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Cited by in F6Publishing
For: Chauvie S, De Maggi A, Baralis I, Dalmasso F, Berchialla P, Priotto R, Violino P, Mazza F, Melloni G, Grosso M;  SOS Study team. Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial. Eur Radiol. 2020;30:4134-4140. [PMID: 32166491 DOI: 10.1007/s00330-020-06783-z] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Prospective clinical research of radiomics and deep learning in oncology: A translational review. Crit Rev Oncol Hematol 2022;179:103823. [PMID: 36152912 DOI: 10.1016/j.critrevonc.2022.103823] [Reference Citation Analysis]
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4 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]
5 Wu Z, Wang F, Cao W, Qin C, Dong X, Yang Z, Zheng Y, Luo Z, Zhao L, Yu Y, Xu Y, Li J, Tang W, Shen S, Wu N, Tan F, Li N, He J. Lung cancer risk prediction models based on pulmonary nodules: A systematic review. Thorac Cancer 2022. [PMID: 35137543 DOI: 10.1111/1759-7714.14333] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Xiao L, Chen Y, Xing Y, Mou L, Zhang L, Li W, Xie S, Sun M. The Analysis and AI Prospect Based on the Clinical Screening Results of Chronic Diseases. Proceedings of the 11th International Conference on Computer Engineering and Networks 2022. [DOI: 10.1007/978-981-16-6554-7_61] [Reference Citation Analysis]
7 Chauvie S, Ceriani L, Zucca E. Radiomics in Malignant Lymphomas. Lymphoma 2021. [DOI: 10.36255/exon-publications.lymphoma.2021.radiomics] [Reference Citation Analysis]
8 Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021; 2(2): 12-24 [DOI: 10.35713/aic.v2.i2.12] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
9 Avanzo M, Porzio M, Lorenzon L, Milan L, Sghedoni R, Russo G, Massafra R, Fanizzi A, Barucci A, Ardu V, Branchini M, Giannelli M, Gallio E, Cilla S, Tangaro S, Lombardi A, Pirrone G, De Martin E, Giuliano A, Belmonte G, Russo S, Rampado O, Mettivier G. Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Physica Medica 2021;83:221-41. [DOI: 10.1016/j.ejmp.2021.04.010] [Cited by in Crossref: 20] [Cited by in F6Publishing: 23] [Article Influence: 10.0] [Reference Citation Analysis]
10 Xing Z, Ding W, Zhang S, Zhong L, Wang L, Wang J, Wang K, Xie Y, Zhao X, Li N, Ye Z. Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images. Biomed Res Int 2020;2020:6287545. [PMID: 33062689 DOI: 10.1155/2020/6287545] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
11 Saini KS, de Las Heras B, Plummer R, Moreno V, Romano M, de Castro J, Aftimos P, Fredriksson J, Bhattacharyya GS, Olivo MS, Schiavon G, Punie K, Garcia-Foncillas J, Rogata E, Pfeiffer R, Orbegoso C, Morrison K, Curigliano G, Chin L, Saini ML, Rekdal Ø, Anderson S, Cortes J, Leone M, Dancey J, Twelves C, Awada A. Reimagining Global Oncology Clinical Trials for the Postpandemic Era: A Call to Arms. JCO Glob Oncol 2020;6:1357-62. [PMID: 32897732 DOI: 10.1200/GO.20.00346] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]