Abstract
OBJECTIVES
The benefits of neoadjuvant therapy prior to surgery for patients with locally advanced oesophageal cancer have been well established by multiple trials. However, there may be socioeconomic barriers impacting equitable administration. We aim to identify whether disparities exist in the uptake of neoadjuvant therapy among patients with loco-regional oesophageal cancer.
METHODS
We queried the National Cancer Database to identify patients with clinical stage II–III oesophageal cancer who underwent surgical resection (2006–2020). Logistic regression was performed to identify associations between sociodemographic factors and uptake of neoadjuvant therapy. In propensity score-matched groups, survival was evaluated using the Kaplan–Meier method.
RESULTS
Among 19 748 clinical stage II–III patients, 85% (n = 16 781) received neoadjuvant therapy and 15% (n = 2967) underwent upfront surgery. Rates of neoadjuvant uptake increased over time. On multivariable analysis after adjusting by clinical stage, factors associated with lower rates of neoadjuvant therapy included older age (age ≥70, adjusted odds ratio 0.52; 95% confidence interval 0.47–0.57; P < 0.001), female sex (0.76; 0.69–0.85; P < 0.001), Black race (0.77; 0.63–0.94; P = 0.009), more comorbidities (0.76; 0.65–0.85; P < 0.001) and government rather than private insurance (0.84; 0.76–0.93; P < 0.001). In a propensity-matched cohort accounting for these variables, neoadjuvant treatment was associated with improved 5-year overall survival compared to upfront surgery (41.1% vs 35.4%, P < 0.001).
CONCLUSIONS
Several sociodemographic factors are associated with the delivery of neoadjuvant therapy in patients with oesophageal cancer, including age, sex, race, and insurance status. Interventions can be put into place to target vulnerable patients and ensure equitable delivery of care.
Keywords: Oesophageal cancer, Neoadjuvant therapy, Locoregional oesophageal disease, Equitable surgical care, Healthcare insurance, Disparities
Oesophageal cancer is the 6th leading cause of cancer-related mortality worldwide, with over 540 000 new cases diagnosed annually [1].
Graphical abstract
INTRODUCTION
Oesophageal cancer is the 6th leading cause of cancer-related mortality worldwide, with over 540 000 new cases diagnosed annually [1]. The incidence and mortality rate of this disease are predicted to increase by more than 50% over the next 15 years [2]. Neoadjuvant therapy (NT) followed by surgical resection has emerged as a cornerstone in the management of locally advanced oesophageal cancer [3–9]. This multimodal approach, particularly the combination of systemic therapy with or without radiation and surgery, has been shown to significantly improve overall survival (OS) compared to surgery alone, without increasing the risk of treatment-related mortality [10, 11]. NT offers several benefits in the treatment of oesophageal cancer, including tumour size reduction, decreased surgical difficulty and an improved rate of complete (R0) resection [12].
Despite these clinical advantages, the adoption of NT across diverse patient populations remains inconsistent, with many clinically eligible patients not receiving this therapy. Previous research has documented significant disparities in the receipt of NT across various cancer types, often influenced by socioeconomic status (SES) and demographic factors [13–17]. In oesophageal cancer, studies have revealed that patients with lower SES are less likely to be offered surgical resection, receive a 2nd opinion or maintain employment during treatment, further exacerbating health inequities [18].
Understanding disparities is crucial for developing targeted interventions to ensure the equitable delivery of care for all patients with oesophageal cancer. However, there is currently a gap in the literature regarding disparities in the delivery of NT for oesophageal cancer. This study aims to investigate whether socioeconomic or demographic disparities exist in the uptake of NT among patients with clinical stage II–III oesophageal cancer undergoing surgical resection in the USA and to assess the impact of NT on OS.
MATERIALS AND METHODS
Data source
The National Cancer Database (NCDB) utilized in this study is a hospital-based tumour registry supported by the American Cancer Society and the American College of Surgeons since 1989 [19]. The database gathers data from ∼1500 hospitals accredited by the American College of Surgeons Commission on Cancer, covering more than 70% of newly diagnosed cancer cases across the country, with coverage varying slightly each year based on hospital reporting. The data are coded and reported following protocols established by the North American Association of Central Cancer Registries. Data within the NCDB comply with the privacy standards set by the Health Insurance Portability and Accountability Act. The NCDB has not verified the statistical validity of the analyses conducted or the conclusions drawn by the authors and is not responsible for them.
