A nomogram to predict survival in patients older than 65 years with rectal cancer: A SEER-based population study

doi:10.21203/rs.3.rs-3397078/v1

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A nomogram to predict survival in patients older than 65 years with rectal cancer: A SEER-based population study

https://doi.org/10.21203/rs.3.rs-3397078/v1

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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.

elderly rectal cancer

nomogram

prognosis

survival

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.

2.1 Data Source

The 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.

2.2 Study Population

To 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.

2.3 Variables and endpoints

The 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.

2.4 Propensity score matching

To 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.

2.5 Statistical Analysis

In 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.

3.1 Patient baseline characteristics

Figure 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.

Table 1 Baseline characteristics of enrolled patients

Variables	Overall cohort (n = 12780)	Training cohort (n =8948)	Validation cohort (n = 3832)	*p value*
Sex, n (%)				0.976
Male	7508(58.7)	5256(58.7)	2252(58.8)
Female	5272(41.3)	3692(41.3)	1580(41.2)
Age, Median (IQR)	74.0(69.0, 80.0)	74.0(69.0, 80.0)	74.0(69.0, 80.0)	0.907
Race, n (%)				0.429
White	10613(83.0)	7413(82.8)	3200(83.5)
Black	856( 6.7)	616(6.9)	240(6.3)
Other	1311(10.3)	919(10.3)	392(10.2)
Year of diagnos, n (%)				0.592
2010-2011	4376(34.2)	3089(34.5)	1287(33.6)
2012-2013	4433(34.7)	3089(34.5)	1344(35.1)
2014-2015	3971(31.1)	2770(31)	1201(31.3)
Marital status, n (%)				0.446
Single	1219( 9.5)	834(9.3)	385(10)
Married	7095(55.5)	4988(55.7)	2107(55)
Divorced	1154( 9.0)	795(8.9)	359(9.4)
Other	3312(25.9)	2331(26.1)	981(25.6)
Grade, n (%)				0.719
Ⅰ	1061( 8.3)	741(8.3)	320(8.4)
Ⅱ	9963(78.0)	6984(78.1)	2979(77.7)
Ⅲ	1591(12.4)	1102(12.3)	489(12.8)
Ⅳ	165( 1.3)	121(1.4)	44(1.1)
CEA, n (%)				0.448
Positive	4917(38.5)	3451(38.6)	1466(38.3)
Negative	7765(60.8)	5434(60.7)	2331(60.8)
Borderline	98( 0.8)	63(0.7)	35(0.9)
Tumor.size, n (%)				0.918
＜3	3550(27.8)	2476(27.7)	1074(28)
3-5	4648(36.4)	3271(36.6)	1377(35.9)
5-7	2896(22.7)	2020(22.6)	876(22.9)
≥7	1686(13.2)	1181(13.2)	505(13.2)
T stage, n (%)				0.734
T1	1832(14.3)	1265(14.1)	567(14.8)
T2	2850(22.3)	2012(22.5)	838(21.9)
T3	7107(55.6)	4976(55.6)	2131(55.6)
T4	991( 7.8)	695(7.8)	296(7.7)
N stage, n (%)				0.684
N0	7988(62.5)	5582(62.4)	2406(62.8)
N1	3587(28.1)	2530(28.3)	1057(27.6)
N2	1205( 9.4)	836(9.3)	369(9.6)
TNM Stage, n (%)				0.851
Ⅰ	3866(30.3)	2694(30.1)	1172(30.6)
Ⅱ	4122(32.3)	2888(32.3)	1234(32.2)
Ⅲ	4792(37.5)	3366(37.6)	1426(37.2)
Months.from.diagnosis.to.treatment,Mean ± SD	1.2 ± 1.2	1.2 ± 1.2	1.1 ± 1.1	0.129
Surgery, n (%)				0.667
No	1242( 9.7)	863(9.6)	379(9.9)
Yes	11538(90.3)	8085(90.4)	3453(90.1)
Radiation, n (%)				0.491
No	5444(42.6)	3794(42.4)	1650(43.1)
Yes	7336(57.4)	5154(57.6)	2182(56.9)
Chemotherapy, n (%)				0.771
No	5288(41.4)	3695(41.3)	1593(41.6)
Yes	7492(58.6)	5253(58.7)	2239(58.4)

3.2 Univariate and Multivariate Analyses for OS and CSS of training cohort

To develop the prognostic model, we used Cox regression models to screen for prognostic factors affecting OS and CSS. First, all 13 original variables were included in the regression model (Table 2). Univariate Cox regression found that 10 variables were risk factors for CSS in the Training cohort: race, marital status, grade, T stage, N stage, TNM Stage, CEA, tumor size, surgery, radiation. In addition, sex, race, marital status, grade, T stage, N stage, TNM Stage, CEA, months from diagnosis to treatment, surgery, radiation and chemotherapy were risk factors for OS. Subsequently, variables with statistical significance (p<0.05) in univariate analysis were included in multivariate analysis (Table 3). Multivariate analysis results indicated that marital status, grade, T stage, N stage, TNM Stage, CEA, tumor size, surgery and radiation were the independent prognostic factors for CSS, whereas sex, race, marital status, grade, T stage, N stage, TNM Stage, CEA, tumor size, months from diagnosis to treatment, surgery, radiation and chemotherapy were the independent prognostic factors for OS, and the multivariate analysis results were presented by forest plot (Figure 2).

