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For: van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection.Insights Imaging. 2020;11:91. [PMID: 32785796 DOI: 10.1186/s13244-020-00887-2] [Cited by in Crossref: 41] [Cited by in F6Publishing: 41] [Article Influence: 20.5] [Reference Citation Analysis]
Number Citing Articles
1 Hu X, Zhou R, Hu M, Wen J, Shen T. Differentiation and prediction of pneumoconiosis stage by computed tomography texture analysis based on U-Net neural network. Computer Methods and Programs in Biomedicine 2022;225:107098. [DOI: 10.1016/j.cmpb.2022.107098] [Reference Citation Analysis]
2 Hewitt DB, Brown ZJ, Pawlik TM. The Role of Biomarkers in the Management of Colorectal Liver Metastases. Cancers 2022;14:4602. [DOI: 10.3390/cancers14194602] [Reference Citation Analysis]
3 Mori M, Palumbo D, De Cobelli F, Fiorino C. Does radiomics play a role in the diagnosis, staging and re-staging of gastroesophageal junction adenocarcinoma? Updates Surg. [DOI: 10.1007/s13304-022-01377-4] [Reference Citation Analysis]
4 Gao J, Ye F, Han F, Jiang H, Zhang J. A radiogenomics biomarker based on immunological heterogeneity for non-invasive prognosis of renal clear cell carcinoma. Front Immunol 2022;13:956679. [DOI: 10.3389/fimmu.2022.956679] [Reference Citation Analysis]
5 Pillai PS, Holmes DR, Carter R, Inoue A, Cook DA, Karwoski R, Fidler JL, Fletcher JG, Leng S, Yu L, Mccollough CH, Hsieh SS. Individualized and generalized models for predicting observer performance on liver metastasis detection using CT. J Med Imag 2022;9. [DOI: 10.1117/1.jmi.9.5.055501] [Reference Citation Analysis]
6 Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, Pinto Dos Santos D, Baeßler B. Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging. Invest Radiol 2022. [PMID: 36070524 DOI: 10.1097/RLI.0000000000000921] [Reference Citation Analysis]
7 Zhang H, Lei H, Pang J. Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis. Front Oncol 2022;12:975183. [DOI: 10.3389/fonc.2022.975183] [Reference Citation Analysis]
8 Luo C, Zhao S, Peng C, Wang C, Hu K, Zhong X, Luo T, Huang J, Lu D. Mammography radiomics features at diagnosis and progression-free survival among patients with breast cancer. Br J Cancer 2022. [PMID: 36050449 DOI: 10.1038/s41416-022-01958-5] [Reference Citation Analysis]
9 Lim EJ, Castellani D, So WZ, Fong KY, Li JQ, Tiong HY, Gadzhiev N, Heng CT, Teoh JY, Naik N, Ghani K, Sarica K, De La Rosette J, Somani B, Gauhar V. Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions. JCM 2022;11:5151. [DOI: 10.3390/jcm11175151] [Reference Citation Analysis]
10 Brunasso L, Bonosi L, Costanzo R, Buscemi F, Giammalva GR, Ferini G, Valenti V, Viola A, Umana GE, Gerardi RM, Sturiale CL, Albanese A, Iacopino DG, Maugeri R. Updated Systematic Review on the Role of Brain Invasion in Intracranial Meningiomas: What, When, Why? Cancers 2022;14:4163. [DOI: 10.3390/cancers14174163] [Reference Citation Analysis]
11 Li Y, Lv X, Wang B, Xu Z, Wang Y, Gao S, Hou D. Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis. Eur J Radiol 2022;155:110499. [PMID: 36049410 DOI: 10.1016/j.ejrad.2022.110499] [Reference Citation Analysis]
12 Zhuo M, Guo J, Tang Y, Tang X, Qian Q, Chen Z. Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors. Front Oncol 2022;12:905036. [DOI: 10.3389/fonc.2022.905036] [Reference Citation Analysis]
13 Bin X, Zhu C, Tang Y, Li R, Ding Q, Xia W, Tang Y, Tang X, Yao D, Tang A. Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma. Clin Oncol (R Coll Radiol) 2022:S0936-6555(22)00319-3. [PMID: 36008245 DOI: 10.1016/j.clon.2022.07.007] [Reference Citation Analysis]
14 Stanzione A, Verde F, Cuocolo R, Romeo V, Paolo Mainenti P, Brunetti A, Maurea S. Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal. Eur J Radiol 2022;155:110497. [PMID: 36030661 DOI: 10.1016/j.ejrad.2022.110497] [Reference Citation Analysis]