Patient selection and study design
This retrospective cohort study used data from the NCDB. The study included adult patients aged ≥18 diagnosed with clinical stage II or III oesophageal cancer who underwent surgical resection between 2006 and 2020. This time frame was selected to ensure consistent data availability in the NCDB and include years prior to the wider standardization of NT to observe historical trends and early adopters. Inclusion criteria were based on the availability of complete sociodemographic and clinical data within the NCDB. Patients who did not receive either NT or upfront surgery (S) were excluded from the analysis. To ensure our study population only included patients who received intended neoadjuvant treatment followed by surgical resection, we excluded those who did not proceed to surgery due to inadequate response to NT. A complete-case analysis was conducted under the assumption of data missing at random. Follow-up time was reported as median and interquartile range (IQR) for each group [20]. The cohort was divided into 2 groups based on whether they received NT or S (Figure 1). NT refers to any form of systemic chemotherapy or radiation before surgery.
Figure 1:
Flow diagram of the study population selection criteria. NCDB: National Cancer Database.
Sociodemographic and clinical characteristics, including age, sex, race, ethnicity, Charlson Comorbidity Index (CCI), insurance status, income level (based on median household income for patient’s zip code), education level (percentage of patients living in ZIP code areas without a high school education), year of diagnosis (continuous), practice setting, facility location (geographic location based on US Census Bureau-designated regions), treatment facility type, distance to facility, clinical stage (II or III), histology, primary tumour site and surgical approach were included for descriptive analysis. Patient demographic data, such as race and ethnicity, were presented in the study as collected and reported by the NCDB [21]. Outcomes variables were evaluated, including resection margins, length of stay, unplanned readmission, 30- and 90-day mortality and OS. All NCDB variables utilized in this study are presented in Supplementary Material 1.
Study objectives
The primary objective was to assess whether sociodemographic factors influence the uptake of NT in patients with loco-regional oesophageal cancer. Secondary objectives included evaluating the impact of NT on OS in a propensity score-matched cohort and identifying trends in NT utilization over the study period.
Statistical analysis
Descriptive statistics were used to summarize patient characteristics, and trends in NT utilization were assessed using the chi-squared test for trend to illustrate the increased utilization of this treatment regimen over time. Continuous variables are presented as medians with IQRs, and categorical variables as frequencies and percentages. The Shapiro–Wilk test and Q–Q plots were employed to confirm the normality of continuous data. Based on these assessments, we used the Student’s t-test for normally distributed variables and the Mann–Whitney U-test for non-normally distributed ones. Chi-squared analyses were applied to categorical variables.
The association of the variables of interest with NT was assessed with a multivariable logistic regression model with backward stepwise conditional elimination based on clinical importance and significance in the univariable model. The model included age, sex, race, ethnicity, insurance status, median income, CCI, geographic location, clinical stage and year of diagnosis. Covariates were selected based on clinical reasoning, as well as the univariable analysis findings. Correlation coefficients were examined to rule out multicollinearity, and the Hosmer–Lemeshow goodness-of-fit test was evaluated.
Propensity score-matching was used to account for potential confounders in the survival analysis when comparing those who underwent NT to those undergoing upfront surgical resection. The groups were matched using the nearest-neighbour logistic regression algorithm with no replacement (calliper size, 0.0001), controlling for age, sex, race, ethnicity, insurance status, CCI, histology and clinical stage (Supplementary Material 2). The balance of covariates between matched groups was assessed using standardized mean differences, with standardized mean differences <0.1 indicating good balance. The overlap in propensity score distributions was visually assessed using density plots to confirm sufficient overlap between groups [22]. To compare outcomes between matched groups, paired statistical tests were used for continuous and categorical variables to account for the dependency structure of matched pairs [23].
Survival analysis was conducted with Kaplan–Meier curves comparing the NT and S groups using the log-rank test stratified by match pairs. OS was defined as the time of surgical resection to the last follow-up or death. The Cox proportional hazard regression method was used to estimate the hazard ratio with a corresponding 95% confidence interval (CI) for receiving NT. Linearized rates for the follow-up period are provided for a more detailed assessment of survival patterns (Supplementary Material 6).
All statistical tests were two-sided and considered significant with a P-value <0.05. Statistical analysis was performed using SPSS v.28 (IBM Corporation, Armonk, NY.) and R Core Team 4.1 (R Foundation for statistical computing, Vienna, Austria).
