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For: Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16:391-403. [PMID: 31092914 DOI: 10.1038/s41585-019-0193-3] [Cited by in Crossref: 83] [Cited by in F6Publishing: 65] [Article Influence: 41.5] [Reference Citation Analysis]
Number Citing Articles
1 Montironi R, Cimadamore A, Scarpelli M, Cheng L, Lopez-Beltran A, Mikuz G. Let us not forget about our past contributions to the field of prostatic neoplasms: To some extent what we value now was already there. Pathol Res Pract 2021;219:153377. [PMID: 33631479 DOI: 10.1016/j.prp.2021.153377] [Reference Citation Analysis]
2 Woo JH, Kim EC, Kim SM. The Current Status of Breakthrough Devices Designation in the United States and Innovative Medical Devices Designation in Korea for Digital Health Software. Expert Rev Med Devices 2022. [PMID: 35255755 DOI: 10.1080/17434440.2022.2051479] [Reference Citation Analysis]
3 Egevad L, Ström P, Kartasalo K, Olsson H, Samaratunga H, Delahunt B, Eklund M. The utility of artificial intelligence in the assessment of prostate pathology. Histopathology 2020;76:790-2. [PMID: 32402150 DOI: 10.1111/his.14060] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
4 Chen MY, Woodruff MA, Dasgupta P, Rukin NJ. Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists. Cancer Med 2020;9:7172-82. [PMID: 32810385 DOI: 10.1002/cam4.3386] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
5 Perera M, Mirchandani R, Papa N, Breemer G, Effeindzourou A, Smith L, Swindle P, Smith E. PSA-based machine learning model improves prostate cancer risk stratification in a screening population. World J Urol 2021;39:1897-902. [PMID: 32747980 DOI: 10.1007/s00345-020-03392-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Fawcett TJ, Cooper CS, Longenecker RJ, Walton JP. Automated classification of acoustic startle reflex waveforms in young CBA/CaJ mice using machine learning. J Neurosci Methods 2020;344:108853. [PMID: 32668315 DOI: 10.1016/j.jneumeth.2020.108853] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
7 Alarcón-zendejas AP, Scavuzzo A, Jiménez-ríos MA, Álvarez-gómez RM, Montiel-manríquez R, Castro-hernández C, Jiménez-dávila MA, Pérez-montiel D, González-barrios R, Jiménez-trejo F, Arriaga-canon C, Herrera LA. The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches. Prostate Cancer Prostatic Dis. [DOI: 10.1038/s41391-022-00537-2] [Reference Citation Analysis]
8 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]
9 Montironi R, Cimadamore A, Lopez-Beltran A, Cheng L, Scarpelli M. Update on Prostate Cancer Diagnosis, Prognosis, and Prediction to Response to Therapy. Cells 2020;10:E20. [PMID: 33374303 DOI: 10.3390/cells10010020] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
10 Wu Y, Cheng M, Huang S, Pei Z, Zuo Y, Liu J, Yang K, Zhu Q, Zhang J, Hong H, Zhang D, Huang K, Cheng L, Shao W. Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications. Cancers 2022;14:1199. [DOI: 10.3390/cancers14051199] [Reference Citation Analysis]
11 Huss R, Coupland SE. Software‐assisted decision support in digital histopathology. J Pathol 2020;250:685-92. [DOI: 10.1002/path.5388] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 7.5] [Reference Citation Analysis]
12 Xu Z, Wang X, Zeng S, Ren X, Yan Y, Gong Z. Applying artificial intelligence for cancer immunotherapy. Acta Pharm Sin B 2021;11:3393-405. [PMID: 34900525 DOI: 10.1016/j.apsb.2021.02.007] [Reference Citation Analysis]
13 Almeida G, Tavares JMR. Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review. J Med Syst 2020;44. [DOI: 10.1007/s10916-020-01641-3] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
14 Gómez Rivas J, Toribio Vázquez C, Ballesteros Ruiz C, Taratkin M, Marenco JL, Cacciamani GE, Checcucci E, Okhunov Z, Enikeev D, Esperto F, Grossmann R, Somani B, Veneziano D. Artificial intelligence and simulation in urology. Actas Urol Esp 2021:S0210-4806(21)00088-7. [PMID: 34127285 DOI: 10.1016/j.acuro.2020.10.012] [Reference Citation Analysis]
15 Hu D, Wang C, Zheng S, Cui X. Investigating the genealogy of the literature on digital pathology: a two-dimensional bibliometric approach. Scientometrics. [DOI: 10.1007/s11192-021-04224-2] [Reference Citation Analysis]
