3574 Background: We used artificial intelligence to perform tissue classification and count CD3 and CD8 in each subclass and determined their role in outcome prediction in PETACC8 cohort of stage III colon cancer treated with FOLFOX or FOLFOX plus cetuximab. Methods: We developed artificial intelligence aimed to detect tumor, healthy mucosa, stroma and immune cells on whole slide of CD3 and CD8 staining. The invasive margin (IM) was also automatically determined. Using a lasso algorithm, the software was able to detect digital parameters within the tumor core (TC) which were related to patients’ outcome (variable called DGMate for DiGital tuMor pArameTErs). CD3 and CD8 lymphocytes density were also quantified automatically by the software in TC and at IM. Associations with disease-free survival (DFS) were evaluated by multivariable Cox regression adjusting for age, T/N stage, sidedness, KRAS/BRAF, DNA mismatch repair (MMR). Results: On 1220 samples collected, data could be generated for 1018 patients. We observed that a high IM stromal area and a high DGMate were associated with a poorer DFS [HR 5.65 (95% CI, 2.34, 13.67), p < 0.0001; HR 2.72 (95% IC, 1.92, 3.85), p<0.001 respectively for the continuous variable]. A higher density of CD3+ TC, CD3+ IM and CD8+ TC were significantly associated with a longer DFS (HR 0.75 (95% IC, .66, .87), p<0.0001; HR 0.78 (95% IC, .68, .88), p<0.0001; HR 0.83 (95% IC, .71, .96), p=0.01). All these immune variables were significantly correlated with each other. ANOVA test demonstrated that CD3+ TC gave a similar prognostic value compared to the classical CD3/CD8 immunoscore (p=0.44). The combination of IM stromal area, DGMate and CD3 outperformed the classical CD3/CD8 immunoscore to estimate patients’ prognosis (C-index= 0.601 vs 0.578, p-value=0.04). Adding this new variable to classical clinical prognostic parameters we generated a nomogram which predicted the risk of relapse of stage III colon cancer with a stronger predictive value compared to clinical parameters or the immunoscore. Conclusions: We propose a new fully automated method of whole slide analysis using a software based on artificial intelligence which classify tissue and determine tumor and immune parameters on one single slide stained with CD3 antibody. This valuable strategy outperforms immunoscore and clinical outcome prediction models.
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