Literature DB >> 29656215

Inter-observer variance and the need for standardization in the morphological classification of myelodysplastic syndrome.

Keiko Sasada1, Noriko Yamamoto1, Hiroki Masuda1, Yoko Tanaka1, Ayako Ishihara1, Yasushi Takamatsu2, Yutaka Yatomi3, Waichiro Katsuda4, Issei Sato5, Hirotaka Matsui6.   

Abstract

In this era of genome medicine, the sub-classification of myeloid neoplasms, including myelodysplastic syndrome (MDS), is now supported by genetic testing in selected cases. However, as the initial suspicion and primary diagnosis of the disease still largely relies on morphological features and numbers of hematopoietic cells, the establishment of a uniform diagnostic basis, especially for cell morphology, is essential. In this study, we collected nearly 100,000 hematopoietic cell images from 499 peripheral blood smear specimens from patients with MDS and used these to evaluate the standardization of morphological classification by medical technologists. The observers in this study ranged between two to eleven for each image, and the images were classified according to MDS criteria through a web-based system. We found considerable inter-observer variance in the assessment of dysplastic features. Observers did not recognize cytoplasmic hypo-granularity unless almost all granules in neutrophils were absent. Pseudo Pelger-Huët anomalies were also often overlooked, except for cells with a very typical "pince-nez" appearance. Taken together, this study suggests a requirement for further standardization in terms of morphological cell classification, and a need for the development of automatic cell classification-supporting devices for the accurate diagnosis of MDS.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cytoplasmic hypo–granularity; Inter-observer variance; Morphological classification; Myelodysplastic syndrome; Pseudo Pelger–Huët anomaly

Mesh:

Year:  2018        PMID: 29656215     DOI: 10.1016/j.leukres.2018.04.003

Source DB:  PubMed          Journal:  Leuk Res        ISSN: 0145-2126            Impact factor:   3.156


  5 in total

1.  Automated bone marrow cytology using deep learning to generate a histogram of cell types.

Authors:  Rohollah Moosavi Tayebi; Youqing Mu; Taher Dehkharghanian; Catherine Ross; Monalisa Sur; Ronan Foley; Hamid R Tizhoosh; Clinton J V Campbell
Journal:  Commun Med (Lond)       Date:  2022-04-20

2.  Utility of clinical comprehensive genomic characterization for diagnostic categorization in patients presenting with hypocellular bone marrow failure syndromes.

Authors:  Piers Blombery; Lucy Fox; Georgina L Ryland; Ella R Thompson; Jennifer Lickiss; Michelle McBean; Satwica Yerneni; David Hughes; Anthea Greenway; Francoise Mechinaud; Erica M Wood; Graham J Lieschke; Jeff Szer; Pasquale Barbaro; John Roy; Joel Wight; Elly Lynch; Melissa Martyn; Clara Gaff; David Ritchie
Journal:  Haematologica       Date:  2021-01-01       Impact factor: 9.941

3.  A geno-clinical decision model for the diagnosis of myelodysplastic syndromes.

Authors:  Nathan Radakovich; Manja Meggendorfer; Luca Malcovati; C Beau Hilton; Mikkael A Sekeres; Jacob Shreve; Yazan Rouphail; Wencke Walter; Stephan Hutter; Anna Galli; Sara Pozzi; Chiara Elena; Eric Padron; Michael R Savona; Aaron T Gerds; Sudipto Mukherjee; Yasunobu Nagata; Rami S Komrokji; Babal K Jha; Claudia Haferlach; Jaroslaw P Maciejewski; Torsten Haferlach; Aziz Nazha
Journal:  Blood Adv       Date:  2021-11-09

4.  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

5.  Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence.

Authors:  Shivam Kalra; H R Tizhoosh; Sultaan Shah; Charles Choi; Savvas Damaskinos; Amir Safarpoor; Sobhan Shafiei; Morteza Babaie; Phedias Diamandis; Clinton J V Campbell; Liron Pantanowitz
Journal:  NPJ Digit Med       Date:  2020-03-10
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.