Table 2 Univariate Cox regression analysis of prognostic factors for Training cohort.

	CSS		OS
Characteristics	HR(95% CI)	P value	HR(95% CI)	P value
Sex
Male	Reference		Reference
Female	0.92(0.86,0.98)	0.01	0.91(0.85-0.97)	0.004
Race
White	Reference		Reference
Black	1.31(1.16-1.47)	<0.001	0.99(0.86-1.13)	0.845
Other	0.87(0.78-0.97)	0.015	1.2(1.09-1.32)	＜0.001
Marital status
Single	Reference		Reference
Married	0.71(0.64-0.79)	<0.001	0.86(0.77-0.96)	0.008
Divorced	0.9(0.78-1.04)	0.157	0.93(0.8-1.08)	0.323
Other	0.99(0.89-1.11)	0.9	0.78(0.69-0.89)	＜0.001
Grade
I	Reference		Reference
II	1.19(1.05-1.35)	0.008	0.85(0.76-0.94)	0.002
III	1.85(1.6-2.14)	<0.001	0.62(0.54-0.72)	＜0.001
IV	2.07(1.58-2.7)	<0.001	0.63(0.44-0.91)	0.013
T stage
T1	Reference		Reference
T2	1.27(1.11-1.45)	＜0.001	0.86(0.78-0.96)	0.006
T3	2.17(1.93-2.44)	＜0.001	1.09(0.99-1.19)	0.068
T4	4.4(3.82-5.06)	＜0.001	1.44(1.23-1.67)	＜0.001
N stage
N0	Reference		Reference
N1	1.52(1.42-1.64)	＜0.001	1.22(1.14-1.31)	＜0.001
N2	2.61(2.38-2.86)	＜0.001	0.87(0.76-1)	0.044
TNM Stage
Ⅰ	Reference		Reference
Ⅱ	1.81(1.65-1.98)	＜0.001	1.17(1.08-1.27)	＜0.001
Ⅲ	2.46(2.26-2.68)	＜0.001	1.23(1.14-1.33)	＜0.001
CEA
Positive	Reference		Reference
Negative	0.57(0.53-0.61)	<0.001	0.92(0.86-0.98)	0.015
Borderline	0.85(0.6-1.2)	0.348	0.78(0.53-1.14)	0.2
Tumor size
＜3	Reference		Reference
3-5	1.39(1.27-1.51)	＜0.001	0.94(0.87-1.02)	0.147
5-7	1.74(1.58-1.91)	＜0.001	1.07(0.97-1.16)	0.163
≥7	2.22(2-2.46)	＜0.001	1.07(0.96-1.19)	0.219
Months.from.diagnosis.to.treatment	1(0.98-1.03)	0.746	1.12(1.1-1.15)	＜0.001
Surgery
No	Reference		Reference
Yes	0.39(0.35-0.42)	＜0.001	0.56(0.51-0.62)	＜0.001
Radiation
No	Reference		Reference
Yes	1.09(1.02-1.16)	0.009	1.27(1.2-1.34)	＜0.001
Chemotherapy
No	Reference		Reference
Yes	1.05(0.98-1.12)	0.154	1.39(1.31-1.47)	＜0.001

Table 3 Multivariate Cox regression analysis of prognostic factors for Training cohort.