15 Ong W, Zhu L, Zhang W, Kuah T, Lim DSW, Low XZ, Thian YL, Teo EC, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Hallinan JTPD. Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. Cancers (Basel) 2022;14:4025. [PMID: 36011018 DOI: 10.3390/cancers14164025] [Reference Citation Analysis]
16 Pasini G, Bini F, Russo G, Comelli A, Marinozzi F, Stefano A. matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model. J Imaging 2022;8:221. [DOI: 10.3390/jimaging8080221] [Reference Citation Analysis]
17 Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022;14:3897. [PMID: 36010891 DOI: 10.3390/cancers14163897] [Reference Citation Analysis]
18 Mehta C, Shah R, Yanamala N, Sengupta PP. Cardiovascular Imaging Databases: Building Machine Learning Algorithms for Regenerative Medicine. Curr Stem Cell Rep. [DOI: 10.1007/s40778-022-00216-x] [Reference Citation Analysis]
19 Lazar AJ, Demicco EG. Human and machine: Better at pathology together? Cancer Cell 2022;40:806-8. [PMID: 35944500 DOI: 10.1016/j.ccell.2022.06.004] [Reference Citation Analysis]
20 Mitchell D, Buszek S, Tran B, Farhat M, Goldman J, Erickson L, Curl B, Suki D, Ferguson SD, Liu H, Kundu S, Chung C. Managing the effect of magnetic resonance imaging pulse sequence on radiomic feature reproducibility in the study of brain metastases. F1000Res 2022;11:892. [DOI: 10.12688/f1000research.122871.1] [Reference Citation Analysis]
21 Murugesan A, Patel S, Viswanathan VS, Bhargava P, Faraji N. Dear Medical Students - Artificial Intelligence is NOT taking away a Radiologist's Job. Current Problems in Diagnostic Radiology 2022. [DOI: 10.1067/j.cpradiol.2022.08.001] [Reference Citation Analysis]
22 Ramlee S, Hulse D, Bernatowicz K, Pérez-lópez R, Sala E, Aloj L. Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers 2022;14:3656. [DOI: 10.3390/cancers14153656] [Reference Citation Analysis]
23 Waldman CE, Hermel M, Hermel JA, Allinson F, Pintea MN, Bransky N, Udoh E, Nicholson L, Robinson A, Gonzalez J, Suhar C, Nayak K, Wesbey G, Bhavnani SP. Artificial intelligence in healthcare: a primer for medical education in radiomics. Per Med 2022. [PMID: 35880428 DOI: 10.2217/pme-2022-0014] [Reference Citation Analysis]
24 Boguszewicz Ł. Predictive Biomarkers for Response and Toxicity of Induction Chemotherapy in Head and Neck Cancers. Front Oncol 2022;12:900903. [PMID: 35875133 DOI: 10.3389/fonc.2022.900903] [Reference Citation Analysis]
25 Kato H, Kawaguchi M, Ando T, Shibata H, Ogawa T, Noda Y, Hyodo F, Matsuo M. Current status of diffusion-weighted imaging in differentiating parotid tumors. Auris Nasus Larynx 2022:S0385-8146(22)00189-4. [PMID: 35879151 DOI: 10.1016/j.anl.2022.07.002] [Reference Citation Analysis]
26 Iliadou V, Kakkos I, Karaiskos P, Kouloulias V, Platoni K, Zygogianni A, Matsopoulos GK. Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach. Cancers 2022;14:3573. [DOI: 10.3390/cancers14153573] [Reference Citation Analysis]
27 Zhang Z, Wu Y, Xiong D, Ibrahim JG, Srivastava A, Zhu H. LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures. Journal of the American Statistical Association. [DOI: 10.1080/01621459.2022.2102984] [Reference Citation Analysis]
28 Müller M, Winz O, Gutsche R, Leijenaar RTH, Kocher M, Lerche C, Filss CP, Stoffels G, Steidl E, Hattingen E, Steinbach JP, Maurer GD, Heinzel A, Galldiks N, Mottaghy FM, Langen K, Lohmann P. Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression. J Neurooncol. [DOI: 10.1007/s11060-022-04089-2] [Reference Citation Analysis]
29 Hacking SM, Yakirevich E, Wang Y. From Immunohistochemistry to New Digital Ecosystems: A State-of-the-Art Biomarker Review for Precision Breast Cancer Medicine. Cancers (Basel) 2022;14:3469. [PMID: 35884530 DOI: 10.3390/cancers14143469] [Reference Citation Analysis]
30 Mirón Mombiela R, Borrás C. The Usefulness of Radiomics Methodology for Developing Descriptive and Prognostic Image-Based Phenotyping in the Aging Population: Results From a Small Feasibility Study. Front Aging 2022;3:853671. [PMID: 35821818 DOI: 10.3389/fragi.2022.853671] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Gatti M, Maino C, Darvizeh F, Serafini A, Tricarico E, Guarneri A, Inchingolo R, Ippolito D, Ricardi U, Fonio P, Faletti R. Role of gadoxetic acid-enhanced liver magnetic resonance imaging in the evaluation of hepatocellular carcinoma after locoregional treatment. World J Gastroenterol 2022; 28(26): 3116-3131 [DOI: 10.3748/wjg.v28.i26.3116] [Reference Citation Analysis]
