. Eur Radiol 27:2916–2927CrossRefPubMed
26.
go back to reference Rios Velazquez E, Parmar C, Liu Y et al (2017) Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 77:3922–3930CrossRefPubMed Rios Velazquez E, Parmar C, Liu Y et al (2017) Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 77:3922–3930CrossRefPubMed
27.
go back to reference Song SH, Park H, Lee G et al (2017) Imaging phenotyping using Radiomics to predict micropapillary pattern within lung adenocarcinoma. J Thorac Oncol 12:624–632CrossRefPubMed Song SH, Park H, Lee G et al (2017) Imaging phenotyping using Radiomics to predict micropapillary pattern within lung adenocarcinoma. J Thorac Oncol 12:624–632CrossRefPubMed
28.
go back to reference Wang J, Wu CJ, Bao ML, Zhang J, Wang XN, Zhang YD (2017) Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol 27:4082–4090CrossRefPubMed Wang J, Wu CJ, Bao ML, Zhang J, Wang XN, Zhang YD (2017) Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol 27:4082–4090CrossRefPubMed
29.
go back to reference Wu S, Zheng J, Li Y et al (2017) A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 23:6904–6911CrossRefPubMed Wu S, Zheng J, Li Y et al (2017) A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 23:6904–6911CrossRefPubMed
30.
go back to reference Yu J, Shi Z, Lian Y et al (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27:3509–3522CrossRefPubMed Yu J, Shi Z, Lian Y et al (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27:3509–3522CrossRefPubMed
31.
go back to reference Yuan M, Zhang YD, Pu XH et al (2017) Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol 27:4857–4865CrossRefPubMed Yuan M, Zhang YD, Pu XH et al (2017) Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol 27:4857–4865CrossRefPubMed
32.
go back to reference Zhang B, Tian J, Dong D et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23:4259–4269CrossRefPubMed Zhang B, Tian J, Dong D et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23:4259–4269CrossRefPubMed
33.
34.
go back to reference Akbari H, Bakas S, Pisapia JM et al (2018) In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol 20:1068–1079CrossRefPubMedPubMedCentral Akbari H, Bakas S, Pisapia JM et al (2018) In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol 20:1068–1079CrossRefPubMedPubMedCentral
35.
go back to reference Bae S, Choi YS, Ahn SS et al (2018) Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 289:797–806CrossRefPubMed Bae S, Choi YS, Ahn SS et al (2018) Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 289:797–806CrossRefPubMed
36.
go back to reference Beukinga RJ, Hulshoff JB, Mul VEM et al (2018) Prediction of response to neoadjuvant chemotherapy and radiation therapy with baseline and restaging (18)F-FDG PET imaging biomarkers in patients with esophageal cancer. Radiology 287:983–992CrossRefPubMed Beukinga RJ, Hulshoff JB, Mul VEM et al (2018) Prediction of response to neoadjuvant chemotherapy and radiation therapy with baseline and restaging (18)F-FDG PET imaging biomarkers in patients with esophageal cancer. Radiology 287:983–992CrossRefPubMed
37.
go back to reference Bickelhaupt S, Jaeger PF, Laun FB et al (2018) Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer. Radiology 287:761–770CrossRefPubMed Bickelhaupt S, Jaeger PF, Laun FB et al (2018) Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer. Radiology 287:761–770CrossRefPubMed
38.
go back to reference Chen T, Ning Z, Xu L et al (2018) Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively. Eur Radiol 29:1074–1082 Chen T, Ning Z, Xu L et al (2018) Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively. Eur Radiol 29:1074–1082
39.
go back to reference Chen Y, Chen TW, Wu CQ et al (2018) Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol 29:4408–4417 Chen Y, Chen TW, Wu CQ et al (2018) Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol 29:4408–4417
40.
go back to reference Cui Y, Yang X, Shi Z et al (2018) Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 29:1211–1220 Cui Y, Yang X, Shi Z et al (2018) Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 29:1211–1220
41.
go back to reference Dong F, Li Q, Xu D et al (2018) Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features. Eur Radiol 29:3968–3975 Dong F, Li Q, Xu D et al (2018) Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features. Eur Radiol 29:3968–3975
42.
go back to reference Dong Y, Feng Q, Yang W et al (2018) Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 28:582–591CrossRefPubMed Dong Y, Feng Q, Yang W et al (2018) Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 28:582–591CrossRefPubMed
43.