	CSS		OS
Characteristics	HR(95% CI)	P value	HR(95% CI)	P value
Sex
Male	Reference		Reference
Female	0.92(0.86-0.98)	0.01	0.92(0.87-0.97)	0.004
Race
White	Reference		Reference
Black	1.31(1.16-1.47)	<0.001	1.01(0.9-1.13)	0.882
Other	0.87(0.78-0.97)	0.015	1.21(1.12-1.31)	<0.001
Marital status
Single	Reference		Reference
Married	0.71(0.64-0.79)	<0.001	0.84(0.77-0.92)	<0.001
Divorced	0.9(0.78-1.04)	0.157	0.91(0.8-1.03)	0.124
Other	0.99(0.89-1.11)	0.9	0.76(0.69-0.85)	<0.001
Grade
I	Reference		Reference
II	1.19(1.05-1.35)	0.008	0.84(0.76-0.91)	<0.001
III	1.85(1.6-2.14)	<0.001	0.6(0.53-0.67)	<0.001
IV	2.07(1.58-2.7)	<0.001	0.6(0.44-0.82)	0.002
T stage
T1	Reference		Reference
T2	1.27(1.11-1.45)	＜0.001	0.89(0.81-0.97)	0.006
T3	2.17(1.93-2.44)	＜0.001	1.09(1.01-1.18)	0.021
T4	4.4(3.82-5.06)	＜0.001	1.4(1.23-1.58)	<0.001
N stage
N0	Reference		Reference
N1	1.52(1.42-1.64)	＜0.001	1.22(1.15-1.3)	<0.001
N2	2.61(2.38-2.86)	＜0.001	0.88(0.79-0.98)	0.023
TNM Stage
Ⅰ	Reference		Reference
Ⅱ	1.81(1.65-1.98)	＜0.001	1.15(1.08-1.23)	＜0.001
Ⅲ	2.46(2.26-2.68)	＜0.001	1.22(1.15-1.31)	＜0.001
CEA
Positive	Reference		Reference
Negative	0.57(0.53-0.61)	<0.001	0.92(0.87-0.97)	0.003
Borderline	0.85(0.6-1.2)	0.348	0.73(0.53-1.02)	0.065
Tumor size
＜3	Reference		Reference
3-5	1.39(1.27-1.51)	＜0.001	0.96(0.9-1.03)	0.27
5-7	1.74(1.58-1.91)	＜0.001	1.08(1-1.17)	0.038
≥7	2.22(2-2.46)	＜0.001	1.11(1.01-1.21)	0.027
Months.from.diagnosis.to.treatment	1(0.98-1.03)	0.746	1.14(1.12-1.16)	＜ 0.001
Surgery
No	Reference		Reference
Yes	0.39(0.35-0.42)	＜0.001	0.56(0.51-0.62)	＜0.001
Radiation
No	Reference		Reference
Yes	1.09(1.02-1.16)	0.009	1.27(1.2-1.34)	＜0.001
Chemotherapy
No	Reference		Reference
Yes	1.05(0.98-1.12)	0.154	1.39(1.31-1.47)	＜0.001

3.3 Nomogram construction

LASSO regression analysis was performed to identify the potential prognostic factors. Figure 3 shows that when the optimal lambda value was 0.02, marital status, grade, T stage, N stage, CEA, tumor size, surgery and chemotherapy were associated with CSS. And when the optimal lambda value was 0.04, grade, chemotherapy and months from diagnosis to treatment, were associated with OS.

Based on multivariate analysis results and LASSO regression analysis, eight prognostic factors including marital status, grade, T stage, N stage, CEA, tumor size, surgery and chemotherapy were used to establish the nomogram of CSS (Figure 4A), And three prognostic factors including grade, chemotherapy and months from diagnosis to treatment were used to establish the nomogram of OS (Figure 4B), These nomograms were adopted to predict the 1-, 3-, and 5-year survival rates of training cohort. In a nomogram, the score of each value level of each variable was assigned according to its contribution to the outcome; then, each score was added to obtain the total score; finally, the predicted value of each individual outcome event was calculated through the function conversion relation between the total score and the survival probability.

3.4 Nomogram validation

The nomogram was assessed using the area under the curve (AUC) and concordance index (C-index). In the training cohort, as shown in (Figure 5), the C-index of CSS and OS prediction nomograms were 0.609 (95%CI, 0.598 to 0.619) and 0.697 (95%CI, 0.686 to 0.708) , and the AUC in predicting 1-, 3- and 5-year CSS were 0.746, 0.741 and 0.731, the AUC in predicting 1-, 3- and 5-year OS was 0.636, 0.642 and 0.633. Respectively, while in the validation cohort the C-index of CSS and OS prediction nomograms were 0.682 (95%CI, 0.665 to 0.698) and 0.605 (95%CI, 0.589 to 0.621), and in predicting 1-, 3-, and 5-year CSS were 0.732, 0.720 and 0.710, the AUC in predicting 1-, 3-, and 5-year OS was 0.609, 0.633 and 0.631(Figure 6). Calibration curves of survival rates at 1, 3, and 5 years in the training cohort(Figure 7) and validation cohort (Figure 8) showed satisfactory internal modelfitting capabilities. Excellent discrimination and calibration demonstrated the accuracy of the prognostic model.

3.5 Risk stratification

According to the nomograms we calculated the risk score of each patient. Meanwhile, the cutoff value of total score of CSS was 161 points, whereas that of OS was 43 points, as calculated by the td-ROC curve analysis. Later, patients were divided into low- and high-risk groups according to the cutoff values. The survival of high-risk patients from training cohort and validation cohort was poorer than that of low-risk patients, either based on the OS or CSS nomogram (Figure 9).

3.6 Subgroup Analyses from cherapy

Furthermore, we explore the impact of different treatments on survival and prognosis. Based on the treatment received by the patients, they were divided into a surgery group and a combined group. In the surgery cohort patients were received rectal cancer surgery only, while in combined group patients were received surgery plus radiation or chemotherapy. The detailed clinicopathologic characteristics of the patients in the two groups before or after PSM are shown in Table 4. Before PSM, the surgery group showed significantly better CSS and OS than the combined group(CSS:p < 0.001, OS: p < 0.001, respectively ; Figure 10A and 10B), After 1:1 matching, 2234 patients in the surgery group were matched and compared with 2234 patients in the combined group. No demographic variables with significant differences were included (p > 0.05).

