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For: 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: 9] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Sandeman K, Blom S, Koponen V, Manninen A, Juhila J, Rannikko A, Ropponen T, Mirtti T. AI Model for Prostate Biopsies Predicts Cancer Survival. Diagnostics 2022;12:1031. [DOI: 10.3390/diagnostics12051031] [Reference Citation Analysis]
2 Liu H, Zhao Y, Yang F, Lou X, Wu F, Li H, Xing X, Peng T, Menze B, Huang J, Zhang S, Han A, Yao J, Fan X. Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning. BME Frontiers 2022;2022:1-12. [DOI: 10.34133/2022/9860179] [Reference Citation Analysis]
3 Jennings CN, Humphries MP, Wood S, Jadhav M, Chabra R, Brown C, Chan G, Kaye D, Bansal D, Colquhoun C, Merzouki N, Arumugam P, Westhead DR, Treanor D. Bridging the gap with the UK Genomics Pathology Imaging Collection. Nat Med 2022. [PMID: 35534568 DOI: 10.1038/s41591-022-01798-z] [Reference Citation Analysis]
4 Jiménez-sánchez D, Ariz M, Chang H, Matias-guiu X, de Andrea CE, Ortiz-de-solórzano C. NaroNet: discovery of tumor microenvironment elements from highly multiplexed images. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102384] [Reference Citation Analysis]
5 Li J, Garfinkel J, Zhang X, Wu D, Zhang Y, de Haan K, Wang H, Liu T, Bai B, Rivenson Y, Rubinstein G, Scumpia PO, Ozcan A. Biopsy-free in vivo virtual histology of skin using deep learning. Light Sci Appl 2021;10:233. [PMID: 34795202 DOI: 10.1038/s41377-021-00674-8] [Reference Citation Analysis]
6 Waljee AK, Weinheimer-Haus EM, Abubakar A, Ngugi AK, Siwo GH, Kwakye G, Singal AG, Rao A, Saini SD, Read AJ, Baker JA, Balis U, Opio CK, Zhu J, Saleh MN. Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa. Gut 2022;71:1259-65. [PMID: 35418482 DOI: 10.1136/gutjnl-2022-327211] [Reference Citation Analysis]
7 Mungenast F, Fernando A, Nica R, Boghiu B, Lungu B, Batra J, Ecker RC. Next-Generation Digital Histopathology of the Tumor Microenvironment. Genes (Basel) 2021;12:538. [PMID: 33917241 DOI: 10.3390/genes12040538] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Bilal M, Raza SEA, Azam A, Graham S, Ilyas M, Cree IA, Snead D, Minhas F, Rajpoot NM. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit Health 2021;3:e763-72. [PMID: 34686474 DOI: 10.1016/S2589-7500(21)00180-1] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Ringborg U, Berns A, Celis JE, Heitor M, Tabernero J, Schüz J, Baumann M, Henrique R, Aapro M, Basu P, Beets-Tan R, Besse B, Cardoso F, Carneiro F, van den Eede G, Eggermont A, Fröhling S, Galbraith S, Garralda E, Hanahan D, Hofmarcher T, Jönsson B, Kallioniemi O, Kásler M, Kondorosi E, Korbel J, Lacombe D, Carlos Machado J, Martin-Moreno JM, Meunier F, Nagy P, Nuciforo P, Oberst S, Oliveiera J, Papatriantafyllou M, Ricciardi W, Roediger A, Ryll B, Schilsky R, Scocca G, Seruca R, Soares M, Steindorf K, Valentini V, Voest E, Weiderpass E, Wilking N, Wren A, Zitvogel L. The Porto European Cancer Research Summit 2021. Mol Oncol 2021;15:2507-43. [PMID: 34515408 DOI: 10.1002/1878-0261.13078] [Reference Citation Analysis]
10 Amgad M, Atteya LA, Hussein H, Mohammed KH, Hafiz E, Elsebaie MAT, Alhusseiny AM, AlMoslemany MA, Elmatboly AM, Pappalardo PA, Sakr RA, Mobadersany P, Rachid A, Saad AM, Alkashash AM, Ruhban IA, Alrefai A, Elgazar NM, Abdulkarim A, Farag AA, Etman A, Elsaeed AG, Alagha Y, Amer YA, Raslan AM, Nadim MK, Elsebaie MAT, Ayad A, Hanna LE, Gadallah A, Elkady M, Drumheller B, Jaye D, Manthey D, Gutman DA, Elfandy H, Cooper LAD. NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer. Gigascience 2022;11:giac037. [PMID: 35579553 DOI: 10.1093/gigascience/giac037] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
