Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas.
暂无分享,去创建一个
[1] W. Chai,et al. TP53 Mutation Estimation Based on MRI Radiomics Analysis for Breast Cancer , 2022, Journal of magnetic resonance imaging : JMRI.
[2] Li Liu,et al. Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer , 2022, Frontiers in Oncology.
[3] Ji-gang Yang,et al. Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma , 2022, BMC Medical Imaging.
[4] S. Saffari,et al. CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma , 2022, Child's Nervous System.
[5] L. States,et al. Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN-amplification in neuroblastomas: preliminary results , 2022, European Radiology.
[6] M. Guckenberger,et al. Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study , 2022, Physics and imaging in radiation oncology.
[7] Jixia Li,et al. Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics , 2022, Frontiers in Medicine.
[8] Matthew A. Zapala,et al. Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma , 2022, Journal of Digital Imaging.
[9] Ji-gang Yang,et al. Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics , 2022, Diagnostics.
[10] D. Koh,et al. Radiomics in Oncology: A Practical Guide , 2021, Radiographics : a review publication of the Radiological Society of North America, Inc.
[11] I. Koch,et al. Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging , 2021, Scientific Reports.
[12] Huan Liu,et al. CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma , 2021, Frontiers in Oncology.
[13] Hannah J. Lee,et al. Radiomics feature robustness as measured using an MRI phantom , 2021, Scientific Reports.
[14] P. D. Di Paolo,et al. Radiogenomics prediction for MYCN amplification in neuroblastoma: A hypothesis generating study , 2021, Pediatric blood & cancer.
[15] OUP accepted manuscript , 2021, Neuro-Oncology.
[16] Hui Zheng,et al. Radiogenomics of neuroblastoma in pediatric patients: CT-based radiomics signature in predicting MYCN amplification , 2020, European Radiology.
[17] Olivier Saut,et al. Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients , 2020, Scientific Reports.
[18] Ellen Poliakoff,et al. Machine learning algorithm validation with a limited sample size , 2019, PloS one.
[19] H. Koo,et al. Clinical significance of MYCN amplification in patients with high‐risk neuroblastoma , 2018, Pediatric blood & cancer.
[20] G. Brodeur. Spontaneous regression of neuroblastoma , 2018, Cell and Tissue Research.
[21] O. Delattre,et al. Radiogenomics of neuroblastomas: Relationships between imaging phenotypes, tumor genomic profile and survival , 2017, PloS one.
[22] M. Arsenian-Henriksson,et al. The MYCN Protein in Health and Disease , 2017, Genes.
[23] William A Weiss,et al. Neuroblastoma and MYCN. , 2013, Cold Spring Harbor perspectives in medicine.
[24] J Khan,et al. International consensus for neuroblastoma molecular diagnostics: report from the International Neuroblastoma Risk Group (INRG) Biology Committee , 2009, British Journal of Cancer.
[25] T. Thompson,et al. Cancer Incidence Among Children and Adolescents in the United States, 2001–2003 , 2008, Pediatrics.
[26] B. Hero,et al. Neuroblastoma , 2007, The Lancet.
[27] Jayaram K. Udupa,et al. New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.