After eliminating the differences in covariates that might affect CSS and OS by using PSM, we found that patients in the surgery group achieved no significantly better CSS and OS than those in the combined group (CSS:p < 0.073, OS: p < 0.057, respectively ; Figure 10C and 10D). We performed univariate and multivariate analyses to investigate the prognostic factors that could predict CSS and OS in the before PSM group and after PSM group. As shown in Table 5, univariate analyses revealed that sex, age, race, marital status, grade, CEA, tumor size, T stage, N stage, TNM stage and cherapy were important factors affecting CSS, and sex, age, race, marital status, grade, CEA, tumor size, T stage, months from diagnosis to treatment and cherapy were important factors affecting OS. And in the multivariate analysis, sex, marital status, grade, tumor size, CEA, T stage N stage, TNM stage and cherapy were independent prognostic factors of CSS, while sex, race, marital status, grade, tumor size, cherapy and months from diagnosis to treatment were independent prognostic factors of OS. The multivariate analysis results were presented by forest plot (Figure 11A and 11B). But in the after PSM cohort, as shown in Table 6, univariate analyses revealed that sex, age, marital status, Grade, CEA, Tumor size, T stage, N stage, TNM stage were important factors affecting CSS, and age, Grade, T stage, Months from diagnosis to treatment were important factors affecting OS. And in the multivariate analysis, grade, T stage, N stage, TNM stage, CEA, tumor size were independent prognostic factors of CSS, while race, grade, tumor size, months from diagnosis to treatment were independent prognostic factors of OS, the multivariate analysis results were presented by a forest plot (Figure 11C and 11D).

Table 4 Baseline characteristics of two groups

	Data before PSM				Data after PSM
Variables	Overall cohort (n = 5846)	Surgery (n=4620)	Combined (n=1226)	p value	Overall cohort (n = 2234)	Surgery (n=1117)	Combined (n=1117)	p value
Sex, n (%)				0.014				0.491
Male	3305(56.5)	2574(55.7)	731(59.6)		1322(59.2)	669(59.9)	653(58.5)
Female	2541(43.5)	2046(44.3)	495(40.4)		912(40.8)	448(40.1)	464(41.5)
Age, Median (IQR)	76.0(70.0, 82.0)	76.5(70.0, 83.0)	75.0(69.2, 80.0)	<0.001	75.0(70.0, 81.0)	75.0(70.0, 82.0)	75.0(70.0, 81.0)	0.545
Race, n (%)				0.003				0.939
White	4899(83.8)	3906(84.5)	993(81)		1814(81.2)	904(80.9)	910(81.5)
Black	375( 6.4)	273(5.9)	102(8.3)		180( 8.1)	92(8.2)	88(7.9)
Other	572( 9.8)	441(9.5)	131(10.7)		240(10.7)	121(10.8)	119(10.7)
Year.of.diagnos.2, n (%)				<0.001				0.609
2010-2011	2366(40.5)	1922(41.6)	444(36.2)		891(39.9)	457(40.9)	434(38.9)
2012-2013	2012(34.4)	1598(34.6)	414(33.8)		748(33.5)	367(32.9)	381(34.1)
2014-2015	1468(25.1)	1100(23.8)	368(30)		595(26.6)	293(26.2)	302(27)
Marital status, n (%)				<0.001				0.63
Single	531( 9.1)	402(8.7)	129(10.5)		213( 9.5)	98(8.8)	115(10.3)
Married	3137(53.7)	2428(52.6)	709(57.8)		1245(55.7)	625(56)	620(55.5)
Divorced	459( 7.9)	363(7.9)	96(7.8)		196( 8.8)	102(9.1)	94(8.4)
Other	1719(29.4)	1427(30.9)	292(23.8)		580(26.0)	292(26.1)	288(25.8)
Grade, n (%)				<0.001				0.861
Ⅰ	516( 8.8)	440(9.5)	76(6.2)		150( 6.7)	77(6.9)	73(6.5)
Ⅱ	4516(77.2)	3595(77.8)	921(75.1)		1676(75.0)	830(74.3)	846(75.7)
Ⅲ	736(12.6)	541(11.7)	195(15.9)		359(16.1)	186(16.7)	173(15.5)
Ⅳ	78( 1.3)	44(1)	34(2.8)		49( 2.2)	24(2.1)	25(2.2)
CEA, n (%)				<0.001				0.951
Positive	1988(34.0)	1494(32.3)	494(40.3)		900(40.3)	452(40.5)	448(40.1)
Negative	3818(65.3)	3093(66.9)	725(59.1)		1321(59.1)	659(59)	662(59.3)
Borderline	40( 0.7)	33(0.7)	7(0.6)		13( 0.6)	6(0.5)	7(0.6)
Tumor size, n (%)				<0.001				0.651
＜3	2071(35.4)	1758(38.1)	313(25.5)		556(24.9)	280(25.1)	276(24.7)
3-5	2140(36.6)	1651(35.7)	489(39.9)		919(41.1)	469(42)	450(40.3)
5-7	1109(19.0)	812(17.6)	297(24.2)		531(23.8)	262(23.5)	269(24.1)
≥7	526( 9.0)	399(8.6)	127(10.4)		228(10.2)	106(9.5)	122(10.9)
T stage, n (%)				<0.001				0.506
T1	1316(22.5)	1206(26.1)	110(9)		229(10.3)	120(10.7)	109(9.8)
T2	1849(31.6)	1613(34.9)	236(19.2)		446(20.0)	230(20.6)	216(19.3)
T3	2362(40.4)	1620(35.1)	742(60.5)		1335(59.8)	650(58.2)	685(61.3)
T4	319( 5.5)	181(3.9)	138(11.3)		224(10.0)	117(10.5)	107(9.6)
N stage, n (%)				<0.001				0.277
N0	4322(73.9)	3740(81)	582(47.5)		1117(50.0)	540(48.3)	577(51.7)
N1	1005(17.2)	580(12.6)	425(34.7)		735(32.9)	377(33.8)	358(32.1)
N2	519( 8.9)	300(6.5)	219(17.9)		382(17.1)	200(17.9)	182(16.3)
TNM Stage, n (%)				<0.001				0.074
Ⅰ	2826(48.3)	2623(56.8)	203(16.6)		419(18.8)	216(19.3)	203(18.2)
Ⅱ	1496(25.6)	1117(24.2)	379(30.9)		698(31.2)	324(29)	374(33.5)
Ⅲ	1524(26.1)	880(19)	644(52.5)		1117(50.0)	577(51.7)	540(48.3)
Months.from.diagnosis.to.treatment,Mean ± SD	1.1 ± 1.3	1.1 ± 1.3	1.1 ± 1.2	0.811	1.1 ± 1.3	1.1 ± 1.4	1.1 ± 1.2	0.452

