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

Diagnostic Pathology

Jeroen A. W. M. van der Laak¹  Annelies M. C. Mavinkurve-Groothuis²  Jikke J. Rutgers²  Marry M. van den Heuvel-Eibrink²  Marijn A. Scheijde-Vermeulen²  Ronald R. de Krijger³  Marta Fiocco⁴  Ananda van der Kamp⁵  Tomas J. Waterlander⁶  Tessa Bánki⁶ 

[1] Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands;Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands;Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands;Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands;Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands;Mathematical Institute Leiden University, Leiden, The Netherlands;Medical Statistics, Biomedical Data Science Department, Leiden University Medical Center, Leiden, The Netherlands;Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands;University of Groningen, Groningen, The Netherlands;Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands;Utrecht University, Utrecht, The Netherlands;

关键词: Wilms tumor;    Interobserver variability;    Machine learning;    Histopathology;    Classification;    AI (artificial intelligence);   

DOI  :  10.1186/s13000-021-01136-w

来源: Springer

【 摘 要 】

BackgroundHistopathological 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.MethodsFour 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).ResultsPairwise 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).ConclusionsInexperienced 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.

【 授权许可】

CC BY