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For: Harmon SA, Tuncer S, Sanford T, Choyke PL, Türkbey B. Artificial intelligence at the intersection of pathology and radiology in prostate cancer. Diagn Interv Radiol 2019;25:183-8. [PMID: 31063138 DOI: 10.5152/dir.2019.19125] [Cited by in Crossref: 28] [Cited by in F6Publishing: 24] [Article Influence: 14.0] [Reference Citation Analysis]
Number Citing Articles
1 Kalyane D, Sanap G, Paul D, Shenoy S, Anup N, Polaka S, Tambe V, Tekade RK. Artificial intelligence in the pharmaceutical sector: current scene and future prospect. The Future of Pharmaceutical Product Development and Research. Elsevier; 2020. pp. 73-107. [DOI: 10.1016/b978-0-12-814455-8.00003-7] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Turkbey B, Haider MA. Deep learning-based artificial intelligence applications in prostate MRI: brief summary. Br J Radiol 2021;:20210563. [PMID: 34860562 DOI: 10.1259/bjr.20210563] [Reference Citation Analysis]
3 Ito R, Iwano S, Naganawa S. A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019. Diagn Interv Radiol 2020;26:443-8. [PMID: 32436845 DOI: 10.5152/dir.2019.20294] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 6.5] [Reference Citation Analysis]
4 Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest 2021;101:412-22. [PMID: 33454724 DOI: 10.1038/s41374-020-00514-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 12.0] [Reference Citation Analysis]
5 Tang HP, Cai, Kong YQ, Ye H, Ma ZX, Lv HS, Tuo LR, Pan QJ, Liu ZH, Han X. Cervical cytology screening facilitated by an artificial intelligence microscope: A preliminary study. Cancer Cytopathol 2021;129:693-700. [PMID: 33826796 DOI: 10.1002/cncy.22425] [Reference Citation Analysis]
6 Xie L, Yang S, Squirrell D, Vaghefi E. Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke. PLoS One 2020;15:e0225015. [PMID: 32275656 DOI: 10.1371/journal.pone.0225015] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
7 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]
8 Sen A, Troncoso P, Venkatesan A, Pagel MD, Nijkamp JA, He Y, Lesage AC, Woodland M, Brock KK. Correlation of in-vivo imaging with histopathology: A review. Eur J Radiol 2021;144:109964. [PMID: 34619617 DOI: 10.1016/j.ejrad.2021.109964] [Reference Citation Analysis]
9 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]
10 Parwani AV, Amin MB. Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions. Adv Anat Pathol 2020;27:221-6. [PMID: 32541593 DOI: 10.1097/PAP.0000000000000271] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
11 Haffner MC, Zwart W, Roudier MP, True LD, Nelson WG, Epstein JI, De Marzo AM, Nelson PS, Yegnasubramanian S. Genomic and phenotypic heterogeneity in prostate cancer. Nat Rev Urol 2021;18:79-92. [PMID: 33328650 DOI: 10.1038/s41585-020-00400-w] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
12 Cheung HMC, Rubin D. Challenges and opportunities for artificial intelligence in oncological imaging. Clin Radiol 2021;76:728-36. [PMID: 33902889 DOI: 10.1016/j.crad.2021.03.009] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
13 Yu C, Helwig EJ. Artificial intelligence in gastric cancer: a translational narrative review. Ann Transl Med 2021;9:269. [PMID: 33708896 DOI: 10.21037/atm-20-6337] [Reference Citation Analysis]
14 Koçak B, Durmaz EŞ, Ateş E, Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol 2019;25:485-95. [PMID: 31650960 DOI: 10.5152/dir.2019.19321] [Cited by in Crossref: 53] [Cited by in F6Publishing: 55] [Article Influence: 26.5] [Reference Citation Analysis]
15 Alaidarous MA. The emergence of new trends in clinical laboratory diagnosis. Saudi Med J 2020;41:1175-80. [PMID: 33130836 DOI: 10.15537/smj.2020.11.25455] [Reference Citation Analysis]
16 Van Booven DJ, Kuchakulla M, Pai R, Frech FS, Ramasahayam R, Reddy P, Parmar M, Ramasamy R, Arora H. A Systematic Review of Artificial Intelligence in Prostate Cancer. Res Rep Urol 2021;13:31-9. [PMID: 33520879 DOI: 10.2147/RRU.S268596] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Brodie A, Dai N, Teoh JY, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021;39:379-99. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Reference Citation Analysis]
18 Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, El-Baz A. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors (Basel) 2021;21:2586. [PMID: 33917035 DOI: 10.3390/s21082586] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Liss MA, Newcomb LF, Zheng Y, Garcia MP, Filson CP, Boyer H, Brooks JD, Carroll PR, Cooperberg MR, Ellis WJ, Gleave ME, Martin FM, Morgan T, Nelson PS, Wagner AA, Thompson IM Jr, Lin DW. Magnetic Resonance Imaging for the Detection of High Grade Cancer in the Canary Prostate Active Surveillance Study. J Urol 2020;204:701-6. [PMID: 32343189 DOI: 10.1097/JU.0000000000001088] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
20 Amirmahani F, Ebrahimi N, Molaei F, Faghihkhorasani F, Jamshidi Goharrizi K, Mirtaghi SM, Borjian‐boroujeni M, Hamblin MR. Approaches for the integration of big data in translational medicine: single‐cell and computational methods. Ann N Y Acad Sci 2021;1493:3-28. [DOI: 10.1111/nyas.14544] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
21 Lin Y, Zhao X, Miao Z, Ling Z, Wei X, Pu J, Hou J, Shen B. Data-driven translational prostate cancer research: from biomarker discovery to clinical decision. J Transl Med 2020;18:119. [PMID: 32143723 DOI: 10.1186/s12967-020-02281-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
22 Chavarriaga J, Moreno C. Precision Medicine, Artificial Intelligence, and Genomic Markers in Urology. Do we need to Tailor our Clinical Practice? Revista Urología Colombiana / Colombian Urology Journal 2020;29:158-67. [DOI: 10.1055/s-0040-1714148] [Reference Citation Analysis]
23 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]
24 Chen M, Ma T, Li J, Zhang HJ, Li Q, Wang JJ, Sang T, Cao CL, Cui XW. Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models. Med Sci Monit 2021;27:e929913. [PMID: 33556045 DOI: 10.12659/MSM.929913] [Reference Citation Analysis]
25 Mikhail AS, Mauda-Havakuk M, Partanen A, Karanian JW, Pritchard WF, Wood BJ. Liver-specific 3D sectioning molds for correlating in vivo CT and MRI with tumor histopathology in woodchucks (Marmota monax). PLoS One 2020;15:e0230794. [PMID: 32214365 DOI: 10.1371/journal.pone.0230794] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
26 O'Connor LP, Lebastchi AH, Horuz R, Rastinehad AR, Siddiqui MM, Grummet J, Kastner C, Ahmed HU, Pinto PA, Turkbey B. Role of multiparametric prostate MRI in the management of prostate cancer. World J Urol 2021;39:651-9. [PMID: 32583039 DOI: 10.1007/s00345-020-03310-z] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
27 Kulac I, Roudier MP, Haffner MC. Molecular Pathology of Prostate Cancer. Surg Pathol Clin 2021;14:387-401. [PMID: 34373091 DOI: 10.1016/j.path.2021.05.004] [Reference Citation Analysis]