go back to reference Guo J, Liu Z, Shen C et al (2018) MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol 28:3872–3881CrossRefPubMed Guo J, Liu Z, Shen C et al (2018) MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol 28:3872–3881CrossRefPubMed
44.
go back to reference Horvat N, Veeraraghavan H, Khan M et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287:833–843CrossRefPubMed Horvat N, Veeraraghavan H, Khan M et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287:833–843CrossRefPubMed
45.
go back to reference Hu HT, Wang Z, Huang XW et al (2018) Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol 29:2890–2901 Hu HT, Wang Z, Huang XW et al (2018) Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol 29:2890–2901
46.
go back to reference Kang D, Park JE, Kim YH et al (2018) Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol 20:1251–1261CrossRefPubMedPubMedCentral Kang D, Park JE, Kim YH et al (2018) Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol 20:1251–1261CrossRefPubMedPubMedCentral
47.
go back to reference Kickingereder P, Neuberger U, Bonekamp D et al (2018) Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol 20:848–857CrossRefPubMed Kickingereder P, Neuberger U, Bonekamp D et al (2018) Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol 20:848–857CrossRefPubMed
48.
go back to reference Kim JY, Park JE, Jo Y et al (2018) Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol 21:404–414 Kim JY, Park JE, Jo Y et al (2018) Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol 21:404–414
49.
go back to reference Kniep HC, Madesta F, Schneider T et al (2018) Radiomics of brain MRI: utility in prediction of metastatic tumor type. Radiology 180946 Kniep HC, Madesta F, Schneider T et al (2018) Radiomics of brain MRI: utility in prediction of metastatic tumor type. Radiology 180946
50.
go back to reference Multiparametric ultrasomics of significant liver fibrosis: a machine learning-based analysis. Eur Radiol 29:1496–1506 Multiparametric ultrasomics of significant liver fibrosis: a machine learning-based analysis. Eur Radiol 29:1496–1506
51.
go back to reference Li Y, Liu X, Qian Z et al (2018) Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature. Eur Radiol 28:2960–2968CrossRefPubMed Li Y, Liu X, Qian Z et al (2018) Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature. Eur Radiol 28:2960–2968CrossRefPubMed
52.
go back to reference Li Y, Liu X, Xu K et al (2018) MRI features can predict EGFR expression in lower grade gliomas: a voxel-based radiomic analysis. Eur Radiol 28:356–362CrossRefPubMed Li Y, Liu X, Xu K et al (2018) MRI features can predict EGFR expression in lower grade gliomas: a voxel-based radiomic analysis. Eur Radiol 28:356–362CrossRefPubMed
53.
go back to reference Li ZC, Bai H, Sun Q et al (2018) Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol 28:3640–3650CrossRefPubMed Li ZC, Bai H, Sun Q et al (2018) Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol 28:3640–3650CrossRefPubMed
54.
go back to reference Liang W, Yang P, Huang R et al (2018) A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors. Clin Cancer Res 25:584–594 Liang W, Yang P, Huang R et al (2018) A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors. Clin Cancer Res 25:584–594
55.
go back to reference Liu H, Zhang C, Wang L et al (2018) MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol 29:4418–4426 Liu H, Zhang C, Wang L et al (2018) MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol 29:4418–4426
56.
go back to reference Lu CF, Hsu FT, Hsieh KL et al (2018) Machine learning-based radiomics for molecular subtyping of gliomas. Clin Cancer Res 24:4429–4436CrossRefPubMed Lu CF, Hsu FT, Hsieh KL et al (2018) Machine learning-based radiomics for molecular subtyping of gliomas. Clin Cancer Res 24:4429–4436CrossRefPubMed
57.
go back to reference Lv W, Yuan Q, Wang Q et al (2018) Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT. Eur Radiol 28:3245–3254CrossRefPubMed Lv W, Yuan Q, Wang Q et al (2018) Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT. Eur Radiol 28:3245–3254CrossRefPubMed
58.
go back to reference Meng X, Xia W, Xie P et al (2018) Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol 29:3200–3209 Meng X, Xia W, Xie P et al (2018) Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol 29:3200–3209
59.
go back to reference Naganawa S, Enooku K, Tateishi R et al (2018) Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol 28:3050–3058CrossRefPubMed Naganawa S, Enooku K, Tateishi R et al (2018) Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol 28:3050–3058CrossRefPubMed
60.
