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For: Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16:703-715. [PMID: 31399699 DOI: 10.1038/s41571-019-0252-y] [Cited by in Crossref: 191] [Cited by in F6Publishing: 144] [Article Influence: 95.5] [Reference Citation Analysis]
Number Citing Articles
1 Laurinavicius A, Rasmusson A, Plancoulaine B, Shribak M, Levenson R. Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance. Am J Pathol 2021:S0002-9440(21)00165-6. [PMID: 33895120 DOI: 10.1016/j.ajpath.2021.04.008] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Schie I, Stiebing C, Popp J. Looking for a perfect match: multimodal combinations of Raman spectroscopy for biomedical applications. J Biomed Opt 2021;26. [PMID: 34387049 DOI: 10.1117/1.JBO.26.8.080601] [Reference Citation Analysis]
3 Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 2021;5:555-70. [PMID: 33649564 DOI: 10.1038/s41551-020-00682-w] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 9.0] [Reference Citation Analysis]
4 Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Shih NNC, Feldman M, Gupta S, McKenney J, Lal P, Madabhushi A. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. Eur Urol Focus 2021;7:722-32. [PMID: 33941504 DOI: 10.1016/j.euf.2021.04.016] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Mishra R, Li B. The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease. Aging Dis 2020;11:1567-84. [PMID: 33269107 DOI: 10.14336/AD.2020.0312] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
6 Daisy PS, Anitha TS. Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy? Med Oncol 2021;38:53. [PMID: 33811540 DOI: 10.1007/s12032-021-01500-2] [Reference Citation Analysis]
7 Cheng JY, Abel JT, Balis UGJ, McClintock DS, Pantanowitz L. Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology. Am J Pathol. 2020;. [PMID: 33245914 DOI: 10.1016/j.ajpath.2020.10.018] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Salvi M, Molinari F, Iussich S, Muscatello LV, Pazzini L, Benali S, Banco B, Abramo F, De Maria R, Aresu L. Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study. Front Vet Sci 2021;8:640944. [PMID: 33869320 DOI: 10.3389/fvets.2021.640944] [Reference Citation Analysis]
9 Wang H, Jiang Y, Li B, Cui Y, Li D, Li R. Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes. Cancers (Basel) 2020;12:E3562. [PMID: 33260561 DOI: 10.3390/cancers12123562] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Calderaro J, Kather JN. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut. 2020;. [PMID: 33214163 DOI: 10.1136/gutjnl-2020-322880] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
11 Inge L, Dennis E. Development and applications of computer image analysis algorithms for scoring of PD-L1 immunohistochemistry. Immuno-Oncology Technology 2020;6:2-8. [DOI: 10.1016/j.iotech.2020.04.001] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 6.0] [Reference Citation Analysis]
12 Mahmood H, Shaban M, Indave BI, Santos-Silva AR, Rajpoot N, Khurram SA. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review. Oral Oncol 2020;110:104885. [PMID: 32674040 DOI: 10.1016/j.oraloncology.2020.104885] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 13.0] [Reference Citation Analysis]
13 Santo BA, Rosenberg AZ, Sarder P. Artificial intelligence driven next-generation renal histomorphometry. Curr Opin Nephrol Hypertens 2020;29:265-72. [PMID: 32205581 DOI: 10.1097/MNH.0000000000000598] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 9.0] [Reference Citation Analysis]
14 Bender A, Cortes-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov Today 2021;26:1040-52. [PMID: 33508423 DOI: 10.1016/j.drudis.2020.11.037] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
15 Bera K, Katz I, Madabhushi A. Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO Clin Cancer Inform 2020;4:1039-50. [PMID: 33166198 DOI: 10.1200/CCI.20.00110] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
16 Bianconi F, Kather JN, Reyes-Aldasoro CC. Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin. Cancers (Basel) 2020;12:E3337. [PMID: 33187299 DOI: 10.3390/cancers12113337] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
