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For: Tătaru OS, Vartolomei MD, Rassweiler JJ, Virgil O, Lucarelli G, Porpiglia F, Amparore D, Manfredi M, Carrieri G, Falagario U, Terracciano D, de Cobelli O, Busetto GM, Del Giudice F, Ferro M. Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021;11:354. [PMID: 33672608 DOI: 10.3390/diagnostics11020354] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Belue MJ, Harmon SA, Lay NS, Daryanani A, Phelps TE, Choyke PL, Turkbey B. The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms. Journal of the American College of Radiology 2022. [DOI: 10.1016/j.jacr.2022.05.022] [Reference Citation Analysis]
2 Lee RS, Ma R, Pham S, Maya-Silva J, Nguyen J, Aron M, Cen SY, Daneshmand S, Hung AJ. Machine learning to delineate surgeon and clinical factors that anticipate positive surgical margins after robot-assisted radical prostatectomy. J Endourol 2022. [PMID: 35414218 DOI: 10.1089/end.2021.0890] [Reference Citation Analysis]
3 Mata C, Walker P, Oliver A, Martí J, Lalande A. Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI. Clinics and Practice 2022;12:350-62. [DOI: 10.3390/clinpract12030040] [Reference Citation Analysis]
4 Marinkovic M, Popovic M, Stojanovic-rundic S, Nikolic M, Cavic M, Gavrilovic D, Teodorovic D, Mitrovic N, Mijatovic Teodorovic L, Chutipongtanate S. Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients. BioMed Research International 2022;2022:1-13. [DOI: 10.1155/2022/7943609] [Reference Citation Analysis]
5 Rajwa P, Huebner NA, Hostermann DI, Grossmann NC, Schuettfort VM, Korn S, Quhal F, König F, Mostafaei H, Laukhtina E, Mori K, Motlagh RS, Yanagisawa T, Aydh A, Bryniarski P, Pradere B, Paradysz A, Baltzer PA, Grubmüller B, Shariat SF. Evaluation of the Predictive Role of Blood-Based Biomarkers in the Context of Suspicious Prostate MRI in Patients Undergoing Prostate Biopsy. J Pers Med 2021;11:1231. [PMID: 34834583 DOI: 10.3390/jpm11111231] [Reference Citation Analysis]
6 Zhu H, Ding XF, Lu SM, Ding N, Pi SY, Liu Z, Xiao Q, Zhu LY, Luan Y, Han YX, Chen HP, Liu Z. The Application of Biopsy Density in Transperineal Templated-Guided Biopsy Patients With PI-RADS<3. Front Oncol 2022;12:918300. [PMID: 35756615 DOI: 10.3389/fonc.2022.918300] [Reference Citation Analysis]
7 Mata LA, Retamero JA, Gupta RT, García Figueras R, Luna A. Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics 2021;41:1676-97. [PMID: 34597215 DOI: 10.1148/rg.2021210020] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Bonmatí LM, Miguel A, Suárez A, Aznar M, Beregi JP, Fournier L, Neri E, Laghi A, França M, Sardanelli F, Penzkofer T, Lambin P, Blanquer I, Menzel MI, Seymour K, Figueiras S, Krischak K, Martínez R, Mirsky Y, Yang G, Alberich-Bayarri Á. CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools. Front Oncol 2022;12:742701. [PMID: 35280732 DOI: 10.3389/fonc.2022.742701] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Belue MJ, Turkbey B. Tasks for artificial intelligence in prostate MRI. Eur Radiol Exp 2022;6:33. [PMID: 35908102 DOI: 10.1186/s41747-022-00287-9] [Reference Citation Analysis]
10 Samtani S, Burotto M, Roman JC, Cortes-Herrera D, Walton-Diaz A. MRI and Targeted Biopsy Essential Tools for an Accurate Diagnosis and Treatment Decision Making in Prostate Cancer. Diagnostics (Basel) 2021;11:1551. [PMID: 34573893 DOI: 10.3390/diagnostics11091551] [Reference Citation Analysis]
11 Mezher MA, Altamimi A, Altamimi R. An enhanced Genetic Folding algorithm for prostate and breast cancer detection. PeerJ Computer Science 2022;8:e1015. [DOI: 10.7717/peerj-cs.1015] [Reference Citation Analysis]
12 Sunoqrot MRS, Saha A, Hosseinzadeh M, Elschot M, Huisman H. Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges. Eur Radiol Exp 2022;6:35. [PMID: 35909214 DOI: 10.1186/s41747-022-00288-8] [Reference Citation Analysis]
13 Ferro M, Crocetto F, Bruzzese D, Imbriaco M, Fusco F, Longo N, Napolitano L, La Civita E, Cennamo M, Liotti A, Lecce M, Russo G, Insabato L, Imbimbo C, Terracciano D. Prostate Health Index and Multiparametric MRI: Partners in Crime Fighting Overdiagnosis and Overtreatment in Prostate Cancer. Cancers (Basel) 2021;13:4723. [PMID: 34572950 DOI: 10.3390/cancers13184723] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Yang L, Li Z, Liang X, Xu J, Cai Y, Huang C, Zhang M, Yao J, Song B. Radiomic Machine Learning and External Validation Based on 3.0 T mpMRI for Prediction of Intraductal Carcinoma of Prostate With Different Proportion. Front Oncol 2022;12:934291. [DOI: 10.3389/fonc.2022.934291] [Reference Citation Analysis]
15 Nevedomskaya E, Haendler B. From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer. Int J Mol Sci 2022;23:6281. [PMID: 35682963 DOI: 10.3390/ijms23116281] [Reference Citation Analysis]
16 Ferro M, de Cobelli O, Vartolomei MD, Lucarelli G, Crocetto F, Barone B, Sciarra A, Del Giudice F, Muto M, Maggi M, Carrieri G, Busetto GM, Falagario U, Terracciano D, Cormio L, Musi G, Tataru OS. Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021;22:9971. [PMID: 34576134 DOI: 10.3390/ijms22189971] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Ferro M, de Cobelli O, Musi G, Del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022;14:17562872221109020. [PMID: 35814914 DOI: 10.1177/17562872221109020] [Reference Citation Analysis]
18 Singh R, Mills IG. The Interplay Between Prostate Cancer Genomics, Metabolism, and the Epigenome: Perspectives and Future Prospects. Front Oncol 2021;11:704353. [PMID: 34660272 DOI: 10.3389/fonc.2021.704353] [Reference Citation Analysis]
19 Basaran E, Kucukoztas N, Aktepe HO, Atak Tel B, Aktas G. An exceptional prostate cancer case: Importance of cancer screening. Precision Medical Sciences. [DOI: 10.1002/prm2.12063] [Reference Citation Analysis]
20 Falagario UG, Sanguedolce F, Dovey Z, Carbonara U, Crocerossa F, Papastefanou G, Autorino R, Recchia M, Ninivaggi A, Busetto GM, Annese P, Carrieri G, Cormio L. Prostate cancer biomarkers: a practical review based on different clinical scenarios. Crit Rev Clin Lab Sci 2022;:1-12. [PMID: 35200064 DOI: 10.1080/10408363.2022.2033161] [Reference Citation Analysis]
21 Gui S, Lan M, Wang C, Nie S, Fan B. Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia. Front Oncol 2022;12:859625. [DOI: 10.3389/fonc.2022.859625] [Reference Citation Analysis]