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Research Article
* These authors contributed equally
This study presents a two-center ambispective workflow for measuring serum carcinoembryonic antigen, squamous cell carcinoma antigen, and carbohydrate antigen 125 and validating their combined diagnostic and prognostic value in esophageal squamous cell carcinoma.
Esophageal squamous cell carcinoma remains difficult to diagnose early and to stratify accurately before treatment. Conventional tumor-node-metastasis staging provides essential anatomical information but does not fully reflect circulating biological heterogeneity. This two-center ambispective observational study developed and externally validated a combined serum biomarker panel incorporating carcinoembryonic antigen, squamous cell carcinoma antigen, and carbohydrate antigen 125 for diagnostic discrimination and prognostic risk assessment in esophageal squamous cell carcinoma. A total of 277 participants were enrolled from a tertiary hospital and a community healthcare center, including 167 patients with esophageal squamous cell carcinoma, 55 patients with benign esophageal disease, and 55 healthy controls. The tertiary hospital cohort was used for model development and internal validation, and the community center cohort was used for external validation. Pretreatment serum biomarkers were measured before initial treatment, logarithmically transformed, standardized using training-cohort parameters, and entered into diagnostic logistic regression and prognostic Cox proportional hazards models. The combined panel showed higher diagnostic discrimination than any individual marker, with an area under the receiver operating characteristic curve of 0.869 in the training cohort and 0.842 in the external validation cohort. A higher combined biomarker burden was associated with tumor length of at least 5 cm, pT3–4 disease, lymph node metastasis, and tumor-node-metastasis stage III–IV disease. In survival analysis, high combined biomarker burden independently predicted poorer overall survival and progression-free survival after adjustment for major clinicopathological factors. The integrated prognostic model, combining biomarker burden with clinicopathological variables, outperformed the tumor-node-metastasis stage alone, with a training-cohort C-index of 0.792 and an external-validation C-index of 0.761. These findings support the use of the serum carcinoembryonic antigen, squamous cell carcinoma antigen, and carbohydrate antigen 125 panel as a practical, noninvasive adjunct for pretreatment assessment and individualized risk stratification in esophageal squamous cell carcinoma.
Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer and remains a substantial clinical burden in East Asia, particularly in China1. Although endoscopic diagnosis, imaging assessment, surgery, chemoradiotherapy, and systemic treatment have improved clinical management, prognosis remains unsatisfactory for many patients2. One important reason is that the disease is frequently detected after local invasion or nodal spread has already occurred3. Another is that patients with the same tumor-node-metastasis stage may still differ in treatment response, recurrence risk, and survival outcome4. These differences indicate that anatomical staging alone cannot fully describe the biological variability of esophageal squamous cell carcinoma.
Current clinical evaluation relies mainly on endoscopy with biopsy, radiological staging, and postoperative pathological assessment. These methods are indispensable, but they have limitations when used for repeated monitoring, pretreatment risk estimation, or broad screening. Endoscopy is invasive and less suitable for frequent follow-up, whereas computed tomography and endoscopic ultrasonography may miss subtle invasion or small metastatic lesions5. Imaging-based and molecular prognostic models have improved risk prediction in esophageal squamous cell carcinoma, but many require specialized platforms, image post-processing, tissue sequencing, or computational workflows that are not routinely available in all clinical laboratories6,7. A low-cost and repeatable blood-based approach may therefore provide useful complementary information, especially when clinicians need pretreatment risk stratification before definitive therapy.
Serum tumor markers are attractive in this setting because they are routinely available, minimally invasive, and easy to repeat. Carcinoembryonic antigen reflects tumor burden and abnormal cellular differentiation, whereas squamous cell carcinoma antigen is more closely associated with malignant transformation of the squamous epithelium8. Carbohydrate antigen 125 has been linked to systemic inflammatory activity, serosal involvement, and disease progression in several malignancies9. These markers do not represent the same biological signal. Instead, they may capture overlapping but distinct dimensions of tumor burden, squamous phenotype, systemic response, and host-tumor interaction. For this reason, a combined panel may perform better than any single marker when the goal is to identify esophageal squamous cell carcinoma and estimate clinical risk10.
Previous studies have evaluated serum tumor markers, inflammatory indicators, imaging signatures, molecular profiles, and composite prognostic models in esophageal squamous cell carcinoma and related gastrointestinal cancers11,12. However, many published models focus on either diagnosis or prognosis, use single-center cohorts, lack independent external validation, or provide limited details about marker transformation, cutoff selection, calibration, and model reproducibility. The novelty of the present study is the development of a practical three-marker serum panel that uses routinely measured carcinoembryonic antigen, squamous cell carcinoma antigen, and carbohydrate antigen 125 to support both diagnostic discrimination and prognostic risk stratification within a single two-center workflow. The model was developed in a tertiary hospital cohort and then tested without re-optimization in an independent community center cohort, allowing evaluation of stability across different clinical settings.