Ethical statement
This research was conducted in accordance with the 1964 Helsinki Declaration and its subsequent amendments and was reviewed by the Institutional Review Board. Since all the data in this study were de-identified, under IRB #2013-2570, the requirement for informed consent was waived on 1 December 2022.
RESULTS
Between 2006 and 2020, 19 748 patients with clinical stage II–III oesophageal cancer underwent surgical resection within the NCDB. Among them, 85% (n = 16 781) received NT and 15% (n = 2967) underwent S upfront. Rates of NT uptake increased over time, with 76.6% (N = 7164) of all clinical stage II and 92.6% (N = 9617) of all clinical stage III patients receiving neoadjuvant treatment (Supplementary Material 3).
Table 1 compares sociodemographic characteristics of patients undergoing NT and S. The NT recipients were younger (age < 70, 87.4% rate of NT versus age ≥ 70, 77.3% rate of NT, P < 0.001), male (85.7% versus female, 81.6%, P < 0.001) and had fewer comorbidities (CCI 0–1, 85.3% versus CCI ≥ 2, 81.4%, P < 0.001). Black patients (Black patients, 81.1% versus White patients, 85.2%, P < 0.001), those with government insurance (82.7% versus private insurance 87.7%, P < 0.001) and lower median income (84.2% vs ≥$57 857, 85.4%, P = 0.02) were less likely to receive NT. NT uptake varied by geographic facility location.
Table 1:
Baseline demographic characteristics of the study population patients
| Variable | Upfront surgery, N = 2967 | Neoadjuvant therapy, N = 16 781 | P-value |
|---|---|---|---|
| Age, n (%)a | <0.001 | ||
| Age <70, N = 14 971 (75.8%) | 1881 (12.6%) | 13 090 (87.4%) | |
| Age >70, N = 4777 (24.2%) | 1086 (22.7%) | 3691 (77.3%) | |
| Sex, n (%)a | <0.001 | ||
| Male, N = 16 362 (82.9%) | 2343 (14.3%) | 14 019 (85.7%) | |
| Female, N = 3386 (17.1%) | 624 (18.4%) | 2762 (81.6%) | |
| Race, n (%)a | <0.001 | ||
| White, N = 18 296 (92.6%) | 2699 (14.8%) | 15 597 (85.2%) | |
| Black, N = 801 (4.1%) | 151 (18.9%) | 650 (81.1%) | |
| Other, N = 651 (3.3%) | 117 (18%) | 534 (82%) | |
| Ethnicity, n (%)a | <0.001 | ||
| Non-Hispanic, N = 18 523 (93.8%) | 2732 (14.7%) | 15 791 (85.3%) | |
| Hispanic, N = 612 (3.1%) | 86 (14.1%) | 526 (85.9%) | |
| Charlson Comorbidity Index, n (%)a | <0.001 | ||
| 0 or 1, N = 18 176 (92%) | 2675 (14.7%) | 15 501 (85.3%) | |
| ≥2, N = 1572 (8%) | 292 (18.6%) | 1280 (81.4%) | |
| Insurance type, n (%)a | <0.001 | ||
| Private, N = 8693 (44%) | 1066 (12.3%) | 7627 (87.7%) | |
| Uninsured, N = 344 (1.7%) | 53 (15.4%) | 291 (84.6%) | |
| Government, N = 10 711 (54.2%) | 1848 (17.3%) | 8863 (82.7%) | |
| Median income, n (%)a | 0.020 | ||
| ≥$57 857, N = 12 535 (63.5%) | 1827 (14.6%) | 10 708 (85.4%) | |
| <$57 857, N = 7213 (36.5%) | 1140 (15.8%) | 6073 (84.2%) |
Percent per row.
Clinical characteristics of the study population are presented in Table 2. The population was relatively balanced between patients with clinical stage II (47.4%, N = 9358) and stage III (52.6%, N = 10 390) cancer, with clinical stage III patients undergoing treatment with NT more often (cII, 76.6%, N = 7164 versus cIII, 92.6%, N = 9617). Of the total population, 79.5% (N = 15 696) had adenocarcinoma and 17.1% (N = 3384) had squamous cell carcinoma, with 86.6% (N = 13 598) and 79.3% (N = 2685) receiving NT, respectively. Patients treated with robot-assisted surgery were more likely to have undergone NT (92.1% versus thoracoscopic, 84.1% versus open, 88%).
