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For: Pantanowitz L, Quiroga-Garza GM, Bien L, Heled R, Laifenfeld D, Linhart C, Sandbank J, Albrecht Shach A, Shalev V, Vecsler M, Michelow P, Hazelhurst S, Dhir R. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit Health 2020;2:e407-16. [PMID: 33328045 DOI: 10.1016/S2589-7500(20)30159-X] [Cited by in Crossref: 26] [Cited by in F6Publishing: 10] [Article Influence: 26.0] [Reference Citation Analysis]
Number Citing Articles
1 Nakata W, Mori H, Tsujimura G, Tsujimoto Y, Gotoh T, Tsujihata M. Pilot study of an artificial intelligence-based deep learning algorithm to predict time to castration-resistant prostate cancer for metastatic hormone-naïve prostate cancer. Jpn J Clin Oncol 2022:hyac089. [PMID: 35750041 DOI: 10.1093/jjco/hyac089] [Reference Citation Analysis]
2 Ruini C, Schlingmann S, Jonke Ž, Avci P, Padrón-Laso V, Neumeier F, Koveshazi I, Ikeliani IU, Patzer K, Kunrad E, Kendziora B, Sattler E, French LE, Hartmann D. Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy. Cancers (Basel) 2021;13:5522. [PMID: 34771684 DOI: 10.3390/cancers13215522] [Reference Citation Analysis]
3 Prabhu S, Prasad K, Robels-kelly A, Lu X. AI-based carcinoma detection and classification using histopathological images: A systematic review. Computers in Biology and Medicine 2022;142:105209. [DOI: 10.1016/j.compbiomed.2022.105209] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Kers J, Bülow RD, Klinkhammer BM, Breimer GE, Fontana F, Abiola AA, Hofstraat R, Corthals GL, Peters-Sengers H, Djudjaj S, von Stillfried S, Hölscher DL, Pieters TT, van Zuilen AD, Bemelman FJ, Nurmohamed AS, Naesens M, Roelofs JJTH, Florquin S, Floege J, Nguyen TQ, Kather JN, Boor P. Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study. Lancet Digit Health 2022;4:e18-26. [PMID: 34794930 DOI: 10.1016/S2589-7500(21)00211-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Perincheri S. Tumor Microenvironment of Lymphomas and Plasma Cell Neoplasms: Broad Overview and Impact on Evaluation for Immune Based Therapies. Front Oncol 2021;11:719140. [DOI: 10.3389/fonc.2021.719140] [Reference Citation Analysis]
6 Tang H, Sun N, Shen S. Improving Generalization of Deep Learning Models for Diagnostic Pathology by Increasing Variability in Training Data: Experiments on Osteosarcoma Subtypes. J Pathol Inform 2021;12:30. [PMID: 34497734 DOI: 10.4103/jpi.jpi_78_20] [Reference Citation Analysis]
7 Dwivedi R, Mehrotra D, Chandra S. Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review. Journal of Oral Biology and Craniofacial Research 2021. [DOI: 10.1016/j.jobcr.2021.11.010] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
8 Ghosh A, Sirinukunwattana K, Khalid Alham N, Browning L, Colling R, Protheroe A, Protheroe E, Jones S, Aberdeen A, Rittscher J, Verrill C. The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer. Cancers (Basel) 2021;13:1325. [PMID: 33809521 DOI: 10.3390/cancers13061325] [Reference Citation Analysis]
9 Feng L, Liu Z, Li C, Li Z, Lou X, Shao L, Wang Y, Huang Y, Chen H, Pang X, Liu S, He F, Zheng J, Meng X, Xie P, Yang G, Ding Y, Wei M, Yun J, Hung MC, Zhou W, Wahl DR, Lan P, Tian J, Wan X. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health 2022;4:e8-e17. [PMID: 34952679 DOI: 10.1016/S2589-7500(21)00215-6] [Reference Citation Analysis]
10 Meier LJ, Hein A, Diepold K, Buyx A. Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept. Am J Bioeth 2022;22:4-20. [PMID: 35293841 DOI: 10.1080/15265161.2022.2040647] [Reference Citation Analysis]
11 Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022;11:2265. [PMID: 35456357 DOI: 10.3390/jcm11082265] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Comba A, Faisal SM, Varela ML, Hollon T, Al-Holou WN, Umemura Y, Nunez FJ, Motsch S, Castro MG, Lowenstein PR. Uncovering Spatiotemporal Heterogeneity of High-Grade Gliomas: From Disease Biology to Therapeutic Implications. Front Oncol 2021;11:703764. [PMID: 34422657 DOI: 10.3389/fonc.2021.703764] [Reference Citation Analysis]
13 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]
14 Appakkannu A, Govindaraj E, Balakrishnan K. Detection of Abnormality in Prostate Tissues Using Two-dimensional Photonic Crystal Tactile Sensor. Plasmonics. [DOI: 10.1007/s11468-022-01635-6] [Reference Citation Analysis]
15 Dadhania V, Gonzalez D, Yousif M, Cheng J, Morgan TM, Spratt DE, Reichert ZR, Mannan R, Wang X, Chinnaiyan A, Cao X, Dhanasekaran SM, Chinnaiyan AM, Pantanowitz L, Mehra R. Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer. BMC Cancer 2022;22:494. [PMID: 35513774 DOI: 10.1186/s12885-022-09559-4] [Reference Citation Analysis]