32 Kido A, Nishio M. MRI-based Radiomics Models for Pretreatment Risk Stratification of Endometrial Cancer. Radiology 2022;:221398. [PMID: 35819329 DOI: 10.1148/radiol.221398] [Reference Citation Analysis]
33 Bianconi F, Palumbo I, Fravolini ML, Rondini M, Minestrini M, Pascoletti G, Nuvoli S, Spanu A, Scialpi M, Aristei C, Palumbo B. Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans. Sensors 2022;22:5044. [DOI: 10.3390/s22135044] [Reference Citation Analysis]
34 Homayoun H, Chan WY, Kuzan TY, Leong WL, Altintoprak KM, Mohammadi A, Vijayananthan A, Rahmat K, Leong SS, Mirza-aghazadeh-attari M, Ejtehadifar S, Faeghi F, Acharya UR, Ardakani AA. Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study. Biocybernetics and Biomedical Engineering 2022;42:921-33. [DOI: 10.1016/j.bbe.2022.07.004] [Reference Citation Analysis]
35 Liu X, Elbanan MG, Luna A, Haider MA, Smith AD, Sabottke CF, Spieler BM, Turkbey B, Fuentes D, Moawad A, Kamel S, Horvat N, Elsayes KM. Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status. AJR Am J Roentgenol 2022. [PMID: 35766531 DOI: 10.2214/AJR.22.27695] [Reference Citation Analysis]
36 Shiiba T, Takano K, Takaki A, Suwazono S. Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson's disease. EJNMMI Res 2022;12:39. [PMID: 35759054 DOI: 10.1186/s13550-022-00910-1] [Reference Citation Analysis]
37 Li Y, Tao Y, Chen Y. Radiomics Model Based on Enhanced Gradient Level Set Segmentation Algorithm to Predict the Prognosis of Endoscopic Treatment of Sinusitis. Computational and Mathematical Methods in Medicine 2022;2022:1-7. [DOI: 10.1155/2022/9511631] [Reference Citation Analysis]
38 Fathalla KM, Youssef SM, Mohammed N. DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes. Applied Sciences 2022;12:6318. [DOI: 10.3390/app12136318] [Reference Citation Analysis]
39 Marfisi D, Tessa C, Marzi C, Del Meglio J, Linsalata S, Borgheresi R, Lilli A, Lazzarini R, Salvatori L, Vignali C, Barucci A, Mascalchi M, Casolo G, Diciotti S, Traino AC, Giannelli M. Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy. Sci Rep 2022;12:10186. [PMID: 35715531 DOI: 10.1038/s41598-022-13937-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
40 Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022:S0001-2998(22)00035-6. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
41 Webb EM, Mongan J. Gastrointestinal Stromal Tumors: Radiomics may Increase the Role of Imaging in Malignant Risk Assessment. Acad Radiol 2022;29:817-8. [PMID: 35248459 DOI: 10.1016/j.acra.2022.01.023] [Reference Citation Analysis]
42 Zhao JW, Shu X, Chen XX, Liu JX, Liu MQ, Ye J, Jiang HJ, Wang GS. Prediction of early recurrence of hepatocellular carcinoma after liver transplantation based on computed tomography radiomics nomogram. Hepatobiliary Pancreat Dis Int 2022:S1499-3872(22)00131-X. [PMID: 35705443 DOI: 10.1016/j.hbpd.2022.05.013] [Reference Citation Analysis]
43 Avesani G, Tran HE, Cammarata G, Botta F, Raimondi S, Russo L, Persiani S, Bonatti M, Tagliaferri T, Dolciami M, Celli V, Boldrini L, Lenkowicz J, Pricolo P, Tomao F, Rizzo SMR, Colombo N, Manganaro L, Fagotti A, Scambia G, Gui B, Manfredi R. CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset. Cancers (Basel) 2022;14:2739. [PMID: 35681720 DOI: 10.3390/cancers14112739] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
44 Brancato V, Cerrone M, Lavitrano M, Salvatore M, Cavaliere C. A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis. Cancers (Basel) 2022;14:2731. [PMID: 35681711 DOI: 10.3390/cancers14112731] [Reference Citation Analysis]
