Abstract
Background: There has been no construction of nomograms specifically geared towards pblueicting the prognosis of older patients with rectal cancer. Our objective was to create a nomogram that can accurately pblueict the chances of cancer-specific survival (CSS) and overall survival (OS) for older patients with rectal cancer,stratifying them into distinct risk groups.
Methods: Data of older than 65 years with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2020 were extracted.
Results: A total of 12,780 patients were collected in order to identify the independent prognostic factors for cancer-specific survival (CSS) and overall survival (OS) and construct nomograms. The factors included age, sex, race,year of diagnos, marital status, grade, tumor size, CEA, T classification, N classification, Months from diagnosis to treatment, surgery, chemotherapy, and radiation. LASSO regression was used for this purpose. The results showed that grade, chemotherapy, and months from diagnosis to treatment were identified as independent prognostic factors for OS, while grade, chemotherapy, and tumor size were identified as independent prognostic factors for CSS. The concordance index of the CSS nomogram was 0.609 (95% confidence interval [CI], 0.598-0.619) in the training cohort and 0.682 (95%CI, 0.665-0.698) in the validation cohort. The concordance index of the OS nomogram was 0.697 (95%CI, 0.686-0.708) in the training cohort and 0.605 (95%CI, 0.589-0.621) in the validation cohort.
Conclusion: Caution should be taken when administering adjuvant therapy after surgery in older patients with rectal cancer,as we have developed and validated a new pblueictive nomogram for CSS and OS.
Keywords: elderly rectal cancer, nomogram, prognosis, survival
Introduction
From 2000 to 2016, there has been a noticeable increase in the rates of occurrence and mortality associated with rectal cancer. In China, the incidence rate of rectal cancer climbed to the second position, while the mortality rate ranked fourth. In the United States, among the 147,950 individuals diagnosed with rectal cancer in 2020, 54% were aged 65 and above. This same age group accounted for 68% of rectal cancer deaths [1-2]. Additionally, the 2020 statistics on rectal cancer highlight that it is the third most commonly diagnosed cancer and the third leading cause of cancer-related deaths for both males and females [3]. Recent data from the National Center for Health Statistics reveals that adults aged 65 and older comprised 44% of newly diagnosed rectal cancer cases in the US in 2020 [4]. Considering Ireland, a substantial majority of rectal cancer patients were even older, with 42% of them being over 70 years old between 2014 and 2017 [5].
During the past three decades, there have been significant advancements in the management of locally advanced rectal cancer (LARC) through the use of multimodal therapy, which includes neoadjuvant chemoradiotherapy and total mesorectal excision. As a result, there has been a notable decrease in the occurrence of local recurrence [6]. The use of total neoadjuvant therapy, also known as TNT, which involves preoperative systemic treatment followed by radiation, could enhance adherence and potentially decrease distant metastasis. While several recent large-scale trials have shown promising outcomes with TNT, these findings may not be entirely applicable to older individuals diagnosed with rectal cancer. For instance, the French trial PRODIGE 23 demonstrated an improvement in three-year disease-free survival (DFS) from 69% to 76%, but subgroup analysis did not reveal a discrepancy in the DFS benefits between patients aged 70 or younger and those over 70 [7]. The overall survival and acute toxicity were enhanced through the trial POLISH II, however, the TNT group had a median age of only 60 and 75% of the participants were 66 years old or younger. This poses a challenge in implementing this approach for older patients [8]. Although, rectal cancer treatment typically involves a combination of therapies and is more frequently associated with higher morbidity rates compared to colon cancer [9], Compared to younger patients, older individuals diagnosed with rectal cancer exhibit physical decline, have a higher prevalence of concurrent ailments, and experience a lengthier recovery period following surgery [10]. Clinical trials for cancer often do not include a sufficient number of elderly patients, resulting in a lack of efficacy data and ambiguous guidance for doctors when making treatment decisions. In the prognosis prediction of rectal cancer patients, the eighth edition of the American Joint Committee on Cancer commonly uses the TNM staging system [11]. Nevertheless, there are certain constraints when it comes to predicting postoperative overall survival. The outcome for individuals diagnosed with rectal cancer can greatly differ, even if they are in the same TNM stage. Numerous factors other than tumor stage, including tumor location, histological type, age, gender, microsatellite status, and RAS/RAF mutation, can influence the overall survival of patients with stage I-III CRC. It is important to note that no individual factor can provide an accurate prediction of survival in individuals with rectal cancer [12-13]. Important prognostic factors have been incorporated and illustrated in nomograms, which are widely utilized in clinical oncology. These nomograms serve as reliable tools for estimating numerical probabilities for individual patients [14-17]. This has been demonstrated in several types of cancers[18-20], the nomograms’ predictions may be more precise in comparison to the traditional TNM staging systems for different types of tumors [21-22]. Furthermore, we have thus far found relatively few nomograms for predicting survival in older patients with rectal cancer.
