Literature DB >> 36138135

Artificial intelligence in histopathology: enhancing cancer research and clinical oncology.

Artem Shmatko1,2, Narmin Ghaffari Laleh3, Moritz Gerstung4,5, Jakob Nikolas Kather6,7,8,9.   

Abstract

Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative information from digital histopathology images. AI is expected to reduce workload for human experts, improve the objectivity and consistency of pathology reports, and have a clinical impact by extracting hidden information from routinely available data. Here, we describe how AI can be used to predict cancer outcome, treatment response, genetic alterations and gene expression from digitized histopathology slides. We summarize the underlying technologies and emerging approaches, noting limitations, including the need for data sharing and standards. Finally, we discuss the broader implications of AI in cancer research and oncology.
© 2022. Springer Nature America, Inc.

Entities:  

Year:  2022        PMID: 36138135     DOI: 10.1038/s43018-022-00436-4

Source DB:  PubMed          Journal:  Nat Cancer        ISSN: 2662-1347


  87 in total

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2.  Quantitative analysis of colorectal lesions observed on magnified endoscopy images.

Authors:  Keiichi Onji; Shigeto Yoshida; Shinji Tanaka; Rie Kawase; Yoshito Takemura; Shiro Oka; Toru Tamaki; Bisser Raytchev; Kazufumi Kaneda; Masaharu Yoshihara; Kazuaki Chayama
Journal:  J Gastroenterol       Date:  2011-09-16       Impact factor: 7.527

Review 3.  Cancer as an evolutionary and ecological process.

Authors:  Lauren M F Merlo; John W Pepper; Brian J Reid; Carlo C Maley
Journal:  Nat Rev Cancer       Date:  2006-11-16       Impact factor: 60.716

Review 4.  Moving pan-cancer studies from basic research toward the clinic.

Authors:  Feng Chen; Michael C Wendl; Matthew A Wyczalkowski; Matthew H Bailey; Yize Li; Li Ding
Journal:  Nat Cancer       Date:  2021-09-16

Review 5.  Evolution of the cancer genome.

Authors:  Lucy R Yates; Peter J Campbell
Journal:  Nat Rev Genet       Date:  2012-10-09       Impact factor: 53.242

6.  Pan-cancer image-based detection of clinically actionable genetic alterations.

Authors:  Alexander T Pearson; Tom Luedde; Jakob Nikolas Kather; Lara R Heij; Heike I Grabsch; Chiara Loeffler; Amelie Echle; Hannah Sophie Muti; Jeremias Krause; Jan M Niehues; Kai A J Sommer; Peter Bankhead; Loes F S Kooreman; Jefree J Schulte; Nicole A Cipriani; Roman D Buelow; Peter Boor; Nadi-Na Ortiz-Brüchle; Andrew M Hanby; Valerie Speirs; Sara Kochanny; Akash Patnaik; Andrew Srisuwananukorn; Hermann Brenner; Michael Hoffmeister; Piet A van den Brandt; Dirk Jäger; Christian Trautwein
Journal:  Nat Cancer       Date:  2020-07-27

7.  Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes.

Authors:  Stefan C Dentro; Ignaty Leshchiner; Kerstin Haase; Maxime Tarabichi; Jeff Wintersinger; Amit G Deshwar; Kaixian Yu; Yulia Rubanova; Geoff Macintyre; Jonas Demeulemeester; Ignacio Vázquez-García; Kortine Kleinheinz; Dimitri G Livitz; Salem Malikic; Nilgun Donmez; Subhajit Sengupta; Pavana Anur; Clemency Jolly; Marek Cmero; Daniel Rosebrock; Steven E Schumacher; Yu Fan; Matthew Fittall; Ruben M Drews; Xiaotong Yao; Thomas B K Watkins; Juhee Lee; Matthias Schlesner; Hongtu Zhu; David J Adams; Nicholas McGranahan; Charles Swanton; Gad Getz; Paul C Boutros; Marcin Imielinski; Rameen Beroukhim; S Cenk Sahinalp; Yuan Ji; Martin Peifer; Inigo Martincorena; Florian Markowetz; Ville Mustonen; Ke Yuan; Moritz Gerstung; Paul T Spellman; Wenyi Wang; Quaid D Morris; David C Wedge; Peter Van Loo
Journal:  Cell       Date:  2021-04-07       Impact factor: 41.582

8.  Unraveling the cartography of the cancer ecosystem.

Authors:  Roy Rabbie; Doreen Lau; Richard M White; David J Adams
Journal:  Genome Biol       Date:  2021-03-24       Impact factor: 13.583

Review 9.  The causes and consequences of genetic heterogeneity in cancer evolution.

Authors:  Rebecca A Burrell; Nicholas McGranahan; Jiri Bartek; Charles Swanton
Journal:  Nature       Date:  2013-09-19       Impact factor: 49.962

10.  Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.

Authors:  Humayun Irshad; Sepehr Jalali; Ludovic Roux; Daniel Racoceanu; Lim Joo Hwee; Gilles Le Naour; Frédérique Capron
Journal:  J Pathol Inform       Date:  2013-03-30
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