Table 2:
Clinical characteristics of the study population patients
| Variable | Upfront surgery, N = 2967 | Neoadjuvant therapy, N = 16 781 | P-value |
|---|---|---|---|
| AJCC Clinical Stage, n (%)a | <0.001 | ||
| II, N = 9358 (47.4%) | 2194 (23.4%) | 7164 (76.6%) | |
| III, N = 10 390 (52.6%) | 773 (7.4%) | 9617 (92.6%) | |
| Histology, n (%)a | <0.001 | ||
| Adenocarcinoma, N = 15 696 (79.5%) | 2098 (13.4%) | 13 598 (86.6%) | |
| Squamous Cell Carcinoma, N = 3384 (17.1%) | 699 (20.7%) | 2685 (79.3%) | |
| Other, N = 669 (3.4%) | 170 (25.4%) | 498 (74.4%) | |
| Primary site, n (%)a | <0.001 | ||
| Cervical, N = 337 (1.7%) | 134 (39.8%) | 203 (60.2%) | |
| Thoracic, N = 2259 (11.4%) | 437 (19.3%) | 1822 (80.7%) | |
| Abdominal, N = 15 413 (78%) | 2047 (13.3%) | 13 366 (86.7%) | |
| Overlapping, N = 819 (4.1%) | 117 (14.3%) | 702 (85.7%) | |
| Surgical approach, n (%)a | <0.001 | ||
| Robot-assisted, N = 1780 (10.4%) | 141 (7.9%) | 1639 (92.1%) | |
| Thoracoscopic, N = 3820 (22.3%) | 609 (15.9%) | 3211 (84.1%) | |
| Open, N = 6889 (40.2%) | 830 (12%) | 6059 (88%) | |
| MIS converted to open, N = 454 (2.6%) | 51 (11.2%) | 403 (88.8%) | |
| Extent of resection, n (%)a | <0.001 | ||
| Partial and less, N = 5742 (29.1%) | 1164 (20.3%) | 4578 (79.7%) | |
| Esophagectomy, N = 14 006 (70.9%) | 1803 (12.9%) | 12 203 (87.1%) |
MIS: minimally invasive surgery.
Percent per row.
Multivariable analyses of sociodemographic factors associated with receiving NT are summarized in Supplementary Material 4. On multivariable analysis after adjusting by clinical stage, factors associated with lower rates of NT included older age [age ≥70, adjusted odds ratio (aOR) 0.52; 95% CI 0.47–0.57; P < 0.001], female sex (aOR 0.76; 95% CI 0.69–0.85; P < 0.001), Black race (aOR 0.77; 95% CI 0.63–0.94; P = 0.009), higher numbers of comorbidities (aOR 0.76; 95% CI 0.65–0.85; P < 0.001), government rather than private insurance (aOR 0.84; 95% CI 0.76–0.93; P < 0.001), being diagnosed in a later year (year of diagnosis is a continuous variable, aOR 1.10; 95% CI 1.09–1.12; P < 0.001) and geographic location (Fig. 2).
Figure 2:
Forest plot showing the association of patient factors with neoadjuvant therapy delivery. Adjusted odds ratios with 95% confidence intervals reflect associations rather than causal effects in log-scale. Variables included in the analysis but not shown in the figure: clinical stage, geographic location and year of diagnosis.
We next sought to characterize perioperative outcomes and survival in those patients undergoing NT compared to upfront S. Those patients who received NT had a higher rate of R0 resection margins (NT, 91% versus S, 76.8%, P < 0.001), as shown in Table 3. The median length of stay was similar between the 2 groups (9 days, P = 0.97). Those in the S group had a higher unplanned 30-day readmission rate (5.4% vs 5.1%, P < 0.001), 30-day mortality rate (4.6% vs 3.1%, P < 0.001) and 90-day mortality rate (8.9% vs 7.4%, P < 0.001) compared to the NT group. The median follow-up time was 41.4 months (IQR, 17.9–133.7) for the NT group and 32.7 months (IQR, 13.4–110.0) for the S group. The follow-up rate for the NT group was 94.5% (2805/2967) and 92.8% (15 578/16 781) for the S group.
