Survival benefit of adjuvant chemotherapy for duodenal adenocarcinoma: a retrospective cohort study and propensity score-matched analysis

Introduction

Duodenal adenocarcinoma (DAC) originates from the glandular epithelial cells of the duodenum and is a rare malignancy of the digestive tract, with an incidence rate of approximately 0.2–0.5 per 100,000 person-years (1,2). Due to its concealed location and subtle early symptoms, most patients present with clinical signs of obstruction, such as nausea, vomiting, or postprandial fullness. Chronic bleeding is common, with over 66% of patients testing positive for fecal occult blood (3). Typically, DAC is diagnosed at a mid-to-late stage, complicating and challenging treatment (4,5). Surgery remains the only curative option available (6). Treatment options range from endoscopic resection to segmental duodenal resection to pancreaticoduodenectomy (3,7,8).

For small bowel adenocarcinoma (SBA) patients at high risk of recurrence, adjuvant chemotherapy (AC) is often recommended (9). Evidence suggests that AC can improve patient outcomes. However, postoperative recurrence rates are substantial, particularly for DAC compared to other small bowel segments, underscoring the critical need for effective adjuvant strategies (10). In this context, AC is increasingly employed in clinical practice, yet its survival benefit for DAC patients remains a subject of considerable debate. The evidence base is limited and contradictory, comprising underpowered single-center retrospective studies and analyses of larger databases, such as the National Cancer Database (NCDB), which have yielded conflicting conclusions (11,12). This controversy stems from several key challenges: the inherent rarity of DAC, which makes large randomized trials difficult; the potential for selection bias in retrospective studies, where patients with more advanced or aggressive disease are preferentially selected for AC; and limited evidence on which patient subgroups benefit most. The Surveillance, Epidemiology, and End Results (SEER) database is well-suited to address these challenges. Its large, population-based sample helps mitigate the limited statistical power of smaller studies. Additionally, statistical methods like multivariable adjustment and propensity score matching (PSM) can be used to account for differences between patient groups who did and did not receive AC.

Therefore, to help clarify the role of AC in DAC, we performed this retrospective cohort study using SEER data. Our primary aims were: (I) to examine the relationship between AC and survival after DAC surgery, and (II) to investigate variations in AC benefit among different patient subgroups. We present this article in accordance with the STROBE reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1155/rc).

Methods

Study cohort

The patient data for our study were sourced from the SEER database (www.seer.cancer.gov). Using SEER*Stat software, we extracted the SEER Research Data from the SEER*Stat Database: Incidence-SEER Research Data, 17 Registries, Nov 2023 Sub (2000–2021), which was released in April 2024, based on the November 2023 submission. We referenced the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) Code C17.0-Duodenum to select all malignant tumors with primary sites in the duodenum from 2000 to 2021. Based on data availability, we harmonized clinical stage information across different coding systems, including: Combined Summary Stage (2004+), Summary stage 2000 (1998–2017), SEER Combined Summary Stage 2000 (2004–2017) and SEER historic stage A (1973–2015), histological grade [Grade Recode (thru 2017)]. To align with the 8th edition of the American Joint Committee on Cancer (AJCC) staging system, where the tumor stage was classified according to the Tumor, Node, Metastasis (TNM) (13), T stage [Derived EOD 2018 T (2018+), Derived AJCC T, 7th ed (2010–2015), Derived SEER Combined T (2016–2017), Derived AJCC T, 6th ed (2004–2015)], N stage [Derived EOD 2018 N (2018+), Derived AJCC N, 7th ed (2010–2015), Derived SEER Combined N (2016–2017), Derived AJCC N, 6th ed (2004–2015)], M stage [Derived EOD 2018 M (2018+), Derived AJCC M, 7th ed (2010–2015), Derived SEER Combined M (2016–2017), Derived AJCC M, 6th ed (2004–2015)] and TNM stage [Derived EOD 2018 Stage Group (2018+), Derived AJCC Stage Group, 7th ed (2010–2015), 7th Edition Stage Group Recode (2016–2017), Derived SEER Cmb Stg Grp (2016–2017), Derived AJCC Stage Group, 6th ed (2004–2015)] were also evaluated.