go back to reference Niu J, Zhang S, Ma S et al (2018) Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images. Eur Radiol 29:1625–1634 Niu J, Zhang S, Ma S et al (2018) Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images. Eur Radiol 29:1625–1634
61.
go back to reference Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D (2018) Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 28:4514–4523 Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D (2018) Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 28:4514–4523
62.
go back to reference Park YW, Oh J, You SC et al (2018) Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol 29:4068–4076 Park YW, Oh J, You SC et al (2018) Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol 29:4068–4076
63.
go back to reference She Y, Zhang L, Zhu H et al (2018) The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol 28:5121–5128CrossRefPubMed She Y, Zhang L, Zhu H et al (2018) The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol 28:5121–5128CrossRefPubMed
64.
go back to reference Shi Z, Zhu C, Degnan AJ et al (2018) Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach. Eur Radiol 28:3912–3921CrossRefPubMedPubMedCentral Shi Z, Zhu C, Degnan AJ et al (2018) Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach. Eur Radiol 28:3912–3921CrossRefPubMedPubMedCentral
65.
go back to reference Su C, Jiang J, Zhang S et al (2018) Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour. Eur Radiol 29:1986–1996 Su C, Jiang J, Zhang S et al (2018) Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour. Eur Radiol 29:1986–1996
66.
go back to reference Suh HB, Choi YS, Bae S et al (2018) Primary central nervous system lymphoma and atypical glioblastoma: differentiation using radiomics approach. Eur Radiol 28:3832–3839CrossRefPubMed Suh HB, Choi YS, Bae S et al (2018) Primary central nervous system lymphoma and atypical glioblastoma: differentiation using radiomics approach. Eur Radiol 28:3832–3839CrossRefPubMed
67.
go back to reference Sun H, Chen Y, Huang Q et al (2018) Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis. Radiology 287:620–630CrossRefPubMed Sun H, Chen Y, Huang Q et al (2018) Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis. Radiology 287:620–630CrossRefPubMed
68.
go back to reference Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C (2018) Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI. Radiology 181352 Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C (2018) Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI. Radiology 181352
69.
go back to reference Wu M, Tan H, Gao F et al (2018) Predicting the grade of hepatocellular carcinoma based on non-contrastenhanced MRI radiomics signature. Eur Radiol 29:2802–2811 Wu M, Tan H, Gao F et al (2018) Predicting the grade of hepatocellular carcinoma based on non-contrastenhanced MRI radiomics signature. Eur Radiol 29:2802–2811
70.
go back to reference Yang L, Dong D, Fang M et al (2018) Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol 28:2058–2067CrossRefPubMed Yang L, Dong D, Fang M et al (2018) Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol 28:2058–2067CrossRefPubMed
71.
go back to reference Yin P, Mao N, Zhao C et al (2018) Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol 29:1841–1847 Yin P, Mao N, Zhao C et al (2018) Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol 29:1841–1847
72.
go back to reference Zhang S, Song G, Zang Y et al (2018) Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery. Eur Radiol 28:3692–3701CrossRefPubMed Zhang S, Song G, Zang Y et al (2018) Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery. Eur Radiol 28:3692–3701CrossRefPubMed
73.
go back to reference Zhang Y, Zhang B, Liang F et al (2018) Radiomics features on noncontrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types. Eur Radiol 29:2157–2165 Zhang Y, Zhang B, Liang F et al (2018) Radiomics features on noncontrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types. Eur Radiol 29:2157–2165
74.
go back to reference Zhang Z, Yang J, Ho A et al (2018) A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 28:2255–2263CrossRefPubMed Zhang Z, Yang J, Ho A et al (2018) A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 28:2255–2263CrossRefPubMed
75.
go back to reference Zhu X, Dong D, Chen Z et al (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28:2772–2778CrossRefPubMed Zhu X, Dong D, Chen Z et al (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28:2772–2778CrossRefPubMed
76.
go back to reference Zinn PO, Singh SK, Kotrotsou A et al (2018) A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models. Clin Cancer Res 24:6288–6299CrossRefPubMedPubMedCentral Zinn PO, Singh SK, Kotrotsou A et al (2018) A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models. Clin Cancer Res 24:6288–6299CrossRefPubMedPubMedCentral
77.
go back to reference Choe J, Lee SM, Do KH et al (2019) Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer. Eur Radiol 29:915–923CrossRefPubMed Choe J, Lee SM, Do KH et al (2019) Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer. Eur Radiol 29:915–923CrossRefPubMed
78.