16 Chaddad A, Katib Y, Hassan L. Future artificial intelligence tools and perspectives in medicine. Curr Opin Urol 2021;31:371-7. [PMID: 33927099 DOI: 10.1097/MOU.0000000000000884] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Henschke CI, Yip R, Shaham D, Zulueta JJ, Aguayo SM, Reeves AP, Jirapatnakul A, Avila R, Moghanaki D, Yankelevitz DF; I-ELCAP Investigators. The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program. J Thorac Imaging 2021;36:6-23. [PMID: 32520848 DOI: 10.1097/RTI.0000000000000538] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
18 Checcucci E, De Cillis S, Granato S, Chang P, Afyouni AS, Okhunov Z; Uro-technology and SoMe Working Group of the Young Academic Urologists Working Party of the European Association of Urology. Applications of neural networks in urology: a systematic review. Curr Opin Urol 2020;30:788-807. [PMID: 32881726 DOI: 10.1097/MOU.0000000000000814] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 9.0] [Reference Citation Analysis]
19 Wang X, Ma J, Bhosale P, Ibarra Rovira JJ, Qayyum A, Sun J, Bayram E, Szklaruk J. Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging. Abdom Radiol (NY) 2021;46:3378-86. [PMID: 33580348 DOI: 10.1007/s00261-021-02964-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
20 Sopyllo K, Erickson AM, Mirtti T. Grading Evolution and Contemporary Prognostic Biomarkers of Clinically Significant Prostate Cancer. Cancers (Basel) 2021;13:628. [PMID: 33562508 DOI: 10.3390/cancers13040628] [Reference Citation Analysis]
21 Verde F, Romeo V, Stanzione A, Maurea S. Current trends of artificial intelligence in cancer imaging. Artif Intell Med Imaging 2020; 1(3): 87-93 [DOI: 10.35711/aimi.v1.i3.87] [Cited by in CrossRef: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
22 Vélez JI, Samper LA, Arcos-Holzinger M, Espinosa LG, Isaza-Ruget MA, Lopera F, Arcos-Burgos M. A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease. Diagnostics (Basel) 2021;11:887. [PMID: 34067584 DOI: 10.3390/diagnostics11050887] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Giannini V, Mazzetti S, Defeudis A, Stranieri G, Calandri M, Bollito E, Bosco M, Porpiglia F, Manfredi M, De Pascale A, Veltri A, Russo F, Regge D. A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation. Front Oncol 2021;11:718155. [PMID: 34660282 DOI: 10.3389/fonc.2021.718155] [Reference Citation Analysis]
24 Hooshmand A. Accurate diagnosis of prostate cancer using logistic regression. Open Med (Wars) 2021;16:459-63. [PMID: 33817323 DOI: 10.1515/med-2021-0238] [Reference Citation Analysis]
25 Liscano Y, Oñate-Garzón J, Delgado JP. Peptides with Dual Antimicrobial-Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides. Molecules 2020;25:E4245. [PMID: 32947811 DOI: 10.3390/molecules25184245] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
26 Hameed BMZ, Shah M, Naik N, Ibrahim S, Somani B, Rice P, Soomro N, Rai BP. Contemporary application of artificial intelligence in prostate cancer: an i-TRUE study. Ther Adv Urol 2021;13:1756287220986640. [PMID: 33633799 DOI: 10.1177/1756287220986640] [Reference Citation Analysis]
27 Nolsøe AB, Østergren PB, Jensen CFS, Fode M. From separation to collaboration: the future of urology. Nat Rev Urol 2019;16:633-4. [PMID: 31575989 DOI: 10.1038/s41585-019-0241-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
28 Montironi R, Cheng L, Cimadamore A, Lopez-Beltran A. Molecular diagnostics in uro-oncology. Expert Rev Mol Diagn 2020;20:117-21. [PMID: 31933387 DOI: 10.1080/14737159.2020.1715799] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
29 Bueschbell B, Caniceiro AB, Suzano PM, Machuqueiro M, Rosário-ferreira N, Moreira IS. Network Biology and Artificial Intelligence Drive the Understanding of the Multidrug Resistance Phenotype in Cancer. Drug Resistance Updates 2022. [DOI: 10.1016/j.drup.2022.100811] [Reference Citation Analysis]
30 Koo J, Choi K, Lee P, Polley A, Pudupakam RS, Tsang J, Fernandez E, Han EJ, Park S, Swartzfager D, Qi NSX, Jung M, Ocnean M, Kim HU, Lim S. Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning Model. Vet Sci 2021;8:301. [PMID: 34941828 DOI: 10.3390/vetsci8120301] [Reference Citation Analysis]
31 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]
32 Li Q, Fan QL, Han QX, Geng WJ, Zhao HH, Ding XN, Yan JY, Zhu HY. Machine learning in nephrology: scratching the surface. Chin Med J (Engl) 2020;:687-98. [PMID: 32049747 DOI: 10.1097/CM9.0000000000000694] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
