ONEST (Observers Needed to Evaluate Subjective Tests) Analysis of Stromal Tumour-Infiltrating Lymphocytes (sTILs) in Breast Cancer and Its Limitations

Tumour-infiltrating lymphocytes (TILs) reflect antitumour immunity. Their evaluation of histopathology specimens is influenced by several factors and is subject to issues of reproducibility. ONEST (Observers Needed to Evaluate Subjective Tests) helps in determining the number of observers that would...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Cserni Bálint
Kilmartin Darren
O’Loughlin Mark
Andreu Xavier
Bagó-Horváth Zsuzsanna
Bianchi Simonetta
Chmielik Ewa
Figueiredo Paulo
Oláh-Németh Orsolya
Pancsa Tamás
Sejben Anita
Vörös András
Zombori Tamás
Nyári Tibor
Cserni Gábor
et al
Dokumentumtípus: Cikk
Megjelent: 2023
Sorozat:CANCERS 15 No. 4
Tárgyszavak:
doi:10.3390/cancers15041199

mtmt:33649494
Online Access:http://publicatio.bibl.u-szeged.hu/26550
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520 3 |a Tumour-infiltrating lymphocytes (TILs) reflect antitumour immunity. Their evaluation of histopathology specimens is influenced by several factors and is subject to issues of reproducibility. ONEST (Observers Needed to Evaluate Subjective Tests) helps in determining the number of observers that would be sufficient for the reliable estimation of inter-observer agreement of TIL categorisation. This has not been explored previously in relation to TILs. ONEST analyses, using an open-source software developed by the first author, were performed on TIL quantification in breast cancers taken from two previous studies. These were one reproducibility study involving 49 breast cancers, 23 in the first circulation and 14 pathologists in the second circulation, and one study involving 100 cases and 9 pathologists. In addition to the estimates of the number of observers required, other factors influencing the results of ONEST were examined. The analyses reveal that between six and nine observers (range 2–11) are most commonly needed to give a robust estimate of reproducibility. In addition, the number and experience of observers, the distribution of values around or away from the extremes, and outliers in the classification also influence the results. Due to the simplicity and the potentially relevant information it may give, we propose ONEST to be a part of new reproducibility analyses. 
650 4 |a Klinikai orvostan 
700 0 1 |a Kilmartin Darren  |e aut 
700 0 1 |a O’Loughlin Mark  |e aut 
700 0 1 |a Andreu Xavier  |e aut 
700 0 2 |a Bagó-Horváth Zsuzsanna  |e aut 
700 0 2 |a Bianchi Simonetta  |e aut 
700 0 2 |a Chmielik Ewa  |e aut 
700 0 2 |a Figueiredo Paulo  |e aut 
700 0 2 |a Oláh-Németh Orsolya  |e aut 
700 0 2 |a Pancsa Tamás  |e aut 
700 0 2 |a Sejben Anita  |e aut 
700 0 2 |a Vörös András  |e aut 
700 0 2 |a Zombori Tamás  |e aut 
700 0 2 |a Nyári Tibor  |e aut 
700 0 2 |a Cserni Gábor  |e aut 
700 0 2 |a et al.  |e aut 
856 4 0 |u http://publicatio.bibl.u-szeged.hu/26550/1/Cancers-2023-15-01199-ONESTforTILs.pdf  |z Dokumentum-elérés