A Postsurgical Prognostic Nomogram for Patients with Lymph Node Positive Rectosigmoid Junction Adenocarcinoma

doi:10.21203/rs.3.rs-2006874/v1

Download PDF

Research Article

A Postsurgical Prognostic Nomogram for Patients with Lymph Node Positive Rectosigmoid Junction Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-2006874/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 18 May, 2023

Read the published version in BMC Gastroenterology →

You are reading this latest preprint version

Objective: The definition of rectosigmoid junction (RSJ) is still in debate. The treatment and prognosis of patients with rectosigmoid junction cancer (RSJC) and positive lymph nodes (PLN-RSJCs) are mostly based on the American Joint Committee on Cancer (AJCC) staging system. Our study aims to assist clinicians in creating a more intuitive and accurate nomogram model for PLN-RSJCs for the prediction of patient overall survival (OS) after surgery.

Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, we extracted 3384 patients with PLN-RSJCs and randomly divided them into development (n=2344) and validation (n=1004) cohorts at a radio of 7:3. Using univariate and multivariate COX regression analysis, we identified independent risk factors associated with OS in PLN-RSJCs in the development cohort, which were further used to establish a nomogram model. To verify the accuracy of the model, the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and an internal validation cohort have been employed. Decision curve analysis (DCA) was used to assess the clinical applicability and benefits of the generated model. Survival curves of the low- and high-risk groups were calculated using the Kaplan–Meier method together with the log-rank test.

Results: Age, marital, chemotherapy, AJCC stage, T and N stage of TNM system, tumor size, and regional lymph nodes were selected as independent risk factors and included in the nomogram model. The C-index of this nomogram in the development (0.751;0.737-0.765) and validation cohorts (0.750;0.764-0.736) were more significant than that of the AJCC 7th staging system (0.681; 0.665-0.697). The ROC curve with the calculated area under the curve (AUC) in the development cohort was 0.845,0.808 and 0.800 for 1-year, 3-year and 5-year OS, AUC in the validation cohort was 0.815,0.833 and 0.814 for 1-year, 3-year and 5-year, respectively. The calibration plots of both cohorts for 1-year,3-year and 5-year OS all demonstrated good agreement between actual clinical observations and predicted outcomes. In the development cohort, the DCA showed that the nomogram prediction model is more advantageous for clinical application than the AJCC 7th staging system. Kaplan-Meier curves in the low and high groups showed significant differences in patient OS.

Conclusions: We established an accurate nomogram model for PLN-RSJCs, intended to support clinicians in the treatment and follow-up of patients.

According to the 2022 Cancer Statistics Report, about 609,360 people died from cancer in the United States. Colorectal cancer is the second leading cause of death, after lung cancer [1]. Roughly, 140 people die of colorectal cancer every day in the United States. [1]. The rectosigmoid junction (RSJ) is the connection between the sigmoid colon and the upper rectum [2, 3]. However, its exact definition is still debatable [4]. Anatomically, the RSJ is the tissue at the end of the sigmoid colon [5], yet some experts still prefer to attribute RSJ to the rectum, as it shares blood vessels and other support with the upper rectum [6]. In general, RSJ is usually attributed to the upper rectum, according to the AJCC staging system [7]. Due to the controversial location of RSJ and the relative rarity RSJ adenocarcinoma (RSJC), relevant clinical data is scarce, making its treatment difficult and patient prognosis hard to predict. The purpose of our study is to generate a reliable RSJC risk prediction model, that supports the prediction of the prognosis of RSJC patients with positive lymph nodes (PLN-RSJCs) after surgery.

The nomogram model is a digital simulation tool widely used in clinical research and its results can be recognized by the public [8–10]. By scoring each single predictor, total scores can be obtained in different stages of the disease, enabling the prognosis prediction in a more intuitive manner [11]. A model for the perioperative treatment of locally advanced rectosigmoid colon has been previously established by Chao Zhang and colleagues [12]. This study showed that the current perioperative neoadjuvant therapy for rectal cancer is different from that of RSJC, and proved that lymph node positivity is an important factor in the selection of treatment for patients with locally advanced RSJC [12].

