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Cited by in F6Publishing
For: 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]
Number Citing Articles
1 Liu YY, Zhang H, Wang L, Lin SS, Lu H, Liang HJ, Liang P, Li J, Lv PJ, Gao JB. Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study. Front Oncol 2021;11:740732. [PMID: 34604085 DOI: 10.3389/fonc.2021.740732] [Reference Citation Analysis]
2 Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J Gastroenterol 2021; 27(27): 4395-4412 [PMID: 34366612 DOI: 10.3748/wjg.v27.i27.4395] [Reference Citation Analysis]
3 Yang H, Hu B. Early gastrointestinal cancer: The application of artificial intelligence. Artif Intell Gastrointest Endosc 2021; 2(4): 185-197 [DOI: 10.37126/aige.v2.i4.185] [Reference Citation Analysis]
4 Liang X, Cai W, Liu X, Jin M, Ruan L, Yan S. A radiomics model that predicts lymph node status in pancreatic cancer to guide clinical decision making: A retrospective study. J Cancer 2021;12:6050-7. [PMID: 34539878 DOI: 10.7150/jca.61101] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Liu X, Liang X, Ruan L, Yan S. A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer. Front Oncol 2021;11:633852. [PMID: 34631512 DOI: 10.3389/fonc.2021.633852] [Reference Citation Analysis]
6 Wang XX, Ding Y, Wang SW, Dong D, Li HL, Chen J, Hu H, Lu C, Tian J, Shan XH. Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer. Cancer Imaging 2020;20:83. [PMID: 33228815 DOI: 10.1186/s40644-020-00358-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Toia GV, Mileto A, Wang CL, Sahani DV. Quantitative dual-energy CT techniques in the abdomen. Abdom Radiol (NY) 2021. [PMID: 34468796 DOI: 10.1007/s00261-021-03266-7] [Reference Citation Analysis]
8 Bonnot PE, Lintis A, Mercier F, Benzerdjeb N, Passot G, Pocard M, Meunier B, Bereder JM, Abboud K, Marchal F, Quenet F, Goere D, Msika S, Arvieux C, Pirro N, Wernert R, Rat P, Gagnière J, Lefevre JH, Courvoisier T, Kianmanesh R, Vaudoyer D, Rivoire M, Meeus P, Villeneuve L, Piessen G, Glehen O; FREGAT and BIG-RENAPE Networks . Prognosis of poorly cohesive gastric cancer after complete cytoreductive surgery with or without hyperthermic intraperitoneal chemotherapy (CYTO-CHIP study). Br J Surg 2021;108:1225-35. [PMID: 34498666 DOI: 10.1093/bjs/znab200] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Tan JW, Wang L, Chen Y, Xi W, Ji J, Xu X, Zou LK, Feng JX, Zhang J, Zhang H. Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation. J Cancer. 2020;11:7224-7236. [PMID: 33193886 DOI: 10.7150/jca.46704] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
10 Liu S, Qiao X, Xu M, Ji C, Li L, Zhou Z. Development and Validation of Multivariate Models Integrating Preoperative Clinicopathological Parameters and Radiographic Findings Based on Late Arterial Phase CT Images for Predicting Lymph Node Metastasis in Gastric Cancer. Acad Radiol 2021:S1076-6332(21)00020-9. [PMID: 33487536 DOI: 10.1016/j.acra.2021.01.011] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
11 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]
12 Chen Y, Yuan F, Wang L, Li E, Xu Z, Wels M, Yao W, Zhang H. Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy. Eur J Surg Oncol 2021:S0748-7983(21)00635-1. [PMID: 34304951 DOI: 10.1016/j.ejso.2021.07.014] [Reference Citation Analysis]
13 Shivakumar N, Chandrashekar A, Handa AI, Lee R. Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review. Postgrad Med J 2021:postgradmedj-2020-139620. [PMID: 33688072 DOI: 10.1136/postgradmedj-2020-139620] [Reference Citation Analysis]