Table 5 Univariate and multivariate Cox regression model analysis of before PSM cohort between the surgery group and the combined group.

	Univariate analysis				Multivariate analysis
	CSS		OS		CSS		OS
Characteristics	HR(95% CI)	P value	HR(95% CI)	P value	HR(95% CI)	P value	HR(95% CI)	P value
Sex						0.023		＜0.001
Male	Reference		Reference		Reference		Reference
Female	0.89(0.81-0.98)	0.023	0.86(0.79-0.93)	< 0.001	0.89(0.81-0.98)	0.023	0.86(0.79-0.93)	＜0.001
Race						0.777		＜0.001
White	Reference		Reference		Reference		Reference
Black	1.34(1.12-1.61)	0.001	1.07(0.88-1.3)	0.517	1.34(1.12-1.61)	0.001	1.07(0.88-1.3)	0.517
Other	0.94(0.8-1.11)	0.489	1.28(1.12-1.45)	< 0.001	0.94(0.8-1.11)	0.489	1.28(1.12-1.45)	＜0.001
Marital status						＜0.001		0.005
Single	Reference		Reference		Reference		Reference
Married	0.74(0.62-0.87)	< 0.001	0.93(0.8-1.08)	0.343	0.74(0.62-0.87)	＜0.001	0.93(0.8-1.08)	0.343
Divorced	0.87(0.69-1.1)	0.243	1.03(0.84-1.27)	0.752	0.87(0.69-1.1)	0.243	1.03(0.84-1.27)	0.752
Other	1.05(0.88-1.24)	0.607	0.8(0.68-0.95)	0.01	1.05(0.88-1.24)	0.607	0.8(0.68-0.95)	0.01
Grade						＜0.001		＜0.001
I	Reference		Reference		Reference		Reference
II	1.34(1.1-1.63)	0.004	0.8(0.7-0.92)	0.001	1.34(1.1-1.63)	0.004	0.8(0.7-0.92)	0.001
III	2.36(1.89-2.94)	＜0.001	0.58(0.47-0.7)	< 0.001	2.36(1.89-2.94)	＜0.001	0.58(0.47-0.7)	＜0.001
IV	3.23(2.22-4.7)	＜0.001	0.53(0.3-0.96)	0.034	3.23(2.22-4.7)	＜0.001	0.53(0.3-0.96)	0.034
T stage						＜0.001		0.072
T1	Reference		Reference		Reference		Reference
T2	1.71(1.44-2.03)	＜0.001	0.81(0.73-0.9)	< 0.001	1.71(1.44-2.03)	＜0.001	0.81(0.73-0.9)	＜0.001
T3	3.68(3.14-4.31)	＜0.001	0.85(0.77-0.95)	0.004	3.68(3.14-4.31)	＜0.001	0.85(0.77-0.95)	0.004
T4	7.53(6.13-9.26)	＜0.001	1.19(0.93-1.54)	0.172	7.53(6.13-9.26)	＜0.001	1.19(0.93-1.54)	0.172
N stage						＜0.001		0.61
N0	Reference		Reference		Reference		Reference
N1	2.3(2.05-2.58)	＜0.001	1.06(0.93-1.2)	0.36	2.3(2.05-2.58)	＜0.001	1.06(0.93-1.2)	0.36
N2	3.82(3.35-4.37)	＜0.001	0.87(0.71-1.07)	0.195	3.82(3.35-4.37)	＜0.001	0.87(0.71-1.07)	0.195
TNM Stage						＜0.001		0.708
Ⅰ	Reference		Reference		Reference		Reference
Ⅱ	2.42(2.14-2.74)	＜0.001	0.95(0.86-1.06)	0.019	2.42(2.14-2.74)	＜0.001	0.95(0.86-1.06)	0.384
Ⅲ	3.92(3.49-4.41)	＜0.001	0.99(0.88-1.11)	＜0.001	3.92(3.49-4.41)	＜0.001	0.99(0.88-1.11)	0.899
CEA						＜0.001		0.703
Positive	Reference		Reference		Reference		Reference
Negative	0.52(0.47-0.57)	< 0.001	1.02(0.93-1.13)	0.659	0.52(0.47-0.57)	＜0.001	1.02(0.93-1.13)	0.659
Borderline	1.17(0.72-1.89)	0.525	0.93(0.48-1.8)	0.831	1.17(0.72-1.89)	0.525	0.93(0.48-1.8)	0.831
Tumor size						＜0.001		＜0.001
＜3	Reference		Reference		Reference		Reference
3-5	1.56(1.38-1.76)	＜0.001	0.85(0.77-0.94)	0.001	1.56(1.38-1.76)	＜0.001	0.85(0.77-0.94)	0.001
5-7	2.2(1.92-2.52)	＜0.001	0.75(0.66-0.86)	< 0.001	2.2(1.92-2.52)	＜0.001	0.75(0.66-0.86)	＜0.001
≥7	2.56(2.17-3.01)	＜0.001	0.72(0.6-0.87)	< 0.001	2.56(2.17-3.01)	＜0.001	0.72(0.6-0.87)	＜0.001
Months.from.diagnosis.to.treatment	1.01(0.97-1.05)	0.597	1.14(1.11-1.17)	＜0.001	1.01(0.97-1.05)	0.597	1.14(1.11-1.17)	＜0.001
Cherapy						＜0.001		＜0.001
Surgery	Reference		Reference		Reference		Reference
Combined	1.52(1.36-1.69)	< 0.001	1.21(1.09-1.35)	＜0.001	1.52(1.36-1.69)	＜0.001	1.21(1.09-1.35)	＜0.001

