Early-onset locally advanced rectal cancer characteristics, a practical nomogram and risk stratification system: a population-based study - Su, Yang; Yang, Da Shuai; Li, Yan qi; Qin, Jichao; Liu, Lu (Frontiers in Oncology, 2023)
Background: The purpose of this study is to construct a novel and practical nomogram and risk stratification system to accurately predict cancer-specific survival (CSS) of early-onset locally advanced rectal cancer (EO-LARC) patients. Methods:  In total, 2440 patients diagnosed with EO-LARC between 2010 and 2019 were screened from the Surveillance, Epidemiology, and End Results (SEER). The pool of potentially eligible patients was randomly split into two groups, a training cohort (N =1708) and a validation cohort (N =732). The nomogram was identified and calibrated applying the coherence index(C-index), subject operating characteristic curve (ROC), calibration curves and decision curves (DCA). A new risk classification system was established based on the nomogram. To compare the performance of this nomogram to that of the American Joint Committee on Cancer (AJCC)staging system, DCA, net reclassification index (NRI), and integrated discrimination improvement (IDI) were employed. Results：Seven variables were included in the model. The area under the ROC curve (AUC) for the training cohort was 0.766, 0.736, and 0.731 at 3, 6, and 9 years, respectively. Calibration plots displayed good consistency between actual observations and the nomogram's predictions. The DCA curve further demonstrated the validity of the nomination form in clinical practice. Based on the scores of the nomogram, all patients were divided into a low-risk group, a middle-risk group, and a high-risk group. NRI for the 3-, 6-, and 9-year CSS(training cohort: 0.48, 0.45, 0.52; validation cohort: 0.42, 0.37, 0.37), IDI for the 3-, 6-, and 9-year CSS (training cohort: 0.09, 0.10, 0.11; validation cohort: 0.07, 0.08, 0.08). The Kaplan-Meier curve revealed that the new risk classification system possesses a more extraordinary ability to identify patients in different risk groups than the AJCC staging. Conclusion: A practical prognostic nomogram and novel risk classification system can efficiently predict the prognosis of EO-LARC and serve as a tool to guide the individualization of patient treatment.