Literature DB >> 34027414

Towards artificial intelligence-driven pathology assessment for hematological malignancies.

Olivier Elemento1.   

Abstract

In this issue of Blood Cancer Discovery, Brück et al applied unsupervised and supervised machine learning to bone marrow histopathology images from Myelodysplastic Syndrome (MDS) patients. Their study provides new insights into the pathobiology of MDS and paves the way for increased use of artificial intelligence for the assessment and diagnosis of hematological malignancies.

Entities:  

Year:  2021        PMID： 34027414      PMCID： PMC8133372          DOI： 10.1158/2643-3230.BCD-21-0048

Source DB:  PubMed          Journal:  Blood Cancer Discov        ISSN： 2643-3230


  8 in total

1.  Deep learning-based classification of mesothelioma improves prediction of patient outcome.

Authors:  Pierre Courtiol; Charles Maussion; Françoise Galateau-Sallé; Gilles Wainrib; Thomas Clozel; Matahi Moarii; Elodie Pronier; Samuel Pilcer; Meriem Sefta; Pierre Manceron; Sylvain Toldo; Mikhail Zaslavskiy; Nolwenn Le Stang; Nicolas Girard; Olivier Elemento; Andrew G Nicholson; Jean-Yves Blay
Journal:  Nat Med       Date:  2019-10-07       Impact factor: 53.440

2.  Dermatologist-level classification of skin cancer with deep neural networks.

Authors:  Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal:  Nature       Date:  2017-01-25       Impact factor: 49.962

Review 3.  The Molecular Pathology of Myelodysplastic Syndrome.

Authors:  Torsten Haferlach
Journal:  Pathobiology       Date:  2018-05-23       Impact factor: 3.916

4.  Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images.

Authors:  Pegah Khosravi; Ehsan Kazemi; Marcin Imielinski; Olivier Elemento; Iman Hajirasouliha
Journal:  EBioMedicine       Date:  2017-12-28       Impact factor: 8.143

5.  Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.

Authors:  Nicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; Navneet Narula; Matija Snuderl; David Fenyö; Andre L Moreira; Narges Razavian; Aristotelis Tsirigos
Journal:  Nat Med       Date:  2018-09-17       Impact factor: 53.440

6.  Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

Authors:  Yasunobu Nagata; Ran Zhao; Hassan Awada; Cassandra M Kerr; Inom Mirzaev; Sunisa Kongkiatkamon; Aziz Nazha; Hideki Makishima; Tomas Radivoyevitch; Jacob G Scott; Mikkael A Sekeres; Brian P Hobbs; Jaroslaw P Maciejewski
Journal:  Blood       Date:  2020-11-12       Impact factor: 25.476

7.  Assessment of dysplasia in bone marrow smear with convolutional neural network.

Authors:  Jinichi Mori; Shizuo Kaji; Hiroki Kawai; Satoshi Kida; Masaharu Tsubokura; Masahiko Fukatsu; Kayo Harada; Hideyoshi Noji; Takayuki Ikezoe; Tomoya Maeda; Akira Matsuda
Journal:  Sci Rep       Date:  2020-09-07       Impact factor: 4.379

8.  Diagnosis and treatment of primary myelodysplastic syndromes in adults: recommendations from the European LeukemiaNet.

Authors:  Luca Malcovati; Eva Hellström-Lindberg; David Bowen; Lionel Adès; Jaroslav Cermak; Consuelo Del Cañizo; Matteo G Della Porta; Pierre Fenaux; Norbert Gattermann; Ulrich Germing; Joop H Jansen; Moshe Mittelman; Ghulam Mufti; Uwe Platzbecker; Guillermo F Sanz; Dominik Selleslag; Mette Skov-Holm; Reinhard Stauder; Argiris Symeonidis; Arjan A van de Loosdrecht; Theo de Witte; Mario Cazzola
Journal:  Blood       Date:  2013-08-26       Impact factor: 22.113
  8 in total

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