BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Zhao B. Understanding Sources of Variation to Improve the Reproducibility of Radiomics. Front Oncol 2021;11:633176. [PMID: 33854969 DOI: 10.3389/fonc.2021.633176] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
Number Citing Articles
1 Wright DE, Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Suman G, Chari ST, Kudva YC, Kline TL, Goenka AH. Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study. Abdom Radiol. [DOI: 10.1007/s00261-022-03668-1] [Reference Citation Analysis]
2 Rai R, Barton MB, Chlap P, Liney G, Brink C, Vinod S, Heinke M, Trada Y, Holloway LC. Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer. J Med Imag 2022;9. [DOI: 10.1117/1.jmi.9.4.044005] [Reference Citation Analysis]
3 Ramlee S, Hulse D, Bernatowicz K, Pérez-lópez R, Sala E, Aloj L. Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers 2022;14:3656. [DOI: 10.3390/cancers14153656] [Reference Citation Analysis]
4 Kothari G, Woon B, Patrick CJ, Korte J, Wee L, Hanna GG, Kron T, Hardcastle N, Siva S. The impact of inter-observer variation in delineation on robustness of radiomics features in non-small cell lung cancer. Sci Rep 2022;12:12822. [PMID: 35896707 DOI: 10.1038/s41598-022-16520-9] [Reference Citation Analysis]
5 Mirón Mombiela R, Arildskov AR, Bruun FJ, Hasselbalch LH, Holst KB, Rasmussen SH, Borrás C. What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies. Int J Mol Sci 2022;23:6504. [PMID: 35742947 DOI: 10.3390/ijms23126504] [Reference Citation Analysis]
6 Moskowitz CS, Welch ML, Jacobs MA, Kurland BF, Simpson AL. Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies. Radiology 2022;:211597. [PMID: 35579522 DOI: 10.1148/radiol.211597] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Wang Z, Yang C, Han W, Sui X, Zheng F, Xue F, Xu X, Wu P, Chen Y, Gu W, Song W, Jiang J. Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study. Insights Imaging 2022;13:82. [PMID: 35482262 DOI: 10.1186/s13244-022-01204-9] [Reference Citation Analysis]
8 Duan J, Qiu Q, Zhu J, Shang D, Dou X, Sun T, Yin Y, Meng X. Reproducibility for Hepatocellular Carcinoma CT Radiomic Features: Influence of Delineation Variability Based on 3D-CT, 4D-CT and Multiple-Parameter MR Images. Front Oncol 2022;12:881931. [DOI: 10.3389/fonc.2022.881931] [Reference Citation Analysis]
9 Xue C, Yuan J, Zhou Y, Wong OL, Cheung KY, Yu SK. Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study. Vis Comput Ind Biomed Art 2022;5:10. [PMID: 35359245 DOI: 10.1186/s42492-022-00106-3] [Reference Citation Analysis]
10 Refaee T, Salahuddin Z, Widaatalla Y, Primakov S, Woodruff HC, Hustinx R, Mottaghy FM, Ibrahim A, Lambin P. CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features. JPM 2022;12:553. [DOI: 10.3390/jpm12040553] [Reference Citation Analysis]
11 Tharmaseelan H, Hertel A, Tollens F, Rink J, Woźnicki P, Haselmann V, Ayx I, Nörenberg D, Schoenberg SO, Froelich MF. Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity. Cancers (Basel) 2022;14:1646. [PMID: 35406418 DOI: 10.3390/cancers14071646] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
12 Ferro M, de Cobelli O, Musi G, Del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022;14:17562872221109020. [PMID: 35814914 DOI: 10.1177/17562872221109020] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
13 Jensen LJ, Kim D, Elgeti T, Steffen IG, Hamm B, Nagel SN. Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study. Tomography 2021;7:866-76. [PMID: 34941645 DOI: 10.3390/tomography7040073] [Reference Citation Analysis]
14 Yoon JH, Sun SH, Xiao M, Yang H, Lu L, Li Y, Schwartz LH, Zhao B. Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies. Tomography 2021;7:877-92. [PMID: 34941646 DOI: 10.3390/tomography7040074] [Reference Citation Analysis]
15 Schmidt RM, Delgadillo R, Ford JC, Padgett KR, Studenski M, Abramowitz MC, Spieler B, Xu Y, Yang F, Dogan N. Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer. Sci Rep 2021;11:22737. [PMID: 34815464 DOI: 10.1038/s41598-021-02154-w] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
16 Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021;11:771787. [PMID: 34790581 DOI: 10.3389/fonc.2021.771787] [Reference Citation Analysis]
17 Lu L, Dercle L, Zhao B, Schwartz LH. Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging. Nat Commun 2021;12:6654. [PMID: 34789774 DOI: 10.1038/s41467-021-26990-6] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
18 Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Reference Citation Analysis]
19 Liu X, Maleki F, Muthukrishnan N, Ovens K, Huang SH, Pérez-Lara A, Romero-Sanchez G, Bhatnagar SR, Chatterjee A, Pusztaszeri MP, Spatz A, Batist G, Payabvash S, Haider SP, Mahajan A, Reinhold C, Forghani B, O'Sullivan B, Yu E, Forghani R. Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models. Cancers (Basel) 2021;13:3723. [PMID: 34359623 DOI: 10.3390/cancers13153723] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]