In a study that aimed to develop a predictive nomogram for postoperative recurrence in stage I colorectal cancer (CRC), a predictive nomogram was developed with a total of 1538 stage I CRC patients and internally validated. This nomogram will assist physicians to more accurately identify high-risk patients who need more active surveillance and will help ensure more efficient disease management. Background: Patients with stage I colorectal cancer (CRC) have excellent prognosis after curative surgery. However, approximately 5% to 10% of patients experience recurrence and have a poor prognosis. Because the incidence of stage I CRC is increasing with active screening programs worldwide, a more accurate and easy-to-use predictive tool for recurrence is becoming more important. This study aimed to develop a predictive nomogram for recurrence in stage I CRC. Patients and Methods: A total of 1538 patients who underwent curative surgery for stage I CRC were enrolled. Predictive factors for recurrence were determined by multivariate Cox regression model and were used to develop a predictive nomogram. This model was internally validated, and performance was evaluated through calibration plots. Results: The cumulative recurrence rate at 5 years after surgery for stage I CRC was 5.3%. In multivariate Cox analysis, independent predictors of recurrence were tumor location at rectum, pT2 stage, and presence of lymphovascular invasion. The 5-year recurrence rate was significantly different depending on the number of risk factors (0.7% for 0, 5.8% for 1, and 9.7% for ≥ 2 risk factors). On this basis, a nomogram for recurrence-free survival was developed and internally validated. The concordance index of the nomogram was 0.71, and the performance was acceptable. Conclusion: We developed and internally validated a nomogram that can predict postoperative recurrence in stage I CRC patients. This nomogram may be used to more accurately stratify the risk of recurrence and to perform personalized postoperative surveillance in stage I CRC patients.
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