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For: Xu H, Guo W, Cui X, Zhuo H, Xiao Y, Ou X, Zhao Y, Zhang T, Ma X. Three-Dimensional Texture Analysis Based on PET/CT Images to Distinguish Hepatocellular Carcinoma and Hepatic Lymphoma. Front Oncol 2019;9:844. [PMID: 31552173 DOI: 10.3389/fonc.2019.00844] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Jiang X, Zou X, Sun J, Zheng A, Su C. A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer. Contrast Media Mol Imaging 2020;2020:5418364. [PMID: 32922222 DOI: 10.1155/2020/5418364] [Reference Citation Analysis]
2 Hussain M, Saher N, Qadri S. Computer Vision Approach for Liver Tumor Classification Using CT Dataset. Applied Artificial Intelligence. [DOI: 10.1080/08839514.2022.2055395] [Reference Citation Analysis]
3 Wang R, Su Y, Mao C, Li S, You M, Xiang S. Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success? Eur Radiol 2021;31:3734-44. [PMID: 33210203 DOI: 10.1007/s00330-020-07498-x] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Piñeiro-Fiel M, Moscoso A, Pubul V, Ruibal Á, Silva-Rodríguez J, Aguiar P. A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021;11:380. [PMID: 33672285 DOI: 10.3390/diagnostics11020380] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
5 Kenawy MA, Khalil MM, Abdelgawad MH, El-Bahnasawy HH. Correlation of texture feature analysis with bone marrow infiltration in initial staging of patients with lymphoma using 18F-fluorodeoxyglucose positron emission tomography combined with computed tomography. Pol J Radiol 2020;85:e586-94. [PMID: 33204373 DOI: 10.5114/pjr.2020.99833] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Wan S, Wei Y, Zhang X, Yang C, Hu F, Song B. Computed Tomography-Based Texture Features for the Risk Stratification of Portal Hypertension and Prediction of Survival in Patients With Cirrhosis: A Preliminary Study. Front Med 2022;9:863596. [DOI: 10.3389/fmed.2022.863596] [Reference Citation Analysis]
7 Rizzo A, Triumbari EKA, Gatta R, Boldrini L, Racca M, Mayerhoefer M, Annunziata S. The role of 18F-FDG PET/CT radiomics in lymphoma. Clin Transl Imaging 2021;9:589-98. [DOI: 10.1007/s40336-021-00451-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Hasani N, Paravastu SS, Farhadi F, Yousefirizi F, Morris MA, Rahmim A, Roschewski M, Summers RM, Saboury B. Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions). PET Clin 2022;17:145-74. [PMID: 34809864 DOI: 10.1016/j.cpet.2021.09.006] [Reference Citation Analysis]
9 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]
10 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]
11 Ripani D, Caldarella C, Za T, Rossi E, De Stefano V, Giordano A. Progression to Symptomatic Multiple Myeloma Predicted by Texture Analysis-Derived Parameters in Patients Without Focal Disease at 18F-FDG PET/CT. Clin Lymphoma Myeloma Leuk 2021;21:536-44. [PMID: 33985932 DOI: 10.1016/j.clml.2021.03.014] [Reference Citation Analysis]
12 Jiang H, Li A, Ji Z, Tian M, Zhang H. Role of Radiomics-Based Baseline PET/CT Imaging in Lymphoma: Diagnosis, Prognosis, and Response Assessment. Mol Imaging Biol 2022. [PMID: 35031945 DOI: 10.1007/s11307-022-01703-7] [Reference Citation Analysis]
13 Cui J, Zou Z, Duan J, Tang W, Li Y, Zhang L, Pan L, Niu T. Predictive Values of PET/CT in Combination With Regulatory B Cells for Therapeutic Response and Survival in Contemporary Patients With Newly Diagnosed Multiple Myeloma. Front Immunol 2021;12:671904. [PMID: 34489930 DOI: 10.3389/fimmu.2021.671904] [Reference Citation Analysis]
14 Xue B, Jiang J, Chen L, Wu S, Zheng X, Zheng X, Tang K. Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front Oncol 2021;11:740111. [PMID: 34765549 DOI: 10.3389/fonc.2021.740111] [Reference Citation Analysis]
15 Zhou N, Guo X, Sun H, Yu B, Zhu H, Li N, Yang Z. The Value of 18F-FDG PET/CT and Abdominal PET/MRI as a One-Stop Protocol in Patients With Potentially Resectable Colorectal Liver Metastases. Front Oncol 2021;11:714948. [PMID: 34858808 DOI: 10.3389/fonc.2021.714948] [Reference Citation Analysis]
16 Lisson C, Lisson C, Mezger M, Wolf D, Schmidt S, Thaiss W, Tausch E, Beer A, Stilgenbauer S, Beer M, Goetz M. Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma. Cancers 2022;14:2008. [DOI: 10.3390/cancers14082008] [Reference Citation Analysis]
17 Zhang S, Wang J, Wang K, Li X, Zhao X, Chen Q, Zhang W, Ai L. Differentiation of high-grade glioma and primary central nervous system lymphoma: Multiparametric imaging of the enhancing tumor and peritumoral regions based on hybrid 18F-FDG PET/MRI. European Journal of Radiology 2022;150:110235. [DOI: 10.1016/j.ejrad.2022.110235] [Reference Citation Analysis]
18 Sharma A, Pandey AK, Sharma A, Arora G, Mohan A, Bhalla AS, Gupta L, Biswal SK, Kumar R. Prognostication Based on Texture Analysis of Baseline 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Nonsmall-Cell Lung Carcinoma Patients Who Underwent Platinum-Based Chemotherapy as First-Line Treatment. Indian J Nucl Med 2021;36:252-60. [PMID: 34658548 DOI: 10.4103/ijnm.ijnm_20_21] [Reference Citation Analysis]