The data filtering process is illustrated in Figure 1. Initially, a cohort of patients with primary DAC was selected for the study. Patients lacking T, N, M, or TNM staging, clinical staging, pathological grading, or with distant metastases were excluded. Additionally, patients with unknown race and marital status were also excluded. Subsequently, among the patients who had undergone surgery, those who died within 1 month post-operation were excluded due to the possibility that their deaths were surgery-related. Patients with unspecified survival times were also excluded. For the classification of AC status, the SEER database variable “Chemotherapy recode (yes, no/unk)” includes only “Yes” and “No/Unknown” categories. Therefore, we incorporated the variable “RX Summ--Systemic/Sur Seq (2007+)” to jointly evaluate whether AC was administered. Patients who met both criteria of “Chemotherapy recode: Yes” and “RX Summ--Systemic/Sur Seq: Systemic therapy after surgery” were classified into the AC group. Conversely, patients who met the criteria of “Chemotherapy recode: No/Unknown” and “RX Summ--Systemic/Sur Seq: No systemic therapy and/or surgical procedures” were classified into the no adjuvant chemotherapy (NAC) group.

Figure 1 Flowchart of study cohort. SEER, Surveillance, Epidemiology, and End Results; TNM, Tumor, Node, Metastasis.

Finally, 3,296 patients meeting the study criteria were included in the formal analysis (Figure 1). The analyzed variables for each patient included: age, sex, race, marital status, clinical stage, histological grade, T stage, N stage, M stage, TNM stage, chemotherapy, radiotherapy, survival time and status. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. It was reported following the Strengthening the Reporting of Cohort Studies in Surgery (STROCSS) guidelines (14). Since the SEER database anonymizes patient information and is openly accessible and freely available to researchers, this study did not require institutional review board approval.

Statistical analysis

All statistical analyses in this study were carried out using R software version 4.2.3 (http://www.r-project.org/). The χ² test was used to examine the relationships between AC and categorical variables. PSM was conducted to calibrate the effects of the baseline data differences. The AC group was matched to the NAC group using the MatchIt package in the R project. PSM algorithm took all potential confounders (including age, sex, race, marital status, clinical stage, histological grade, and TNM stage) into account based on the nearest neighbor method, without a replacement (caliper width =0.05 of the standard deviation of the logit of the propensity score). The balance of covariates before and after matching was assessed using standardized mean differences (SMD), with an SMD <0.10 indicating a negligible difference between groups.

Kaplan-Meier curves were generated using GraphPad Prism 9.5 software (GraphPad Software Inc., San Diego, CA, USA) to assess overall survival (OS) and cancer-specific survival (CSS) differences in both the overall population and subgroups. A log-rank test was used to compare survival differences by patient, tumor, and treatment-related characteristics. OS was defined as the time from diagnosis to death due to any cause or the last follow-up. Cancer CSS was defined as the time from diagnosis to death from cancer. Univariate and multivariate Cox proportional hazards regression models were applied to identifying independent prognostic factors. For multivariate analysis in both the overall and matched populations, we used the Cox proportional hazards model adjusting related variables with P values <0.05 in univariate analyses. Furthermore, we examined the interactions between AC and each significant prognostic variable by adding interaction terms to multivariable Cox regression model that was adjusted for all factors. For each interaction coefficient in the subgroups, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. All P values less than 0.05 were considered statistically significant.

Results

Patient characteristics and propensity score matching

This study analyzed data from 3,296 patients with pathologically confirmed DAC. The cohort consisted of 1,738 men (52.7%) and 1,558 women (47.3%). The majority of patients were White (n=2,350, 71.3%). Due to variations in the AJCC TNM staging criteria across different periods, we standardized the TNM staging to the 8th edition, categorizing patients into stage I (n=1,388), stage II (n=842), and stage III (n=1,066). Patients were divided into two groups based on AC status: the AC group (n=704) and the NAC group (n=2,592) (Table 1). Additionally, data on radiotherapy were collected. Our analysis identified several factors significantly associated with the likelihood of receiving AC. Patients who received AC tended to be younger, of specific race, and had more advanced disease characteristics—including clinical stage, tumor grade, T stage, N stage, and TNM stage. Receipt of radiotherapy and marital status were also influencing factors. To mitigate this substantial selection bias, we constructed a propensity score-matched cohort for sensitivity analysis. After matching, adequate balance was achieved for all included variables in the newly constituted cohort of 878 patients (439 per group). The SMDs for all covariates were below 0.1, except for race (SMD =0.171) (Table 1, Figure 2).