33 Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol 2020;38:2329-47. [PMID: 31691082 DOI: 10.1007/s00345-019-03000-5] [Cited by in Crossref: 21] [Cited by in F6Publishing: 20] [Article Influence: 7.0] [Reference Citation Analysis]
34 Egger J, Wild D, Weber M, Bedoya CAR, Karner F, Prutsch A, Schmied M, Dionysio C, Krobath D, Jin Y, Gsaxner C, Li J, Pepe A. Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform. J Digit Imaging. [DOI: 10.1007/s10278-021-00574-8] [Reference Citation Analysis]
35 Peng Y, Yang N, Xu Q, Dai Y, Wang Z. Recent Advances in Flexible Tactile Sensors for Intelligent Systems. Sensors (Basel) 2021;21:5392. [PMID: 34450833 DOI: 10.3390/s21165392] [Reference Citation Analysis]
36 Steiner DF, Chen PC, Mermel CH. Closing the translation gap: AI applications in digital pathology. Biochim Biophys Acta Rev Cancer 2021;1875:188452. [PMID: 33065195 DOI: 10.1016/j.bbcan.2020.188452] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
37 Hameed BMZ, S Dhavileswarapu AVL, Naik N, Karimi H, Hegde P, Rai BP, Somani BK. Big Data Analytics in urology: the story so far and the road ahead. Ther Adv Urol 2021;13:1756287221998134. [PMID: 33747134 DOI: 10.1177/1756287221998134] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomedicine & Pharmacotherapy 2020;128:110255. [DOI: 10.1016/j.biopha.2020.110255] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 9.5] [Reference Citation Analysis]
39 Guo K, Fu X, Zhang H, Wang M, Hong S, Ma S. Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data. Transl Pediatr 2021;10:33-43. [PMID: 33633935 DOI: 10.21037/tp-20-238] [Reference Citation Analysis]
40 Senturk N, Tuncel G, Dogan B, Aliyeva L, Dundar MS, Ozemri Sag S, Mocan G, Temel SG, Dundar M, Ergoren MC. BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models. Genes (Basel) 2021;12:1774. [PMID: 34828379 DOI: 10.3390/genes12111774] [Reference Citation Analysis]
41 Wu J, Wang P, Han Z, Li T, Yi C, Qiu C, Yang Q, Sun G, Dai L, Shi J, Wang K, Ye H. A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system. Cancer Sci 2021. [PMID: 34821436 DOI: 10.1111/cas.15217] [Reference Citation Analysis]
42 Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ, Gasbarrini A, Tortora G. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 2020;17:635-48. [DOI: 10.1038/s41575-020-0327-3] [Cited by in Crossref: 31] [Cited by in F6Publishing: 25] [Article Influence: 15.5] [Reference Citation Analysis]
43 Smith SC, Gandhi JS, Moch H, Aron M, Compérat E, Paner GP, McKenney JK, Amin MB. Similarities and Differences in the 2019 ISUP and GUPS Recommendations on Prostate Cancer Grading: A Guide for Practicing Pathologists. Adv Anat Pathol 2021;28:1-7. [PMID: 33027069 DOI: 10.1097/PAP.0000000000000287] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
44 Greenberg A, Aizic A, Zubkov A, Borsekofsky S, Hagege RR, Hershkovitz D. Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis. Sci Rep 2021;11:3306. [PMID: 33558593 DOI: 10.1038/s41598-021-82869-y] [Reference Citation Analysis]
45 Abedi V, Razavi SM, Khan A, Avula V, Tompe A, Poursoroush A, Vafaei Sadr A, Li J, Zand R. Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021;10:5710. [PMID: 34884412 DOI: 10.3390/jcm10235710] [Reference Citation Analysis]
46 Deniffel D, Abraham N, Namdar K, Dong X, Salinas E, Milot L, Khalvati F, Haider MA. Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment. Eur Radiol 2020;30:6867-76. [PMID: 32591889 DOI: 10.1007/s00330-020-07030-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
47 Alhassan Z, Watson M, Budgen D, Alshammari R, Alessa A, Al Moubayed N. Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records. JMIR Med Inform 2021;9:e25237. [PMID: 34028357 DOI: 10.2196/25237] [Reference Citation Analysis]
48 Shen YT, Chen L, Yue WW, Xu HX. Artificial intelligence in ultrasound. Eur J Radiol 2021;139:109717. [PMID: 33962110 DOI: 10.1016/j.ejrad.2021.109717] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Reference Citation Analysis]
50 Ahmad Z, Rahim S, Zubair M, Abdul-Ghafar J. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagn Pathol 2021;16:24. [PMID: 33731170 DOI: 10.1186/s13000-021-01085-4] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
51 Kalman JM, Lavandero S, Mahfoud F, Nahrendorf M, Yacoub MH, Zhao D. Looking back and thinking forwards - 15 years of cardiology and cardiovascular research. Nat Rev Cardiol 2019;16:651-60. [PMID: 31570832 DOI: 10.1038/s41569-019-0261-7] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
