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Prognostic Value of Immunoscore in Colorectal Carcinomas.

Helal ImenHmidi AmiraKhanchel FatmaJouini RajaSabbah MariemZaafouri HaithemBen Brahim EhseneChadlidebbiche Aschraf
Published in: International journal of surgical pathology (2023)
Aims. Immunoscore, based on the evaluation of CD3+ and CD8+ densities in the center of the tumor and its invasive margin, is currently considered as a potential prognostic factor, particularly in colorectal carcinomas. In the current study, we aimed to assess the prognostic value of immunoscore in colorectal cancer stage I to IV, through a survival study. Methods and Results. It was a descriptive and retrospective study involving 104 cases of colorectal cancer. Data were collected over 3 years (2014-2016). An immunohistochemical study (anti-CD3, anti-CD8) by the tissue microarray technique was carried out in the areas of "hot spot" in the tumor center and invasive margin. A percentage was assigned to each marker and within each region. Then, the density was classified as "low" or "high," according to a cutoff fixed at the median of percentages. immunoscore was calculated by the method described by Galon et al. The prognostic value of the immunoscore was assessed through a survival study. The mean age of patients was 61.6 years. immunoscore was low in 60.6% (n = 63). Our study had shown that low immunoscore significantly deteriorates survival and a high immunoscore enhances survival significantly ( P  < .001). We found a correlation between immunoscore and T stage ( P  = .026). A multivariate showed that the predictive factors for survival were immunoscore ( P  = .001) and age ( P  = .035). Conclusions. Our study highlights the potential role of immunoscore as a prognostic factor in colorectal cancer. Its reproducibility and reliability allow its introduction into daily practice for better therapeutic management.
Keyphrases
  • prognostic factors
  • healthcare
  • newly diagnosed
  • primary care
  • chronic kidney disease
  • free survival
  • risk assessment
  • deep learning
  • quality improvement
  • high grade
  • machine learning
  • artificial intelligence