45 Wong KL, Cheng KH, Lam SK, Liu C, Cai J. Review of functional magnetic resonance imaging in the assessment of nasopharyngeal carcinoma treatment response. Precision Radiation Oncology. [DOI: 10.1002/pro6.1161] [Reference Citation Analysis]
46 Ugga L, Spadarella G, Pinto L, Cuocolo R, Brunetti A. Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization. Cancers (Basel) 2022;14:2605. [PMID: 35681585 DOI: 10.3390/cancers14112605] [Reference Citation Analysis]
47 Villani U, Silvestri E, Castellaro M, Schiavi S, Anglani M, Facchini S, Monai E, D'Avella D, Della Puppa A, Cecchin D, Corbetta M, Bertoldo A. Diffusion-based microstructure models in brain tumours: Fitting in presence of a model-microstructure mismatch. Neuroimage Clin 2022;34:102968. [PMID: 35220105 DOI: 10.1016/j.nicl.2022.102968] [Reference Citation Analysis]
48 Kothari G. Role of radiomics in predicting immunotherapy response. J Med Imaging Radiat Oncol 2022. [PMID: 35581928 DOI: 10.1111/1754-9485.13426] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
49 Moskowitz CS, Welch ML, Jacobs MA, Kurland BF, Simpson AL. Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies. Radiology 2022;:211597. [PMID: 35579522 DOI: 10.1148/radiol.211597] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
50 Wu YQ, Gao RZ, Lin P, Wen R, Li HY, Mou MY, Chen FH, Huang F, Zhou WJ, Yang H, He Y, Wu J. An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer. BMC Med Imaging 2022;22:84. [PMID: 35538520 DOI: 10.1186/s12880-022-00813-6] [Reference Citation Analysis]
51 Battineni G, Hossain MA, Chintalapudi N, Amenta F. A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review. Diagnostics (Basel) 2022;12:1179. [PMID: 35626333 DOI: 10.3390/diagnostics12051179] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
52 Lu J, Ji X, Wang L, Jiang Y, Liu X, Ma Z, Ning Y, Dong J, Peng H, Sun F, Guo Z, Ji Y, Xing J, Lu Y, Lu D, Yang Y. Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma. Disease Markers 2022;2022:1-14. [DOI: 10.1155/2022/2056837] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
53 Defeudis A, Mazzetti S, Panic J, Micilotta M, Vassallo L, Giannetto G, Gatti M, Faletti R, Cirillo S, Regge D, Giannini V. MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study. Eur Radiol Exp 2022;6:19. [PMID: 35501512 DOI: 10.1186/s41747-022-00272-2] [Reference Citation Analysis]
54 Fatania K, Clark A, Frood R, Scarsbrook A, Al-qaisieh B, Currie S, Nix M. Harmonisation of scanner-dependent contrast variations in magnetic resonance imaging for radiation oncology, using style-blind auto-encoders. Physics and Imaging in Radiation Oncology 2022. [DOI: 10.1016/j.phro.2022.05.005] [Reference Citation Analysis]
55 Tham E, Sestito M, Markovich B, Garland-Kledzik M. Current and future imaging modalities in gastric cancer. J Surg Oncol 2022;125:1123-34. [PMID: 35481912 DOI: 10.1002/jso.26875] [Reference Citation Analysis]
56 Park T, Yoon MA, Cho YC, Ham SJ, Ko Y, Kim S, Jeong H, Lee J. Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy. Sci Rep 2022;12:6735. [PMID: 35468985 DOI: 10.1038/s41598-022-10807-7] [Reference Citation Analysis]
57 Wu Y, Wu F, Yang S, Tang E, Liang C. Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education. Diagnostics 2022;12:1064. [DOI: 10.3390/diagnostics12051064] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
58 Wang Y, Zhou M, Ding Y, Li X, Zhou Z, Xie T, Shi Z, Fu W. A radiomics model for predicting the outcome of endovascular abdominal aortic aneurysm repair based on machine learning. Vascular 2022;:17085381221091061. [PMID: 35440250 DOI: 10.1177/17085381221091061] [Reference Citation Analysis]
59 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] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
60 Heo J, Lim JH, Lee HR, Jang JY, Shin YS, Kim D, Lim JY, Park YM, Koh YW, Ahn SH, Chung EJ, Lee DY, Seok J, Kim CH. Deep learning model for tongue cancer diagnosis using endoscopic images. Sci Rep 2022;12:6281. [PMID: 35428854 DOI: 10.1038/s41598-022-10287-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
