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For: Moradmand H, Aghamiri SMR, Ghaderi R. Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma. J Appl Clin Med Phys 2020;21:179-90. [PMID: 31880401 DOI: 10.1002/acm2.12795] [Cited by in Crossref: 58] [Cited by in F6Publishing: 65] [Article Influence: 14.5] [Reference Citation Analysis]
Number Citing Articles
1 Chilaca-Rosas MF, Garcia-Lezama M, Moreno-Jimenez S, Roldan-Valadez E. Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation. Diagnostics (Basel) 2023;13. [PMID: 36899993 DOI: 10.3390/diagnostics13050849] [Reference Citation Analysis]
2 Destito M, Marzullo A, Leone R, Zaffino P, Steffanoni S, Erbella F, Calimeri F, Anzalone N, De Momi E, Ferreri AJM, Calimeri T, Spadea MF. Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients. Bioengineering 2023;10:285. [DOI: 10.3390/bioengineering10030285] [Reference Citation Analysis]
3 Stogiannos N, Bougias H, Georgiadou E, Leandrou S, Papavasileiou P. Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study. Radiography (Lond) 2023;29:355-61. [PMID: 36758380 DOI: 10.1016/j.radi.2023.01.019] [Reference Citation Analysis]
4 Wang Y. Smart Vehicle Status Service Guarantee Framework Integrating Vehicle-Machine Smart Modules. 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) 2023. [DOI: 10.1109/idciot56793.2023.10053469] [Reference Citation Analysis]
5 Ahmed T. Biomaterial-based in vitro 3D modeling of glioblastoma multiforme. Cancer Pathogenesis and Therapy 2023. [DOI: 10.1016/j.cpt.2023.01.002] [Reference Citation Analysis]
6 Jiang ZY, Qi LS, Li JT, Cui N, Li W, Liu W, Wang KZ. Radiomics: Status quo and future challenges. Artif Intell Med Imaging 2022; 3(4): 87-96 [DOI: 10.35711/aimi.v3.i4.87] [Reference Citation Analysis]
7 McAnena P, Moloney BM, Browne R, O'Halloran N, Walsh L, Walsh S, Sheppard D, Sweeney KJ, Kerin MJ, Lowery AJ. A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer. BMC Med Imaging 2022;22:225. [PMID: 36564734 DOI: 10.1186/s12880-022-00956-6] [Reference Citation Analysis]
8 Stadlbauer A, Heinz G, Marhold F, Meyer-Bäse A, Ganslandt O, Buchfelder M, Oberndorfer S. Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning. Metabolites 2022;12. [PMID: 36557302 DOI: 10.3390/metabo12121264] [Reference Citation Analysis]
9 Demircioğlu A. The effect of preprocessing filters on predictive performance in radiomics. Eur Radiol Exp 2022;6:40. [PMID: 36045274 DOI: 10.1186/s41747-022-00294-w] [Reference Citation Analysis]
10 Zhang R, Ai QYH, Wong LM, Green C, Qamar S, So TY, Vlantis AC, King AD. Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used? Cancers (Basel) 2022;14. [PMID: 36497285 DOI: 10.3390/cancers14235804] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Won SY, Lee N, Park YW, Ahn SS, Ku CR, Kim EH, Lee SK. Quality reporting of radiomics analysis in pituitary adenomas: promoting clinical translation. Br J Radiol 2022;95:20220401. [PMID: 36018049 DOI: 10.1259/bjr.20220401] [Reference Citation Analysis]
12 Nikulshina YO, Redkin AN, Kolpakov AV, Zakharov MA. Radiomic Study for Objectification of Diagnostics and Complex Treatment of Glioblastoma. Kreativnaâ hirurgiâ i onkologiâ 2022;12:237-243. [DOI: 10.24060/2076-3093-2022-12-3-237-243] [Reference Citation Analysis]
13 Teng X, Zhang J, Ma Z, Zhang Y, Lam S, Li W, Xiao H, Li T, Li B, Zhou T, Ren G, Lee FK, Au K, Lee VH, Chang ATY, Cai J. Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma. Front Oncol 2022;12:974467. [DOI: 10.3389/fonc.2022.974467] [Reference Citation Analysis]
14 Poirot MG, Caan MWA, Ruhe HG, Bjørnerud A, Groote I, Reneman L, Marquering HA. Robustness of radiomics to variations in segmentation methods in multimodal brain MRI. Sci Rep 2022;12:16712. [PMID: 36202934 DOI: 10.1038/s41598-022-20703-9] [Reference Citation Analysis]
15 Veres G, Kiss J, Vas NF, Kallos-balogh P, Máthé NB, Lassen ML, Berényi E, Balkay L. Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms. Diagnostics 2022;12:2196. [DOI: 10.3390/diagnostics12092196] [Reference Citation Analysis]
16 Horvat N, Miranda J, El Homsi M, Peoples JJ, Long NM, Simpson AL, Do RKG. A primer on texture analysis in abdominal radiology. Abdom Radiol (NY) 2022;47:2972-85. [PMID: 34825946 DOI: 10.1007/s00261-021-03359-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
