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For: Ma Z, Fang M, Huang Y, He L, Chen X, Liang C, Huang X, Cheng Z, Dong D, Liang C, Xie J, Tian J, Liu Z. CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma. Eur J Radiol. 2017;91:142-147. [PMID: 28629560 DOI: 10.1016/j.ejrad.2017.04.007] [Cited by in Crossref: 49] [Cited by in F6Publishing: 50] [Article Influence: 9.8] [Reference Citation Analysis]
Number Citing Articles
1 E L, Lu L, Li L, Yang H, Schwartz LH, Zhao B. Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. Academic Radiology 2019;26:1245-52. [DOI: 10.1016/j.acra.2018.10.013] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 5.3] [Reference Citation Analysis]
2 Hou Z, Yang Y, Li S, Yan J, Ren W, Liu J, Wang K, Liu B, Wan S. Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis. Quant Imaging Med Surg 2018;8:410-20. [PMID: 29928606 DOI: 10.21037/qims.2018.05.01] [Cited by in Crossref: 15] [Cited by in F6Publishing: 21] [Article Influence: 3.8] [Reference Citation Analysis]
3 Gao L, Li Y, Zhai Z, Liang T, Zhang Q, Xie S, Chen H. Radiomics study on pulmonary infarction mimicking community-acquired pneumonia. Clin Respir J 2021;15:661-9. [PMID: 33686798 DOI: 10.1111/crj.13341] [Reference Citation Analysis]
4 Zhang W, Chen T, Zhang M, Liu P, Lu Z. [A radiomics-based model for differentiation between benign and malignant gastrointestinal stromal tumors]. Nan Fang Yi Ke Da Xue Xue Bao 2018;38:55-61. [PMID: 33177032 DOI: 10.3969/j.issn.1673-4254.2018.01.09] [Reference Citation Analysis]
5 Shu J, Tang Y, Cui J, Yang R, Meng X, Cai Z, Zhang J, Xu W, Wen D, Yin H. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade. Eur J Radiol 2018;109:8-12. [PMID: 30527316 DOI: 10.1016/j.ejrad.2018.10.005] [Cited by in Crossref: 50] [Cited by in F6Publishing: 45] [Article Influence: 12.5] [Reference Citation Analysis]
6 Meng X, Shu J, Xia Y, Yang R. A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma. Biomed Res Int 2020;2020:7103647. [PMID: 32775436 DOI: 10.1155/2020/7103647] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
7 Liu D, Zhang W, Hu F, Yu P, Zhang X, Yin H, Yang L, Fang X, Song B, Wu B, Hu J, Huang Z. A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study. Front Oncol 2021;11:777760. [PMID: 34926287 DOI: 10.3389/fonc.2021.777760] [Reference Citation Analysis]
8 Zhou C, Tian S, Lv F, Shang R, Zheng X, Pallikonda Rajasekaran M. Computerized Tomography Imaging Omics under Iterative Reconstruction Algorithm in Diagnosis of Gastric Cancer. Scientific Programming 2021;2021:1-8. [DOI: 10.1155/2021/2987080] [Reference Citation Analysis]
9 Chen Y, Wei K, Liu D, Xiang J, Wang G, Meng X, Peng J. A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer. Front Oncol 2021;11:675458. [PMID: 34141620 DOI: 10.3389/fonc.2021.675458] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
10 Shao S, Zheng N, Mao N, Xue X, Cui J, Gao P, Wang B. A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging. Clin Radiol 2021;76:472.e11-8. [PMID: 33752882 DOI: 10.1016/j.crad.2020.10.019] [Reference Citation Analysis]
11 Hu Y, Weng Q, Xia H, Chen T, Kong C, Chen W, Pang P, Xu M, Lu C, Ji J. A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer. Abdom Radiol (NY) 2021;46:2384-92. [PMID: 34086094 DOI: 10.1007/s00261-021-03120-w] [Reference Citation Analysis]
12 Liu S, He J, Liu S, Ji C, Guan W, Chen L, Guan Y, Yang X, Zhou Z. Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer. Eur Radiol 2020;30:239-46. [DOI: 10.1007/s00330-019-06368-5] [Cited by in Crossref: 17] [Cited by in F6Publishing: 16] [Article Influence: 5.7] [Reference Citation Analysis]