17 Song Z, Zou S, Zhou W, Huang Y, Shao L, Yuan J, Gou X, Jin W, Wang Z, Chen X, Ding X, Liu J, Yu C, Ku C, Liu C, Sun Z, Xu G, Wang Y, Zhang X, Wang D, Wang S, Xu W, Davis RC, Shi H. Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning. Nat Commun. 2020;11:4294. [PMID: 32855423 DOI: 10.1038/s41467-020-18147-8] [Cited by in Crossref: 16] [Cited by in F6Publishing: 10] [Article Influence: 16.0] [Reference Citation Analysis]
18 Nam S, Chong Y, Jung CK, Kwak TY, Lee JY, Park J, Rho MJ, Go H. Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med 2020;54:125-34. [PMID: 32045965 DOI: 10.4132/jptm.2019.12.31] [Cited by in Crossref: 16] [Cited by in F6Publishing: 11] [Article Influence: 16.0] [Reference Citation Analysis]
19 Bhargava HK, Leo P, Elliott R, Janowczyk A, Whitney J, Gupta S, Fu P, Yamoah K, Khani F, Robinson BD, Rebbeck TR, Feldman M, Lal P, Madabhushi A. Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. Clin Cancer Res 2020;26:1915-23. [PMID: 32139401 DOI: 10.1158/1078-0432.CCR-19-2659] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
20 Tizhoosh HR, Diamandis P, Campbell CJV, Safarpoor A, Kalra S, Maleki D, Riasatian A, Babaie M. Searching Images for Consensus: Can AI Remove Observer Variability in Pathology? Am J Pathol 2021:S0002-9440(21)00072-9. [PMID: 33636179 DOI: 10.1016/j.ajpath.2021.01.015] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
21 Schmitt M, Maron RC, Hekler A, Stenzinger A, Hauschild A, Weichenthal M, Tiemann M, Krahl D, Kutzner H, Utikal JS, Haferkamp S, Kather JN, Klauschen F, Krieghoff-Henning E, Fröhling S, von Kalle C, Brinker TJ. Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study. J Med Internet Res 2021;23:e23436. [PMID: 33528370 DOI: 10.2196/23436] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
22 Försch S, Klauschen F, Hufnagl P, Roth W. Artificial Intelligence in Pathology. Dtsch Arztebl Int 2021;118:194-204. [PMID: 34024323 DOI: 10.3238/arztebl.m2021.0011] [Reference Citation Analysis]
23 Formica V, Morelli C, Riondino S, Renzi N, Nitti D, Roselli M. Artificial intelligence for the study of colorectal cancer tissue slides. AIG 2020;1:51-9. [DOI: 10.35712/aig.v1.i3.51] [Reference Citation Analysis]
24 Jiang M, Li Y, Jiang C, Zhao L, Zhang X, Lipsky PE. Machine Learning in Rheumatic Diseases. Clin Rev Allergy Immunol 2021;60:96-110. [PMID: 32681407 DOI: 10.1007/s12016-020-08805-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
25 Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2020:S1044-579X(20)30264-9. [PMID: 33290844 DOI: 10.1016/j.semcancer.2020.12.005] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
26 Quénéhervé L, Olivier R, Gora MJ, Bossard C, Mosnier JF, Benoit A la Guillaume E, Boccara C, Brochard C, Neunlist M, Coron E. Full-field optical coherence tomography: novel imaging technique for extemporaneous high-resolution analysis of mucosal architecture in human gut biopsies. Gut 2021;70:6-8. [PMID: 32447309 DOI: 10.1136/gutjnl-2020-321228] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
27 Hartl D, de Luca V, Kostikova A, Laramie J, Kennedy S, Ferrero E, Siegel R, Fink M, Ahmed S, Millholland J, Schuhmacher A, Hinder M, Piali L, Roth A. Translational precision medicine: an industry perspective. J Transl Med 2021;19:245. [PMID: 34090480 DOI: 10.1186/s12967-021-02910-6] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Voith von Voithenberg L, Fomitcheva Khartchenko A, Huber D, Schraml P, Kaigala GV. Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity. Nucleic Acids Res 2020;48:e17. [PMID: 31853536 DOI: 10.1093/nar/gkz1151] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 9.0] [Reference Citation Analysis]
29 Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020;18:2312-25. [PMID: 32994890 DOI: 10.1016/j.csbj.2020.08.003] [Cited by in Crossref: 16] [Cited by in F6Publishing: 9] [Article Influence: 16.0] [Reference Citation Analysis]
30 Braun M, Piasecka D, Bobrowski M, Kordek R, Sadej R, Romanska HM. A 'Real-Life' Experience on Automated Digital Image Analysis of FGFR2 Immunohistochemistry in Breast Cancer. Diagnostics (Basel) 2020;10:E1060. [PMID: 33297384 DOI: 10.3390/diagnostics10121060] [Reference Citation Analysis]
31 Liu JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A. Harnessing non-destructive 3D pathology. Nat Biomed Eng 2021;5:203-18. [PMID: 33589781 DOI: 10.1038/s41551-020-00681-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