61 Brunasso L, Ferini G, Bonosi L, Costanzo R, Musso S, Benigno UE, Gerardi RM, Giammalva GR, Paolini F, Umana GE, Graziano F, Scalia G, Sturiale CL, Di Bonaventura R, Iacopino DG, Maugeri R. A Spotlight on the Role of Radiomics and Machine-Learning Applications in the Management of Intracranial Meningiomas: A New Perspective in Neuro-Oncology: A Review. Life 2022;12:586. [DOI: 10.3390/life12040586] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
62 Ge X, Wang L, Pan L, Ye H, Zhu X, Feng Q, Ding Z. Risk Factors for Unilateral Trigeminal Neuralgia Based on Machine Learning. Front Neurol 2022;13:862973. [DOI: 10.3389/fneur.2022.862973] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
63 Filippi L, Bianconi F, Schillaci O, Spanu A, Palumbo B. The Role and Potential of 18F-FDG PET/CT in Malignant Melanoma: Prognostication, Monitoring Response to Targeted and Immunotherapy, and Radiomics. Diagnostics 2022;12:929. [DOI: 10.3390/diagnostics12040929] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
64 Okuda K, Saito H, Yamashita S, Yamamoto H, Ichikawa H, Kato T, Yokoyama K, Doai M, Hashimoto M, Matoba M. Beads phantom for evaluating heterogeneity of SUV on 18F-FDG PET images. Ann Nucl Med 2022. [PMID: 35377093 DOI: 10.1007/s12149-022-01740-w] [Reference Citation Analysis]
65 Zhong J, Zhang C, Hu Y, Zhang J, Liu Y, Si L, Xing Y, Ding D, Geng J, Jiao Q, Zhang H, Yang G, Yao W. Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram. Eur Radiol 2022. [PMID: 35364712 DOI: 10.1007/s00330-022-08735-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
66 Koka K, Verma A, Dwarakanath BS, Papineni RV. Technological Advancements in External Beam Radiation Therapy (EBRT): An Indispensable Tool for Cancer Treatment. CMAR 2022;Volume 14:1421-9. [DOI: 10.2147/cmar.s351744] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
67 Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022;42:1-13. [PMID: 35671432 DOI: 10.1200/EDBK_350931] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
68 Rinaldi L, Pezzotta F, Santaniello T, De Marco P, Bianchini L, Origgi D, Cremonesi M, Milani P, Mariani M, Botta F. HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images. Phys Med 2022;97:13-24. [PMID: 35334407 DOI: 10.1016/j.ejmp.2022.03.010] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
69 Bijlstra OD, Boreel MME, van Mossel S, Burgmans MC, Kapiteijn EHW, Oprea-lager DE, Rietbergen DDD, van Velden FHP, Vahrmeijer AL, Swijnenburg R, Mieog JSD, de Geus-oei L. The Value of 18F-FDG-PET-CT Imaging in Treatment Evaluation of Colorectal Liver Metastases: A Systematic Review. Diagnostics 2022;12:715. [DOI: 10.3390/diagnostics12030715] [Reference Citation Analysis]
70 Yang Y, Zou X, Zhou W, Yuan G, Hu D, Shen Y, Xie Q, Zhang Q, Kuang D, Hu X, Li Z. DWI-based radiomic signature: potential role for individualized adjuvant chemotherapy in intrahepatic cholangiocarcinoma after partial hepatectomy. Insights Imaging 2022;13:37. [PMID: 35244793 DOI: 10.1186/s13244-022-01179-7] [Reference Citation Analysis]
71 Jiang L, Zhang C, Wang S, Ai Z, Shen T, Zhang H, Duan S, Yin X, Chen Y. MRI Radiomics Features From Infarction and Cerebrospinal Fluid for Prediction of Cerebral Edema After Acute Ischemic Stroke. Front Aging Neurosci 2022;14:782036. [DOI: 10.3389/fnagi.2022.782036] [Reference Citation Analysis]
72 Lyra V, Chatziioannou S, Kallergi M. Clinical Perspectives for 18F-FDG PET Imaging in Pediatric Oncology: Μetabolic Tumor Volume and Radiomics. Metabolites 2022;12:217. [DOI: 10.3390/metabo12030217] [Reference Citation Analysis]
73 Stanzione A, Galatola R, Cuocolo R, Romeo V, Verde F, Mainenti PP, Brunetti A, Maurea S. Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study. Diagnostics 2022;12:578. [DOI: 10.3390/diagnostics12030578] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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92 Wei P. Radiomics, deep learning and early diagnosis in oncology. Emerg Top Life Sci 2021;5:829-35. [PMID: 34874454 DOI: 10.1042/ETLS20210218] [Reference Citation Analysis]