13 Li Q, Liu YJ, Dong D, Bai X, Huang QB, Guo AT, Ye HY, Tian J, Wang HY. Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma. J Magn Reson Imaging 2020;52:1557-66. [PMID: 32462799 DOI: 10.1002/jmri.27182] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
14 Ye Y, Qiu X, Mei J, He D, Zou A. Primary gastric Ewing sarcoma/primitive neuroectodermal tumor. J Int Med Res 2021;49:300060520986681. [PMID: 33530793 DOI: 10.1177/0300060520986681] [Reference Citation Analysis]
15 Dong D, Tang L, Li ZY, Fang MJ, Gao JB, Shan XH, Ying XJ, Sun YS, Fu J, Wang XX, Li LM, Li ZH, Zhang DF, Zhang Y, Li ZM, Shan F, Bu ZD, Tian J, Ji JF. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol. 2019;30:431-438. [PMID: 30689702 DOI: 10.1093/annonc/mdz001] [Cited by in Crossref: 114] [Cited by in F6Publishing: 108] [Article Influence: 57.0] [Reference Citation Analysis]
16 Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1(2): 37-50 [DOI: 10.35712/aig.v1.i2.37] [Reference Citation Analysis]
17 Feng B, Huang L, Li C, Quan Y, Chen Y, Xue H, Chen Q, Sun S, Li R, Long W. A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer. J Comput Assist Tomogr 2021;45:191-202. [PMID: 33273161 DOI: 10.1097/RCT.0000000000001117] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Li Q, Dong F, Wang Q, Xu F, Zhang M. A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of CT features. Eur Radiol 2021. [PMID: 34109487 DOI: 10.1007/s00330-021-08061-y] [Reference Citation Analysis]
19 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]
20 Gao X, Ma T, Cui J, Zhang Y, Wang L, Li H, Ye Z. A CT-based Radiomics Model for Prediction of Lymph Node Metastasis in Early Stage Gastric Cancer. Acad Radiol 2021;28:e155-64. [PMID: 32507613 DOI: 10.1016/j.acra.2020.03.045] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Eid M, Iannicelli E, Laghi A. Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications. Cancers (Basel) 2021;13:2681. [PMID: 34072366 DOI: 10.3390/cancers13112681] [Reference Citation Analysis]
22 Chen X, Feng B, Li C, Duan X, Chen Y, Li Z, Liu Z, Zhang C, Long W. A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography. Oncol Rep 2020;43:1256-66. [PMID: 32323834 DOI: 10.3892/or.2020.7497] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
23 Zhou H, Dong D, Chen B, Fang M, Cheng Y, Gan Y, Zhang R, Zhang L, Zang Y, Liu Z, Zheng H, Li W, Tian J. Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features. Transl Oncol. 2018;11:31-36. [PMID: 29156383 DOI: 10.1016/j.tranon.2017.10.010] [Cited by in Crossref: 27] [Cited by in F6Publishing: 23] [Article Influence: 5.4] [Reference Citation Analysis]
24 Rowe SP, Chu LC, Fishman EK. Evaluation of Stomach Neoplasms With 3-Dimensional Computed Tomography: Focus on the Potential Role of Cinematic Rendering. Journal of Computer Assisted Tomography 2018;42:661-6. [DOI: 10.1097/rct.0000000000000761] [Cited by in Crossref: 11] [Cited by in F6Publishing: 2] [Article Influence: 2.8] [Reference Citation Analysis]
25 Chen X, Huang Y, He L, Zhang T, Zhang L, Ding H. CT-Based Radiomics to Differentiate Pelvic Rhabdomyosarcoma From Yolk Sac Tumors in Children. Front Oncol 2020;10:584272. [PMID: 33330062 DOI: 10.3389/fonc.2020.584272] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
26 Zhang R, Zhu L, Cai Z, Jiang W, Li J, Yang C, Yu C, Jiang B, Wang W, Xu W, Chai X, Zhang X, Tang Y. Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions. European Journal of Radiology 2019;121:108735. [DOI: 10.1016/j.ejrad.2019.108735] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