32 Aboujaoude E, Gega L, Parish MB, Hilty DM. Editorial: Digital Interventions in Mental Health: Current Status and Future Directions. Front Psychiatry 2020;11:111. [PMID: 32174858 DOI: 10.3389/fpsyt.2020.00111] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
33 Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, Lennerz J, Eschrich J, Kazdal D, Schirmacher P, Wagner AH, Tacke F, Capper D, Müller KR, Klauschen F. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 2021:S1044-579X(21)00034-1. [PMID: 33631297 DOI: 10.1016/j.semcancer.2021.02.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
34 Abraham J, Heimberger AB, Marshall J, Heath E, Drabick J, Helmstetter A, Xiu J, Magee D, Stafford P, Nabhan C, Antani S, Johnston C, Oberley M, Korn WM, Spetzler D. Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type. Transl Oncol 2021;14:101016. [PMID: 33465745 DOI: 10.1016/j.tranon.2021.101016] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
35 Mulliqi N, Kartasalo K, Olsson H, Ji X, Egevad L, Eklund M, Ruusuvuori P. OpenPhi: An interface to access Philips iSyntax whole slide images for computational pathology. Bioinformatics 2021:btab578. [PMID: 34358287 DOI: 10.1093/bioinformatics/btab578] [Reference Citation Analysis]
36 Pischon H, Mason D, Lawrenz B, Blanck O, Frisk AL, Schorsch F, Bertani V. Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats. Toxicol Pathol 2021;49:928-37. [PMID: 33397216 DOI: 10.1177/0192623320983244] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Rafique R, Islam SMR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021;19:4003-17. [PMID: 34377366 DOI: 10.1016/j.csbj.2021.07.003] [Reference Citation Analysis]
38 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: 4] [Article Influence: 12.0] [Reference Citation Analysis]
39 Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey. Med Image Anal 2021;67:101813. [PMID: 33049577 DOI: 10.1016/j.media.2020.101813] [Cited by in Crossref: 26] [Cited by in F6Publishing: 15] [Article Influence: 26.0] [Reference Citation Analysis]
40 Kuntz S, Krieghoff-Henning E, Kather JN, Jutzi T, Höhn J, Kiehl L, Hekler A, Alwers E, von Kalle C, Fröhling S, Utikal JS, Brenner H, Hoffmeister M, Brinker TJ. Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review. Eur J Cancer 2021;155:200-15. [PMID: 34391053 DOI: 10.1016/j.ejca.2021.07.012] [Reference Citation Analysis]
41 Diao S, Hou J, Yu H, Zhao X, Sun Y, Lambo RL, Xie Y, Liu L, Qin W, Luo W. Computer-Aided Pathological Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning. Am J Pathol. 2020;. [PMID: 32360568 DOI: 10.1016/j.ajpath.2020.04.008] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
42 Angerilli V, Galuppini F, Pagni F, Fusco N, Malapelle U, Fassan M. The Role of the Pathologist in the Next-Generation Era of Tumor Molecular Characterization. Diagnostics (Basel) 2021;11:339. [PMID: 33670699 DOI: 10.3390/diagnostics11020339] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
43 Kräter M, Abuhattum S, Soteriou D, Jacobi A, Krüger T, Guck J, Herbig M. AIDeveloper: Deep Learning Image Classification in Life Science and Beyond. Adv Sci (Weinh) 2021;8:e2003743. [PMID: 34105281 DOI: 10.1002/advs.202003743] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
44 Jang HJ, Song IH, Lee SH. Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images. Cancers (Basel) 2021;13:3811. [PMID: 34359712 DOI: 10.3390/cancers13153811] [Reference Citation Analysis]
45 Liu K, Xia W, Qiang M, Chen X, Liu J, Guo X, Lv X. Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy. Cancer Med 2020;9:1298-306. [PMID: 31860791 DOI: 10.1002/cam4.2802] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
46 Taylor-Weiner A, Pokkalla H, Han L, Jia C, Huss R, Chung C, Elliott H, Glass B, Pethia K, Carrasco-Zevallos O, Shukla C, Khettry U, Najarian R, Taliano R, Subramanian GM, Myers RP, Wapinski I, Khosla A, Resnick M, Montalto MC, Anstee QM, Wong VW, Trauner M, Lawitz EJ, Harrison SA, Okanoue T, Romero-Gomez M, Goodman Z, Loomba R, Beck AH, Younossi ZM. A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH. Hepatology 2021;74:133-47. [PMID: 33570776 DOI: 10.1002/hep.31750] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