52 Chen SH, Xu LY, Wu YP, Ke ZB, Huang P, Lin F, Li XD, Xue XY, Wei Y, Zheng QS, Xu N. Tumor volume: a new prognostic factor of oncological outcome of localized clear cell renal cell carcinoma. BMC Cancer 2021;21:79. [PMID: 33468079 DOI: 10.1186/s12885-021-07795-8] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
53 Bulten W, Kartasalo K, Chen PC, Ström P, Pinckaers H, Nagpal K, Cai Y, Steiner DF, van Boven H, Vink R, Hulsbergen-van de Kaa C, van der Laak J, Amin MB, Evans AJ, van der Kwast T, Allan R, Humphrey PA, Grönberg H, Samaratunga H, Delahunt B, Tsuzuki T, Häkkinen T, Egevad L, Demkin M, Dane S, Tan F, Valkonen M, Corrado GS, Peng L, Mermel CH, Ruusuvuori P, Litjens G, Eklund M; PANDA challenge consortium. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med 2022. [PMID: 35027755 DOI: 10.1038/s41591-021-01620-2] [Reference Citation Analysis]
54 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]
55 Li Y, Tian S, Huang Y, Dong W. Driverless artificial intelligence framework for the identification of malignant pleural effusion. Transl Oncol 2021;14:100896. [PMID: 33045678 DOI: 10.1016/j.tranon.2020.100896] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
56 Yang S, Guan H, Chen Z, Wang S, Wu H, Zhong W, Khalaf OI. Analysis of the Role of Comprehensive Treatment Model in the Treatment of Prostate Cancer. Computational and Mathematical Methods in Medicine 2022;2022:1-7. [DOI: 10.1155/2022/2118823] [Reference Citation Analysis]
57 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]
58 Elbadawi M, McCoubrey LE, Gavins FKH, Ong JJ, Goyanes A, Gaisford S, Basit AW. Disrupting 3D printing of medicines with machine learning. Trends Pharmacol Sci 2021;42:745-57. [PMID: 34238624 DOI: 10.1016/j.tips.2021.06.002] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
59 Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res 2021;23:e22394. [PMID: 33792552 DOI: 10.2196/22394] [Reference Citation Analysis]
60 Cimadamore A, Cheng L, Scarpelli M, Lopez-Beltran A, Montironi R. Digital diagnostics and artificial intelligence in prostate cancer treatment in 5 years from now. Transl Androl Urol 2021;10:1499-505. [PMID: 33850784 DOI: 10.21037/tau-2021-01] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
61 Kalaiselvan V, Sharma A, Gupta SK. “Feasibility test and application of AI in healthcare”—with special emphasis in clinical, pharmacovigilance, and regulatory practices. Health Technol 2021;11:1-15. [DOI: 10.1007/s12553-020-00495-6] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
62 Gupta R, Le H, Van Arnam J, Belinsky D, Hasan M, Samaras D, Kurc T, Saltz JH. Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics. Curr Pathobiol Rep 2020;8:133-48. [DOI: 10.1007/s40139-020-00217-7] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
63 Safdar S, Rizwan M, Gadekallu TR, Javed AR, Rahmani MKI, Jawad K, Bhatia S. Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection. Diagnostics 2022;12:1134. [DOI: 10.3390/diagnostics12051134] [Reference Citation Analysis]
64 Shao Y, Nir G, Fazli L, Goldenberg L, Gleave M, Black P, Wang J, Salcudean S. Improving prostate cancer classification in H&E tissue micro arrays using Ki67 and P63 histopathology. Comput Biol Med 2020;127:104053. [PMID: 33126125 DOI: 10.1016/j.compbiomed.2020.104053] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
65 Erle A, Moazemi S, Lütje S, Essler M, Schultz T, Bundschuh RA. Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans. Tomography 2021;7:301-12. [PMID: 34449727 DOI: 10.3390/tomography7030027] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
66 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]
67 Egevad L, Swanberg D, Delahunt B, Ström P, Kartasalo K, Olsson H, Berney DM, Bostwick DG, Evans AJ, Humphrey PA, Iczkowski KA, Kench JG, Kristiansen G, Leite KRM, McKenney JK, Oxley J, Pan CC, Samaratunga H, Srigley JR, Takahashi H, Tsuzuki T, van der Kwast T, Varma M, Zhou M, Clements M, Eklund M. Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading. Virchows Arch 2020;477:777-86. [PMID: 32542445 DOI: 10.1007/s00428-020-02858-w] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
68 Thomas T. Automated systems comparable to expert pathologists for prostate cancer Gleason grading. Nat Rev Urol 2020;17:131. [PMID: 32055009 DOI: 10.1038/s41585-020-0294-z] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
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