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For: Bologna M, Corino VDA, Montin E, Messina A, Calareso G, Greco FG, Sdao S, Mainardi LT. Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images. J Digit Imaging 2018;31:879-94. [PMID: 29725965 DOI: 10.1007/s10278-018-0092-9] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 6.3] [Reference Citation Analysis]
Number Citing Articles
1 Gitto S, Cuocolo R, Emili I, Tofanelli L, Chianca V, Albano D, Messina C, Imbriaco M, Sconfienza LM. Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors. J Digit Imaging 2021;34:820-32. [PMID: 34405298 DOI: 10.1007/s10278-021-00498-3] [Reference Citation Analysis]
2 Gitto S, Bologna M, Corino VDA, Emili I, Albano D, Messina C, Armiraglio E, Parafioriti A, Luzzati A, Mainardi L, Sconfienza LM. Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance. Radiol Med 2022. [PMID: 35320464 DOI: 10.1007/s11547-022-01468-7] [Reference Citation Analysis]
3 Corino VDA, Bologna M, Calareso G, Licitra L, Ghi M, Rinaldi G, Caponigro F, Morelli F, Airoldi M, Allegrini G, Cassano A, Ferrari D, Mirabile A, Tosoni A, Galizia D, Merlano M, Sponghini A, Moretti G, Mainardi L, Bossi P. A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study. Diagnostics (Basel) 2021;11:979. [PMID: 34071518 DOI: 10.3390/diagnostics11060979] [Reference Citation Analysis]
4 Bologna M, Corino V, Calareso G, Tenconi C, Alfieri S, Iacovelli NA, Cavallo A, Cavalieri S, Locati L, Bossi P, Romanello DA, Ingargiola R, Rancati T, Pignoli E, Sdao S, Pecorilla M, Facchinetti N, Trama A, Licitra L, Mainardi L, Orlandi E. Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients. Cancers (Basel) 2020;12:E2958. [PMID: 33066161 DOI: 10.3390/cancers12102958] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
5 Schwier M, van Griethuysen J, Vangel MG, Pieper S, Peled S, Tempany C, Aerts HJWL, Kikinis R, Fennessy FM, Fedorov A. Repeatability of Multiparametric Prostate MRI Radiomics Features. Sci Rep 2019;9:9441. [PMID: 31263116 DOI: 10.1038/s41598-019-45766-z] [Cited by in Crossref: 78] [Cited by in F6Publishing: 72] [Article Influence: 26.0] [Reference Citation Analysis]
6 Dreher C, Kuder TA, König F, Mlynarska-Bujny A, Tenconi C, Paech D, Schlemmer HP, Ladd ME, Bickelhaupt S. Radiomics in diffusion data: a test-retest, inter- and intra-reader DWI phantom study. Clin Radiol 2020;75:798.e13-22. [PMID: 32723501 DOI: 10.1016/j.crad.2020.06.024] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
7 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]
8 Arimura H, Soufi M, Ninomiya K, Kamezawa H, Yamada M. Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis. Radiol Phys Technol 2018;11:365-74. [PMID: 30374837 DOI: 10.1007/s12194-018-0486-x] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
9 Park BW, Kim JK, Heo C, Park KJ. Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters.Sci Rep. 2020;10:3852. [PMID: 32123281 DOI: 10.1038/s41598-020-60868-9] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
10 Gutmann DAP, Rospleszcz S, Rathmann W, Schlett CL, Peters A, Wachinger C, Gatidis S, Bamberg F. MRI-Derived Radiomics Features of Hepatic Fat Predict Metabolic States in Individuals without Cardiovascular Disease. Acad Radiol 2020:S1076-6332(20)30408-6. [PMID: 32800693 DOI: 10.1016/j.acra.2020.06.030] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
11 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] [Reference Citation Analysis]
12 Mitchell-Hay RN, Ahearn TS, Murray AD, Waiter GD. Investigation of the Inter- and Intrascanner Reproducibility and Repeatability of Radiomics Features in T1-Weighted Brain MRI. J Magn Reson Imaging 2022. [PMID: 35396777 DOI: 10.1002/jmri.28191] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019;104:302-15. [PMID: 30711529 DOI: 10.1016/j.ijrobp.2019.01.087] [Cited by in Crossref: 18] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
14 Cattell R, Chen S, Huang C. Robustness of radiomic features in magnetic resonance imaging: review and a phantom study. Vis Comput Ind Biomed Art 2019;2:19. [PMID: 32240418 DOI: 10.1186/s42492-019-0025-6] [Cited by in Crossref: 20] [Cited by in F6Publishing: 18] [Article Influence: 6.7] [Reference Citation Analysis]
15 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: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
16 Jeon SH, Song C, Chie EK, Kim B, Kim YH, Chang W, Lee YJ, Chung JH, Chung JB, Lee KW, Kang SB, Kim JS. Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer. Radiat Oncol 2019;14:43. [PMID: 30866965 DOI: 10.1186/s13014-019-1246-8] [Cited by in Crossref: 25] [Cited by in F6Publishing: 28] [Article Influence: 8.3] [Reference Citation Analysis]
17 Bologna M, Calareso G, Resteghini C, Sdao S, Montin E, Corino V, Mainardi L, Licitra L, Bossi P. Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer. NMR Biomed 2020;:e4265. [PMID: 32009265 DOI: 10.1002/nbm.4265] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Raisi-Estabragh Z, Gkontra P, Jaggi A, Cooper J, Augusto J, Bhuva AN, Davies RH, Manisty CH, Moon JC, Munroe PB, Harvey NC, Lekadir K, Petersen SE. Repeatability of Cardiac Magnetic Resonance Radiomics: A Multi-Centre Multi-Vendor Test-Retest Study. Front Cardiovasc Med 2020;7:586236. [PMID: 33344517 DOI: 10.3389/fcvm.2020.586236] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
19 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] [Reference Citation Analysis]
20 Traverso A, Kazmierski M, Shi Z, Kalendralis P, Welch M, Nissen HD, Jaffray D, Dekker A, Wee L. Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing. Phys Med 2019;61:44-51. [PMID: 31151578 DOI: 10.1016/j.ejmp.2019.04.009] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]