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For: Giganti F, Antunes S, Salerno A, Ambrosi A, Marra P, Nicoletti R, Orsenigo E, Chiari D, Albarello L, Staudacher C, Esposito A, Del Maschio A, De Cobelli F. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol. 2017;27:1831-1839. [PMID: 27553932 DOI: 10.1007/s00330-016-4540-y] [Cited by in Crossref: 49] [Cited by in F6Publishing: 56] [Article Influence: 8.2] [Reference Citation Analysis]
Number Citing Articles
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3 Zhang P, Min X, Feng Z, Kang Z, Li B, Cai W, Fan C, Yin X, Xie J, Lv W, Wang L. Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions. Cancer Manag Res 2021;13:839-47. [PMID: 33536790 DOI: 10.2147/CMAR.S288378] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Feng QX, Liu C, Qi L, Sun SW, Song Y, Yang G, Zhang YD, Liu XS. An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer. J Am Coll Radiol 2019;16:952-60. [PMID: 30733162 DOI: 10.1016/j.jacr.2018.12.017] [Cited by in Crossref: 17] [Cited by in F6Publishing: 16] [Article Influence: 5.7] [Reference Citation Analysis]
5 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, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021;13:2522. [PMID: 34063937 DOI: 10.3390/cancers13112522] [Reference Citation Analysis]
6 Liu S, Shi H, Ji C, Zheng H, Pan X, Guan W, Chen L, Sun Y, Tang L, Guan Y, Li W, Ge Y, He J, Liu S, Zhou Z. Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging. Clin Radiol. 2018;73:756.e1-756.e9. [PMID: 29625746 DOI: 10.1016/j.crad.2018.03.005] [Cited by in Crossref: 18] [Cited by in F6Publishing: 23] [Article Influence: 4.5] [Reference Citation Analysis]
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9 Yardimci AH, Sel I, Bektas CT, Yarikkaya E, Dursun N, Bektas H, Afsar CU, Gursu RU, Yardimci VH, Ertas E, Kilickesmez O. Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment. Jpn J Radiol 2020;38:553-60. [PMID: 32140880 DOI: 10.1007/s11604-020-00936-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
10 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]
11 Nardone V, Tini P, Croci S, Carbone SF, Sebaste L, Carfagno T, Battaglia G, Pastina P, Rubino G, Mazzei MA, Pirtoli L. 3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures. Quant Imaging Med Surg 2018;8:14-24. [PMID: 29541619 DOI: 10.21037/qims.2018.02.01] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
12 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]
13 Ma XT, He XW, Lian H, Wang XY, Wang WJ, Peng ML. Value of double contrast-enhanced ultrasonography in determining pathological features of advanced gastric cancer. Shijie Huaren Xiaohua Zazhi 2018; 26(2): 87-92 [DOI: 10.11569/wcjd.v26.i2.87] [Reference Citation Analysis]
14 Liu S, Liu S, Ji C, Zheng H, Pan X, Zhang Y, Guan W, Chen L, Guan Y, Li W, He J, Ge Y, Zhou Z. Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol 2017;27:4951-9. [PMID: 28643092 DOI: 10.1007/s00330-017-4881-1] [Cited by in Crossref: 57] [Cited by in F6Publishing: 58] [Article Influence: 11.4] [Reference Citation Analysis]
15 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]
16 Tsurumaru D, Nishimuta Y, Muraki T, Asayama Y, Nishie A, Oki E, Honda H. Gastric cancer with synchronous and metachronous hepatic metastasis predicted by enhancement pattern on multiphasic contrast-enhanced CT. European Journal of Radiology 2018;108:165-71. [DOI: 10.1016/j.ejrad.2018.09.030] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
17 Wang L, Zhang Y, Chen Y, Tan J, Wang L, Zhang J, Yang C, Ma Q, Ge Y, Xu Z, Pan Z, Du L, Yan F, Yao W, Zhang H. The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images. Front Oncol 2020;10:562945. [PMID: 33585186 DOI: 10.3389/fonc.2020.562945] [Reference Citation Analysis]
