Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors: a pilot study for future algorithmic approach

Jikke J. Rutgers, Tessa Bánki, Ananda van der Kamp, Tomas J. Waterlander, Marijn A. Scheijde-Vermeulen, Marry M. van den Heuvel-Eibrink, Jeroen A.W.M. van der Laak, Marta Fiocco, Annelies M.C. Mavinkurve-Groothuis, Ronald R. de Krijger

Onderzoeksoutput: Bijdrage aan tijdschriftArtikelpeer review

1 Citaat (Scopus)

Samenvatting

Background: Histopathological classification of Wilms tumors determines treatment regimen. Machine learning has been shown to contribute to histopathological classification in various malignancies but requires large numbers of manually annotated images and thus specific pathological knowledge. This study aimed to assess whether trained, inexperienced observers could contribute to reliable annotation of Wilms tumor components for classification performed by machine learning. Methods: Four inexperienced observers (medical students) were trained in histopathology of normal kidneys and Wilms tumors by an experienced observer (pediatric pathologist). Twenty randomly selected scanned Wilms tumor-slides (from n = 1472 slides) were annotated, and annotations were independently classified by both the inexperienced observers and two experienced pediatric pathologists. Agreement between the six observers and for each tissue element was measured using kappa statistics (κ). Results: Pairwise interobserver agreement between all inexperienced and experienced observers was high (range: 0.845–0.950). The interobserver variability for the different histological elements, including all vital tumor components and therapy-related effects, showed high values for all κ-coefficients (> 0.827). Conclusions: Inexperienced observers can be trained to recognize specific histopathological tumor and tissue elements with high interobserver agreement with experienced observers. Nevertheless, supervision by experienced pathologists remains necessary. Results of this study can be used to facilitate more rapid progress for supervised machine learning-based algorithm development in pediatric pathology and beyond.

Originele taal-2Engels
Artikelnummer77
TijdschriftDiagnostic pathology
Volume16
Nummer van het tijdschrift1
DOI's
StatusGepubliceerd - dec. 2021

Vingerafdruk

Duik in de onderzoeksthema's van 'Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors: a pilot study for future algorithmic approach'. Samen vormen ze een unieke vingerafdruk.

Citeer dit