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For: Tomaszewski MR, Gillies RJ. The Biological Meaning of Radiomic Features. Radiology. 2021;298:505-516. [PMID: 33399513 DOI: 10.1148/radiol.2021202553] [Cited by in Crossref: 12] [Cited by in F6Publishing: 16] [Article Influence: 12.0] [Reference Citation Analysis]
Number Citing Articles
1 Tenório APM, Ferreira-Junior JR, Dalto VF, Faleiros MC, Assad RL, Louzada-Junior P, Nogueira-Barbosa MH, Rangayyan RM, de Azevedo-Marques PM. Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis. J Digit Imaging 2022. [PMID: 34997373 DOI: 10.1007/s10278-021-00559-7] [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 Lv K, Cao X, Wang R, Du P, Fu J, Geng D, Zhang J. Neuroplasticity of Glioma Patients: Brain Structure and Topological Network. Front Neurol 2022;13:871613. [DOI: 10.3389/fneur.2022.871613] [Reference Citation Analysis]
4 Li MD, Cheng MQ, Chen LD, Hu HT, Zhang JC, Ruan SM, Huang H, Kuang M, Lu MD, Li W, Wang W. Reproducibility of radiomics features from ultrasound images: influence of image acquisition and processing. Eur Radiol 2022. [PMID: 35314881 DOI: 10.1007/s00330-022-08662-1] [Reference Citation Analysis]
5 Hou M, Sun JH. Emerging applications of radiomics in rectal cancer: State of the art and future perspectives. World J Gastroenterol 2021; 27(25): 3802-3814 [PMID: 34321845 DOI: 10.3748/wjg.v27.i25.3802] [Reference Citation Analysis]
6 Kim YJ. Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography. Int J Environ Res Public Health 2021;18:8710. [PMID: 34444459 DOI: 10.3390/ijerph18168710] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Shao D, Du D, Liu H, Lv J, Cheng Y, Zhang H, Lv W, Wang S, Lu L. Identification of Stage IIIC/IV EGFR-Mutated Non-Small Cell Lung Cancer Populations Sensitive to Targeted Therapy Based on a PET/CT Radiomics Risk Model. Front Oncol 2021;11:721318. [PMID: 34796106 DOI: 10.3389/fonc.2021.721318] [Reference Citation Analysis]
8 Duff L, Scarsbrook AF, Mackie SL, Frood R, Bailey M, Morgan AW, Tsoumpas C. A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis. J Nucl Cardiol 2022. [PMID: 35322380 DOI: 10.1007/s12350-022-02927-4] [Reference Citation Analysis]
9 Tian Y, Komolafe TE, Chen T, Zhou B, Yang X. Prediction of TACE Treatment Response in a Preoperative MRI via Analysis of Integrating Deep Learning and Radiomics Features. J Med Biol Eng . [DOI: 10.1007/s40846-022-00692-w] [Reference Citation Analysis]
10 Shim KY, Chung SW, Jeong JH, Hwang I, Park CK, Kim TM, Park SH, Won JK, Lee JH, Lee ST, Yoo RE, Kang KM, Yun TJ, Kim JH, Sohn CH, Choi KS, Choi SH. Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI. Sci Rep 2021;11:9974. [PMID: 33976264 DOI: 10.1038/s41598-021-89218-z] [Reference Citation Analysis]
11 Bluemke DA. Top Publications in Radiology, 2021. Radiology 2022;:219030. [PMID: 35076305 DOI: 10.1148/radiol.219030] [Reference Citation Analysis]
12 Gaebe K, Li AY, Das S. Clinical Biomarkers for Early Identification of Patients with Intracranial Metastatic Disease. Cancers (Basel) 2021;13:5973. [PMID: 34885083 DOI: 10.3390/cancers13235973] [Reference Citation Analysis]
13 Fathi Kazerooni A, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, Chawla S, Nabavizadeh A, Mohan S, Bakas S, Davatzikos C, Nasrallah MP. Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Cancers (Basel) 2021;13:5921. [PMID: 34885031 DOI: 10.3390/cancers13235921] [Reference Citation Analysis]
