A lymphocyte spatial distribution graph-based method for automated classification of recurrence risk on lung cancer images

17 November 2017 A lymphocyte spatial distribution graph-based method for automated classification of recurrence risk on lung cancer images

Juan D. Garciá-Arteaga, Germán Corredor, Xiangxue Wang, Vamsidhar Velcheti, Anant Madabhushi, Eduardo Romero

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Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057203 (2017) https://doi.org/10.1117/12.2285653
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

Abstract

Tumor-infiltrating lymphocytes occurs when various classes of white blood cells migrate from the blood stream towards the tumor, infiltrating it. The presence of TIL is predictive of the response of the patient to therapy. In this paper, we show how the automatic detection of lymphocytes in digital H and E histopathological images and the quantitative evaluation of the global lymphocyte configuration, evaluated through global features extracted from non-parametric graphs, constructed from the lymphocytes’ detected positions, can be correlated to the patient’s outcome in early-stage non-small cell lung cancer (NSCLC). The method was assessed on a tissue microarray cohort composed of 63 NSCLC cases. From the evaluated graphs, minimum spanning trees and K-nn showed the highest predictive ability, yielding F1 Scores of 0.75 and 0.72 and accuracies of 0.67 and 0.69, respectively. The predictive power of the proposed methodology indicates that graphs may be used to develop objective measures of the infiltration grade of tumors, which can, in turn, be used by pathologists to improve the decision making and treatment planning processes.

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Juan D. Garciá-Arteaga, Germán Corredor, Xiangxue Wang, Vamsidhar Velcheti, Anant Madabhushi, and Eduardo Romero "A lymphocyte spatial distribution graph-based method for automated classification of recurrence risk on lung cancer images", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057203 (17 November 2017); https://doi.org/10.1117/12.2285653

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