14 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]
15 Lennartz S, O'Shea A, Parakh A, Persigehl T, Baessler B, Kambadakone A. Robustness of dual-energy CT-derived radiomic features across three different scanner types. Eur Radiol 2021. [PMID: 34542695 DOI: 10.1007/s00330-021-08249-2] [Reference Citation Analysis]
16 Kruis MF. Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT. J Appl Clin Med Phys 2021. [PMID: 34743405 DOI: 10.1002/acm2.13468] [Reference Citation Analysis]
17 Zhao H, Li W, Huang W, Yang Y, Shen W, Liang P, Gao J. Dual-Energy CT-Based Nomogram for Decoding HER2 Status in Patients With Gastric Cancer. AJR Am J Roentgenol 2021;216:1539-48. [PMID: 33852330 DOI: 10.2214/AJR.20.23528] [Reference Citation Analysis]
18 Ding Y, Meyer M, Lyu P, Rigiroli F, Ramirez-Giraldo JC, Lafata K, Yang S, Marin D. Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions? Acta Radiol 2021;:2841851211010396. [PMID: 33878931 DOI: 10.1177/02841851211010396] [Reference Citation Analysis]
19 Bonde A, Daly S, Kirsten J, Kondapaneni S, Mellnick V, Menias CO, Katabathina VS. Human Gut Microbiota-associated Gastrointestinal Malignancies: A Comprehensive Review. Radiographics 2021;41:1103-22. [PMID: 33989072 DOI: 10.1148/rg.2021200168] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 An C, Li D, Li S, Li W, Tong T, Liu L, Jiang D, Jiang L, Ruan G, Hai N, Fu Y, Wang K, Zhuo S, Tian J. Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging 2021. [PMID: 34651229 DOI: 10.1007/s00259-021-05573-z] [Reference Citation Analysis]
21 Reginelli A, Nardone V, Giacobbe G, Belfiore MP, Grassi R, Schettino F, Del Canto M, Grassi R, Cappabianca S. Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021;11:1796. [PMID: 34679494 DOI: 10.3390/diagnostics11101796] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. Artif Intell Gastroenterol 2020; 1(4): 71-85 [DOI: 10.35712/aig.v1.i4.71] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Han D, Yu Y, He T, Yu N, Dang S, Wu H, Ren J, Duan X. Effect of radiomics from different virtual monochromatic images in dual-energy spectral CT on the WHO/ISUP classification of clear cell renal cell carcinoma. Clin Radiol 2021;76:627.e23-9. [PMID: 33985770 DOI: 10.1016/j.crad.2021.02.033] [Reference Citation Analysis]
24 Jin C, Jiang Y, Yu H, Wang W, Li B, Chen C, Yuan Q, Hu Y, Xu Y, Zhou Z, Li G, Li R. Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer. Br J Surg 2021;108:542-9. [PMID: 34043780 DOI: 10.1002/bjs.11928] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
25 Heinrich A, Schenkl S, Buckreus D, Güttler FV, Teichgräber UK. CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning-based reconstruction. Eur Radiol 2021. [PMID: 34327575 DOI: 10.1007/s00330-021-08206-z] [Reference Citation Analysis]
26 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]
27 Ebrahimian S, Singh R, Netaji A, Madhusudhan KS, Homayounieh F, Primak A, Lades F, Saini S, Kalra MK, Sharma S. Characterization of Benign and Malignant Pancreatic Lesions with DECT Quantitative Metrics and Radiomics. Acad Radiol 2021:S1076-6332(21)00316-0. [PMID: 34412944 DOI: 10.1016/j.acra.2021.07.008] [Reference Citation Analysis]
28 Zheng Q, Yang L, Zeng B, Li J, Guo K, Liang Y, Liao G. Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis. EClinicalMedicine 2021;31:100669. [PMID: 33392486 DOI: 10.1016/j.eclinm.2020.100669] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]