1. Estudio del valor pronóstico del microambiente inmune tumoral en el carcinoma seroso de alto grado de ovario mediante análisis digital de imagen
- Author
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Idoate Gastearena, Miguel Ángel, Ríos Martín, Juan José, Universidad de Sevilla. Departamento de Citología e Histología Normal y Patológica, Machuca Aguado, Jesús, Idoate Gastearena, Miguel Ángel, Ríos Martín, Juan José, Universidad de Sevilla. Departamento de Citología e Histología Normal y Patológica, and Machuca Aguado, Jesús
- Abstract
Introduction: High-grade serous ovarian carcinoma is a neoplasm with a bleak prognosis. Despite advancements in therapies, altering the disease trajectory has proven elusive. In this context, immunotherapy emerges as a prospective alternative. Investigation into the tumor immune microenvironment, particularly the characterization and quantification of tumor-infiltrating lymphocytes (TILs), assumes critical significance. Two types of TILs are discerned based on their location within the neoplasm—stromal and intraepithelial. The latter is defined as TILs strongly associated with tumor cords. To precisely quantify TILs, novel methodologies such as digital image analysis are imperative, enabling the derivation of algorithms useful in estimating the prognostic role of TILs, with the potential to identify patients whose tumor microenvironment may be more conducive to immunotherapy. Objectives: Given the aforementioned context, our aim was to quantify the tumor immune microenvironment in a substantial cohort of patients diagnosed with high-grade serous ovarian carcinoma and establish its correlation with the BRCA gene mutation and pertinent oncological parameters. These parameters include the impact of neoadjuvancy, degree of tumor regression, overall survival, and platinum-free interval. To achieve this, original algorithms were devised to discern the prognostic value of TILs based on their location and quantity, with particular emphasis on CD8+ effector T lymphocytes. Materials and Methods: We conducted an analytical observational study involving 76 patients diagnosed with high-grade serous ovarian carcinoma between 2013 and 2022, with updated clinical follow-up. Techniques for quantifying intraepithelial and stromal TILs, encompassing tissue segmentation and cell classification through algorithms based on automatic image application (machine learning), were employed. This comprehensive evaluation was complemented by an immunohistochemical study focusing on CD8+ T lympho
- Published
- 2024