Table 6 Univariate and multivariate Cox regression model analysis of after PSM cohort between the surgery group and the combined group.

	Univariate analysis				Multivariate analysis
	CSS		OS		CSS		OS
Characteristics	HR(95% CI)	P value	HR(95% CI)	P value	HR(95% CI)	P value	HR(95% CI)	P value
Sex						0.036		0.148
Male	Reference		Reference		Reference		Reference
Female	0.86(0.75-0.99)	0.036	0.9(0.77-1.04)	0.148	0.86(0.75-0.99)	0.036	0.9(0.77-1.04)	0.148
Race						0.762		0.022
White	Reference		Reference		Reference		Reference
Black	1.24(0.98-1.58)	0.077	1.07(0.78-1.45)	0.68	1.24(0.98-1.58)	0.077	1.07(0.78-1.45)	0.68
Other	0.97(0.78-1.21)	0.801	1.29(1.04-1.59)	0.02	0.97(0.78-1.21)	0.801	1.29(1.04-1.59)	0.02
Marital status						0.054		0.431
Single	Reference		Reference		Reference		Reference
Married	0.71(0.57-0.89)	0.003	1.04(0.8-1.37)	0.765	0.71(0.57-0.89)	0.003	1.04(0.8-1.37)	0.765
Divorced	0.87(0.64-1.18)	0.365	1.09(0.75-1.58)	0.65	0.87(0.64-1.18)	0.365	1.09(0.75-1.58)	0.65
Other	0.94(0.74-1.19)	0.586	0.94(0.69-1.27)	0.688	0.94(0.74-1.19)	0.586	0.94(0.69-1.27)	0.688
Grade						＜0.001		＜0.001
I	Reference		Reference		Reference		Reference
II	1.05(0.79-1.4)	0.743	0.77(0.59-1.01)	0.055	1.05(0.79-1.4)	0.743	0.77(0.59-1.01)	0.055
III	1.61(1.17-2.2)	0.003	0.54(0.38-0.76)	< 0.001	1.61(1.17-2.2)	0.003	0.54(0.38-0.76)	＜0.001
IV	2.31(1.44-3.71)	< 0.001	0.42(0.15-1.15)	0.091	2.31(1.44-3.71)	0.001	0.42(0.15-1.15)	0.091
T stage						＜0.001		0.889
T1	Reference		Reference		Reference		Reference
T2	1.32(0.95-1.83)	0.096	0.76(0.59-0.98)	0.034	1.32(0.95-1.83)	0.096	0.76(0.59-0.98)	0.034
T3	2.24(1.67-2.99)	< 0.001	0.8(0.64-1.01)	0.058	2.24(1.67-2.99)	＜0.001	0.8(0.64-1.01)	0.058
T4	4.59(3.3-6.38)	< 0.001	1.17(0.82-1.67)	0.382	4.59(3.3-6.38)	＜0.001	1.17(0.82-1.67)	0.382
N stage								0.181
N0	Reference		Reference		Reference		Reference
N1	1.49(1.28-1.74)	< 0.001	0.97(0.82-1.14)	0.688	1.49(1.28-1.74)	＜ 0.001	0.97(0.82-1.14)	0.688
N2	2.55(2.14-3.04)	< 0.001	0.83(0.65-1.07)	0.145	2.55(2.14-3.04)	＜ 0.001	0.83(0.65-1.07)	0.145
TNM Stage						＜0.001		0.465
Ⅰ	Reference		Reference		Reference		Reference
Ⅱ	1.81(1.43-2.29)	＜0.001	1.03(0.84-1.25)	0.797	1.81(1.43-2.29)	＜0.001	1.03(0.84-1.25)	0.797
Ⅲ	2.65(2.13-3.31)	＜0.001	0.94(0.78-1.14)	0.543	2.65(2.13-3.31)	＜0.001	0.94(0.78-1.14)	0.543
CEA						＜ 0.001		0.231
Positive	Reference		Reference		Reference		Reference
Negative	0.63(0.55-0.72)	< 0.001	1.09(0.93-1.28)	0.289	0.63(0.55-0.72)	＜0.001	1.09(0.93-1.28)	0.289
Borderline	2.04(1.01-4.11)	0.046	1.97(0.63-6.16)	0.244	2.04(1.01-4.11)	0.046	1.97(0.63-6.16)	0.244
Tumor size						＜ 0.001		0.019
＜3	Reference		Reference		Reference		Reference
3-5	1.28(1.07-1.54)	0.007	0.89(0.74-1.06)	0.189	1.28(1.07-1.54)	0.007	0.89(0.74-1.06)	0.189
5-7	1.48(1.21-1.81)	< 0.001	0.8(0.65-0.99)	0.039	1.48(1.21-1.81)	＜ 0.001	0.8(0.65-0.99)	0.039
≥7	1.96(1.54-2.48)	< 0.001	0.76(0.56-1.03)	0.078	1.96(1.54-2.48)	＜ 0.001	0.76(0.56-1.03)	0.078
Months.from.diagnosis.to.treatment	1.02(0.96-1.07)	0.542	1.19(1.14-1.24)	< 0.001	1.02(0.96-1.07)	0.542	1.19(1.14-1.24)	＜0.001
Cherapy						0.073		0.059
Surgery	Reference		Reference		Reference		Reference
Combined	0.88(0.77-1.01)	0.073	1.15(0.99-1.34)	0.059	0.88(0.77-1.01)	0.073	1.15(0.99-1.34)	0.059

