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For: Guo J, Liu Z, Shen C, Li Z, Yan F, Tian J, Xian J. MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol 2018;28:3872-81. [PMID: 29632999 DOI: 10.1007/s00330-018-5381-7] [Cited by in Crossref: 25] [Cited by in F6Publishing: 23] [Article Influence: 6.3] [Reference Citation Analysis]
Number Citing Articles
1 Su Y, Xu X, Zuo P, Xia Y, Qu X, Chen Q, Guo J, Wei W, Xian J. Value of MR-based radiomics in differentiating uveal melanoma from other intraocular masses in adults. European Journal of Radiology 2020;131:109268. [DOI: 10.1016/j.ejrad.2020.109268] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Wu W, Li J, Ye J, Wang Q, Zhang W, Xu S. Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning. Front Oncol 2021;11:639062. [PMID: 33791225 DOI: 10.3389/fonc.2021.639062] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Weng Q, Zhou L, Wang H, Hui J, Chen M, Pang P, Zheng L, Xu M, Wang Z, Ji J. A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules. Clin Radiol 2019;74:933-43. [PMID: 31521324 DOI: 10.1016/j.crad.2019.07.026] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
4 Li L, Mu W, Wang Y, Liu Z, Liu Z, Wang Y, Ma W, Kong Z, Wang S, Zhou X, Wei W, Cheng X, Lin Y, Tian J. A Non-invasive Radiomic Method Using 18F-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma. Front Oncol 2019;9:1183. [PMID: 31803608 DOI: 10.3389/fonc.2019.01183] [Cited by in Crossref: 17] [Cited by in F6Publishing: 14] [Article Influence: 5.7] [Reference Citation Analysis]
5 Li Z, Guo J, Xu X, Wei W, Xian J. MRI-based radiomics model can improve the predictive performance of postlaminar optic nerve invasion in retinoblastoma. Br J Radiol 2021;:20211027. [PMID: 34826253 DOI: 10.1259/bjr.20211027] [Reference Citation Analysis]
6 Xiong Q, Zhou X, Liu Z, Lei C, Yang C, Yang M, Zhang L, Zhu T, Zhuang X, Liang C, Liu Z, Tian J, Wang K. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy. Clin Transl Oncol. 2020;22:50-59. [PMID: 30977048 DOI: 10.1007/s12094-019-02109-8] [Cited by in Crossref: 24] [Cited by in F6Publishing: 24] [Article Influence: 8.0] [Reference Citation Analysis]
7 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] [Reference Citation Analysis]
8 Han Y, Yang Y, Shi ZS, Zhang AD, Yan LF, Hu YC, Feng LL, Ma J, Wang W, Cui GB. Distinguishing brain inflammation from grade II glioma in population without contrast enhancement: a radiomics analysis based on conventional MRI. Eur J Radiol 2021;134:109467. [PMID: 33307462 DOI: 10.1016/j.ejrad.2020.109467] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Abdel Razek AAK, Khaled R, Helmy E, Naglah A, AbdelKhalek A, El-Baz A. Artificial Intelligence and Deep Learning of Head and Neck Cancer. Magn Reson Imaging Clin N Am 2022;30:81-94. [PMID: 34802583 DOI: 10.1016/j.mric.2021.06.016] [Reference Citation Analysis]
10 Li X, Ma Q, Nie P, Zheng Y, Dong C, Xu W. A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study. Br J Radiol 2021;:20210534. [PMID: 34735296 DOI: 10.1259/bjr.20210534] [Reference Citation Analysis]
11 Yuan Y, Chu G, Gong T, Du L, Xie L, Yuan Q, Han Q. To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease. Biomed Res Int 2021;2021:6668510. [PMID: 33628805 DOI: 10.1155/2021/6668510] [Reference Citation Analysis]
12 Capobianco E, Deng J. Radiomics at a Glance: A Few Lessons Learned from Learning Approaches. Cancers (Basel) 2020;12:E2453. [PMID: 32872466 DOI: 10.3390/cancers12092453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
13 He M, Liu Z, Lin Y, Wan J, Li J, Xu K, Wang Y, Jin Z, Tian J, Xue H. Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics. Eur J Radiol 2019;117:102-11. [PMID: 31307634 DOI: 10.1016/j.ejrad.2019.05.024] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 3.3] [Reference Citation Analysis]
14 Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 2019;46:2656-72. [PMID: 31214791 DOI: 10.1007/s00259-019-04372-x] [Cited by in Crossref: 56] [Cited by in F6Publishing: 52] [Article Influence: 18.7] [Reference Citation Analysis]
15 Lee J, Steinmann A, Ding Y, Lee H, Owens C, Wang J, Yang J, Followill D, Ger R, MacKin D, Court LE. Radiomics feature robustness as measured using an MRI phantom. Sci Rep 2021;11:3973. [PMID: 33597610 DOI: 10.1038/s41598-021-83593-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