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94 Parmeggiani A, Miceli M, Errani C, Facchini G. State of the Art and New Concepts in Giant Cell Tumor of Bone: Imaging Features and Tumor Characteristics. Cancers (Basel) 2021;13:6298. [PMID: 34944917 DOI: 10.3390/cancers13246298] [Reference Citation Analysis]
95 Wan Y, Zhou S, Zhang Y, Deng X, Xu L. Radiomic Analysis of Contrast-Enhanced MRI Predicts DNA Copy-Number Subtype and Outcome in Lower-Grade Gliomas. Acad Radiol 2021:S1076-6332(21)00488-8. [PMID: 34916150 DOI: 10.1016/j.acra.2021.10.014] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
96 Morgan HE, Wang K, Dohopolski M, Liang X, Folkert MR, Sher DJ, Wang J. Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features. Quant Imaging Med Surg 2021;11:4781-96. [PMID: 34888189 DOI: 10.21037/qims-21-274] [Reference Citation Analysis]
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98 Stumpo V, Kernbach JM, van Niftrik CHB, Sebök M, Fierstra J, Regli L, Serra C, Staartjes VE. Machine Learning Algorithms in Neuroimaging: An Overview. Acta Neurochir Suppl 2022;134:125-38. [PMID: 34862537 DOI: 10.1007/978-3-030-85292-4_17] [Reference Citation Analysis]
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100 Sotoudeh H, Sarrami AH, Roberson GH, Shafaat O, Sadaatpour Z, Rezaei A, Choudhary G, Singhal A, Sotoudeh E, Tanwar M. Emerging Applications of Radiomics in Neurological Disorders: A Review. Cureus 2021;13:e20080. [PMID: 34987940 DOI: 10.7759/cureus.20080] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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102 Guglielmo P, Marturano F, Bettinelli A, Gregianin M, Paiusco M, Evangelista L. Additional Value of PET Radiomic Features for the Initial Staging of Prostate Cancer: A Systematic Review from the Literature. Cancers (Basel) 2021;13:6026. [PMID: 34885135 DOI: 10.3390/cancers13236026] [Reference Citation Analysis]
103 Ponsiglione A, Stanzione A, Cuocolo R, Ascione R, Gambardella M, De Giorgi M, Nappi C, Cuocolo A, Imbriaco M. Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol 2021. [PMID: 34812912 DOI: 10.1007/s00330-021-08375-x] [Reference Citation Analysis]
104 Xv Y, Lv F, Guo H, Zhou X, Tan H, Xiao M, Zheng Y. Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study. Insights Imaging 2021;12:170. [PMID: 34800179 DOI: 10.1186/s13244-021-01107-1] [Reference Citation Analysis]
105 Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021;11:771787. [PMID: 34790581 DOI: 10.3389/fonc.2021.771787] [Reference Citation Analysis]
106 Winder M, Owczarek AJ, Chudek J, Pilch-Kowalczyk J, Baron J. Are We Overdoing It? Changes in Diagnostic Imaging Workload during the Years 2010-2020 including the Impact of the SARS-CoV-2 Pandemic. Healthcare (Basel) 2021;9:1557. [PMID: 34828603 DOI: 10.3390/healthcare9111557] [Reference Citation Analysis]
107 Peng F, Zheng T, Tang X, Liu Q, Sun Z, Feng Z, Zhao H, Gong L. Magnetic Resonance Texture Analysis in Myocardial Infarction. Front Cardiovasc Med 2021;8:724271. [PMID: 34778395 DOI: 10.3389/fcvm.2021.724271] [Reference Citation Analysis]
108 Zheng X, Shao J, Zhou L, Wang L, Ge Y, Wang G, Feng F. A Comprehensive Nomogram Combining CT Imaging with Clinical Features for Prediction of Lymph Node Metastasis in Stage I-IIIB Non-small Cell Lung Cancer. Ther Innov Regul Sci 2021. [PMID: 34699046 DOI: 10.1007/s43441-021-00345-1] [Reference Citation Analysis]
109 Bleker J, Yakar D, van Noort B, Rouw D, de Jong IJ, Dierckx RAJO, Kwee TC, Huisman H. Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer. Insights Imaging 2021;12:150. [PMID: 34674058 DOI: 10.1186/s13244-021-01099-y] [Reference Citation Analysis]
110 Luo S, Wei R, Lu S, Lai S, Wu J, Wu Z, Pang X, Wei X, Jiang X, Zhen X, Yang R. Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis. Eur Radiol 2021. [PMID: 34636962 DOI: 10.1007/s00330-021-08322-w] [Reference Citation Analysis]
111 Davey MG, Davey MS, Boland MR, Ryan ÉJ, Lowery AJ, Kerin MJ. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis. Eur J Radiol 2021;144:109996. [PMID: 34624649 DOI: 10.1016/j.ejrad.2021.109996] [Reference Citation Analysis]