17 Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022;14:3897. [PMID: 36010891 DOI: 10.3390/cancers14163897] [Reference Citation Analysis]
18 Cui Y, Yin F. Impact of image quality on radiomics applications. Phys Med Biol 2022;67:15TR03. [DOI: 10.1088/1361-6560/ac7fd7] [Reference Citation Analysis]
19 Mitchell D, Buszek S, Tran B, Farhat M, Goldman J, Erickson L, Curl B, Suki D, Ferguson SD, Liu H, Kundu S, Chung C. Managing the effect of magnetic resonance imaging pulse sequence on radiomic feature reproducibility in the study of brain metastases. F1000Res 2022;11:892. [DOI: 10.12688/f1000research.122871.1] [Reference Citation Analysis]
20 Verma R, Hill VB, Statsevych V, Bera K, Correa R, Leo P, Ahluwalia M, Madabhushi A, Tiwari P. Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study. AJNR Am J Neuroradiol 2022;43:1115-23. [PMID: 36920774 DOI: 10.3174/ajnr.A7591] [Reference Citation Analysis]
21 Zhang JZ, Ganesh H, Raslau FD, Nair R, Escott E, Wang C, Wang G, Zhang J. Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study. Phys Med Biol 2022;67:145009. [DOI: 10.1088/1361-6560/ac7999] [Reference Citation Analysis]
22 Keek SA, Beuque M, Primakov S, Woodruff HC, Chatterjee A, van Timmeren JE, Vallières M, Hendriks LEL, Kraft J, Andratschke N, Braunstein SE, Morin O, Lambin P. Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics. Front Oncol 2022;12:920393. [DOI: 10.3389/fonc.2022.920393] [Reference Citation Analysis]
23 Shaheen A, Bukhari ST, Nadeem M, Burigat S, Bagci U, Mohy-Ud-Din H. Overall Survival Prediction of Glioma Patients With Multiregional Radiomics. Front Neurosci 2022;16:911065. [PMID: 35873825 DOI: 10.3389/fnins.2022.911065] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
24 Qian C, Jiang Y, Soh ZD, Sakthi Selvam G, Xiao S, Tham Y, Xu X, Liu Y, Li J, Zhong H, Cheng C. Smartphone-Acquired Anterior Segment Images for Deep Learning Prediction of Anterior Chamber Depth: A Proof-of-Concept Study. Front Med 2022;9:912214. [DOI: 10.3389/fmed.2022.912214] [Reference Citation Analysis]
25 Teng X, Zhang J, Zwanenburg A, Sun J, Huang Y, Lam S, Zhang Y, Li B, Zhou T, Xiao H, Liu C, Li W, Han X, Ma Z, Li T, Cai J. Building reliable radiomic models using image perturbation. Sci Rep 2022;12:10035. [PMID: 35710850 DOI: 10.1038/s41598-022-14178-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
26 Chen M, Sun G. Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model. Computational Intelligence and Neuroscience 2022;2022:1-12. [DOI: 10.1155/2022/5156532] [Reference Citation Analysis]
27 Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, Yang G. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. Inf Fusion 2022;82:99-122. [PMID: 35664012 DOI: 10.1016/j.inffus.2022.01.001] [Cited by in Crossref: 23] [Cited by in F6Publishing: 22] [Article Influence: 23.0] [Reference Citation Analysis]
28 Kwok HC, Charbel C, Danilova S, Miranda J, Gangai N, Petkovska I, Chakraborty J, Horvat N. Rectal MRI radiomics inter- and intra-reader reliability: should we worry about that? Abdom Radiol (NY) 2022;47:2004-13. [PMID: 35366088 DOI: 10.1007/s00261-022-03503-7] [Reference Citation Analysis]
29 Stadlbauer A, Marhold F, Oberndorfer S, Heinz G, Buchfelder M, Kinfe TM, Meyer-Bäse A. Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data. Cancers (Basel) 2022;14:2363. [PMID: 35625967 DOI: 10.3390/cancers14102363] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022;67:09TR03. [DOI: 10.1088/1361-6560/ac60b9] [Reference Citation Analysis]
31 Strzelecki M, Piórkowski A, Obuchowicz R. Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging. JCM 2022;11:2526. [DOI: 10.3390/jcm11092526] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Linsalata S, Borgheresi R, Marfisi D, Barca P, Sainato A, Paiar F, Neri E, Traino AC, Giannelli M, Bonifacio C. Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging. BioMed Research International 2022;2022:1-21. [DOI: 10.1155/2022/2003286] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
33 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] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
