Predicting survival and prognosis in early-onset locally advanced colon cancer: a retrospective observational study.

Objective: To predict cancer-specific survival, a refined nomogram model and brand-new risk-stratifying system were established to classify the risk levels of patients with early-onset locally advanced colon cancer (LACC).
Methods: The clinical factors and survival outcomes of LACC cases from the SEER database from 2010 to 2019 were retrieved retrospectively. Early-onset and late-onset colon cancer were grouped according to the age (50 years old) at diagnosis. Differences between groups were compared to identify mutual significant variables. A multivariate Cox regression analysis was further performed and then constructed a nomogram. We compared it with the AJCC-TNM system. The external validation was performed for evaluation. Finally, a risk-stratifying system of patients with early-onset LACC was established.
Results: A total of 32,855 LACC patients were enrolled in, 4548 (13.84%) patients were included in the early-onset LACC group, and 28,307 (86.16%) patients were included in the late-onset LACC group. The external validation set included 228 early-onset LACC patients. Early-onset colon cancers had poorer prognosis (T4, N2, TNM stage III, CEA, tumor deposit, and nerve invasion), and a higher proportion received radiotherapy and systemic therapy (P<0.001). In the survival analysis, cancer-specific survival (CSS) was better in patients with early-onset LACC than in those with late-onset LACC (P <0.001). This nomogram constructed based on the results of COX analysis showed better accuracy in CSS prediction of early-onset LACC patients than AJCC-TNM system in the training set and external validation set (0.783 vs 0.728; 0.852 vs 0.773).
Conclusion: We developed a novel nomogram model to predict CSS in patients with early-onset LACC it provided a reference in prognosis prediction and selection of individualized treatment, helping clinicians in decision-making.
(© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)