The Surveillance, Epidemiology, and End Results (SEER) database, established by the National Cancer Institute (NCI), collects data on cancer diagnosis, treatment, and survival in the U.S. population. On average, around 400,000 cancer cases are collected each year, providing researchers with a large amount of retrospective data. The SEER medical database has been use in clinical research aiming at improving the outcome of colorectal cancer patients [13].

To the best of our knowledge, this is the first study to establish a prognostic model for PLN-RSJCs after surgery.

Patient selection and data processing

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).

Variables defined

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 (≤ 3.7cm, 3.8-5.6cm, ≥ 5.7cm), 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.

Statistical analysis

For the analysis of continuous and categorical variables in both development and validation cohort, the T and Chi-square tests were used. Univariate COX regression was used to extract the potential significant predictors in the development cohort. Predictors with p value lower than 0.05 were included in the multivariate COX proportional risk regression model. The independent prognostic factors with p lower 0.05 in the multivariate COX model were incorporated into the nomogram model, to establish a visual prediction model to evaluate the 1-year, 3-year and 5-year survival rates of patients. Hazard ratios and 95% confidence intervals are presented for all results.

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.

Characterization of eligible patients

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.

Prognostic factors in development cohort

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).

Nomogram construction

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, 3year, and 5-year OS. Higher scores were negatively associated with patient prognosis

Validation of the nomogram

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. 3A). 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. 4A-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. 3B). As previously observed for the development cohort, the calibration curve confirmed the positive correlation between nomogram prediction and actual patient outcome (Fig. 4D-F).

Performance of the nomogram in patient of low risk group and high risk group

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 significant different in OS between the two groups (P < 0.05; Fig. 6).

In ICD-O-3, the location codes for sigmoid colon, rectosigmoid junction and rectal cancers are, respectively, C18, C19 and C20, suggesting distinct anatomical tissues, and thus, different associated pathologies [14]. However, most clinical systems, such as the AJCC system, classify diseases of the rectosigmoid junction as rectal disease. A conclusive definition of RSJ is yet to be reached by the scientific and clinical community. However, it has been shown that RSJC is more prone to present lymphatic metastasis than cancer in the sigmoid and the rectum [15], and that the first is more likely to originate distant metastases than the latter [16]. Despite being from the same pathological subtype, rectosigmoid junction and rectal tumors are associated with different patient OS [15], [17], thus requiring distinct treatment modalities [18–20].

A nomogram risk prediction model for colorectal cancer patients has been developed, however lacking data on its pathological type and on the prognosis of lymph node positive patients [21–23]. Moreover, these reports have not distinguished RSJ from coloretal cancer [24, 25]. A study by B. Morcos et al. showed that the number of positive lymph nodes during surgery directly affects the choice of neoadjuvant chemoradiotherapy after colorectal cancer surgery [26]. Our study established a risk assessment of the presence of positive lymph nodes (PLN) in patients with RSJ adenocarcinoma after surgery. Age, marital, AJCC stage, tumor size, and regional lymph nodes exam were selected as independent risk predictors of PLN-RSJCs. Using these parameters, we validated the nomogram accuracy by C-index, AUC in the ROC, and calibration curves. To evaluate the clinical utility and potential benefits of the model, the decision curve analysis was applied to current study [27]. Overall, our results showed that our newly generated model performs better in the prediction of patient outcome than the currently used AJCC staging system.

