Cancers (Feb 2023)

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

  • Bálint Cserni,
  • Darren Kilmartin,
  • Mark O’Loughlin,
  • Xavier Andreu,
  • Zsuzsanna Bagó-Horváth,
  • Simonetta Bianchi,
  • Ewa Chmielik,
  • Paulo Figueiredo,
  • Giuseppe Floris,
  • Maria Pia Foschini,
  • Anikó Kovács,
  • Päivi Heikkilä,
  • Janina Kulka,
  • Anne-Vibeke Laenkholm,
  • Inta Liepniece-Karele,
  • Caterina Marchiò,
  • Elena Provenzano,
  • Peter Regitnig,
  • Angelika Reiner,
  • Aleš Ryška,
  • Anna Sapino,
  • Elisabeth Specht Stovgaard,
  • Cecily Quinn,
  • Vasiliki Zolota,
  • Mark Webber,
  • Sharon A. Glynn,
  • Rita Bori,
  • Erika Csörgő,
  • Orsolya Oláh-Németh,
  • Tamás Pancsa,
  • Anita Sejben,
  • István Sejben,
  • András Vörös,
  • Tamás Zombori,
  • Tibor Nyári,
  • Grace Callagy,
  • Gábor Cserni

DOI
https://doi.org/10.3390/cancers15041199
Journal volume & issue
Vol. 15, no. 4
p. 1199

Abstract

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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.

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