go back to reference Hu T, Wang S, Huang L et al (2019) A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules. Eur Radiol 29:439–449CrossRefPubMed Hu T, Wang S, Huang L et al (2019) A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules. Eur Radiol 29:439–449CrossRefPubMed
79.
go back to reference Ji GW, Zhang YD, Zhang H et al (2019) Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology 290:90–98CrossRefPubMed Ji GW, Zhang YD, Zhang H et al (2019) Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology 290:90–98CrossRefPubMed
80.
go back to reference Kontos D, Winham SJ, Oustimov A et al (2019) Radiomic phenotypes of mammographic parenchymal complexity: toward augmenting breast density in breast cancer risk assessment. Radiology 290:41–49CrossRefPubMed Kontos D, Winham SJ, Oustimov A et al (2019) Radiomic phenotypes of mammographic parenchymal complexity: toward augmenting breast density in breast cancer risk assessment. Radiology 290:41–49CrossRefPubMed
81.
go back to reference Qu J, Shen C, Qin J et al (2019) The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer. Eur Radiol 29:906–914CrossRefPubMed Qu J, Shen C, Qin J et al (2019) The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer. Eur Radiol 29:906–914CrossRefPubMed
82.
go back to reference Tan X, Ma Z, Yan L, Ye W, Liu Z, Liang C (2019) Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol 29:392–400CrossRefPubMed Tan X, Ma Z, Yan L, Ye W, Liu Z, Liang C (2019) Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol 29:392–400CrossRefPubMed
83.
go back to reference Wei J, Yang G, Hao X et al (2019) A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol 29:877–888CrossRefPubMed Wei J, Yang G, Hao X et al (2019) A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol 29:877–888CrossRefPubMed
84.
go back to reference Bonekamp D, Kohl S, Wiesenfarth M et al (2018) Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology 289:128–137CrossRefPubMed Bonekamp D, Kohl S, Wiesenfarth M et al (2018) Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology 289:128–137CrossRefPubMed
85.
go back to reference Park H, Lim Y, Ko ES et al (2018) Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res 24:4705–4714CrossRefPubMed Park H, Lim Y, Ko ES et al (2018) Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res 24:4705–4714CrossRefPubMed
86.
go back to reference Sun R, Limkin EJ, Vakalopoulou M et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180–1191CrossRefPubMed Sun R, Limkin EJ, Vakalopoulou M et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180–1191CrossRefPubMed
87.
go back to reference Wang K, Lu X, Zhou H et al (2018) Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 68:729–741 Wang K, Lu X, Zhou H et al (2018) Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 68:729–741
88.
go back to reference Kessler LG, Barnhart HX, Buckler AJ et al (2015) The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 24:9–26CrossRefPubMed Kessler LG, Barnhart HX, Buckler AJ et al (2015) The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 24:9–26CrossRefPubMed
89.
go back to reference McShane LM, Altman DG, Sauerbrei W et al (2005) Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 97:1180–1184CrossRefPubMed McShane LM, Altman DG, Sauerbrei W et al (2005) Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 97:1180–1184CrossRefPubMed
90.
go back to reference Korevaar DA, van Enst WA, Spijker R, Bossuyt PM, Hooft L (2014) Reporting quality of diagnostic accuracy studies: a systematic review and meta-analysis of investigations on adherence to STARD. Evid Based Med 19:47–54CrossRefPubMed Korevaar DA, van Enst WA, Spijker R, Bossuyt PM, Hooft L (2014) Reporting quality of diagnostic accuracy studies: a systematic review and meta-analysis of investigations on adherence to STARD. Evid Based Med 19:47–54CrossRefPubMed
91.
go back to reference Korevaar DA, Wang J, van Enst WA et al (2015) Reporting diagnostic accuracy studies: some improvements after 10 years of STARD. Radiology 274:781–789CrossRefPubMed Korevaar DA, Wang J, van Enst WA et al (2015) Reporting diagnostic accuracy studies: some improvements after 10 years of STARD. Radiology 274:781–789CrossRefPubMed
Metadata
Title
Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement
Authors
Ji Eun Park
Donghyun Kim
Ho Sung Kim
Seo Young Park
Jung Youn Kim
Se Jin Cho
Jae Ho Shin
Jeong Hoon Kim
Publication date
01-01-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 1/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-019-06360-z

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