A postsurgical prognostic nomogram for patients with lymph node positive rectosigmoid junction adenocarcinoma

Data from PLN-RSJCs patients were selected from the SEER database using the SEER*Stat software version 8.4.0 (www.​seer.​cancer.​gov/​seerstat). Patients were selected based on the third edition of the International Classification of Diseases for Oncology (ICD-O-3). Inclusion criteria comprised: surgery performed, including sigmoidectomy, prerectotomy or proctosigmoidectomy; histologic type: adenocarcinoma M8140/3(morphological coding for adenocarcinoma diagnosis); and positive regional nodes ≥ 1. Exclusion criteria were as follows: race unknown, marital status unknown; radiation unknown; summary stage unknown; tumor size unknown; Tx; Nx; M1 and Mx. According to the above criteria, we included 3348 eligible PLN-RSJCs patients in our retrospective study. The data were then randomly split into development (n = 2344) and validation (n = 1004) cohort at a ratio of 7:3 (Fig. 1).

The variables in the selected cohort included: age, sex, race, marital, AJCC stage (7th), T stage, N stage, summary stage, radiotherapy, chemotherapy, tumor size, examined and positive regional nodes. To facilitate the analysis, several continuous variables (age, tumor size, examined regional nodes, and positive regional nodes positive) were transformed into categorical variables using the X-Tile software (Yale School of Medicine, New Haven, CT, USA), which calculates the best cutoff of the continuous variables: age (< 45, 45–64, 65–84, and ≥ 85), tumor size (≤ 3.7 cm, 3.8–5.6 cm, ≥ 5.7 cm), examined regional nodes (1–12, 13–16, ≥ 17), positive regional nodes (1–3, 4–8, ≥ 9). Sex was divided into female and male, and race included White, Black, Asian or pacific islander, and American Indian or Alaska native. We defined marital as single, married, divorced (or separated), widowed and unmarried or domestic partner.

To assess the performance of the model, we used concordance index (C-index) and receiver operating characteristic (ROC) curves with the calculated area under the curve (AUC). Furthermore, calibration plots were used to evaluate the consistency of predicted and actual survival time at 1-year, 3-year and 5-year points in time. The clinical applicability and benefits of the prediction model were estimated using decision curve analyses (DCA). Finally, the development cohort was divided in two risk groups, based on the respective total points. The Kaplan–Meier method, combined with the log-rank test, was applied to analyze the differences in the OS between the low- and high-risk groups. Statistical analysis was performed with the SPSS 22.0 (IBM Corp, Armonk, NY) and R version 4.2.0 software.

The PLN-RSJCs patients (n = 3348) from the SEER database were randomly divided at a ratio of 7:3 into development (n = 2344) and validation (n = 1004) cohort. A summary of the demographic and clinicopathological characteristics of the study population is presented in Table 1. No significant differences were observed in any of the considered parameters between the two cohorts.

Univariate and multivariate analyses were applied to extract independent prognostic factors from the development cohort. The results revealed that age, marital, chemotherapy, AJCC stage, T stage, N stage, tumor size, number of examined regional nodes were independent prognostic factors for PLN-RSJCs patients (Table 2).

Based on the previous results of multivariate analysis in the development cohort, we integrated these independent prognostic factors to establish a nomogram model for OS prediction in PLN-RSJCs (Fig. 2). Each variable in the nomogram was assigned a corresponding score from 0 to 100, based on the contribution to the nomogram model (Table 3). Therefore, for each patient we obtained a total number of points by adding the scores in each subgroup. By this method, we were able to predict the possibility of 1-year, 3 year, and 5-year OS. Higher scores were negatively associated with patient prognosis.

In the development cohort, the calculated C-index of the generated nomogram for patient OS was 0.751 (0.737–0.765), which was more significant than that of the 7th AJCC stage 0.681 (0.665–0.697). Additionally, the performances of the nomogram were assessed by ROC curves, with AUC values of 0.845, 0.808 and 0.800 for 1-year, 3-year and 5-year OS, respectively (Fig. 3 A). Moreover, calibration plots for 1-year, 3-year and 5-year OS in the development cohort, confirmed the correlation between actual observations and predicted outcomes (Fig. 4 A-C). Decision curve analysis further showed that this nomogram prediction model performs better in terms of clinical prediction than of the 7th AJCC staging system (Fig. 5). In addition, an internal verification of the nomogram was performed in the validation cohort to evaluate its applicability. In this cohort, the calculated C-index was 0.750 (0.764–0.736), with AUC values of 0.815, 0.833 and 0.814 for 1-year, 3-year and 5-year OS, respectively (Fig. 3 B). As previously observed for the development cohort, the calibration curve confirmed the positive correlation between nomogram prediction and actual patient outcome (Fig. 4 D-F).

The development cohort was stratified in two subgroups, according to the obtained score: low risk group: score < 94 points; high risk group: score ≥ 94 points. Kaplan–Meier survival curves showed a significant difference in OS between the two groups (P < 0.05; Fig. 6).