18 Pan B, Zhang W, Chen W, Zheng J, Yang X, Sun J, Sun X, Chen X, Shen X. Establishment of the Radiologic Tumor Invasion Index Based on Radiomics Splenic Features and Clinical Factors to Predict Serous Invasion of Gastric Cancer. Front Oncol 2021;11:682456. [PMID: 34434892 DOI: 10.3389/fonc.2021.682456] [Reference Citation Analysis]
19 Liu S, Shi H, Ji C, Guan W, Chen L, Sun Y, Tang L, Guan Y, Li W, Ge Y, He J, Liu S, Zhou Z. CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers. Sci Rep 2018;8:11844. [PMID: 30087428 DOI: 10.1038/s41598-018-30352-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 1.5] [Reference Citation Analysis]
20 Jiang Y, Wang W, Chen C, Zhang X, Zha X, Lv W, Xie J, Huang W, Sun Z, Hu Y, Yu J, Li T, Zhou Z, Xu Y, Li G. Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer. Front Oncol 2019;9:340. [PMID: 31106158 DOI: 10.3389/fonc.2019.00340] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 6.7] [Reference Citation Analysis]
21 Nougaret S, Tardieu M, Vargas H, Reinhold C, Vande Perre S, Bonanno N, Sala E, Thomassin-naggara I. Ovarian cancer: An update on imaging in the era of radiomics. Diagnostic and Interventional Imaging 2019;100:647-55. [DOI: 10.1016/j.diii.2018.11.007] [Cited by in Crossref: 31] [Cited by in F6Publishing: 30] [Article Influence: 10.3] [Reference Citation Analysis]
22 Zhang W, Fang M, Dong D, Wang X, Ke X, Zhang L, Hu C, Guo L, Guan X, Zhou J, Shan X, Tian J. Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer. Radiotherapy and Oncology 2020;145:13-20. [DOI: 10.1016/j.radonc.2019.11.023] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 8.5] [Reference Citation Analysis]
23 Klaassen R, Larue RTHM, Mearadji B, van der Woude SO, Stoker J, Lambin P, van Laarhoven HWM. Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients. PLoS One 2018;13:e0207362. [PMID: 30440002 DOI: 10.1371/journal.pone.0207362] [Cited by in Crossref: 14] [Cited by in F6Publishing: 13] [Article Influence: 3.5] [Reference Citation Analysis]
24 Guerrisi A, Russillo M, Loi E, Ganeshan B, Ungania S, Desiderio F, Bruzzaniti V, Falcone I, Renna D, Ferraresi V, Caterino M, Solivetti FM, Cognetti F, Morrone A. Exploring CT Texture Parameters as Predictive and Response Imaging Biomarkers of Survival in Patients With Metastatic Melanoma Treated With PD-1 Inhibitor Nivolumab: A Pilot Study Using a Delta-Radiomics Approach. Front Oncol 2021;11:704607. [PMID: 34692481 DOI: 10.3389/fonc.2021.704607] [Reference Citation Analysis]
25 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]
26 Liu G, Yin H, Cheng X, Wang Y, Hu Y, Liu T, Shi H. Intra-tumor metabolic heterogeneity of gastric cancer on 18F-FDG PETCT indicates patient survival outcomes. Clin Exp Med 2021;21:129-38. [PMID: 32880779 DOI: 10.1007/s10238-020-00659-8] [Reference Citation Analysis]
27 Li W, Zhang L, Tian C, Song H, Fang M, Hu C, Zang Y, Cao Y, Dai S, Wang F, Dong D, Wang R, Tian J. Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection. Eur Radiol. 2019;29:3079-3089. [PMID: 30519931 DOI: 10.1007/s00330-018-5861-9] [Cited by in Crossref: 27] [Cited by in F6Publishing: 27] [Article Influence: 6.8] [Reference Citation Analysis]
28 Li Y, Xu X, Weng S, Yan C, Chen J, Ye R. CT Image-Based Texture Analysis to Predict Microvascular Invasion in Primary Hepatocellular Carcinoma. J Digit Imaging 2020;33:1365-75. [PMID: 32968880 DOI: 10.1007/s10278-020-00386-2] [Reference Citation Analysis]
29 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]
