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For: Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res 2021;10:1186-99. [PMID: 33718055 DOI: 10.21037/tlcr-20-708] [Cited by in Crossref: 15] [Cited by in F6Publishing: 17] [Article Influence: 7.5] [Reference Citation Analysis]
Number Citing Articles
1 Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Patrone R, Ottaiano A, Nasti G, Silvestro L, Cassata A, Grassi F, Avallone A, Izzo F, Petrillo A. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer 2023;18:18. [PMID: 36927442 DOI: 10.1186/s13027-023-00495-x] [Reference Citation Analysis]
2 Bidzińska J, Szurowska E. See Lung Cancer with an AI. Cancers (Basel) 2023;15. [PMID: 36831662 DOI: 10.3390/cancers15041321] [Reference Citation Analysis]
3 De Muzio F, Fusco R, Cutolo C, Giacobbe G, Bruno F, Palumbo P, Danti G, Grazzini G, Flammia F, Borgheresi A, Agostini A, Grassi F, Giovagnoni A, Miele V, Barile A, Granata V. Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications. J Clin Med 2023;12. [PMID: 36836024 DOI: 10.3390/jcm12041489] [Reference Citation Analysis]
4 Feuerecker B, Heimer MM, Geyer T, Fabritius MP, Gu S, Schachtner B, Beyer L, Ricke J, Gatidis S, Ingrisch M, Cyran CC. Artificial Intelligence in Oncological Hybrid Imaging. Rofo 2023;195:105-14. [PMID: 36170852 DOI: 10.1055/a-1909-7013] [Reference Citation Analysis]
5 Thong LT, Chou HS, Chew HSJ, Lau Y. Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis. Lung Cancer 2023;176:4-13. [PMID: 36566582 DOI: 10.1016/j.lungcan.2022.12.002] [Reference Citation Analysis]
6 Granata V, Fusco R, De Muzio F, Cutolo C, Grassi F, Brunese MC, Simonetti I, Catalano O, Gabelloni M, Pradella S, Danti G, Flammia F, Borgheresi A, Agostini A, Bruno F, Palumbo P, Ottaiano A, Izzo F, Giovagnoni A, Barile A, Gandolfo N, Miele V. Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence. Biology (Basel) 2023;12. [PMID: 36829492 DOI: 10.3390/biology12020213] [Reference Citation Analysis]
7 Granata V, Fusco R, Setola SV, Simonetti I, Picone C, Simeone E, Festino L, Vanella V, Vitale MG, Montanino A, Morabito A, Izzo F, Ascierto PA, Petrillo A. Immunotherapy Assessment: A New Paradigm for Radiologists. Diagnostics (Basel) 2023;13. [PMID: 36673112 DOI: 10.3390/diagnostics13020302] [Reference Citation Analysis]
8 Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Silvestro L, De Bellis M, Di Girolamo E, Grazzini G, Chiti G, Brunese MC, Belli A, Patrone R, Palaia R, Avallone A, Petrillo A, Izzo F. Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence. Cancers (Basel) 2023;15. [PMID: 36672301 DOI: 10.3390/cancers15020351] [Reference Citation Analysis]
9 Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023;24:e13869. [PMID: 36527376 DOI: 10.1002/acm2.13869] [Reference Citation Analysis]
10 梁 核. Support Vector Machine Model Based on SWI Sequences to Discriminate High-Grade Glio-ma from Intracerebral Solitary Metastases. ACM 2023;13:1146-1153. [DOI: 10.12677/acm.2023.131158] [Reference Citation Analysis]
11 Li Y, Lv X, Wang B, Xu Z, Wang Y, Sun M, Hou D. Predicting EGFR T790M Mutation in Brain Metastases Using Multisequence MRI-Based Radiomics Signature. Acad Radiol 2022:S1076-6332(22)00686-9. [PMID: 36586758 DOI: 10.1016/j.acra.2022.12.030] [Reference Citation Analysis]
12 Li S, Zhou B. A review of radiomics and genomics applications in cancers: the way towards precision medicine. Radiat Oncol 2022;17:217. [PMID: 36585716 DOI: 10.1186/s13014-022-02192-2] [Reference Citation Analysis]
13 Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022;68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [Reference Citation Analysis]
14 Ferrante M, Rinaldi L, Botta F, Hu X, Dolp A, Minotti M, De Piano F, Funicelli G, Volpe S, Bellerba F, De Marco P, Raimondi S, Rizzo S, Shi K, Cremonesi M, Jereczek-Fossa BA, Spaggiari L, De Marinis F, Orecchia R, Origgi D. Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models. J Clin Med 2022;11. [PMID: 36555950 DOI: 10.3390/jcm11247334] [Reference Citation Analysis]