34 Xue H. Intelligent System of Sports Injury Evaluation Based on MRI Image Analysis. 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) 2022. [DOI: 10.1109/icais53314.2022.9742839] [Reference Citation Analysis]
35 Krauze AV, Zhuge Y, Zhao R, Tasci E, Camphausen K. AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models. J Biotechnol Biomed 2022;5:1-19. [PMID: 35106480 DOI: 10.26502/jbb.2642-91280046] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Adachi T, Nakamura M, Kakino R, Hirashima H, Iramina H, Tsuruta Y, Ono T, Mukumoto N, Miyabe Y, Matsuo Y, Mizowaki T. Dosiomic feature comparison between dose-calculation algorithms used for lung stereotactic body radiation therapy. Radiol Phys Technol. [DOI: 10.1007/s12194-022-00651-9] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Park CJ, Park YW, Ahn SS, Kim D, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation. Korean J Radiol 2022;23:77-88. [PMID: 34983096 DOI: 10.3348/kjr.2021.0421] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
38 Seo H, So S, Yun S, Lee S, Barg J. Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Segmentation from DCE-MRI. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-17721-7_13] [Reference Citation Analysis]
39 Granzier RWY, Ibrahim A, Primakov S, Keek SA, Halilaj I, Zwanenburg A, Engelen SME, Lobbes MBI, Lambin P, Woodruff HC, Smidt ML. Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability. J Magn Reson Imaging 2021. [PMID: 34936160 DOI: 10.1002/jmri.28027] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
40 Pfaehler E, Zhovannik I, Wei L, Boellaard R, Dekker A, Monshouwer R, El Naqa I, Bussink J, Gillies R, Wee L, Traverso A. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 2021;20:69-75. [PMID: 34816024 DOI: 10.1016/j.phro.2021.10.007] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
41 Stefano A, Leal A, Richiusa S, Trang P, Comelli A, Benfante V, Cosentino S, Sabini MG, Tuttolomondo A, Altieri R, Certo F, Barbagallo GMV, Ippolito M, Russo G. Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. Applied Sciences 2021;11:10170. [DOI: 10.3390/app112110170] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
42 You H, Yu L, Tian S, Cai W. DR-Net: dual-rotation network with feature map enhancement for medical image segmentation. Complex Intell Syst 2022;8:611-23. [DOI: 10.1007/s40747-021-00525-4] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
43 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: 12] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
44 Fernández Patón M, Cerdá Alberich L, Sangüesa Nebot C, Martínez de Las Heras B, Veiga Canuto D, Cañete Nieto A, Martí-Bonmatí L. MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging. J Digit Imaging 2021;34:1134-45. [PMID: 34505958 DOI: 10.1007/s10278-021-00512-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
45 Korte JC, Cardenas C, Hardcastle N, Kron T, Wang J, Bahig H, Elgohari B, Ger R, Court L, Fuller CD, Ng SP. Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer. Sci Rep 2021;11:17633. [PMID: 34480036 DOI: 10.1038/s41598-021-96600-4] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
46 Eck B, Chirra PV, Muchhala A, Hall S, Bera K, Tiwari P, Madabhushi A, Seiberlich N, Viswanath SE. Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters. J Magn Reson Imaging 2021;54:1009-21. [PMID: 33860966 DOI: 10.1002/jmri.27635] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
47 Moradmand H, Aghamiri SMR, Ghaderi R, Emami H. The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma. Cancer Med 2021;10:7048-59. [PMID: 34453413 DOI: 10.1002/cam4.4230] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
48 Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
49 Stamoulou E, Manikis GC, Tsiknakis M, Marias K. ComBat harmonization for multicenter MRI based radiomics features. 2021 IEEE International Conference on Imaging Systems and Techniques (IST) 2021. [DOI: 10.1109/ist50367.2021.9745836] [Reference Citation Analysis]
50 Spohn SKB, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021;11:8027-42. [PMID: 34335978 DOI: 10.7150/thno.61207] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