27 Sun YW, Ji CF, Wang H, He J, Liu S, Ge Y, Zhou ZY. Differentiating gastric cancer and gastric lymphoma using texture analysis (TA) of positron emission tomography (PET). Chin Med J (Engl) 2020;134:439-47. [PMID: 33230019 DOI: 10.1097/CM9.0000000000001206] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
28 Gao X, Ma T, Cui J, Zhang Y, Wang L, Li H, Ye Z. A radiomics-based model for prediction of lymph node metastasis in gastric cancer. Eur J Radiol 2020;129:109069. [PMID: 32464581 DOI: 10.1016/j.ejrad.2020.109069] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
29 Deng X, Liu M, Sun J, Li M, Liu D, Li L, Fang J, Wang X, Zhang J. Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer. Eur J Radiol 2021;134:109429. [PMID: 33290975 DOI: 10.1016/j.ejrad.2020.109429] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
30 E L, Xu Y, Wu Z, Li L, Zhang N, Yang H, Schwartz LH, Lu L, Zhao B. Differentiation of Focal-Type Autoimmune Pancreatitis From Pancreatic Ductal Adenocarcinoma Using Radiomics Based on Multiphasic Computed Tomography. J Comput Assist Tomogr 2020;44:511-8. [DOI: 10.1097/rct.0000000000001049] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
31 Wang Y, Liu W, Yu Y, Liu JJ, Jiang L, Xue HD, Lei J, Jin Z, Yu JC. Prediction of the Depth of Tumor Invasion in Gastric Cancer: Potential Role of CT Radiomics. Acad Radiol 2020;27:1077-84. [PMID: 31761666 DOI: 10.1016/j.acra.2019.10.020] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
32 Amiri S, Akbarabadi M, Abdolali F, Nikoofar A, Esfahani AJ, Cheraghi S. Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models. Comput Biol Med 2021;133:104409. [PMID: 33940534 DOI: 10.1016/j.compbiomed.2021.104409] [Reference Citation Analysis]
33 Wang Y, Liu W, Yu Y, Liu JJ, Xue HD, Qi YF, Lei J, Yu JC, Jin ZY. CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Eur Radiol 2020;30:976-86. [PMID: 31468157 DOI: 10.1007/s00330-019-06398-z] [Cited by in Crossref: 22] [Cited by in F6Publishing: 23] [Article Influence: 7.3] [Reference Citation Analysis]
34 Liu S, Zhang C, Liu R, Li S, Xu F, Liu X, Li Z, Hu Y, Ge Y, Chen J, Zhang Z. CT Texture Analysis for Preoperative Identification of Lymphoma from Other Types of Primary Small Bowel Malignancies. Biomed Res Int 2021;2021:5519144. [PMID: 33884262 DOI: 10.1155/2021/5519144] [Reference Citation Analysis]
35 Liu J, Sun D, Chen L, Fang Z, Song W, Guo D, Ni T, Liu C, Feng L, Xia Y, Zhang X, Li C. Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer. Front Oncol. 2019;9:980. [PMID: 31632912 DOI: 10.3389/fonc.2019.00980] [Cited by in Crossref: 23] [Cited by in F6Publishing: 22] [Article Influence: 7.7] [Reference Citation Analysis]
36 Wang S, Dong D, Zhang W, Hu H, Li H, Zhu Y, Zhou J, Shan X, Tian J. Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research. Med Phys 2021. [PMID: 34260756 DOI: 10.1002/mp.15094] [Reference Citation Analysis]
37 Li J, Zhang C, Wei J, Zheng P, Zhang H, Xie Y, Bai J, Zhu Z, Zhou K, Liang X, Xie Y, Qin T. Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer. Front Oncol 2020;10:552270. [PMID: 33425719 DOI: 10.3389/fonc.2020.552270] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Mao N, Jiao Z, Duan S, Xu C, Xie H. Preoperative prediction of histologic grade in invasive breast cancer by using contrast-enhanced spectral mammography-based radiomics. J Xray Sci Technol 2021. [PMID: 34151880 DOI: 10.3233/XST-210886] [Reference Citation Analysis]
39 Ning P, Gao F, Hai J, Wu M, Chen J, Zhu S, Wang M, Shi D. Application of CT radiomics in prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY). 2020;45:64-72. [PMID: 31486869 DOI: 10.1007/s00261-019-02198-7] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 8.0] [Reference Citation Analysis]