30 Li Y, Xie X, Yang X, Guo L, Liu Z, Zhao X, Luo Y, Jia W, Huang F, Zhu S, Chen Z, Chen X, Wei Z, Zhang W. Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial-least-square discriminant analysis and support vector machine. J Biophotonics 2019;12:e201800324. [PMID: 30585424 DOI: 10.1002/jbio.201800324] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
31 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]
32 Zhang T, Zhang Y, Liu X, Xu H, Chen C, Zhou X, Liu Y, Ma X. Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades. Front Oncol 2020;10:521831. [PMID: 33643890 DOI: 10.3389/fonc.2020.521831] [Reference Citation Analysis]
33 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]
34 Guo C, Zhuge X, Wang Z, Wang Q, Sun K, Feng Z, Chen X. Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade. Abdom Radiol 2019;44:576-85. [DOI: 10.1007/s00261-018-1763-1] [Reference Citation Analysis]
35 Desbordes P, Ruan S, Modzelewski R, Pineau P, Vauclin S, Gouel P, Michel P, Di Fiore F, Vera P, Gardin I. Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier. PLoS One 2017;12:e0173208. [PMID: 28282392 DOI: 10.1371/journal.pone.0173208] [Cited by in Crossref: 27] [Cited by in F6Publishing: 17] [Article Influence: 5.4] [Reference Citation Analysis]
36 Zhang C, Wen HL, Zhang R, Xie SY, Xie CM. Computed tomography radiomics to predict EBER positivity in Epstein-Barr virus-associated gastric adenocarcinomas: a retrospective study. Acta Radiol 2021;:2841851211029083. [PMID: 34233501 DOI: 10.1177/02841851211029083] [Reference Citation Analysis]
37 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]
38 Guo C, Zhuge X, Wang Z, Wang Q, Sun K, Feng Z, Chen X. Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade. Abdom Radiol (NY). 2019;44:576-585. [PMID: 30182253 DOI: 10.1007/s00261-018-1763-1] [Cited by in Crossref: 27] [Cited by in F6Publishing: 22] [Article Influence: 13.5] [Reference Citation Analysis]
39 Jiang Y, Yuan Q, Lv W, Xi S, Huang W, Sun Z, Chen H, Zhao L, Liu W, Hu Y, Lu L, Ma J, Li T, Yu J, Wang Q, Li G. Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits. Theranostics. 2018;8:5915-5928. [PMID: 30613271 DOI: 10.7150/thno.28018] [Cited by in Crossref: 41] [Cited by in F6Publishing: 41] [Article Influence: 10.3] [Reference Citation Analysis]
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41 Cheng S, Fang M, Cui C, Chen X, Yin G, Prasad SK, Dong D, Tian J, Zhao S. LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results. Eur Radiol 2018;28:4615-24. [PMID: 29728817 DOI: 10.1007/s00330-018-5391-5] [Cited by in Crossref: 25] [Cited by in F6Publishing: 19] [Article Influence: 6.3] [Reference Citation Analysis]
42 Li Z, Zhang D, Dai Y, Dong J, Wu L, Li Y, Cheng Z, Ding Y, Liu Z. Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study. Chin J Cancer Res. 2018;30:406-414. [PMID: 30210220 DOI: 10.21147/j.issn.1000-9604.2018.04.03] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 5.0] [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 Feng C, Lu F, Shen Y, Li A, Yu H, Tang H, Li Z, Hu D. Tumor heterogeneity in gastrointestinal stromal tumors of the small bowel: volumetric CT texture analysis as a potential biomarker for risk stratification. Cancer Imaging. 2018;18:46. [PMID: 30518436 DOI: 10.1186/s40644-018-0182-4] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 4.3] [Reference Citation Analysis]
45 Shin J, Lim JS, Huh YM, Kim JH, Hyung WJ, Chung JJ, Han K, Kim S. A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting. Sci Rep 2021;11:1879. [PMID: 33479398 DOI: 10.1038/s41598-021-81408-z] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
46 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]