51 Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. Radiol Med 2021. [PMID: 34213702 DOI: 10.1007/s11547-021-01389-x] [Cited by in Crossref: 46] [Cited by in F6Publishing: 31] [Article Influence: 23.0] [Reference Citation Analysis]
52 Li Y, Ammari S, Balleyguier C, Lassau N, Chouzenoux E. Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features. Cancers (Basel) 2021;13:3000. [PMID: 34203896 DOI: 10.3390/cancers13123000] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 4.5] [Reference Citation Analysis]
53 Taha B, Boley D, Sun J, Chen C. Potential and limitations of radiomics in neuro-oncology. J Clin Neurosci 2021;90:206-11. [PMID: 34275550 DOI: 10.1016/j.jocn.2021.05.015] [Reference Citation Analysis]
54 Granzier RWY, Ibrahim A, Primakov SP, Samiei S, van Nijnatten TJA, de Boer M, Heuts EM, Hulsmans FJ, Chatterjee A, Lambin P, Lobbes MBI, Woodruff HC, Smidt ML. MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study. Cancers (Basel) 2021;13:2447. [PMID: 34070016 DOI: 10.3390/cancers13102447] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
55 Hu Z, Zhuang Q, Xiao Y, Wu G, Shi Z, Chen L, Wang Y, Yu J. MIL normalization -- prerequisites for accurate MRI radiomics analysis. Comput Biol Med 2021;133:104403. [PMID: 33932645 DOI: 10.1016/j.compbiomed.2021.104403] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
56 Vils A, Bogowicz M, Tanadini-Lang S, Vuong D, Saltybaeva N, Kraft J, Wirsching HG, Gramatzki D, Wick W, Rushing E, Reifenberger G, Guckenberger M, Weller M, Andratschke N. Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial. Front Oncol 2021;11:636672. [PMID: 33937035 DOI: 10.3389/fonc.2021.636672] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
57 Won SY, Park YW, Ahn SS, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications. Eur J Radiol 2021;138:109673. [PMID: 33774441 DOI: 10.1016/j.ejrad.2021.109673] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 6.5] [Reference Citation Analysis]
58 Bodalal Z, Wamelink I, Trebeschi S, Beets-Tan RGH. Radiomics in immuno-oncology. Immunooncol Technol 2021;9:100028. [PMID: 35756864 DOI: 10.1016/j.iotech.2021.100028] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
59 Ma Y. Editorial for "Assessment of Repeatability, Reproducibility, and Performances of T2-Mapping-Based Radiomics Features: A Comparative Study". J Magn Reson Imaging 2021;54:549-50. [PMID: 33634896 DOI: 10.1002/jmri.27582] [Reference Citation Analysis]
60 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: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
61 Li XT, Huang RY. Standardization of imaging methods for machine learning in neuro-oncology. Neurooncol Adv 2020;2:iv49-55. [PMID: 33521640 DOI: 10.1093/noajnl/vdaa054] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
62 Ammari S, Pitre-Champagnat S, Dercle L, Chouzenoux E, Moalla S, Reuze S, Talbot H, Mokoyoko T, Hadchiti J, Diffetocq S, Volk A, El Haik M, Lakiss S, Balleyguier C, Lassau N, Bidault F. Influence of Magnetic Field Strength on Magnetic Resonance Imaging Radiomics Features in Brain Imaging, an In Vitro and In Vivo Study. Front Oncol 2020;10:541663. [PMID: 33552944 DOI: 10.3389/fonc.2020.541663] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 7.5] [Reference Citation Analysis]
63 Galavis PE. Reproducibility and standardization in Radiomics: Are we there yet? PROCEEDINGS OF THE XVI MEXICAN SYMPOSIUM ON MEDICAL PHYSICS 2021. [DOI: 10.1063/5.0051609] [Reference Citation Analysis]
64 Pati S, Verma R, Akbari H, Bilello M, Hill VB, Sako C, Correa R, Beig N, Venet L, Thakur S, Serai P, Ha SM, Blake GD, Shinohara RT, Tiwari P, Bakas S. Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset. Med Phys 2020;47:6039-52. [PMID: 33118182 DOI: 10.1002/mp.14556] [Cited by in Crossref: 13] [Cited by in F6Publishing: 16] [Article Influence: 4.3] [Reference Citation Analysis]
65 Saint Martin MJ, Orlhac F, Akl P, Khalid F, Nioche C, Buvat I, Malhaire C, Frouin F. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. MAGMA 2021;34:355-66. [PMID: 33180226 DOI: 10.1007/s10334-020-00892-y] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
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