Figure 2 Propensity score distribution before and after PSM. PSM, propensity score matching.

Survival and chemotherapy

The median OS was 125 months [95% confidence interval (CI): 111–138], while the median CSS was not reached. The 1-, 3-, and 5-year survival rates were 88.7%, 73.7%, and 64.3%, with corresponding CSS rates of 92.7%, 83.3%, and 77.6% (Figure 3A,3B). Patients were categorized based on whether they received AC. Unadjusted Kaplan-Meier curves of the entire cohort suggested a survival advantage for the NAC group (Figure 3C,3D). However, after PSM created balanced comparison groups, the results were reversed. The analysis indicated a significant improvement in median survival for the AC group compared to the NAC group [60 months (95% CI: 47–92) vs. 35 months (95% CI: 28–49), P<0.001, Figure 3E]. The 1-, 3-, and 5-year OS rates were 83.4%, 47.8%, and 27.6% in the AC group, compared to 67.9%, 40.3%, and 27.6% in the NAC group, respectively. Consistent with the OS benefit, AC also demonstrated a significant improvement in CSS for the matched cohort (median: 102 vs. 114 months; P=0.03, Figure 3F). The 1-, 3-, and 5-year CSS for the AC group were 84.5%, 47.8%, and 27.6%, respectively, compared to 69.7%, 40.3%, and 27.6% in the NAC group.

Figure 3 Kaplan-Meier curves of overall survival and cancer-specific survival before and after PSM. (A,B) All patients before PSM; (C,D) patients stratified by AC before PSM; (E,F) patients stratified by AC after PSM. AC, adjuvant chemotherapy; NAC, no adjuvant chemotherapy; PSM, propensity score matching.

Prognostic factors for survival

To identify independent prognostic factors, we first performed univariable and multivariable Cox regression analyses on the entire cohort as the primary analysis. The univariable Cox regression identified several variables associated with OS and CSS (Table S1), which were subsequently included in the multivariable model. Further analysis revealed that male gender (P<0.001, HR =1.24, 95% CI: 1.09–1.40), age ≥65 years (P<0.001, HR =2.11, 95% CI: 1.85−2.41), moderately differentiated grade (P<0.001, HR =1.51, 95% CI: 1.27−1.78), poor/undifferentiated grade (P<0.001, HR =2.07, 95% CI: 1.69−2.52), T3 stage (P=0.002, HR =1.38, 95% CI: 1.12−1.69), T4 stage (P<0.001, HR =2.03, 95% CI: 1.64−2.52), N1 stage (P<0.001, HR =1.54, 95% CI: 1.32−1.79) and N2 stage (P<0.001, HR =2.56, 95% CI: 2.07−3.16) were independent prognostic risk factors for OS. Conversely, married status (P=0.03, HR =0.82, 95% CI: 0.69−0.98) and receipt of AC (P<0.001, HR =0.63, 95% CI: 0.53−0.74) were independent protective factors for OS. For CSS, independent prognostic risk factors included age ≥65 years (P<0.001, HR =1.64, 95% CI: 1.38−1.96), moderate differentiation (P<0.001, HR =2.8, 95% CI: 2.1−3.74), poor/undifferentiated grade (P<0.001, HR =4.59, 95% CI: 3.37−6.26), T3 stage (P<0.001, HR =2.11, 95% CI: 1.51−2.95), T4 stage (P<0.001 HR =3.36, 95% CI: 2.39−4.74), N1 (P<0.001, HR =2.06, 95% CI: 1.67−2.54) and N2 (P<0.001, HR =3.17, 95% CI: 2.44−4.1). Receipt of AC (P<0.001, HR =0.67, 95% CI: 0.55−0.82) was also an independent protective factor for CSS (Table 2). To verify the robustness of these factors, we performed the same analysis in the matched cohort. The results showed that, except for marital status and T stage, which were no longer independent prognostic factors for OS, all other identified factors remained consistent with the findings from the entire patient cohort (Table 2; Table S1).