The objective of this study was to create a predictive model that incorporates all relevant prognostic factors and can determine the overall survival (OS) and cancer-specific survival (CSS) for elderly patients with rectal cancer. In addition, we aimed to classify patients into various risk groups using this nomogram. By providing personalized prognostic information, this tool can assist healthcare professionals and patients in making well-informed decisions regarding treatment and management.
  1. Methods
  2. Data SourceThe SEER database comprises multiple tumor registries across various regions, providing comprehensive cancer-related information, including treatment details. The SEER data is accessible to the public. Our study focused on collecting clinical data of women who were diagnosed with rectal cancer from 2000 to 2020, specifically from the SEER database submission of November 2022, known as SEER 17. This data encompasses various parameters such as gender, age at diagnosis, year of diagnosis, race, marital status, grade, CEA levels, tumor size, TMN stage, treatment information, and follow-up data.
  3. Study PopulationTo extract data from the SEER database (https://seer.cancer.gov), we utilized SEER*-Stat 8.4.2 software from the National Cancer Institute in Bethesda, MD, USA. Our study included patients diagnosed with primary rectal cancer, confirmed by positive histology, in American Joint Committee on Cancer (AJCC) stages I to III between 2000 and 2020. We only included patients with complete clinical and demographic information. Patients diagnosed between 2000 and 2010 were staged using the 6th edition of AJCC rectal cancer, while those after 2010 were staged using the 7th edition. Patients who lacked clinical and follow-up information were excluded from the study.
  4. Variables and endpointsThe variables obtained from the SEER database comprised of age, gender, ethnicity, year of diagnosis, marital status, tumor grade, tumor size, CEA levels, T classification, N classification, time period between diagnosis and treatment, surgical procedures, chemotherapy administration, and radiation therapy. The study primarily examined overall survival (OS), rectal cancer-specific survival (BCSS), and the hazard ratio (HR). OS indicated the duration of survival from the diagnosis of rectal cancer until death from any cause, while BCSS denoted deaths specifically attributed to rectal cancer.
  5. Propensity score matchingTo minimize selection bias, propensity score matching (PSM) was carried out for all relevant confounding factors, as proposed by Rosenbaum and Rubin in 1983. This method of matching is not constrained by the number of events [22-23]. In our research, we employed the techniques of nearest available neighbor matching and caliper matching in PSM [24-25]. For the patients who received surgery or combination therapy (surgery plus RT or CT), to balance the effects of confounding a PSM with a 1:1 ratio and the caliper of 0.02 was set. Only when the propensity score of the control group (surgery plus RT or CT) is within a certain distance (0.02), the control group will be matched with the case. Matched covariates include sex, age, race, year of diagnosis, marital status, grade, CEA, tumor size, stage and months from diagnosis to treatment.
  6. Statistical AnalysisIn order to ensure the accuracy of the model, we randomly divided the patients into two groups, a development cohort and a validation cohort, with a ratio of 7:3. We compared the categorical characteristics between the training cohort and the validation cohort using the Pearson chi-square test. Then, we used univariate and multivariate Cox regression models to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in the training cohort. The potential risk factors identified in the multivariate analysis of the training cohort were further analyzed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. We created dummy variables for categorical variables and used cross-validation to determine the appropriate tuning parameters for LASSO logistic regression. Finally, LASSO selected the most significant features. We performed multivariate Cox proportional hazards analyses using the most significant features selected by LASSO from the training dataset. Variables with a P<0.05 by multivariate analysis were then included in the construction of nomograms. These nomograms were designed to predict the 1-, 3-, and 5-year CSS and OS rates. The results of the subgroup analysis were presented using a forest plot. To evaluate the discrimination of the nomograms, we calculated the concordance index (C-index) and area under the receiver operating characteristic (ROC) curve (AUC) values. Additionally, calibration curves were plotted at 1, 3, and 5 years using a bootstrap involving 100 resamples to assess the consistency between the actual prognosis and the survival predicted by the nomograms. Furthermore, we determined the optimal cutoff value based on the risk scores obtained from the nomograms using time-dependent ROC (tdROC) curve analysis, next, the patients were categorized into low-risk and high-risk categories based on the established threshold. Subsequently, the Kaplan-Meier method was employed to create OS and CSS curves, and the log-rank test was utilized to compare the survival between the groups. All the statistical analysis mentioned above were conducted using R software (http://www.R-project.org, The R Foundation). The Free Statistics software versions 1.8 were employed for all the statistical analyses. A p-value of less than 0.05 was deemed to represent a statistically significant difference.
  7. Results
  8. Patient baseline characteristicsFigure 1 illustrates the data filtering process. Following a rigorous application of inclusion and exclusion criteria, a cohort of 12,780 individuals diagnosed with rectal cancer was selected for the study. Through the utilization of R software, these patients were randomly split into a training cohort and a validation cohort in a 7:3 ratio, resulting in 8948 patients in the training cohort and 3832 patients in the validation cohort. The baseline characteristics of the patients in both cohorts were found to be similar, as showed by Table 1.