47 Leo P, Janowczyk A, Elliott R, Janaki N, Bera K, Shiradkar R, Farré X, Fu P, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Eklund L, Jambor I, Merisaari H, Ettala O, Taimen P, Aronen HJ, Boström PJ, Tewari A, Magi-Galluzzi C, Klein E, Purysko A, Nc Shih N, Feldman M, Gupta S, Lal P, Madabhushi A. Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ Precis Oncol 2021;5:35. [PMID: 33941830 DOI: 10.1038/s41698-021-00174-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
48 Peyster EG, Arabyarmohammadi S, Janowczyk A, Azarianpour-Esfahani S, Sekulic M, Cassol C, Blower L, Parwani A, Lal P, Feldman MD, Margulies KB, Madabhushi A. An automated computational image analysis pipeline for histological grading of cardiac allograft rejection. Eur Heart J 2021;42:2356-69. [PMID: 33982079 DOI: 10.1093/eurheartj/ehab241] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
49 Rahman A, Jahangir C, Lynch SM, Alattar N, Aura C, Russell N, Lanigan F, Gallagher WM. Advances in tissue-based imaging: impact on oncology research and clinical practice. Expert Rev Mol Diagn 2020;20:1027-37. [PMID: 32510287 DOI: 10.1080/14737159.2020.1770599] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
50 Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein LJ, Davidson NE, Glasgow A, Harbhajanka A, Gilmore H, Madabhushi A. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer 2021;7:104. [PMID: 34362928 DOI: 10.1038/s41523-021-00310-z] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
51 Marble HD, Huang R, Dudgeon SN, Lowe A, Herrmann MD, Blakely S, Leavitt MO, Isaacs M, Hanna MG, Sharma A, Veetil J, Goldberg P, Schmid JH, Lasiter L, Gallas BD, Abels E, Lennerz JK. A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients. J Pathol Inform 2020;11:22. [PMID: 33042601 DOI: 10.4103/jpi.jpi_27_20] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
52 Tian M, He X, Jin C, He X, Wu S, Zhou R, Zhang X, Zhang K, Gu W, Wang J, Zhang H. Transpathology: molecular imaging-based pathology. Eur J Nucl Med Mol Imaging 2021;48:2338-50. [PMID: 33585964 DOI: 10.1007/s00259-021-05234-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
53 Zhao L, Liu C, Xie G, Wu F, Hu C. Multiple Primary Lung Cancers: A New Challenge in the Era of Precision Medicine. Cancer Manag Res 2020;12:10361-74. [PMID: 33116891 DOI: 10.2147/CMAR.S268081] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
54 Rakaee M, Kilvaer TK, Jamaly S, Berg T, Paulsen EE, Berglund M, Richardsen E, Andersen S, Al-Saad S, Poehl M, Pezzella F, Kwiatkowski DJ, Bremnes RM, Busund LR, Donnem T. Tertiary lymphoid structure score: a promising approach to refine the TNM staging in resected non-small cell lung cancer. Br J Cancer 2021;124:1680-9. [PMID: 33723388 DOI: 10.1038/s41416-021-01307-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
55 Diao JA, Wang JK, Chui WF, Mountain V, Gullapally SC, Srinivasan R, Mitchell RN, Glass B, Hoffman S, Rao SK, Maheshwari C, Lahiri A, Prakash A, McLoughlin R, Kerner JK, Resnick MB, Montalto MC, Khosla A, Wapinski IN, Beck AH, Elliott HL, Taylor-Weiner A. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat Commun 2021;12:1613. [PMID: 33712588 DOI: 10.1038/s41467-021-21896-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
56 Rathore S, Niazi T, Iftikhar MA, Chaddad A. Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning. Cancers (Basel) 2020;12:E578. [PMID: 32131409 DOI: 10.3390/cancers12030578] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 9.0] [Reference Citation Analysis]
57 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]
58 Kleppe A, Skrede OJ, De Raedt S, Liestøl K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 2021;21:199-211. [PMID: 33514930 DOI: 10.1038/s41568-020-00327-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
59 Lu C, Bera K, Wang X, Prasanna P, Xu J, Janowczyk A, Beig N, Yang M, Fu P, Lewis J, Choi H, Schmid RA, Berezowska S, Schalper K, Rimm D, Velcheti V, Madabhushi A. A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study. Lancet Digit Health 2020;2:e594-606. [PMID: 33163952 DOI: 10.1016/s2589-7500(20)30225-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
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