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For: Liao H, Zhang Z, Chen J, Liao M, Xu L, Wu Z, Yuan K, Song B, Zeng Y. Preoperative Radiomic Approach to Evaluate Tumor-Infiltrating CD8+ T Cells in Hepatocellular Carcinoma Patients Using Contrast-Enhanced Computed Tomography. Ann Surg Oncol. 2019;26:4537-4547. [PMID: 31520208 DOI: 10.1245/s10434-019-07815-9] [Cited by in Crossref: 22] [Cited by in F6Publishing: 20] [Article Influence: 7.3] [Reference Citation Analysis]
Number Citing Articles
1 Cho CS. Radiomics: A Well-Intentioned Leap of Faith. Ann Surg Oncol 2019;26:4178-9. [PMID: 31520206 DOI: 10.1245/s10434-019-07818-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
2 Tian Y, Komolafe TE, Zheng J, Zhou G, Chen T, Zhou B, Yang X. Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features. Diagnostics (Basel) 2021;11:1875. [PMID: 34679573 DOI: 10.3390/diagnostics11101875] [Reference Citation Analysis]
3 Borhani AA, Catania R, Velichko YS, Hectors S, Taouli B, Lewis S. Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response. Abdom Radiol (NY) 2021;46:3674-85. [PMID: 33891149 DOI: 10.1007/s00261-021-03085-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
4 Li J, Shi Z, Liu F, Fang X, Cao K, Meng Y, Zhang H, Yu J, Feng X, Li Q, Liu Y, Wang L, Jiang H, Lu J, Shao C, Bian Y. XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma. Front Oncol 2021;11:671333. [PMID: 34094971 DOI: 10.3389/fonc.2021.671333] [Reference Citation Analysis]
5 Bian Y, Liu C, Li Q, Meng Y, Liu F, Zhang H, Fang X, Li J, Yu J, Feng X, Ma C, Zhao Z, Wang L, Xu J, Shao C, Lu J. Preoperative Radiomics Approach to Evaluating Tumor-Infiltrating CD8+ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging. J Magn Reson Imaging 2021. [PMID: 34355834 DOI: 10.1002/jmri.27871] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
6 Porcu M, Solinas C, Mannelli L, Micheletti G, Lambertini M, Willard-Gallo K, Neri E, Flanders AE, Saba L. Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians. Crit Rev Oncol Hematol 2020;154:103068. [PMID: 32805498 DOI: 10.1016/j.critrevonc.2020.103068] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Bian Y, Liu YF, Jiang H, Meng Y, Liu F, Cao K, Zhang H, Fang X, Li J, Yu J, Feng X, Li Q, Wang L, Lu J, Shao C. Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2021. [PMID: 34189612 DOI: 10.1007/s00261-021-03159-9] [Reference Citation Analysis]
8 Jeon SH, Lim YJ, Koh J, Chang WI, Kim S, Kim K, Chie EK. A radiomic signature model to predict the chemoradiation-induced alteration in tumor-infiltrating CD8+ cells in locally advanced rectal cancer. Radiother Oncol 2021;162:124-31. [PMID: 34265357 DOI: 10.1016/j.radonc.2021.07.004] [Reference Citation Analysis]
9 Wang CY, Ginat DT. Preliminary Computed Tomography Radiomics Model for Predicting Pretreatment CD8+ T-Cell Infiltration Status for Primary Head and Neck Squamous Cell Carcinoma. J Comput Assist Tomogr 2021;45:629-36. [PMID: 34519454 DOI: 10.1097/RCT.0000000000001149] [Reference Citation Analysis]
10 Huang X, Long L, Wei J, Li Y, Xia Y, Zuo P, Chai X. Radiomics for diagnosis of dual-phenotype hepatocellular carcinoma using Gd-EOB-DTPA-enhanced MRI and patient prognosis. J Cancer Res Clin Oncol. 2019;145:2995-3003. [PMID: 31664520 DOI: 10.1007/s00432-019-03062-3] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 3.7] [Reference Citation Analysis]