Table 3:
Clinical outcomes and perioperative data of the study population patients
| Variable | Upfront surgery, N = 2967 | Neoadjuvant therapy, N = 16 781 | P-value |
|---|---|---|---|
| Negative margins, n (%)a, N = 17 549 | 2278 (76.8%) | 15 271 (91%) | <0.001 |
| Length of stay (days), median (IQR) | 9 (6–15) | 9 (7–13) | 0.97 |
| Unplanned 30-day readmission, n (%)a, N = 1007 | 159 (5.4%) | 848 (5.1%) | <0.001 |
| 30-day mortality, n (%)a, N = 604 | 124 (4.6%) | 480 (3.1%) | <0.001 |
| 90-day mortality, n (%)a, N = 1389 | 242 (8.9%) | 1147 (7.4%) | 0.011 |
IQR: interquartile range.
Percent per column.
Kaplan–Meier survival curves illustrate the OS after surgical resection for patients treated with NT versus upfront S in both matched and unmatched cohorts (Fig. 3). Those who received NT had a longer median OS after surgical resection compared to those who received S only both before (36.5 vs 30.2 months; log-rank P < 0.001) and after (38.4 vs 30.2 months; log-rank P < 0.001) matching. Cox regression analysis revealed a hazard ratio of 1.16 (95% CI 1.09–1.24; P < 0.001) in favour of NT. After propensity score-matching, with 2730 patients in each group, NT was associated with improved 5-year OS compared to (41.1% vs 35.4%; P < 0.001). Supplementary Materials 5 displays the absolute standardized mean differences for key covariates before and after propensity score matching. After matching, all covariates achieved absolute standardized mean differences below the threshold of 0.1, indicating excellent covariate balance and minimizing confounding.
Figure 3:
Kaplan–Meier survival curves comparing overall survival (OS) between neoadjuvant therapy (NT) and upfront surgery (S) groups. (A) In the matched cohort, and (B) in the unmatched cohort.
DISCUSSION
NT has played a crucial role in improving survival outcomes for patients with locally advanced oesophageal cancer when compared to surgery alone, even as treatment standards have evolved over time [24–26]. Despite these documented benefits, our analysis shows that the adoption of NT in clinical practice has been inconsistent. While 85% of patients with clinical stage II–III oesophageal cancer were treated with NT before surgery, a significant portion of eligible patients did not receive this treatment.
The underutilization of NT appears to be influenced by multiple factors. In our study, disparities were evident across different demographic patient groups. Older age, female sex, Black race, geographic variability and reliance on government insurance were all associated with lower rates of NT delivery. These findings are consistent with existing literature, which highlights how non-clinical factors such as race, insurance status and geographic location contribute to inequities in cancer treatment and outcomes [27–31].
NT was associated with improved OS and better perioperative outcomes, including higher rates of complete (R0) resection and reduced 30-day and 90-day mortality. These findings reinforce the importance of NT in the treatment paradigm for oesophageal cancer and challenge the notion that NT increases surgical risk or complexity [32, 33]. Despite the clear survival benefits associated with NT, its underutilization in certain populations underscores a critical issue in cancer care delivery.
Several underlying factors may contribute to the disparities observed in this study. For older patients, the lower uptake of NT could stem from a combination of physician concerns about treatment-related toxicity and comorbid conditions, patient reluctance due to perceived frailty and possible age-related biases in treatment recommendations [34]. The lower rates of NT among women and Black patients may be reflective of broader systemic issues, including disparities in access to healthcare, differences in health-seeking behaviour and potential biases within the healthcare system. Black patients often face barriers such as lower SES, reduced access to high-quality cancer care facilities and less frequent referrals for advanced treatments, all which may contribute to the observed treatment gap [35].
Addressing these disparities is not only a matter of improving clinical outcomes but also a pressing issue of healthcare equity. Healthcare systems should consider patient navigation programs that assist individuals in overcoming barriers to care, including those related to insurance coverage, transportation and health literacy [36]. Expanding insurance coverage, increasing funding for safety-net hospitals that serve lower-income populations and implementing quality improvement programs focused on reducing disparities in cancer care are necessary steps to ensure that NT and other advanced treatments are accessible to all [37]. Furthermore, integrating sociodemographic considerations into clinical guidelines and decision-making processes could help to standardize care across diverse patient populations, reducing the influence of biases and other non-clinical factors on treatment delivery.
Limitations
The retrospective nature of the analysis inherently introduces the potential for selection bias. Since the study relies on previously collected data, certain patient groups may be more likely to be included or excluded based on unmeasured factors, which could skew the results. The use of registry data imposes constraints on the level of detail available for key variables. The NCDB does not capture data on several important aspects of patient care that could influence the findings.