16 Eccher A, Girolami I, Troncone G, Pantanowitz L. Digital Slide Assessment for Programmed Death-Ligand 1 Combined Positive Score in Head and Neck Squamous Carcinoma: Focus on Validation and Vision. Front Artif Intell 2021;4:684034. [PMID: 34151256 DOI: 10.3389/frai.2021.684034] [Reference Citation Analysis]
17 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]
18 Huang W, Randhawa R, Jain P, Iczkowski KA, Hu R, Hubbard S, Eickhoff J, Basu H, Roy R. Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification. JAMA Netw Open 2021;4:e2132554. [PMID: 34730818 DOI: 10.1001/jamanetworkopen.2021.32554] [Reference Citation Analysis]
19 Nussbaum S, Shoukry M, Ashary MA, Kasbi AA, Baksh M, Gabriel E. Advanced Tumor Imaging Approaches in Human Tumors. Cancers 2022;14:1549. [DOI: 10.3390/cancers14061549] [Reference Citation Analysis]
20 Ito Y, Unagami M, Yamabe F, Mitsui Y, Nakajima K, Nagao K, Kobayashi H. A method for utilizing automated machine learning for histopathological classification of testis based on Johnsen scores. Sci Rep 2021;11:9962. [PMID: 33967273 DOI: 10.1038/s41598-021-89369-z] [Reference Citation Analysis]
21 Beinecke JM, Anders P, Schurrat T, Heider D, Luster M, Librizzi D, Hauschild A. Evaluation of machine learning strategies for imaging confirmed prostate cancer recurrence prediction on electronic health records. Computers in Biology and Medicine 2022;143:105263. [DOI: 10.1016/j.compbiomed.2022.105263] [Reference Citation Analysis]
22 Glass C, Lafata KJ, Jeck W, Horstmeyer R, Cooke C, Everitt J, Glass M, Dov D, Seidman MA. The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol 2021:S0828-282X(21)00867-9. [PMID: 34813876 DOI: 10.1016/j.cjca.2021.11.008] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Jung M, Jin MS, Kim C, Lee C, Nikas IP, Park JH, Ryu HS. Artificial intelligence system shows performance at the level of uropathologists for the detection and grading of prostate cancer in core needle biopsy: an independent external validation study. Mod Pathol 2022. [PMID: 35487950 DOI: 10.1038/s41379-022-01077-9] [Reference Citation Analysis]
24 Coulter C, McKay F, Hallowell N, Browning L, Colling R, Macklin P, Sorell T, Aslam M, Bryson G, Treanor D, Verrill C. Understanding the ethical and legal considerations of Digital Pathology. J Pathol Clin Res 2022;8:101-15. [PMID: 34796679 DOI: 10.1002/cjp2.251] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
25 Shakir MN, Dugger BN. Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future. J Neuropathol Exp Neurol 2022:nlab122. [PMID: 34981115 DOI: 10.1093/jnen/nlab122] [Reference Citation Analysis]
26 da Silva LM, Pereira EM, Salles PG, Godrich R, Ceballos R, Kunz JD, Casson A, Viret J, Chandarlapaty S, Ferreira CG, Ferrari B, Rothrock B, Raciti P, Reuter V, Dogdas B, DeMuth G, Sue J, Kanan C, Grady L, Fuchs TJ, Reis-Filho JS. Independent real-world application of a clinical-grade automated prostate cancer detection system. J Pathol 2021;254:147-58. [PMID: 33904171 DOI: 10.1002/path.5662] [Reference Citation Analysis]
27 Li H, Wang J, Li Z, Dababneh M, Wang F, Zhao P, Smith GH, Teodoro G, Li M, Kong J, Li X. Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score. Front Med 2022;9:886763. [DOI: 10.3389/fmed.2022.886763] [Reference Citation Analysis]
28 Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, Di Tommaso L. Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology. Cells 2021;10:787. [PMID: 33918173 DOI: 10.3390/cells10040787] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Cimadamore A, Lopez-Beltran A, Scarpelli M, Cheng L, Montironi R. Artificial intelligence and prostate cancer: Advances and challenges. Urologia 2021;:3915603211062409. [PMID: 34877911 DOI: 10.1177/03915603211062409] [Reference Citation Analysis]
30 Chen PC, Mermel CH, Liu Y. Evaluation of artificial intelligence on a reference standard based on subjective interpretation. Lancet Digit Health 2021;3:e693-5. [PMID: 34561202 DOI: 10.1016/S2589-7500(21)00216-8] [Reference Citation Analysis]
31 Konnaris MA, Brendel M, Fontana MA, Otero M, Ivashkiv LB, Wang F, Bell RD. Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges. Arthritis Res Ther 2022;24:68. [PMID: 35277196 DOI: 10.1186/s13075-021-02716-3] [Reference Citation Analysis]
32 Colling R, Protheroe A, Sullivan M, Macpherson R, Tuthill M, Redgwell J, Traill Z, Molyneux A, Johnson E, Abdullah N, Taibi A, Mercer N, Haynes HR, Sackville A, Craft J, Reis J, Rees G, Soares M, Roberts ISD, Siiankoski D, Hemsworth H, Roskell D, Roberts-gant S, White K, Rittscher J, Davies J, Browning L, Verrill C. Digital Pathology Transformation in a Supraregional Germ Cell Tumour Network. Diagnostics 2021;11:2191. [DOI: 10.3390/diagnostics11122191] [Reference Citation Analysis]