16 Tian Y, Liu Z, Tang Z, Li M, Lou X, Dong E, Liu G, Wang Y, Wang Y, Bian X, Wei S, Tian J, Ma L. Radiomics Analysis of DTI Data to Assess Vision Outcome After Intravenous Methylprednisolone Therapy in Neuromyelitis Optic Neuritis. J Magn Reson Imaging 2019;49:1365-73. [PMID: 30252996 DOI: 10.1002/jmri.26326] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
17 Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019;9:1303-1322. [PMID: 30867832 DOI: 10.7150/thno.30309] [Cited by in Crossref: 149] [Cited by in F6Publishing: 146] [Article Influence: 49.7] [Reference Citation Analysis]
18 Kitahara T, Imamura S, Ohta M, Okoshi T, Kobori A, Miyakoshi A, Oichi Y, Toda H. Two cases of primary ocular adnexal lymphomas diagnosed after pre-biopsy corticosteroid treatment using polymerase chain reaction-based gene rearrangement analysis. Am J Ophthalmol Case Rep 2019;15:100520. [PMID: 31372582 DOI: 10.1016/j.ajoc.2019.100520] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
19 Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021;13:97-104. [PMID: 34513082 DOI: 10.17691/stm2021.13.2.11] [Reference Citation Analysis]
20 Duron L, Heraud A, Charbonneau F, Zmuda M, Savatovsky J, Fournier L, Lecler A. A Magnetic Resonance Imaging Radiomics Signature to Distinguish Benign From Malignant Orbital Lesions. Invest Radiol 2021;56:173-80. [PMID: 32932375 DOI: 10.1097/RLI.0000000000000722] [Reference Citation Analysis]
21 Wu Q, Wang S, Chen X, Wang Y, Dong L, Liu Z, Tian J, Wang M. Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother Oncol 2019;138:141-8. [PMID: 31252296 DOI: 10.1016/j.radonc.2019.04.035] [Cited by in Crossref: 25] [Cited by in F6Publishing: 24] [Article Influence: 8.3] [Reference Citation Analysis]
22 Wei W, Liu Z, Rong Y, Zhou B, Bai Y, Wei W, Wang S, Wang M, Guo Y, Tian J. A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study. Front Oncol 2019;9:255. [PMID: 31024855 DOI: 10.3389/fonc.2019.00255] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 5.7] [Reference Citation Analysis]
23 Hu H, Chen L, Zhang JL, Chen W, Chen HH, Liu H, Shi HB, Wu FY, Xu XQ. T2 -Weighted MR Imaging-Derived Radiomics for Pretreatment Determination of Therapeutic Response to Glucocorticoid in Patients With Thyroid-Associated Ophthalmopathy: Comparison With Semiquantitative Evaluation. J Magn Reson Imaging 2022. [PMID: 35092642 DOI: 10.1002/jmri.28088] [Reference Citation Analysis]
24 Tang Z, Zhang X, Liu Z, Li X, Shi Y, Wang S, Fang M, Shen C, Dong E, Sun Y, Tian J. Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer. Radiotherapy and Oncology 2019;132:100-8. [DOI: 10.1016/j.radonc.2018.11.007] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
25 Guo J, Li Z, Qu X, Xian J. Value of MRI-based radiomics analysis for differentiation of benign and malignant epithelial neoplasms in the lacrimal gland: a retrospective study. Acta Radiol 2021;62:743-51. [PMID: 32660315 DOI: 10.1177/0284185120940258] [Reference Citation Analysis]
26 Li L, Zhang J, Zhe X, Tang M, Zhang X, Lei X, Zhang L. A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer. European Journal of Radiology 2022. [DOI: 10.1016/j.ejrad.2022.110243] [Reference Citation Analysis]
27 Park JE, Kim D, Kim HS, Park SY, Kim JY, Cho SJ, Shin JH, Kim JH. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol. 2020;30:523-536. [PMID: 31350588 DOI: 10.1007/s00330-019-06360-z] [Cited by in Crossref: 54] [Cited by in F6Publishing: 60] [Article Influence: 18.0] [Reference Citation Analysis]
28 Zhou X, Yi Y, Liu Z, Cao W, Lai B, Sun K, Li L, Zhou Z, Feng Y, Tian J. Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer. Ann Surg Oncol. 2019;26:1676-1684. [PMID: 30887373 DOI: 10.1245/s10434-019-07300-3] [Cited by in Crossref: 28] [Cited by in F6Publishing: 26] [Article Influence: 9.3] [Reference Citation Analysis]
29 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]
30 Hou Y, Xie X, Chen J, Lv P, Jiang S, He X, Yang L, Zhao F. Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI. Eur Radiol 2021;31:24-33. [PMID: 32789530 DOI: 10.1007/s00330-020-07110-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
31 Wang X, Dai S, Wang Q, Chai X, Xian J. Investigation of MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas. Jpn J Radiol 2021;39:755-62. [PMID: 33860416 DOI: 10.1007/s11604-021-01116-6] [Reference Citation Analysis]