Multicentric evaluation of the variability of digital morphology performances also respect to the reference methods by optical microscopy
© 2022 John Wiley & Sons Ltd..
INTRODUCTION: Despite the important diagnostic role of peripheral blood morphology, cell classification is subjective. Automated image-processing systems (AIS) provide more accurate and objective morphological evaluation. The aims of this multicenter study were the evaluation of the intra and inter-laboratory variation between different AIS in cell pre-classification and after reclassification, compared with manual optical microscopy, the reference method.
METHODS: Six peripheral blood samples were included in this study, for each sample, 70 May-Grunwald and Giemsa stained PB smears were prepared from each specimen and 10 slides were delivered to the seven laboratories involved. Smears were processed by both optical microscopy (OM) and AIS. In addition, the assessment times of both methods were recorded.
RESULTS: Within-laboratory Reproducibility ranged between 4.76% and 153.78%; between-laboratory Precision ranged between 2.10% and 82.2%, while Total Imprecision ranged between 5.21% and 20.60%. The relative Bland Altman bias ranged between -0.01% and 20.60%. The mean of assessment times were 326 ± 110 s and 191 ± 68 s for AIS post reclassification and OM, respectively.
CONCLUSIONS: AIS can be helpful when the number of cell counted are low and can give advantages in terms of efficiency, objectivity and time saving in the morphological analysis of blood cells. They can also help in the interpretation of some morphological features and can serve as learning and investigation tools.
| Errataetall: |
ErratumIn: Int J Lab Hematol. 2023 Jun;45(3):403. - PMID 36872617 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:44 |
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| Enthalten in: |
International journal of laboratory hematology - 44(2022), 6 vom: 02. Dez., Seite 1040-1049 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Da Rin, Giorgio [VerfasserIn] |
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| Links: |
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| Themen: |
Automated image-processing systems |
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| Anmerkungen: |
Date Completed 18.11.2022 Date Revised 06.03.2023 published: Print-Electronic ErratumIn: Int J Lab Hematol. 2023 Jun;45(3):403. - PMID 36872617 Citation Status MEDLINE |
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| doi: |
10.1111/ijlh.13943 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a ErratumIn: Int J Lab Hematol. 2023 Jun;45(3):403. - PMID 36872617 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022 John Wiley & Sons Ltd. | ||
| 520 | |a INTRODUCTION: Despite the important diagnostic role of peripheral blood morphology, cell classification is subjective. Automated image-processing systems (AIS) provide more accurate and objective morphological evaluation. The aims of this multicenter study were the evaluation of the intra and inter-laboratory variation between different AIS in cell pre-classification and after reclassification, compared with manual optical microscopy, the reference method | ||
| 520 | |a METHODS: Six peripheral blood samples were included in this study, for each sample, 70 May-Grunwald and Giemsa stained PB smears were prepared from each specimen and 10 slides were delivered to the seven laboratories involved. Smears were processed by both optical microscopy (OM) and AIS. In addition, the assessment times of both methods were recorded | ||
| 520 | |a RESULTS: Within-laboratory Reproducibility ranged between 4.76% and 153.78%; between-laboratory Precision ranged between 2.10% and 82.2%, while Total Imprecision ranged between 5.21% and 20.60%. The relative Bland Altman bias ranged between -0.01% and 20.60%. The mean of assessment times were 326 ± 110 s and 191 ± 68 s for AIS post reclassification and OM, respectively | ||
| 520 | |a CONCLUSIONS: AIS can be helpful when the number of cell counted are low and can give advantages in terms of efficiency, objectivity and time saving in the morphological analysis of blood cells. They can also help in the interpretation of some morphological features and can serve as learning and investigation tools | ||
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a automated image-processing systems | |
| 650 | 4 | |a between-laboratory precision | |
| 650 | 4 | |a digital morphology | |
| 650 | 4 | |a optical microscopy | |
| 650 | 4 | |a within-laboratory reproducibility | |
| 700 | 1 | |a Seghezzi, Michela |e verfasserin |4 aut | |
| 700 | 1 | |a Padoan, Andrea |e verfasserin |4 aut | |
| 700 | 1 | |a Pajola, Rachele |e verfasserin |4 aut | |
| 700 | 1 | |a Bengiamo, Anna |e verfasserin |4 aut | |
| 700 | 1 | |a Di Fabio, Anna Maria |e verfasserin |4 aut | |
| 700 | 1 | |a Dima, Francesco |e verfasserin |4 aut | |
| 700 | 1 | |a Fanelli, Alessandra |e verfasserin |4 aut | |
| 700 | 1 | |a Francione, Sara |e verfasserin |4 aut | |
| 700 | 1 | |a Germagnoli, Luca |e verfasserin |4 aut | |
| 700 | 1 | |a Lorubbio, Maria |e verfasserin |4 aut | |
| 700 | 1 | |a Marzoni, Alessandro |e verfasserin |4 aut | |
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