11 Chauhan C, Gullapalli RR. Ethics of AI in Pathology: Current Paradigms and Emerging Issues. Am J Pathol 2021:S0002-9440(21)00303-5. [PMID: 34252382 DOI: 10.1016/j.ajpath.2021.06.011] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Cheng S, Liu S, Yu J, Rao G, Xiao Y, Han W, Zhu W, Lv X, Li N, Cai J, Wang Z, Feng X, Yang F, Geng X, Ma J, Li X, Wei Z, Zhang X, Quan T, Zeng S, Chen L, Hu J, Liu X. Robust whole slide image analysis for cervical cancer screening using deep learning. Nat Commun 2021;12:5639. [PMID: 34561435 DOI: 10.1038/s41467-021-25296-x] [Reference Citation Analysis]
13 Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer 2021. [PMID: 34663944 DOI: 10.1038/s41568-021-00408-3] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Huang K, Lou S, Wang C, Thanawala MS, Turner J, Fink A, Ji L, Sadaghiani M, Huang P, Dai H. DeepNeurite™: Identification of neurites from non‐specific binding of fluorescence probes through deep learning. FASEB BioAdvances. [DOI: 10.1096/fba.2021-00072] [Reference Citation Analysis]
15 Echle A, Laleh NG, Schrammen PL, West NP, Trautwein C, Brinker TJ, Gruber SB, Buelow RD, Boor P, Grabsch HI, Quirke P, Kather JN. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review. ImmunoInformatics 2021;3-4:100008. [DOI: 10.1016/j.immuno.2021.100008] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
16 Amgad M, Atteya L, Hussein H, Mohammed KH, Hafiz E, Elsebaie MAT, Mobadersany P, Manthey D, Gutman DA, Elfandy H, Cooper LAD. Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings. Bioinformatics 2021:btab670. [PMID: 34586355 DOI: 10.1093/bioinformatics/btab670] [Reference Citation Analysis]
17 Schrammen PL, Ghaffari Laleh N, Echle A, Truhn D, Schulz V, Brinker TJ, Brenner H, Chang-Claude J, Alwers E, Brobeil A, Kloor M, Heij LR, Jäger D, Trautwein C, Grabsch HI, Quirke P, West NP, Hoffmeister M, Kather JN. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J Pathol 2021. [PMID: 34561876 DOI: 10.1002/path.5800] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Zhang C, Gu J, Zhu Y, Meng Z, Tong T, Li D, Liu Z, Du Y, Wang K, Tian J. AI in spotting high-risk characteristics of medical imaging and molecular pathology. Precision Clinical Medicine 2021;4:271-86. [DOI: 10.1093/pcmedi/pbab026] [Reference Citation Analysis]
19 Thagaard J, Stovgaard ES, Vognsen LG, Hauberg S, Dahl A, Ebstrup T, Doré J, Vincentz RE, Jepsen RK, Roslind A, Kümler I, Nielsen D, Balslev E. Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers. Cancers (Basel) 2021;13:3050. [PMID: 34207414 DOI: 10.3390/cancers13123050] [Reference Citation Analysis]
20 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: 9] [Article Influence: 3.0] [Reference Citation Analysis]
21 Hauser K, Kurz A, Haggenmüller S, Maron RC, von Kalle C, Utikal JS, Meier F, Hobelsberger S, Gellrich FF, Sergon M, Hauschild A, French LE, Heinzerling L, Schlager JG, Ghoreschi K, Schlaak M, Hilke FJ, Poch G, Kutzner H, Berking C, Heppt MV, Erdmann M, Haferkamp S, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather JN, Fröhling S, Lipka DB, Hekler A, Krieghoff-Henning E, Brinker TJ. Explainable artificial intelligence in skin cancer recognition: A systematic review. Eur J Cancer 2022;167:54-69. [PMID: 35390650 DOI: 10.1016/j.ejca.2022.02.025] [Reference Citation Analysis]
22 Kolmar L, Autour A, Ma X, Vergier B, Eduati F, Merten CA. Technological and computational advances driving high-throughput oncology. Trends in Cell Biology 2022. [DOI: 10.1016/j.tcb.2022.04.008] [Reference Citation Analysis]
23 Diao JA, Chen RJ, Kvedar JC. Efficient cellular annotation of histopathology slides with real-time AI augmentation. NPJ Digit Med 2021;4:161. [PMID: 34811479 DOI: 10.1038/s41746-021-00534-0] [Reference Citation Analysis]