Still, we acknowledge the limitations of the present study. First, given that the SEER database collects information from the American population, which are mostly Caucasian, the findings may be biased, even though we did not include race as a predictor in our model. Additionally, available data is incomplete namely for initial records. For example, immunohistochemistry results have only been recorded since 2010. Moreover, detailed therapeutic strategies for patients include chemotherapy plan and dose were not reported in the database. Radiation therapy also includes only radiotherapy sites and some techniques, such as seed implantation and external irradiation. Furthermore, we did not include any data on RSJC-associated tumor markers, such as CEA, Ca199, and Ca242. Finally, our study is limited by two important factors: the lack of analysis of cancer-specific survival (CSS), and the lack of external multi-center validation using patient cohorts in the clinical context. Therefore, additional research is advised to support our preliminary findings.

We declare that none of the work contained in this manuscript is published in any language or currently under consideration at any other journal,and there are no conflicts of interest to declare.All authors have contributed to read and approved this submitted manuscript in its current form.

Special statement: All methods were carried out in accordance with relevant guidelines and regulations.

Ethics approval and consent to participate: Ethical approval and informed consent were obtained for this study. (The second Affiliated Hospital of Nanchang University Medical Research Ethics Committee)

Consent to publish: No Applicable.

Availability of data and materials: If anyone wants to request the data from this study,please contact the author named Wu Yan long([email protected])

Competing interests:The authors declare that they have no competing interests.

Acknowledgement: No Applicable.

Authors’ contribution: Wu Yan long and Wang Yi bing designed the research study. Wu Yan long and Wu Yun xiao took responsibility for statistical analyses. Wu Yan long wrote the manuscript. Wang Yi bing evaluated and revised the manuscript.

Funding: This work was supported by the following funds: Jiang xi Youth Science Fundation (20161BAB215250);Youth Fundation of Second Affiliated Hospital of Nanchang University (2016YNQN12029);Jiangxi Provincial Health Fund for Traditional Chinese Medicine (2019A027);Jiangxi Health and Family Planning Commission Fundation (202130387).

