17 November 2017 Quantifying expert diagnosis variability when grading tumor-infiltrating lymphocytes
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Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057202 (2017) https://doi.org/10.1117/12.2286717
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia
Abstract
Tumor-infiltrating lymphocytes (TILs) have proved to play an important role in predicting prognosis, survival, and response to treatment in patients with a variety of solid tumors. Unfortunately, currently, there are not a standardized methodology to quantify the infiltration grade. The aim of this work is to evaluate variability among the reports of TILs given by a group of pathologists who examined a set of digitized Non-Small Cell Lung Cancer samples (n=60). 28 pathologists answered a different number of histopathological images. The agreement among pathologists was evaluated by computing the Kappa index coefficient and the standard deviation of their estimations. Furthermore, TILs reports were correlated with patient’s prognosis and survival using the Pearson’s correlation coefficient. General results show that the agreement among experts grading TILs in the dataset is low since Kappa values remain below 0.4 and the standard deviation values demonstrate that in none of the images there was a full consensus. Finally, the correlation coefficient for each pathologist also reveals a low association between the pathologists’ predictions and the prognosis/survival data. Results suggest the need of defining standardized, objective, and effective strategies to evaluate TILs, so they could be used as a biomarker in the daily routine.
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Paula Toro, Paula Toro, Germán Corredor, Germán Corredor, Xiangxue Wang, Xiangxue Wang, Viviana Arias, Viviana Arias, Vamsidhar Velcheti, Vamsidhar Velcheti, Anant Madabhushi, Anant Madabhushi, Eduardo Romero, Eduardo Romero, } "Quantifying expert diagnosis variability when grading tumor-infiltrating lymphocytes", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057202 (17 November 2017); doi: 10.1117/12.2286717; https://doi.org/10.1117/12.2286717
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