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For: Wang H, Zhou Y, Li L, Hou W, Ma X, Tian R. Current status and quality of radiomics studies in lymphoma: a systematic review. Eur Radiol 2020;30:6228-40. [PMID: 32472274 DOI: 10.1007/s00330-020-06927-1] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Abdurixiti M, Nijiati M, Shen R, Ya Q, Abuduxiku N, Nijiati M. Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review. Br J Radiol 2021;94:20201272. [PMID: 33882244 DOI: 10.1259/bjr.20201272] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Wijetunga NA, Imber BS, Caravelli JF, Mikhaeel NG, Yahalom J. A picture is worth a thousand words: a history of diagnostic imaging for lymphoma. Br J Radiol 2021;:20210285. [PMID: 34111961 DOI: 10.1259/bjr.20210285] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Marias K. The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics. J Imaging 2021;7:124. [PMID: 34460760 DOI: 10.3390/jimaging7080124] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Bathla G, Priya S, Liu Y, Ward C, Le NH, Soni N, Maheshwarappa RP, Monga V, Zhang H, Sonka M. Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques. Eur Radiol 2021. [PMID: 33890149 DOI: 10.1007/s00330-021-07845-6] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Modiri A, Vogelius I, Rechner LA, Nygård L, Bentzen SM, Specht L. Outcome-based multiobjective optimization of lymphoma radiation therapy plans. Br J Radiol 2021;94:20210303. [PMID: 34541859 DOI: 10.1259/bjr.20210303] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Lue KH, Wu YF, Lin HH, Hsieh TC, Liu SH, Chan SC, Chen YH. Prognostic Value of Baseline Radiomic Features of 18F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma. Diagnostics (Basel) 2020;11:E36. [PMID: 33379166 DOI: 10.3390/diagnostics11010036] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
7 Lisson C, Lisson C, Mezger M, Wolf D, Schmidt S, Thaiss W, Tausch E, Beer A, Stilgenbauer S, Beer M, Goetz M. Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma. Cancers 2022;14:2008. [DOI: 10.3390/cancers14082008] [Reference Citation Analysis]
8 Shi Z, Zhang Z, Liu Z, Zhao L, Ye Z, Dekker A, Wee L. Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 2021. [PMID: 34939174 DOI: 10.1007/s00259-021-05658-9] [Reference Citation Analysis]
9 Chang S, Han K, Suh YJ, Choi BW. Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review. Eur Radiol 2022. [PMID: 35230519 DOI: 10.1007/s00330-022-08587-9] [Reference Citation Analysis]
10 Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021;11:4431-60. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
11 Han EJ, O JH, Yoon H, Ha S, Yoo IR, Min JW, Choi J, Choi B, Park G, Lee HH, Jeon Y, Min G, Cho S. Comparison of FDG PET/CT and Bone Marrow Biopsy Results in Patients with Diffuse Large B Cell Lymphoma with Subgroup Analysis of PET Radiomics. Diagnostics 2022;12:222. [DOI: 10.3390/diagnostics12010222] [Reference Citation Analysis]
12 de Jesus FM, Yin Y, Mantzorou-Kyriaki E, Kahle XU, de Haas RJ, Yakar D, Glaudemans AWJM, Noordzij W, Kwee TC, Nijland M. Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features. Eur J Nucl Med Mol Imaging 2021. [PMID: 34850248 DOI: 10.1007/s00259-021-05626-3] [Reference Citation Analysis]
13 Ferjaoui R, Cherni MA, Boujnah S, Kraiem NEH, Kraiem T. Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images. Comput Methods Programs Biomed 2021;209:106320. [PMID: 34390938 DOI: 10.1016/j.cmpb.2021.106320] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]