Patient survival varies significantly in cases of rectal cancer, irrespective of whether all patients undergo surgical resection and receive chemotherapy [22]. High-risk patients with rectal cancer may experience improved overall survival if they receive personalized treatment following surgical resection. Currently, the TNM staging system is commonly used to predict individual survival in patients with rectal cancer, but it has limitations in its accuracy. In recent years, several prognostic models have been developed to address this issue [23-25], however, there is currently no ideal survival prediction model specifically designed for older patients with rectal cancer. This is concerning as elderly patients make up a significant portion of rectal cancer patients and have distinct biological and clinical characteristics compared to younger patients. The physical condition of elderly patients naturally deteriorates with age, and they often have multiple chronic illnesses and a reduced tolerance for treatment. Furthermore, the efficacy of therapy in elderly patients is still debated due to the limited number of studies conducted on this specific population. As a result, treatment decisions are often based on the subjective opinions of clinicians rather than concrete evidence. Therefore, the development of an individualized risk assessment model for this demographic is of great importance.

Through the use of univariate and multivariate COX regression analyses, as well as implementing the LASSO regression algorithm, it was determined that variables such as marital status, grade, T stage, N stage, CEA, tumor size, surgery, and chemotherapy are independent risk factors affecting the prognosis of CSS. Additionally, variables like grade, chemotherapy, and the time period between diagnosis and treatment are independent risk factors influencing the prognosis of OS. To present the findings of the regression analysis, a nomogram was developed, which not only provides a visual representation of the results but also allows for the prediction of the survival probability in elderly patients with rectal cancer. Traditionally, CEA has served as one of the tumor biomarkers employed to anticipate recurrence, prognosis, and treatment outcomes in these patients [26-27]. An elevated CEA level generally signifies the potential presence of larger tumors, increased lymph node metastases, and lower quality of differentiation [28]. In our research, the CEA level showed significance in both univariate and multivariate regression analyses, and was ultimately included in the development of the nomogram model. Similar to our study, Shang et al. discovered that combining surgery with chemotherapy leads to higher survival rates for patients [29]. However, Liu et al. found that patients over the age of 75 with stage II-III colorectal cancer who receive adjuvant chemotherapy or not face similar risks of postoperative recurrence. It should be noted that the number of participants in the study was relatively small and the chemotherapy regimens used were varied [30]. Hence, it is crucial to factor in various treatment methods during the formulation of a treatment strategy, encompassing surgical procedures as well as radiotherapy/chemotherapy, in order to enhance patient survival rates and overall well-being.