Subgroup interaction with chemotherapy

Our previous analysis of the entire cohort identified variables potentially associated with patient prognosis (Table S1). To determine which subgroups derived greater benefit from chemotherapy, we performed interaction analyses for OS and CSS. The results indicated a significant interaction between chemotherapy and age ≥65 years, with older patients deriving a substantial OS benefit (P for interaction <0.001, P<0.001, HR =0.51; 95% CI: 0.42–0.63; Figure 4). A significant interaction was also observed with T stage. While patients with T3 and T4 tumors showed substantial OS benefit from AC, those with T1 stage faced a potential risk of reduced OS (P for interaction =0.003, P<0.001, HR =0.62; 95% CI: 0.47–0.81 for T3; P<0.001, HR =0.56; 95% CI: 0.45–0.69 for T4; P=0.01, HR =2.26; 95% CI: 1.2–4.25 for T1, Figure 4). Furthermore, patients with lymph node involvement also exhibited heightened sensitivity to chemotherapy, demonstrating significant improvements in both OS (P for interaction =0.001; P<0.001, HR =0.60; 95% CI: 0.48–0.75 for N1; P<0.001, HR =0.42; 95% CI: 0.31–0.58 for N2; Figure 4), and CSS (P for interaction =0.003; P =0.002, HR =0.52; 95% CI: 0.34–0.79 for N1; P<0.001, HR =0.17; 95% CI: 0.08–0.35 for N2; Figure 4). In the OS analysis, the interaction between tumor grade and AC approached statistical significance. A trend suggested a more pronounced OS benefit for patients with moderately differentiated and poor/undifferentiated tumors compared to those with well-differentiated tumors (P for interaction =0.068; P<0.001, HR =0.62; 95% CI: 0.47–0.81 for moderately differentiated; P<0.001, HR =0.56; 95% CI: 0.45–0.69 for poor/undifferentiated; Figure 4). No significant interactions were observed in other subgroups.

Figure 4 Forest plot of the association of AC with (A) OS and (B) CSS in subgroup interactions. AC, adjuvant chemotherapy; CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; NAC, no adjuvant chemotherapy; OS, overall survival.

Discussion

Based on a comprehensive analysis of a large, multicenter database covering 21 years, our study provides robust evidence supporting the use of AC in selected patients with DAC following surgical resection. Our primary analysis, based on multivariable Cox regression of the entire cohort, demonstrated that AC was independently associated with significantly improved OS and CSS. Through comprehensive subgroup analysis, we identified specific patient characteristics associated with a superior response to chemotherapy. Specifically, patients aged ≥65 years, those with lymph node involvement (N1/N2), and those with advanced T-stage (T3/T4) were found to benefit more from AC.

Our findings regarding established prognostic factors, such as lymph node metastasis and poor tumor differentiation, align consistently with prior literature (3,15,16). Despite this consensus on baseline risk, a profound controversy persists regarding the therapeutic role of AC. Some studies suggest that chemotherapy does not improve survival in DAC patients (17,18), while others report favorable outcomes (19,20). Ecker et al. (21) analyzed postoperative SBA patients from the NCDB database and discovered that the most significant survival benefit was observed in AJCC stage III patients after matching. Additionally, Overman et al. (22) found that, among postoperative patients with non-metastatic, margin-negative DAC, AC did not show a survival benefit but did improve disease-free survival (DFS). However, some studies have not found a survival benefit from AC for postoperative patients, such as the systematic review by Meijer et al. (23), which included six heterogeneous studies predating 2017. They found no significant difference in 5-year OS with “any type of adjuvant therapy”. The authors identified lymph node metastasis as a key prognostic factor but noted that most studies lacked proper adjustment for such confounders. More recently, the 2024 international multimethod study by Uijterwijk et al. (24), despite employing a rigorous PSM approach, found no significant OS benefit for AC in a matched cohort of approximately 90 DAC patients per group. Building upon previous research, our study represents the largest to date (n=3,296). Our primary analysis was conducted on all enrolled patients, with simultaneous assessment of both OS and CSS. To identify prognostic factors, our models were adjusted for key variables. Furthermore, we employed PSM as a sensitivity analysis, which enhanced the robustness of our findings and increased the likelihood of detecting the true effect of AC.