Additionally, the database does not provide detailed reasons for patients not receiving NT, which could include medical contraindications, logistical challenges or personal choices. The SES variables in this study are derived from aggregated data representing median population characteristics within zip code areas rather than individual-level data, which may not fully capture SES variability within each region. The data do not capture shared decision-making processes, provider-level or organizational factors, which may influence NT uptake and contribute to variations in its utilization across clinical centres. Future research should consider these variables to more fully understand the mechanisms underlying NT utilization.
Furthermore, the reliance on a complete-case analysis under the assumption of data missing at random may introduce bias if the missingness mechanism is non-random or systematically related to outcomes. There are missing data in variables within the NCDB, which can compromise the internal validity of the study. Despite these exclusions, the study population remains substantial, and important findings can be interpreted meaningfully within this cohort.
Future studies should aim to address the identified limitations by incorporating more granular data and exploring the underlying causes of treatment disparities, ultimately contributing to improved outcomes for all patients with oesophageal cancer.
CONCLUSIONS
Significant disparities are observed in the utilization of NT for oesophageal cancer, with the association of sociodemographic factors such as age, sex, race and insurance status. Despite the clear survival benefits of NT, its uneven application across different patient groups suggests potential inequities in cancer care delivery. As cancer care advances into an era of more personalized and effective treatments, prioritizing equity in care delivery remains essential. Addressing the disparities discussed in this study and considering targeted interventions may support improved access to NT for all patients with oesophageal cancer.
Supplementary Material
ABBREVIATIONS
- CCI
Charlson Comorbidity Index
- IQR
Interquartile range
- NCDB
National Cancer Database
- NT
Neoadjuvant therapy
- OS
Overall survival
- S
Upfront surgery
- SES
Socioeconomic status
Contributor Information
Rajika Jindani, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Jorge Humberto Rodriguez-Quintero, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Isaac Loh, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Grace Ha, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Justin Olivera, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Justin Rosario, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Roger Zhu, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Mohamed K Kamel, Divison of Thoracic and Foregut Surgery, University of Rochester Medical Center, Rochester, NY, USA.
Marc Vimolratana, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Neel P Chudgar, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
Brendon M Stiles, Department of Cardiothoracic Surgery, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA.
SUPPLEMENTARY MATERIAL
Supplementary material is available at EJCTS online.
FUNDING
This work was supported by the National Institutes of Health (grant number 5T32CA200561-10 to R.J.).
Conflict of interest: Brendon M. Stiles: AstraZeneca, Bristol Myers Squibb, Genentech/Roche, Regeneron, Pfizer, Gala Therapeutics, Medtronic, Merck, Genentech/Roche, Bristol Myers Squibb Foundation, Lung Cancer Research Foundation, Lungevity, SIGA Technologies. Neel P. Chudgar: AstraZeneca. The remaining authors have no other conflicts of interest to declare.
DATA AVAILABILITY
The data underlying this article were provided by the National Cancer Institute/American College of Surgeon’s National Cancer Database by request.
Author contributions
Rajika Jindani: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software; Writing—original draft. Jorge Humberto Rodriguez-Quintero: Investigation; Methodology; Resources; Software. Isaac Loh: Conceptualization; Formal analysis; Software. Grace Ha: Conceptualization; Methodology; Writing—original draft. Justin Olivera: Conceptualization; Investigation; Methodology. Justin Rosario: Conceptualization; Investigation; Methodology. Roger Zhu: Conceptualization; Investigation; Methodology. Mohamed K. Kamel: Conceptualization; Investigation; Methodology. Marc Vimolratana: Conceptualization; Investigation; Methodology. Neel Chudgar: Conceptualization; Investigation; Methodology; Supervision; Writing—review & editing. Brendon M. Stiles: Conceptualization; Investigation; Methodology; Supervision; Writing—review & editing.
Reviewer information
European Journal of Cardio-Thoracic Surgery thanks Nuria M Novoa, Oriana Ciani and the other anonymous reviewers for their contribution to the peer review process of this article.
Presented as an oral presentation at the ESTS 31st Annual Meeting, Barcelona, Spain, May 2024.
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Data Availability Statement
The data underlying this article were provided by the National Cancer Institute/American College of Surgeon’s National Cancer Database by request.