Siegel, R.L., et al., Cancer statistics, 2022. CA Cancer J Clin, 2022. 72(1): p. 7-33.
D'Souza, N., et al., The rectosigmoid problem. Surg Oncol, 2018. 27(3): p. 521-525.
Luna-Abanto, J., The rectosigmoid junction: are limits important? Rev Esp Enferm Dig, 2019. 111(5): p. 410.
Massalou, D., et al., Is it possible to give a single definition of the rectosigmoid junction? Surg Radiol Anat, 2018. 40(4): p. 431-438.
S, A.S., <Rectosigmoid junction anatomical, histological, and radiological studies with special reference to a sphincteric function.pdf>. Int J Colorectal Dis, 1999. 14: p. 237–244.
Mukai, M., et al., Stage II/III cancer of the rectosigmoid junction: an independent tumor type? Oncol Rep, 2011. 26(3): p. 737-41.
Weiser, M.R., AJCC 8th Edition: Colorectal Cancer. Ann Surg Oncol, 2018. 25(6): p. 1454-1455.
Park, S.Y., Nomogram: An analogue tool to deliver digital knowledge. J Thorac Cardiovasc Surg, 2018. 155(4): p. 1793.
Yu, C. and Y. Zhang, Development and validation of prognostic nomogram for young patients with gastric cancer. Ann Transl Med, 2019. 7(22): p. 641.
Wu, J., et al., A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis. Cancer Commun (Lond), 2020. 40(7): p. 301-312.
Iasonos, A., et al., How to build and interpret a nomogram for cancer prognosis. J Clin Oncol, 2008. 26(8): p. 1364-70.
Zhang, C., S. Zhao, and X. Wang, A Postsurgical Prognostic Nomogram for Locally Advanced Rectosigmoid Cancer to Assist in Patient Selection for Adjuvant Chemotherapy. Front Oncol, 2021. 11: p. 772482.
Daly, M.C. and I.M. Paquette, Surveillance, Epidemiology, and End Results (SEER) and SEER-Medicare Databases: Use in Clinical Research for Improving Colorectal Cancer Outcomes. Clin Colon Rectal Surg, 2019. 32(1): p. 61-68.
Alves, C.M.M., P.C. de Oliveira Prado, and R.R. Bastos, Net survival for colorectal cancer in Cuiaba and Varzea Grande (state of Mato Grosso), Brazil. Ecancermedicalscience, 2021. 15: p. 1196.
Falch, C., et al., Oncological outcome of carcinomas in the rectosigmoid junction compared to the upper rectum or sigmoid colon - A retrospective cohort study. Eur J Surg Oncol, 2019. 45(11): p. 2037-2044.
Majek, O., et al., Survival from colorectal cancer in Germany in the early 21st century. Br J Cancer, 2012. 106(11): p. 1875-80.
Cai, W., et al., Rectal NETs and rectosigmoid junction NETs may need to be treated differently. Cancer Med, 2020. 9(3): p. 971-979.
Vogel, J.D., et al., The American Society of Colon and Rectal Surgeons Clinical Practice Guidelines for the Management of Colon Cancer. Dis Colon Rectum, 2022. 65(2): p. 148-177.
You, Y.N., et al., The American Society of Colon and Rectal Surgeons Clinical Practice Guidelines for the Management of Rectal Cancer. Dis Colon Rectum, 2020. 63(9): p. 1191-1222.
Watanabe, T., et al., Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2016 for the treatment of colorectal cancer. Int J Clin Oncol, 2018. 23(1): p. 1-34.
Yu, C. and Y. Zhang, Establishment of prognostic nomogram for elderly colorectal cancer patients: a SEER database analysis. BMC Gastroenterol, 2020. 20(1): p. 347.
Huang, Y.Q., et al., Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol, 2016. 34(18): p. 2157-64.
Lv, J., et al., A nomogram model for predicting prognosis of obstructive colorectal cancer. World J Surg Oncol, 2021. 19(1): p. 337.
Kaser, S.A., et al., Adenocarcinomas of the upper third of the rectum and the rectosigmoid junction seem to have similar prognosis as colon cancers even without radiotherapy, SAKK 40/87. Langenbecks Arch Surg, 2015. 400(6): p. 675-82.
Yamamoto, S., <Prospective evaluation of laparoscopic surgery for rectosigmoidal and rectal carcinoma.pdf>. LAPAROSCOPIC SURGERY FOR RECTAL CARCINOMA, 2002. 45: p. 1648-1654.
Morcos, B., et al., Lymph node yield in rectal cancer surgery: Effect of preoperative chemoradiotherapy. European Journal of Surgical Oncology (EJSO), 2010. 36(4): p. 345-349.
Zhang, Z., et al., Decision curve analysis: a technical note. Ann Transl Med, 2018. 6(15): p. 308.

Tables 1 to 3 are available in the Supplementary Files section.

No competing interests reported.

Download PDF

Journal Publication

published 18 May, 2023

Read the published version in BMC Gastroenterology →

Editorial decision: Major revision
20 Mar, 2023
Reviews received at journal
09 Feb, 2023
Reviewers agreed at journal
05 Feb, 2023
Reviews received at journal
27 Jan, 2023
Reviewers agreed at journal
27 Jan, 2023
Reviewers agreed at journal
19 Nov, 2022
Reviewers agreed at journal
07 Nov, 2022
Reviewers invited by journal
31 Oct, 2022
Editor assigned by journal
31 Oct, 2022
Editor invited by journal
20 Oct, 2022
Submission checks completed at journal
20 Oct, 2022
First submitted to journal
28 Aug, 2022

You are reading this latest preprint version

A Postsurgical Prognostic Nomogram for Patients with Lymph Node Positive Rectosigmoid Junction Adenocarcinoma

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Methods

Patient selection and data processing

Variables defined

Statistical analysis

Results

Characterization of eligible patients

Prognostic factors in development cohort

Nomogram construction

Validation of the nomogram

Performance of the nomogram in patient of low risk group and high risk group

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1