15 Wang H, Tang N, Zhang C, Hao Y, Meng X, Li J. Practice toward standardized performance testing of computer-aided detection algorithms for pulmonary nodule. Front Public Health 2022;10:1071673. [PMID: 36568775 DOI: 10.3389/fpubh.2022.1071673] [Reference Citation Analysis]
16 Luo Y, Li J, Huang L, Han Y, Tian X, Ma W, Wang L, Liu J, Zhou J. Value of dynamic metabolic curves and artificial neural network prediction models based on 18F-FDG PET/CT multiphase imaging in differentiating nonspecific solitary pulmonary lesions: a pilot study. Nuclear Medicine Communications 2022;43:1204-1216. [DOI: 10.1097/mnm.0000000000001627] [Reference Citation Analysis]
17 Liu JA, Yang IY, Tsai EB. Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications. AJR Am J Roentgenol 2022;219:703-12. [PMID: 35544377 DOI: 10.2214/AJR.22.27487] [Reference Citation Analysis]
18 Grenier PA, Brun AL, Mellot F. The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography. Diagnostics (Basel) 2022;12:2435. [PMID: 36292124 DOI: 10.3390/diagnostics12102435] [Reference Citation Analysis]
19 Cui R, Yang Z, Liu L. What does radiomics do in PD-L1 blockade therapy of NSCLC patients? Thorac Cancer 2022. [PMID: 36039482 DOI: 10.1111/1759-7714.14620] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Teixeira M, Pereira T, Silva F, Cunha A, Oliveira HP. Unsupervised Approach for Malignancy Assessment of Lung Nodules in Computed Tomography Scans Using Radiomic Features. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022. [DOI: 10.1109/embc48229.2022.9871704] [Reference Citation Analysis]
21 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]
22 Vaiyapuri T, Liyakathunisa, Alaskar H, Parvathi R, Pattabiraman V, Hussain A. Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images. Applied Sciences 2022;12:5491. [DOI: 10.3390/app12115491] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
23 Wang Z, Yang C, Han W, Sui X, Zheng F, Xue F, Xu X, Wu P, Chen Y, Gu W, Song W, Jiang J. Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study. Insights Imaging 2022;13:82. [PMID: 35482262 DOI: 10.1186/s13244-022-01204-9] [Reference Citation Analysis]
24 Ghazal TM, Hussain S, Khan MF, Khan MA, Said RAT, Ahmad M. Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning. Comput Intell Neurosci 2022;2022:4826892. [PMID: 35371238 DOI: 10.1155/2022/4826892] [Reference Citation Analysis]
25 Yuan L, Yifan L, Yuwei Z, Jianwei Z, Meng L, Lin J, Li G. Parameter tuning in machine learning based on radiomics biomarkers of lung cancer. J Xray Sci Technol 2022. [PMID: 35342074 DOI: 10.3233/XST-211096] [Reference Citation Analysis]
26 Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3(1): 1-7 [DOI: 10.35711/aimi.v3.i1.1] [Reference Citation Analysis]
27 Guerrini S, Del Roscio D, Zanoni M, Cameli P, Bargagli E, Volterrani L, Mazzei MA, Luzzi L. Lung Cancer Imaging: Screening Result and Nodule Management. Int J Environ Res Public Health 2022;19:2460. [PMID: 35206646 DOI: 10.3390/ijerph19042460] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Dyer DS, Sandler KL. Lung Cancer Screening Results and Tracking. Lung Cancer Screening 2022. [DOI: 10.1007/978-3-031-10662-0_6] [Reference Citation Analysis]
29 Ayres AS, Ferraciolli SF, Mota AL, Polsin LLM, da Costa Leite C. Radiology and Radiomics: Towards Oncology Prediction with IA and Big Data. Trends of Artificial Intelligence and Big Data for E-Health 2022. [DOI: 10.1007/978-3-031-11199-0_12] [Reference Citation Analysis]
30 Zhang K, Chen K. Artificial intelligence: opportunities in lung cancer. Curr Opin Oncol 2022;34:44-53. [PMID: 34636351 DOI: 10.1097/CCO.0000000000000796] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Wang Z, Zhao J, Wang M. [Advances and Clinical Application of Malignant Probability Prediction Models for 
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32 Marias K. The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics. J Imaging 2021;7:124. [PMID: 34460760 DOI: 10.3390/jimaging7080124] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
33 Alrujaib M, Bakheet D. What Is the Value of Artificial Intelligence in Radiology? Innovations in Surgery and Interventional Medicine 2021;1:23-24. [DOI: 10.36401/isim-21-03] [Reference Citation Analysis]