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Cited by in F6Publishing
For: Chen L, Liang J, Wu H, Wang Z, Li S, Li W, Zhang X, Chen J, Ye J, Li X, Xie X, Lu M, Kuang M, Xu J, Wang W. Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics. Life Sciences 2018;208:55-63. [DOI: 10.1016/j.lfs.2018.07.007] [Cited by in Crossref: 20] [Cited by in F6Publishing: 23] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Li M, Li X, Guo Y, Miao Z, Liu X, Guo S, Zhang H. Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases. Quant Imaging Med Surg 2020;10:397-414. [PMID: 32190566 DOI: 10.21037/qims.2019.12.16] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Zhang J, Pan Z, Yang J, Yan X, Li Y, Lyu J. A nomogram for determining the disease-specific survival in Ewing sarcoma: a population study. BMC Cancer 2019;19:667. [PMID: 31277591 DOI: 10.1186/s12885-019-5893-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 3.3] [Reference Citation Analysis]
3 Bedrikovetski S, Dudi-Venkata NN, Maicas G, Kroon HM, Seow W, Carneiro G, Moore JW, Sammour T. Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis. Artif Intell Med 2021;113:102022. [PMID: 33685585 DOI: 10.1016/j.artmed.2021.102022] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Fu Y, Liu X, Yang Q, Sun J, Xie Y, Zhang Y, Zhang H. Radiomic features based on MRI for prediction of lymphovascular invasion in rectal cancer. Chin J Acad Radiol 2019;2:13-22. [DOI: 10.1007/s42058-019-00016-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Xian MF, Zheng X, Xu JB, Li X, Chen LD, Wang W. Prediction of lymph node metastasis in rectal cancer: comparison between shear-wave elastography based ultrasomics and MRI. Diagn Interv Radiol 2021;27:424-31. [PMID: 34003129 DOI: 10.5152/dir.2021.20031] [Reference Citation Analysis]
6 Horvat N, Bates DDB, Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? Abdom Radiol (NY). 2019;44:3764-3774. [PMID: 31055615 DOI: 10.1007/s00261-019-02042-y] [Cited by in Crossref: 24] [Cited by in F6Publishing: 21] [Article Influence: 12.0] [Reference Citation Analysis]
7 Chen LD, Li W, Xian MF, Zheng X, Lin Y, Liu BX, Lin MX, Li X, Zheng YL, Xie XY, Lu MD, Kuang M, Xu JB, Wang W. Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model. Eur Radiol 2020;30:1969-79. [PMID: 31828415 DOI: 10.1007/s00330-019-06558-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
8 Min X, Zhu J, Shang M, Liu J, Zhang K, Guo L, Li L, Cheng L, Li J. Stiffness Could be a Predictor of AJCC Prognostic Stage Groups in Preoperative Invasive Ductal Carcinoma. J Ultrasound Med 2021. [PMID: 33629753 DOI: 10.1002/jum.15657] [Reference Citation Analysis]
9 Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review [Internet]. Clin Colorectal Cancer. 2021;20:52-71. [PMID: 33349519 DOI: 10.1016/j.clcc.2020.11.001] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
10 Zhang J, Pan Z, Zhao F, Feng X, Huang Y, Hu C, Li Y, Lyu J. Development and validation of a nomogram containing the prognostic determinants of chondrosarcoma based on the Surveillance, Epidemiology, and End Results database. Int J Clin Oncol 2019;24:1459-67. [PMID: 31243629 DOI: 10.1007/s10147-019-01489-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
11 Zhao L, Liang M, Shi Z, Xie L, Zhang H, Zhao X. Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer. Quant Imaging Med Surg 2021;11:1805-16. [PMID: 33936966 DOI: 10.21037/qims-20-659] [Reference Citation Analysis]
12 Cao Y, Zhang J, Bao H, Zhang G, Yan X, Wang Z, Ren J, Chai Y, Zhao Z, Zhou J. Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer. Front Oncol 2021;11:689176. [PMID: 34631524 DOI: 10.3389/fonc.2021.689176] [Reference Citation Analysis]
13 Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G, Moore JW, Sammour T. Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer 2021;21:1058. [PMID: 34565338 DOI: 10.1186/s12885-021-08773-w] [Reference Citation Analysis]
14 Xu X, Huang L, Chen J, Wen J, Liu D, Cao J, Wang J, Fan M. Application of radiomics signature captured from pretreatment thoracic CT to predict brain metastases in stage III/IV ALK-positive non-small cell lung cancer patients. J Thorac Dis 2019;11:4516-28. [PMID: 31903240 DOI: 10.21037/jtd.2019.11.01] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 0.7] [Reference Citation Analysis]
15 Guo Y, Wang Q, Guo Y, Zhang Y, Fu Y, Zhang H. Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer.Sci Rep. 2021;11:9429. [PMID: 33941817 DOI: 10.1038/s41598-021-88831-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Xu H, Zhao W, Guo W, Cao S, Gao C, Song T, Yang L, Liu Y, Han Y, Zhang L, Wang K. Prediction Model Combining Clinical and MR Data for Diagnosis of Lymph Node Metastasis in Patients With Rectal Cancer. J Magn Reson Imaging 2021;53:874-83. [PMID: 32978993 DOI: 10.1002/jmri.27369] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
17 Bogowicz M, Vuong D, Huellner MW, Pavic M, Andratschke N, Gabrys HS, Guckenberger M, Tanadini-Lang S. CT radiomics and PET radiomics: ready for clinical implementation? Q J Nucl Med Mol Imaging 2019;63:355-70. [PMID: 31527578 DOI: 10.23736/S1824-4785.19.03192-3] [Cited by in Crossref: 20] [Cited by in F6Publishing: 13] [Article Influence: 6.7] [Reference Citation Analysis]
18 Li J, Zhou Y, Wang X, Zhou M, Chen X, Luan K. An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer. Abdom Radiol (NY) 2021;46:1816-24. [PMID: 33241428 DOI: 10.1007/s00261-020-02863-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
19 Hu HT, Shan QY, Chen SL, Li B, Feng ST, Xu EJ, Li X, Long JY, Xie XY, Lu MD, Kuang M, Shen JX, Wang W. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners.Radiol Med. 2020;125:697-705. [PMID: 32200455 DOI: 10.1007/s11547-020-01174-2] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
20 Zhang J, Yang J, Wang HQ, Pan Z, Yan X, Hu C, Li Y, Lyu J. Development and validation of a nomogram for osteosarcoma-specific survival: A population-based study. Medicine (Baltimore) 2019;98:e15988. [PMID: 31169737 DOI: 10.1097/MD.0000000000015988] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 2.7] [Reference Citation Analysis]
21 Feng Y, Peng C, Zhu Y, Liu L. Biplane transrectal ultrasonography plus ultrasonic elastosonography and contrast-enhanced ultrasonography in T staging of rectal cancer. BMC Cancer 2020;20:862. [PMID: 32894078 DOI: 10.1186/s12885-020-07369-0] [Reference Citation Analysis]
22 Liu X, Yang Q, Zhang C, Sun J, He K, Xie Y, Zhang Y, Fu Y, Zhang H. Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer. Front Oncol 2020;10:585767. [PMID: 33680919 DOI: 10.3389/fonc.2020.585767] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
23 Zhang Y, He K, Guo Y, Liu X, Yang Q, Zhang C, Xie Y, Mu S, Guo Y, Fu Y, Zhang H. A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer. Front Oncol 2020;10:457. [PMID: 32328460 DOI: 10.3389/fonc.2020.00457] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]