Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.

OBJECTIVE: Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools. DESIGN: We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes. RESULTS: Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated 'DGMuneS', outperformed Immunoscore when used in estimating patients' prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk. CONCLUSION: These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients' prognosis.

Assessment of water enema computed tomography: an effective imaging technique for the diagnosis of colon cancer: Colon cancer: computed tomography using a water enema.

AIM: The aim of this study was to assess the accuracy of water enema computed tomography (WECT) for the diagnosis of colon cancer. METHODS: A total of 191 patients referred for clinically suspected colon cancer were prospectively evaluated by WECT in a multicenter trial. Examination was contrast enhanced helical CT after colon filling through a rectal tube. For all the cases, final diagnosis was obtained by colonoscopy and/or surgery. CT data were interpreted both locally and at a centralized site by a specialized and general radiologist. RESULTS: Seventy-one patients were diagnosed with colon cancer. Overall, WECT sensitivity and specificity were 98.6 and 95.0%, respectively. Positive and negative predictive values were 92.1 and 99.1%, respectively. In a subgroup of 33 patients with unclean bowel, the sensitivity and specificity of WECT were 95.0 and 92.3%, respectively. The correlation between local radiologists and the specialized radiologist was excellent (Kappa = 0.87) as was the correlation between the general radiologist and the specialist (Kappa = 0.92). CONCLUSION: This prospective analysis demonstrates that WECT is an effective, safe, and simple imaging technique for the diagnosis of colon cancer and can be proposed when a strong clinical suspicion of colon cancer is present, especially in frail patients.