47 Huang W, Zhou K, Jiang Y, Chen C, Yuan Q, Han Z, Xie J, Yu S, Sun Z, Hu Y, Yu J, Liu H, Xiao R, Xu Y, Zhou Z, Li G. Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer. Front Oncol 2020;10:1416. [PMID: 32974149 DOI: 10.3389/fonc.2020.01416] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
48 Wang S, Feng C, Dong D, Li H, Zhou J, Ye Y, Liu Z, Tian J, Wang Y. Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study. Med Phys 2020;47:4862-71. [PMID: 32592224 DOI: 10.1002/mp.14350] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
49 Ding L, Wu S, Shen Y, Hu X, Hu D, Kamel I, Li Z. Primary Gastro-Intestinal Lymphoma and Gastro-Intestinal Adenocarcinoma: An Initial Study of CT Texture Analysis as Quantitative Biomarkers for Differentiation. Life (Basel) 2021;11:264. [PMID: 33806817 DOI: 10.3390/life11030264] [Reference Citation Analysis]
50 Ungureanu BS, Sacerdotianu VM, Turcu-Stiolica A, Cazacu IM, Saftoiu A. Endoscopic Ultrasound vs. Computed Tomography for Gastric Cancer Staging: A Network Meta-Analysis. Diagnostics (Basel) 2021;11:134. [PMID: 33467164 DOI: 10.3390/diagnostics11010134] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
51 Nougaret S, Tibermacine H, Tardieu M, Sala E. Radiomics: an Introductory Guide to What It May Foretell.Curr Oncol Rep. 2019;21:70. [PMID: 31240403 DOI: 10.1007/s11912-019-0815-1] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 3.0] [Reference Citation Analysis]
52 Cahalane AM, Kilcoyne A, Tabari A, Mcdermott S, Gee MS. Computed tomography texture features can discriminate benign from malignant lymphadenopathy in pediatric patients: a preliminary study. Pediatr Radiol 2019;49:737-45. [DOI: 10.1007/s00247-019-04350-3] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
53 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]
54 Li J, Dong D, Fang M, Wang R, Tian J, Li H, Gao J. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. Eur Radiol. 2020;30:2324-2333. [PMID: 31953668 DOI: 10.1007/s00330-019-06621-x] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 8.5] [Reference Citation Analysis]
55 Wang YY, Wu Q, Chen L, Chen W, Yang T, Xu XQ, Wu FY, Hu H, Chen HH. Texture analysis of orbital magnetic resonance imaging for monitoring and predicting treatment response to glucocorticoids in patients with thyroid-associated ophthalmopathy. Endocr Connect 2021;10:676-84. [PMID: 34077388 DOI: 10.1530/EC-21-0162] [Reference Citation Analysis]
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57 Lin X, Xu L, Wu A, Guo C, Chen X, Wang Z. Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography. Acta Radiol 2019;60:553-60. [DOI: 10.1177/0284185118788895] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
58 Gondim Teixeira PA, Leplat C, Chen B, De Verbizier J, Beaumont M, Badr S, Cotten A, Blum A. Contrast-enhanced 3T MR Perfusion of Musculoskeletal Tumours: T1 Value Heterogeneity Assessment and Evaluation of the Influence of T1 Estimation Methods on Quantitative Parameters. Eur Radiol 2017;27:4903-12. [DOI: 10.1007/s00330-017-4891-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
59 Jiang Y, Chen C, Xie J, Wang W, Zha X, Lv W, Chen H, Hu Y, Li T, Yu J, Zhou Z, Xu Y, Li G. Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine. 2018;36:171-182. [PMID: 30224313 DOI: 10.1016/j.ebiom.2018.09.007] [Cited by in Crossref: 55] [Cited by in F6Publishing: 56] [Article Influence: 13.8] [Reference Citation Analysis]
60 Kim HY, Kim YH, Yun G, Chang W, Lee YJ, Kim B. Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer? PLoS One. 2018;13:e0194755. [PMID: 29596522 DOI: 10.1371/journal.pone.0194755] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 3.0] [Reference Citation Analysis]
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