14 Zhu D, Chen Y, Zheng K, Chen C, Li Q, Zhou J, Jia X, Xia N, Wang H, Lin B, Ni Y, Pang P, Yang Y. Classifying Ruptured Middle Cerebral Artery Aneurysms With a Machine Learning Based, Radiomics-Morphological Model: A Multicentral Study. Front Neurosci 2021;15:721268. [PMID: 34456680 DOI: 10.3389/fnins.2021.721268] [Reference Citation Analysis]
15 Diniz de Paula W. Editorial for "Radiomic Analysis of Pharmacokinetic Heterogeneity Within Tumor Based on the Unsupervised Decomposition of DCE-MRI for Predicting Histological Characteristics of Breast Cancer". J Magn Reson Imaging 2021. [PMID: 34957642 DOI: 10.1002/jmri.28042] [Reference Citation Analysis]
16 Shur JD, Doran SJ, Kumar S, Ap Dafydd D, Downey K, O'Connor JPB, Papanikolaou N, Messiou C, Koh DM, Orton MR. Radiomics in Oncology: A Practical Guide. Radiographics 2021;41:1717-32. [PMID: 34597235 DOI: 10.1148/rg.2021210037] [Reference Citation Analysis]
17 Mckenney AS, Weg E, Bale TA, Wild AT, Um H, Fox MJ, Lin A, Yang JT, Yao P, Birger ML, Tixier F, Sellitti M, Moss NS, Young RJ, Veeraraghavan H. Radiomic analysis to predict histopathologically confirmed pseudoprogression in glioblastoma patients. Advances in Radiation Oncology 2022. [DOI: 10.1016/j.adro.2022.100916] [Reference Citation Analysis]
18 Cui LB, Zhang YJ, Lu HL, Liu L, Zhang HJ, Fu YF, Wu XS, Xu YQ, Li XS, Qiao YT, Qin W, Yin H, Cao F. Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia. Front Neurosci 2021;15:682777. [PMID: 34290581 DOI: 10.3389/fnins.2021.682777] [Reference Citation Analysis]
19 Kelahan LC, Kim D, Soliman M, Avery RJ, Savas H, Agrawal R, Magnetta M, Liu BP, Velichko YS. Role of hepatic metastatic lesion size on inter-reader reproducibility of CT-based radiomics features. Eur Radiol 2022. [PMID: 35080646 DOI: 10.1007/s00330-021-08526-0] [Reference Citation Analysis]
20 de Koster EJ, Noortman WA, Mostert JM, Booij J, Brouwer CB, de Keizer B, de Klerk JMH, Oyen WJG, van Velden FHP, de Geus-Oei LF, Vriens D; EfFECTS trial study group. Quantitative classification and radiomics of [18F]FDG-PET/CT in indeterminate thyroid nodules. Eur J Nucl Med Mol Imaging 2022. [PMID: 35138444 DOI: 10.1007/s00259-022-05712-0] [Reference Citation Analysis]
21 Quan G, Ban R, Ren JL, Liu Y, Wang W, Dai S, Yuan T. FLAIR and ADC Image-Based Radiomics Features as Predictive Biomarkers of Unfavorable Outcome in Patients With Acute Ischemic Stroke. Front Neurosci 2021;15:730879. [PMID: 34602971 DOI: 10.3389/fnins.2021.730879] [Reference Citation Analysis]
22 Rigiroli F, Hoye J, Lerebours R, Lafata KJ, Li C, Meyer M, Lyu P, Ding Y, Schwartz FR, Mettu NB, Zani S Jr, Luo S, Morgan DE, Samei E, Marin D. CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study. Radiology 2021;301:610-22. [PMID: 34491129 DOI: 10.1148/radiol.2021210699] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Akinci D'Antonoli T, Santini F, Deligianni X, Garcia Alzamora M, Rutz E, Bieri O, Brunner R, Weidensteiner C. Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy. Front Neurol 2021;12:633808. [PMID: 33828520 DOI: 10.3389/fneur.2021.633808] [Reference Citation Analysis]
24 Chen Z, Ye N, Teng C, Li X. Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review. Front Neurosci 2022;16:856808. [DOI: 10.3389/fnins.2022.856808] [Reference Citation Analysis]
25 Bang M, Eom J, An C, Kim S, Park YW, Ahn SS, Kim J, Lee SK, Lee SH. An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum. Transl Psychiatry 2021;11:462. [PMID: 34489405 DOI: 10.1038/s41398-021-01586-2] [Reference Citation Analysis]
26 Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022;12:773840. [DOI: 10.3389/fonc.2022.773840] [Reference Citation Analysis]