112 Midiri F, Vernuccio F, Purpura P, Alongi P, Bartolotta TV. Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature. Diagnostics (Basel) 2021;11:1829. [PMID: 34679527 DOI: 10.3390/diagnostics11101829] [Reference Citation Analysis]
113 Yu TT, Ma D, Lo J, Ju MJ, Beg MF, Sarunic MV. Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification. Biomed Opt Express 2021;12:6660-73. [PMID: 34745763 DOI: 10.1364/BOE.431992] [Reference Citation Analysis]
114 Palumbo D, Mori M, Prato F, Crippa S, Belfiori G, Reni M, Mushtaq J, Aleotti F, Guazzarotti G, Cao R, Steidler S, Tamburrino D, Spezi E, Del Vecchio A, Cascinu S, Falconi M, Fiorino C, De Cobelli F. Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach. Cancers (Basel) 2021;13:4938. [PMID: 34638421 DOI: 10.3390/cancers13194938] [Reference Citation Analysis]
115 Wang M, Perucho JAU, Vardhanabhuti V, Ip P, Ngan HYS, Lee EYP. Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma. Acad Radiol 2021:S1076-6332(21)00376-7. [PMID: 34583867 DOI: 10.1016/j.acra.2021.08.018] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
116 Euler A, Laqua FC, Cester D, Lohaus N, Sartoretti T, Pinto Dos Santos D, Alkadhi H, Baessler B. Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics. Cancers (Basel) 2021;13:4710. [PMID: 34572937 DOI: 10.3390/cancers13184710] [Reference Citation Analysis]
117 Wagner MW, Namdar K, Biswas A, Monah S, Khalvati F, Ertl-Wagner BB. Radiomics, machine learning, and artificial intelligence-what the neuroradiologist needs to know. Neuroradiology 2021. [PMID: 34537858 DOI: 10.1007/s00234-021-02813-9] [Reference Citation Analysis]
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119 Davey MS, Davey MG, Ryan ÉJ, Hogan AM, Kerin MJ, Joyce M. The use of radiomic analysis of magnetic resonance imaging in predicting distant metastases of rectal carcinoma following surgical resection: A systematic review and meta-analysis. Colorectal Dis 2021. [PMID: 34536962 DOI: 10.1111/codi.15919] [Reference Citation Analysis]
120 McCombe KD, Craig SG, Viratham Pulsawatdi A, Quezada-Marín JI, Hagan M, Rajendran S, Humphries MP, Bingham V, Salto-Tellez M, Gault R, James JA. HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks. Comput Struct Biotechnol J 2021;19:4840-53. [PMID: 34522291 DOI: 10.1016/j.csbj.2021.08.033] [Reference Citation Analysis]
121 Klontzas ME, Manikis GC, Nikiforaki K, Vassalou EE, Spanakis K, Stathis I, Kakkos GA, Matthaiou N, Zibis AH, Marias K, Karantanas AH. Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip. Diagnostics (Basel) 2021;11:1686. [PMID: 34574027 DOI: 10.3390/diagnostics11091686] [Reference Citation Analysis]
122 Yan C, Shen DS, Chen XB, Su DK, Liang ZG, Chen KH, Li L, Liang X, Liao H, Zhu XD. CT-Based Radiomics Nomogram for Prediction of Progression-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma. Cancer Manag Res 2021;13:6911-23. [PMID: 34512030 DOI: 10.2147/CMAR.S325373] [Reference Citation Analysis]
123 Davey MG, Davey MS, Ryan ÉJ, Boland MR, McAnena PF, Lowery AJ, Kerin MJ. Is radiomic MRI a feasible alternative to OncotypeDX® recurrence score testing? A systematic review and meta-analysis. BJS Open 2021;5:zrab081. [PMID: 34633438 DOI: 10.1093/bjsopen/zrab081] [Reference Citation Analysis]
124 Adamou A, Beltsios ET, Papanagiotou P. The T2-FLAIR Mismatch Sign as an Imaging Indicator of IDH-Mutant, 1p/19q Non-Codeleted Lower Grade Gliomas: A Systematic Review and Diagnostic Accuracy Meta-Analysis. Diagnostics (Basel) 2021;11:1620. [PMID: 34573962 DOI: 10.3390/diagnostics11091620] [Reference Citation Analysis]
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126 Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27(32): 5306-5321 [PMID: 34539134 DOI: 10.3748/wjg.v27.i32.5306] [Cited by in CrossRef: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
127 Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
