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Proceedings Paper

Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer receiving adjuvant chemotherapy
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Paper Abstract

The presence of tumor-infiltrating lymphocytes (TILs) is correlated with outcome and prognosis in epithelial ovarian cancer (EOC). In this study, automated image analysis was used to analyze the association between overall survival (OS) and TIL spatial arrangement and density in a multi-site cohort of 102 EOC patients who received adjuvant chemotherapy following debulking surgery. Features of the spatial arrangement of TILs (SpaTIL) were used to quantify the spatial co-localization of TILs and tumor cells on digitized pathology slides of the malignant neoplasm of excised specimens. A multivariable Cox regression model of SpaTIL features was fit on the n1 = 51 patient training set and was evaluated in the n2 = 51 patient validation set. The SpaTIL signature was significantly associated with OS, both in the training set (hazard ratio (HR) = 2.81, 95% confidence interval (CI) = 1.33 − 5.92, and p = 0.003) and the validation set (HR = 2.06, 95% CI = 1.04 − 4.07, and p = 0.008). In addition, fusing our spaTIL risk score and the clinical staging further improved the results of the predictive model (HR = 4.045, 95% CI = 4.11−5.41, and p = 0.0002 in the validation set) and outperformed clinical staging alone. This finding illustrates that a spaTIL risk score is not only able to predict OS independent of clinical data, but also offers prognostic value complementary to current clinical standard-of-care. Patients with longer survival times had significantly higher heterogeneity of non-TIL cluster area, while shorter time survivors had mostly same-sized, evenly-distributed non-TIL clusters and smaller average TIL cluster area. These findings suggest that dispersion of TILs throughout the tumor is associated with better treatment response to post-treatment adjuvant chemotherapy, and therefore longer survival time.

Paper Details

Date Published: 16 March 2020
PDF: 10 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200Q (16 March 2020); doi: 10.1117/12.2550188
Show Author Affiliations
Sepideh Azarianpour, Case Western Reserve Univ. (United States)
Germán Corredor, Case Western Reserve Univ. (United States)
Kaustav Bera, Case Western Reserve Univ. (United States)
Patrick Leo, Case Western Reserve Univ. (United States)
Nathaniel Braman, Case Western Reserve Univ. (United States)
Pingfu Fu, Case Western Reserve Univ. (United States)
Haider Mahdi, Cleveland Clinic (United States)
Anant Madabhushi, Western Reserve Univ. (United States)
Louis Stokes Cleveland Veterans Administration Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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