Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement
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S. Park | H. Kim | J. E. Park | Se Jin Cho | Jeong Hoon Kim | Jung Youn Kim | Donghyun Kim | Jae Ho Shin | J. Kim
[1] Yupeng Zhang,et al. Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types , 2018, European Radiology.
[2] Chia-Feng Lu,et al. Machine Learning–Based Radiomics for Molecular Subtyping of Gliomas , 2018, Clinical Cancer Research.
[3] Christos Davatzikos,et al. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature , 2018, Neuro-oncology.
[4] M. Götz,et al. Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. , 2016, Radiology.
[5] Dorit Merhof,et al. Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. , 2019, Radiology.
[6] C. Vachon,et al. Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment. , 2019, Radiology.
[7] Qianjin Feng,et al. Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT , 2018, European Radiology.
[8] Lifen Yan,et al. Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma , 2018, European Radiology.
[9] I. Sohn,et al. Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma , 2017, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.
[10] Nan Hong,et al. 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 , 2018, European Radiology.
[11] Gary S Collins,et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.
[12] Yuanyuan Wang,et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma , 2017, European Radiology.
[13] Ginu A. Thomas,et al. A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models , 2018, Clinical Cancer Research.
[14] Chaofeng Liang,et al. Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study , 2018, European Radiology.
[15] Xiaotang Yang,et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer , 2018, European Radiology.
[16] Weijun Peng,et al. A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules , 2018, European Radiology.
[17] Jie Tian,et al. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study , 2018, Gut.
[18] D. Dong,et al. Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma , 2017, Clinical Cancer Research.
[19] Se Hoon Kim,et al. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach , 2018, European Radiology.
[20] Tian-wu Chen,et al. Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis , 2018, European Radiology.
[21] Tianye Niu,et al. A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors , 2018, Clinical Cancer Research.
[22] Martin Sill,et al. Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response , 2016, Clinical Cancer Research.
[23] D. Oldridge,et al. MRI features predict survival and molecular markers in diffuse lower-grade gliomas , 2017, Neuro-oncology.
[24] Jie Tian,et al. Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images , 2018, European Radiology.
[25] Andrés Larroza,et al. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study , 2018, European Radiology.
[26] J. E. van Timmeren,et al. Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score. , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[27] Leland S. Hu,et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma , 2016, Neuro-oncology.
[28] Erich P Huang,et al. 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. , 2016, Radiology.
[29] T. Jiang,et al. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature , 2018, European Radiology.
[30] N. Shah,et al. Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans , 2017, European Radiology.
[31] Tobias Gauer,et al. Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type. , 2019, Radiology.
[32] Tong-fu Yu,et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival , 2017, European Radiology.
[33] I. Kamel,et al. The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer , 2018, European Radiology.
[34] Lotty Hooft,et al. Reporting quality of diagnostic accuracy studies: a systematic review and meta-analysis of investigations on adherence to STARD , 2013, Evidence-Based Medicine.
[35] Steven D Chang,et al. Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment , 2016, Neuro-oncology.
[36] Jing Li,et al. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images , 2018, European Radiology.
[37] Jie Tian,et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma , 2018, European Radiology.
[38] David T. W. Jones,et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma , 2018, Neuro-oncology.
[39] T. Jiang,et al. MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis , 2017, European Radiology.
[40] Jie Tian,et al. MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation , 2018, European Radiology.
[41] Xinzhong Zhu,et al. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer , 2018, European Radiology.
[42] Susan Mallett,et al. QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies , 2011, Annals of Internal Medicine.
[43] Kensaku Mori,et al. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively , 2018, European Radiology.
[44] Sebastian Bickelhaupt,et al. Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer. , 2018, Radiology.
[45] S. Choi,et al. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients , 2018, Neuro-oncology.
[46] John Quackenbush,et al. Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer. , 2017, Cancer research.
[47] Shaocheng Zhu,et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature , 2018, European Radiology.
[48] O. Abe,et al. Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis , 2018, European Radiology.
[49] W. Sauerbrei,et al. Reporting recommendations for tumor marker prognostic studies (REMARK). , 2005, Journal of the National Cancer Institute.
[50] Xin Gao,et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer , 2018, European Radiology.
[51] Martin Sill,et al. Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. , 2016, Radiology.
[52] Hyunjin Park,et al. Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer , 2018, Clinical Cancer Research.
[53] Sang Min Lee,et al. Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer , 2018, European Radiology.
[54] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[55] G. Collins,et al. Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement , 2018, BMC Medicine.
[56] D. Dong,et al. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication , 2018, European Radiology.
[57] Y. She,et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules , 2018, European Radiology.
[58] Minming Zhang,et al. Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features , 2018, European Radiology.
[59] Lawrence H. Schwartz,et al. Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab , 2017, Neuro-oncology.
[60] F. Zhu,et al. Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes. , 2019, Radiology.
[61] S. Park,et al. Does the Reporting Quality of Diagnostic Test Accuracy Studies, as Defined by STARD 2015, Affect Citation? , 2016, Korean journal of radiology.
[62] Wenzhen Zhu,et al. Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour , 2018, European Radiology.
[63] Yanqi Huang,et al. Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. , 2016, Radiology.
[64] Se Hoon Kim,et al. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging , 2018, European Radiology.
[65] S. Lui,et al. Psychoradiologic Utility of MR Imaging for Diagnosis of Attention Deficit Hyperactivity Disorder: A Radiomics Analysis. , 2017, Radiology.
[66] Michael Götz,et al. Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values. , 2018, Radiology.
[67] John C Waterton,et al. Qualification of imaging biomarkers for oncology drug development. , 2012, European journal of cancer.
[68] Walter Noordzij,et al. Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer. , 2018, Radiology.
[69] Stuart A. Taylor,et al. Imaging biomarker roadmap for cancer studies , 2016, Nature Reviews Clinical Oncology.
[70] D. Dong,et al. Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery , 2018, European Radiology.
[71] Hugo J. W. L. Aerts,et al. Radiomic‐Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC , 2017, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.
[72] T. Niu,et al. Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI , 2016, Clinical Cancer Research.
[73] Lotty Hooft,et al. Reporting diagnostic accuracy studies: some improvements after 10 years of STARD. , 2015, Radiology.
[74] Yanqi Huang,et al. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[75] Di Dong,et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? , 2018, European Radiology.
[76] Jing Wang,et al. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer , 2017, European Radiology.
[77] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[78] Shao-Feng Duan,et al. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer , 2018, European Radiology.
[79] Zhenchao Tang,et al. Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer , 2017, Clinical Cancer Research.
[80] Tianxin Lin,et al. A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer , 2017, Clinical Cancer Research.
[81] Iva Petkovska,et al. MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy. , 2018, Radiology.
[82] Sung Tae Kim,et al. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation , 2018, Neuro-oncology.
[83] D. Saloner,et al. Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach , 2018, European Radiology.
[84] Ming-de Lu,et al. Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis , 2018, European Radiology.
[85] Stephen B. Johnson,et al. Central challenges facing the national clinical research enterprise. , 2003, JAMA.
[86] N. Paragios,et al. 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. , 2018, The Lancet. Oncology.
[87] Qianjin Feng,et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI , 2018, European Radiology.
[88] H. Barnhart,et al. The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions , 2015, Statistical methods in medical research.
[89] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[90] Seok-Gu Kang,et al. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. , 2018, Radiology.
[91] Samuel H. Hawkins,et al. Predicting Malignant Nodules from Screening CT Scans , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.