27 Starosolski Z, Courtney AN, Srivastava M, Guo L, Stupin I, Metelitsa LS, Annapragada A, Ghaghada KB. A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden. Contrast Media Mol Imaging 2021;2021:6641384. [PMID: 34220380 DOI: 10.1155/2021/6641384] [Reference Citation Analysis]
28 Fan M, Cui Y, You C, Liu L, Gu Y, Peng W, Bai Q, Gao X, Li L. Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor-Positive Breast Cancer: A Multicohort Study. Radiology 2021;:210738. [PMID: 34846204 DOI: 10.1148/radiol.2021210738] [Reference Citation Analysis]
29 Hsu W, Sohn JH. Using Radiomics for Risk Stratification: Where We Need to Go. Radiology 2021;:212085. [PMID: 34726541 DOI: 10.1148/radiol.2021212085] [Reference Citation Analysis]
30 Zhang X, Zhang Q, Guo J, Zhao J, Xie L, Zhang J, An J, Yu X, Zhao X. Added-value of texture analysis of ADC in predicting the survival of patients with 2018 FIGO stage IIICr cervical cancer treated by concurrent chemoradiotherapy. Eur J Radiol 2022;150:110272. [PMID: 35334244 DOI: 10.1016/j.ejrad.2022.110272] [Reference Citation Analysis]
31 Perik TH, van Genugten EAJ, Aarntzen EHJG, Smit EJ, Huisman HJ, Hermans JJ. Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review. Abdom Radiol (NY) 2021. [PMID: 34223961 DOI: 10.1007/s00261-021-03190-w] [Reference Citation Analysis]
32 Li X, Hou R, Yu W, Zhu X, Li H, Yang Y, Qian D, Fu X. Detailed Analysis and Radiomic Prediction of First Progression Sites of First-Line Targeted Therapy for EGFR-Mutant Lung Adenocarcinoma Patients With Systemic Metastasis. Front Oncol 2021;11:757892. [PMID: 34676174 DOI: 10.3389/fonc.2021.757892] [Reference Citation Analysis]
33 Li Y, Liu Y, Yin P, Hao C, Sun C, Chen L, Wang S, Hong N. MRI-Based Bone Marrow Radiomics Nomogram for Prediction of Overall Survival in Patients With Multiple Myeloma. Front Oncol 2021;11:709813. [PMID: 34926240 DOI: 10.3389/fonc.2021.709813] [Reference Citation Analysis]
34 Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. Rofo 2022. [PMID: 35211929 DOI: 10.1055/a-1718-4128] [Reference Citation Analysis]
35 Sun D, Du Y, Chen Q, Ye L, Chen H, Li M, He J, Zhu J, Wang L, Fan Y, Xu X. Imaging Features by Machine Learning for Quantification of Optic Disc Changes and Impact on Choroidal Thickness in Young Myopic Patients. Front Med (Lausanne) 2021;8:657566. [PMID: 33996860 DOI: 10.3389/fmed.2021.657566] [Reference Citation Analysis]
36 Bernatowicz K, Grussu F, Ligero M, Garcia A, Delgado E, Perez-Lopez R. Robust imaging habitat computation using voxel-wise radiomics features. Sci Rep 2021;11:20133. [PMID: 34635786 DOI: 10.1038/s41598-021-99701-2] [Reference Citation Analysis]
37 Brossard C, Lemasson B, Attyé A, de Busschère JA, Payen JF, Barbier EL, Grèze J, Bouzat P. Contribution of CT-Scan Analysis by Artificial Intelligence to the Clinical Care of TBI Patients. Front Neurol 2021;12:666875. [PMID: 34177773 DOI: 10.3389/fneur.2021.666875] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
38 Pang S, Field M, Dowling J, Vinod S, Holloway L, Sowmya A. Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings. Artif Intell Med 2022;123:102230. [PMID: 34998514 DOI: 10.1016/j.artmed.2021.102230] [Reference Citation Analysis]
39 Zhang M, Zeng X, Huang C, Liu J, Liu X, Xie X, Wang R. An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators. Int J Med Inform 2021;154:104545. [PMID: 34464848 DOI: 10.1016/j.ijmedinf.2021.104545] [Reference Citation Analysis]
40 Zheng H, Miao Q, Liu Y, Mirak SA, Hosseiny M, Scalzo F, Raman SS, Sung K. Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer. Eur Radiol 2022. [PMID: 35238971 DOI: 10.1007/s00330-022-08625-6] [Reference Citation Analysis]