Certain studies have even explored the benefits of chemotherapy within specific subgroups. For instance, Ecker et al. (25) conducted a retrospective analysis of 1,743 postoperative DAC patients from NCDB. After PSM, they found no survival benefit from chemotherapy, regardless of adequate lymph node assessment. However, their study did not explore the relationship between AC and other factors. Building on this, our study evaluated the differential sensitivity to chemotherapy among patient subgroups. A recent literature analysis of 1,320 patients from the SEER database (2004–2019) examined the effect of AC on survival in DAC (26), which likewise observed a survival benefit with AC. However, our cohort features more recent enrollment dates and a larger sample size. Notably, while previous subgroup analyses typically assessed the effect of AC within separate subgroups, our exploratory analysis went a step further by performing a formal test for interaction. Thus, our research further addresses the heterogeneity of AC, specifically identifying that patients aged ≥65 years or with N1/N2 or T3/T4 disease experience a superior magnitude of benefit from AC, which represents the most clinically valuable finding of our study. Chemotherapy for patients with SBA has long been a topic of debate, with conflicting results in previous studies. Retrospective studies on SBA often utilize 5-fluorouracil (5-FU)-based regimens, including drugs like 5-FU, oxaliplatin, and capecitabine. Due to the rarity of DAC, no chemotherapy regimen has been definitively proven effective for DAC, and no randomized Phase III clinical trials have been conducted to assess its benefits. At the same time, research specific to DAC chemotherapy is scarce. One study involving 33 advanced SBA patients, 78.8% of whom had DAC, evaluated a modified FOLFOX regimen combining oxaliplatin and capecitabine, reporting a response rate of 48.5% (27), higher than previously reported retrospective studies. Despite enrolling patients with poorer performance status and more advanced disease, this study found the modified FOLFOX regimen to be as effective as the standard FOLFOX regimen in other Phase II trials (28). The superior response observed in older patients and those with advanced disease may be attributed to their higher baseline risk of recurrence, resulting in a greater absolute risk reduction from effective systemic therapy. This is consistent with a previous study which reported that patients in high-risk categories showed significant improvement in OS after AC, whereas those in low-risk subgroups did not demonstrate substantial survival benefit from AC, suggesting that the latter may be spared from potential treatment-related toxicities (29). On the other hand, patients with early-stage (T1) DAC had poorer responses to chemotherapy, suggesting that in some cases, survival outcomes may have been negatively impacted. This aligns with another study in which 44% of the 32 DAC patients experienced treatment toxicity following postoperative adjuvant therapy (30). Our findings provide decision-making support for treating high-risk patients, while suggesting that for early-stage, low-risk patients, a postoperative surveillance strategy may be superior to AC, thereby avoiding overtreatment.

Our study presents several strengths. It represents the largest retrospective study to date focusing on AC following surgery for DAC. By employing a primary analysis supplemented by sensitivity analysis, we concurrently evaluated two key prognostic indicators—OS and CSS—thereby enhancing the robustness and reliability of our findings and providing a comprehensive understanding of postoperative outcomes in DAC patients. Additionally, previous studies using the SEER database have not investigated AC effectiveness nor identified sensitive patient subgroups through interaction analysis. Our pioneering focus on chemotherapy sensitivity provides valuable insights for clinical decision-making. The constraints of our study stem from its retrospective design and reliance on the SEER database. The absence of detailed information on specific chemotherapy protocols, surgical procedures, resection margin status, patient comorbidities, tumor biomarkers, and molecular subtypes hindered our ability to refine treatment recommendations. Although propensity score matching achieved adequate balance for most covariates, unmeasured confounders related to the above factors may remain.

Conclusions

In conclusion, this study establishes the survival benefit of postoperative AC in the management of resected DAC, demonstrated in both OS and CSS. By addressing both “whether AC is effective” and “which patients benefit most”, we provide a conceptual framework for treatment individualization. Our results confirm the advantage of AC in DAC and suggest that its use in older patients and those with node-positive disease may be particularly effective, while routine application in early-stage, low-risk patients should be avoided. These findings will help guide more precise and effective clinical application of adjuvant therapy for this rare malignancy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1155/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1155/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1155/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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