128 Barry N, Rowshanfarzad P, Francis RJ, Nowak AK, Ebert MA. Repeatability of image features extracted from FET PET in application to post-surgical glioblastoma assessment. Phys Eng Sci Med 2021;44:1131-40. [PMID: 34436751 DOI: 10.1007/s13246-021-01049-4] [Reference Citation Analysis]
129 Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, Müller H, Primakov S, Salahuddin Z, Chatterjee A, Lambin P. Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods. J Pers Med 2021;11:842. [PMID: 34575619 DOI: 10.3390/jpm11090842] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
130 Shaker R, Wilke C, Ober C, Lawrence J. Machine learning model development for quantitative analysis of CT heterogeneity in canine hepatic masses may predict histologic malignancy. Vet Radiol Ultrasound 2021. [PMID: 34448312 DOI: 10.1111/vru.13012] [Reference Citation Analysis]
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132 Joo L, Jung SC, Lee H, Park SY, Kim M, Park JE, Choi KM. Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patients. Sci Rep 2021;11:17143. [PMID: 34433881 DOI: 10.1038/s41598-021-96621-z] [Reference Citation Analysis]
133 Ghidini M, Vuozzo M, Galassi B, Mapelli P, Ceccarossi V, Caccamo L, Picchio M, Dondossola D. The Role of Positron Emission Tomography/Computed Tomography (PET/CT) for Staging and Disease Response Assessment in Localized and Locally Advanced Pancreatic Cancer. Cancers (Basel) 2021;13:4155. [PMID: 34439307 DOI: 10.3390/cancers13164155] [Reference Citation Analysis]
134 Jain M, Rai CS, Jain J. A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers. Comput Math Methods Med 2021;2021:7965677. [PMID: 34394708 DOI: 10.1155/2021/7965677] [Reference Citation Analysis]
135 Kwong JCC, McLoughlin LC, Haider M, Goldenberg MG, Erdman L, Rickard M, Lorenzo AJ, Hung AJ, Farcas M, Goldenberg L, Nguan C, Braga LH, Mamdani M, Goldenberg A, Kulkarni GS. Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework. Eur Urol Focus 2021;7:672-82. [PMID: 34362709 DOI: 10.1016/j.euf.2021.07.004] [Reference Citation Analysis]
136 Charalambous S, Klontzas ME, Kontopodis N, Ioannou CV, Perisinakis K, Maris TG, Damilakis J, Karantanas A, Tsetis D. Radiomics and machine learning to predict aggressive type 2 endoleaks after endovascular aneurysm repair: a proof of concept. Acta Radiol 2021;:2841851211032443. [PMID: 34313492 DOI: 10.1177/02841851211032443] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
137 Bouchareb Y, Moradi Khaniabadi P, Al Kindi F, Al Dhuhli H, Shiri I, Zaidi H, Rahmim A. Artificial intelligence-driven assessment of radiological images for COVID-19. Comput Biol Med 2021;136:104665. [PMID: 34343890 DOI: 10.1016/j.compbiomed.2021.104665] [Reference Citation Analysis]
138 Huber FA, Guggenberger R. AI MSK clinical applications: spine imaging. Skeletal Radiol 2021. [PMID: 34263344 DOI: 10.1007/s00256-021-03862-0] [Reference Citation Analysis]
139 Olthof EP, van der Aa MA, Adam JA, Stalpers LJA, Wenzel HHB, van der Velden J, Mom CH. The role of lymph nodes in cervical cancer: incidence and identification of lymph node metastases-a literature review. Int J Clin Oncol 2021;26:1600-10. [PMID: 34241726 DOI: 10.1007/s10147-021-01980-2] [Reference Citation Analysis]
140 Bianconi F, Fravolini ML, Palumbo I, Pascoletti G, Nuvoli S, Rondini M, Spanu A, Palumbo B. Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study. Diagnostics (Basel) 2021;11:1224. [PMID: 34359305 DOI: 10.3390/diagnostics11071224] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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144 La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021;13:3015. [PMID: 34208595 DOI: 10.3390/cancers13123015] [Reference Citation Analysis]
145 Lal S, Rehman SU, Shah JH, Meraj T, Rauf HT, Damaševičius R, Mohammed MA, Abdulkareem KH. Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition. Sensors (Basel) 2021;21:3922. [PMID: 34200216 DOI: 10.3390/s21113922] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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