40 Sah BR, Owczarczyk K, Siddique M, Cook GJR, Goh V. Radiomics in esophageal and gastric cancer. Abdom Radiol (NY) 2019;44:2048-58. [PMID: 30116873 DOI: 10.1007/s00261-018-1724-8] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 7.0] [Reference Citation Analysis]
41 Zhang L, Kang L, Li G, Zhang X, Ren J, Shi Z, Li J, Yu S. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. Radiol Med. 2020;125:465-473. [PMID: 32048155 DOI: 10.1007/s11547-020-01138-6] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
42 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]
43 Qin Y, Deng Y, Jiang H, Hu N, Song B. Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction. Front Oncol 2021;11:631686. [PMID: 34367946 DOI: 10.3389/fonc.2021.631686] [Reference Citation Analysis]
44 Zhao H, Li W, Lyu P, Zhang X, Liu H, Liang P, Gao J. TCGA-TCIA-Based CT Radiomics Study for Noninvasively Predicting Epstein-Barr Virus Status in Gastric Cancer. AJR Am J Roentgenol 2021;217:124-34. [PMID: 33955777 DOI: 10.2214/AJR.20.23534] [Reference Citation Analysis]
45 Gao X, Ma T, Bai S, Liu Y, Zhang Y, Wu Y, Li H, Ye Z. A CT-based radiomics signature for evaluating tumor infiltrating Treg cells and outcome prediction of gastric cancer. Ann Transl Med 2020;8:469. [PMID: 32395513 DOI: 10.21037/atm.2020.03.114] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
46 Zhuo E, Zhang W, Li H, Zhang G, Jing B, Zhou J, Cui C, Chen M, Sun Y, Liu L, Cai H. Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups. Eur Radiol 2019;29:5590-9. [DOI: 10.1007/s00330-019-06075-1] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 4.7] [Reference Citation Analysis]
47 Chidambaram S, Sounderajah V, Maynard N, Markar SR. Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy. Ann Surg Oncol 2021. [PMID: 34762214 DOI: 10.1245/s10434-021-10882-6] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
48 Li K, Sun H, Lu Z, Xin J, Zhang L, Guo Y, Guo Q. Value of [18F]FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma. Eur J Radiol 2018;106:160-6. [PMID: 30150039 DOI: 10.1016/j.ejrad.2018.07.024] [Cited by in Crossref: 21] [Cited by in F6Publishing: 20] [Article Influence: 5.3] [Reference Citation Analysis]
49 Wang T, Gong J, Duan HH, Wang LJ, Ye XD, Nie SD. Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer. J Xray Sci Technol 2019;27:773-803. [PMID: 31450540 DOI: 10.3233/XST-190526] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
50 Wang R, Liu H, Liang P, Zhao H, Li L, Gao J. Radiomics analysis of CT imaging for differentiating gastric neuroendocrine carcinomas from gastric adenocarcinomas. Eur J Radiol 2021;138:109662. [PMID: 33774440 DOI: 10.1016/j.ejrad.2021.109662] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
51 Ou X, Wang J, Zhou R, Zhu S, Pang F, Zhou Y, Tian R, Ma X. Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma. Contrast Media Mol Imaging 2019;2019:4507694. [PMID: 30930700 DOI: 10.1155/2019/4507694] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]
52 Wang Y, Jin ZY. Radiomics approaches in gastric cancer: a frontier in clinical decision making. Chin Med J (Engl) 2019;132:1983-9. [PMID: 31348029 DOI: 10.1097/CM9.0000000000000360] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 3.5] [Reference Citation Analysis]
53 Ge YX, Li J, Zhang JQ, Duan SF, Liu YK, Hu SD. Radiomics analysis of multicenter CT images for discriminating mucinous adenocarcinoma from nomucinous adenocarcinoma in rectal cancer and comparison with conventional CT values. J Xray Sci Technol 2020;28:285-97. [PMID: 32116286 DOI: 10.3233/XST-190614] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]