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For: Verduin M, Primakov S, Compter I, Woodruff HC, van Kuijk SMJ, Ramaekers BLT, te Dorsthorst M, Revenich EGM, ter Laan M, Pegge SAH, Meijer FJA, Beckervordersandforth J, Speel EJ, Kusters B, de Leng WWJ, Anten MM, Broen MPG, Ackermans L, Schijns OEMG, Teernstra O, Hovinga K, Vooijs MA, Tjan-Heijnen VCG, Eekers DBP, Postma AA, Lambin P, Hoeben A. Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma. Cancers (Basel) 2021;13:722. [PMID: 33578746 DOI: 10.3390/cancers13040722] [Cited by in Crossref: 4] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Shen N, Lv W, Li S, Liu D, Xie Y, Zhang J, Zhang J, Jiang J, Jiang R, Zhu W. Noninvasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-institutional Study. J Magn Reson Imaging 2022. [PMID: 35929909 DOI: 10.1002/jmri.28378] [Reference Citation Analysis]
2 Caramanti R, Aprígio RM, D`aglio Rocha CE, Morais DF, Góes MJ, Chaddad-neto F, Tognola WA. Is Edema Zone Volume Associated With Ki-67 Index in Glioblastoma Patients? Cureus. [DOI: 10.7759/cureus.24246] [Reference Citation Analysis]
3 Chiu FY, Yen Y. Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach. Cancers (Basel) 2022;14:1475. [PMID: 35326626 DOI: 10.3390/cancers14061475] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
4 Yi Z, Long L, Zeng Y, Liu Z. Current Advances and Challenges in Radiomics of Brain Tumors. Front Oncol 2021;11:732196. [PMID: 34722274 DOI: 10.3389/fonc.2021.732196] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
5 Gao Y, Mao Y, Lu S, Tan L, Li G, Chen J, Huang D, Zhang X, Qiu Y, Liu Y. Magnetic resonance imaging-based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma. Head Neck 2021. [PMID: 34516714 DOI: 10.1002/hed.26867] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
6 Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021;11:1523. [PMID: 34573865 DOI: 10.3390/diagnostics11091523] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
7 Kommers I, Bouget D, Pedersen A, Eijgelaar RS, Ardon H, Barkhof F, Bello L, Berger MS, Conti Nibali M, Furtner J, Fyllingen EH, Hervey-Jumper S, Idema AJS, Kiesel B, Kloet A, Mandonnet E, Müller DMJ, Robe PA, Rossi M, Sagberg LM, Sciortino T, van den Brink WA, Wagemakers M, Widhalm G, Witte MG, Zwinderman AH, Reinertsen I, Solheim O, De Witt Hamer PC. Glioblastoma Surgery Imaging-Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations. Cancers (Basel) 2021;13:2854. [PMID: 34201021 DOI: 10.3390/cancers13122854] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
8 Compter I, Verduin M, Shi Z, Woodruff HC, Smeenk RJ, Rozema T, Leijenaar RTH, Monshouwer R, Eekers DBP, Hoeben A, Postma AA, Dekker A, De Ruysscher D, Lambin P, Wee L. Deciphering the glioblastoma phenotype by computed tomography radiomics. Radiother Oncol 2021;160:132-9. [PMID: 33984349 DOI: 10.1016/j.radonc.2021.05.002] [Cited by in F6Publishing: 1] [Reference Citation Analysis]