11 Xu N, Zhou J, He X, Ye S, Miao H, Liu H, Chen Z, Zhao Y, Pan Z, Wang M. Radiomics Model for Evaluating the Level of Tumor-Infiltrating Lymphocytes in Breast Cancer Based on Dynamic Contrast-Enhanced MRI. Clin Breast Cancer 2020:S1526-8209(20)30336-0. [PMID: 33795199 DOI: 10.1016/j.clbc.2020.12.008] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Wen Q, Yang Z, Zhu J, Qiu Q, Dai H, Feng A, Xing L. Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC. Onco Targets Ther. 2020;13:12003-12013. [PMID: 33244242 DOI: 10.2147/ott.s261068] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
13 Feng B, Ma XH, Wang S, Cai W, Liu XB, Zhao XM. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives. World J Gastroenterol 2021; 27(32): 5341-5350 [PMID: 34539136 DOI: 10.3748/wjg.v27.i32.5341] [Reference Citation Analysis]
14 Wang JH, Wahid KA, van Dijk LV, Farahani K, Thompson RF, Fuller CD. Radiomic biomarkers of tumor immune biology and immunotherapy response. Clin Transl Radiat Oncol 2021;28:97-115. [PMID: 33937530 DOI: 10.1016/j.ctro.2021.03.006] [Reference Citation Analysis]
15 Sun L, Mu L, Zhou J, Tang W, Zhang L, Xie S, Chen J, Wang J. Imaging features of gadoxetic acid-enhanced MR imaging for evaluation of tumor-infiltrating CD8 cells and PD-L1 expression in hepatocellular carcinoma. Cancer Immunol Immunother 2021. [PMID: 33993366 DOI: 10.1007/s00262-021-02957-w] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
16 Li Q, Yu J, Zhang H, Meng Y, Liu YF, Jiang H, Zhu M, Li N, Zhou J, Liu F, Fang X, Li J, Feng X, Lu J, Shao C, Bian Y. Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Perceptron Network Classifier Based on Non-contrast MRI. Acad Radiol 2021:S1076-6332(21)00541-9. [PMID: 34922828 DOI: 10.1016/j.acra.2021.11.013] [Reference Citation Analysis]
17 Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2020:S1044-579X(20)30264-9. [PMID: 33290844 DOI: 10.1016/j.semcancer.2020.12.005] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
18 Lo Gullo R, Wen H, Reiner JS, Hoda R, Sevilimedu V, Martinez DF, Thakur SB, Jochelson MS, Gibbs P, Pinker K. Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results. Cancers 2021;13:6273. [DOI: 10.3390/cancers13246273] [Reference Citation Analysis]
19 Liao H, Xiong T, Peng J, Xu L, Liao M, Zhang Z, Wu Z, Yuan K, Zeng Y. Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning. Ann Surg Oncol. 2020;27:2359-2369. [PMID: 31916093 DOI: 10.1245/s10434-019-08190-1] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
20 Seckler F, Doussot A, Colpart P, Turco C, Calame P, Aubin F, Algros MP, Borg C, Nardin C, Heyd B. Preoperative immunotherapy for resectable hepatocellular carcinoma: Toward a paradigm shift? J Hepatol 2020;73:1588-90. [PMID: 32951909 DOI: 10.1016/j.jhep.2020.05.048] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021;54:890-901. [PMID: 34390014 DOI: 10.1111/apt.16563] [Reference Citation Analysis]
22 Chen Q, Chen AZ, Jia G, Li J, Zheng C, Chen K. Molecular Imaging of Tumor Microenvironment to Assess the Effects of Locoregional Treatment for Hepatocellular Carcinoma. Hepatol Commun 2021. [PMID: 34738743 DOI: 10.1002/hep4.1850] [Reference Citation Analysis]
23 Wei J, Jiang H, Gu D, Niu M, Fu F, Han Y, Song B, Tian J. Radiomics in liver diseases: Current progress and future opportunities. Liver Int 2020;40:2050-63. [PMID: 32515148 DOI: 10.1111/liv.14555] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
24 Liu G, Wu D, Wen Y, Cang S. Immune-associated molecular occurrence and prognosis predictor of hepatocellular carcinoma: an integrated analysis of GEO datasets. Bioengineered 2021;12:5253-65. [PMID: 34424809 DOI: 10.1080/21655979.2021.1962147] [Reference Citation Analysis]