Patients who cannot undergo surgery have been found to benefit from radiotherapy and chemotherapy in terms of survival. The methods of treating locally advanced rectal cancer have changed over time, giving patients various standard treatment options. These options include different radiation schedules, sequencing of different treatment modalities, and in some cases, the possibility of not needing surgery. A neoadjuvant therapy approach called TNT has proven successful in certain patients, enabling them to achieve a complete response to chemotherapy and radiation, potentially eliminating the need for surgery [31]. In our study, the receipt of radiation therapy did not significantly impact the prognosis of survival and was not considered in the risk assessment model. One possible reason for this was that patients experienced severe acute and long-term toxic effects, making it difficult for them to tolerate the therapy. The RAPIDO study found no notable differences in the frequency or severity of side effects between short and long course radiation. Additionally, the use of radio-sensitizing chemotherapy alongside radiation therapy could increase the occurrence of toxicities. The decision to undergo surgery also influenced gastrointestinal toxicities and the time required for recovery. Late toxicities resulting from radiation therapy typically emerge months after the completion of treatment and can persist or appear throughout a person's lifetime. These toxicities may not be reversible, and they are challenging to assess due to the lack of patient follow-up visits and potential loss to follow-up [32-33].

In our analysis of subgroups, we compared patients who underwent surgery alone to those who underwent surgery in addition to radiation therapy or chemotherapy. By adjusting for variables that could affect CSS and OS using PSM, we discovered that there was no significant difference in CSS or OS between the surgery-only group and the combined treatment group. These findings may contrast with certain studies that found neoadjuvant SCRT and sequential chemotherapy followed by delayed surgery to be safe and effective for older patients with locally advanced rectal cancer compared to younger patients [34]. While a combined therapy approach can reduce the extent of locally advanced rectal cancer and slightly decrease the likelihood of distant recurrence, older patients are exposed to the potential toxicity of doublet or triplet oxaliplatin-based therapy when receiving adjuvant chemotherapy after surgery, with questionable benefits in the adjuvant and metastatic stages [35]. Bowel and rectal inflammation caused by radiation is responsible for the majority of the toxic side effects, including diarrhea, cramping, and urgency. When surgery and radiation are combined, these symptoms can worsen. In patients undergoing doublet chemotherapy, neurotoxicity is the most frequently observed long-term toxicity [33]. In the context of longer life spans, it is important to tailor the treatment of older patients in order to find a balance between the advantages of therapy and the potential decrease in quality of life caused by the side effects of additional treatments. Age should not be viewed as a hindrance to receiving chemotherapy and proper care [36-37].

While the TNM staging system is an essential tool for prognosis prediction, it fails to include certain significant prognostic factors like age, resulting in inadequate accuracy in its predictive outcomes. Our nomogram, on the other hand, not only incorporates the parameters of the AJCC staging system but also takes into account individual demographic and pathological characteristics, enabling doctors to distinguish the group that will benefit from chemotherapy. As a result, it offers a more comprehensive and convenient approach. Furthermore, elderly individuals with rectal cancer often succumb to chronic diseases such as heart disease, lung disease, and cerebrovascular disease, rather than the rectal cancer itself [38]. Survival analysis methods commonly used, such as Kaplan-Meier analysis and Cox proportional hazards regression analysis, have a tendency to overestimate cancer mortality by considering deaths from other causes as censored events [39]. Therefore, CSS was selected as the endpoint of this research, thereby eliminating the influence of mortality caused by other illnesses and enhancing the precision of the findings.

Despite successfully creating and confirming a nomogram for predicting the survival probability of older patients with Rectal cancer, our study also had some limitations. Firstly, we had a limited amount of data available, and we utilized the SEER database for a retrospective study. This may have resulted in information bias, so we encourage further randomized controlled trials to validate our models. Secondly, we did not have external validation from other institutions. Many popular nomograms currently in use incorporate clinical examination data from patients, most of whom are from the American population. Although our model performed well in our own cohort, which underwent strict validation, external validation from multiple institutions would offer more convincing evidence.

Overall, our research developed and verified a novel predictive nomogram that can be used by doctors to accurately assess the individual survival of postoperative older patients with rectal cancer. This tool will also help identify high-risk patients who may require more intensive treatment approaches. Additionally, our results highlight the importance of carefully administering adjuvant therapy and follow-up plans to older patients following surgery.

Author contributions

Study conception and design: Wei-Ming Zhang. Data Acquisition and quality control: all authors. Statistical analysis: Si-Kai Nong and Qi-Yan Mo. Manuscript preparation: Si-Kai Nong and Huan Huang. Manuscript review: all authors.

Acknowledgments

This work was supported by the Guangxi Medicine and Health Self-financing Program(Z20201311).

Conflict of interest

The author reports no conflict of interest in this work.

Data availability statement

The data that support the findings of this study are available in the Surveillance, Epidemiology, and End Results database (http://seer.cancer.gov/SEERstat).

Ethics statement

This study was approved by the Ethics and Human Subject Committee of Wuming Hospital of Guangxi Medical University.

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No competing interests reported.

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A nomogram to predict survival in patients older than 65 years with rectal cancer: A SEER-based population study

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