This study developed and validated a combined serum biomarker panel based on carcinoembryonic antigen, squamous cell carcinoma antigen, and carbohydrate antigen 125. The analysis followed two linked objectives. The first was to evaluate whether the combined panel improved diagnostic discrimination between esophageal squamous cell carcinoma and non-malignant controls compared with individual serum markers. The second was to determine whether pretreatment combined biomarker burden added prognostic information beyond conventional clinicopathological variables and tumor-node-metastasis stage. This structure allowed the study to assess whether a routinely available serum biomarker panel could function as a noninvasive adjunct to pathological diagnosis, imaging assessment, and anatomical staging in pretreatment clinical evaluation.
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This study involving human participants, clinical records, and serum samples was conducted in accordance with the Declaration of Helsinki and institutional requirements for human-subject research13. The study protocol was reviewed and approved by Jiangsu Province Hospital of Chinese Medicine under approval number CJ28J12-45. For retrospectively collected anonymized clinical data and residual serum samples, the requirement for additional written informed consent was waived in accordance with institutional ethics regulations. For prospectively collected samples, written informed consent was obtained before enrollment. All participant information and serum samples were anonymized before analysis. Study identifiers were used for data linkage, and no personally identifiable information was included in the analytical dataset.
Study design and cohort allocation
This two-center ambispective observational study evaluated the diagnostic and prognostic value of a combined serum biomarker panel incorporating carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC-Ag), and carbohydrate antigen 125 (CA125) in esophageal squamous cell carcinoma (ESCC). Retrospective data were obtained from eligible clinical records and residual pretreatment serum samples, and prospective enrollment was used to collect additional pretreatment serum samples and to complete follow-up within the same study framework. Participants were recruited from one tertiary hospital and one community healthcare center between January 2021 and December 2024, with follow-up continuing until December 31, 2025. Center A was the tertiary hospital and served as the training cohort for model development, coefficient estimation, cutoff selection, and internal validation. Center B was the community healthcare center and served as the external validation cohort. The study was reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines14.
A total of 277 participants were included, with 194 from Center A and 83 from Center B. The training cohort included 114 ESCC patients, 40 benign esophageal disease controls, and 40 healthy controls. The external validation cohort included 53 ESCC patients, 15 benign esophageal disease controls, and 15 healthy controls. Center-based allocation was used instead of random splitting to reduce information leakage and test model stability in an independent clinical setting. The diagnostic analysis compared serum CEA, SCC-Ag, and CA125 levels among ESCC patients, patients with benign esophageal disease, and healthy controls. The prognostic analysis was restricted to ESCC patients and assessed whether combined biomarker burden improved risk stratification for overall survival (OS) and progression-free survival (PFS). The study design, participant screening, cohort allocation, serum biomarker workflow, diagnostic model development, external validation, and prognostic model comparison are summarized in Figure 1.
The participant flow included screening, exclusions, diagnostic grouping, and center-based allocation. A total of 326 individuals were screened. Forty-nine individuals were excluded, including 12 with antitumor treatment before blood sampling, 8 with another primary malignancy, 9 with severe infection, autoimmune disease, or uncontrolled comorbidities likely to affect serum tumor markers, 11 with hemolysis, lipemia, jaundice interference, insufficient serum volume, or prolonged processing interval, and 9 with missing key clinical, pathological, or follow-up data. The final analytical population comprised 277 participants: 167 ESCC patients, 55 benign esophageal disease controls, and 55 healthy controls.
Sample size rationale
The sample size was determined by the consecutive availability of eligible participants, pretreatment serum samples, and follow-up information during the predefined study period. The diagnostic model used three continuous serum biomarkers as candidate predictors. In the training cohort, 114 ESCC cases and 80 non-ESCC controls were available, corresponding to 38.0 ESCC cases and 26.7 non-ESCC controls per candidate biomarker predictor. This supported low-dimensional logistic regression and reduced the risk of overfitting. The external validation cohort included 53 ESCC cases and 30 non-ESCC controls and was used only for validation without coefficient re-estimation. The sample-size rationale was aligned with current prediction-model principles, which emphasize outcome frequency, number of candidate predictor parameters, anticipated model performance, and independent validation rather than random data splitting15.
Participant recruitment, eligibility, and data collection
Participants were enrolled continuously during the study period. In Center A, ESCC patients were recruited from thoracic surgery, gastroenterology, and oncology departments. In Center B, ESCC patients were identified through community-based screening, follow-up records, or referral pathways and were included only when pathological confirmation and pretreatment serum samples were available. Benign esophageal disease controls were recruited from individuals who underwent gastroscopy for dysphagia, reflux symptoms, or suspected esophageal abnormalities and had non-malignant pathological findings. Healthy controls were selected from routine health examinations during the same period and had no history of malignancy, active infection, severe hepatic or renal dysfunction, or imaging/endoscopic evidence of esophageal space-occupying lesions.
ESCC patients were eligible if they had primary ESCC confirmed by gastroscopic biopsy or postoperative pathology, had no antitumor treatment before blood sampling, and had complete baseline clinical data, pathological records, and pretreatment serum samples. Participants were excluded if they had another primary malignancy, prior antitumor treatment before blood sampling, severe acute infection, active autoimmune disease, uncontrolled comorbidities likely to affect serum tumor marker levels, visible hemolysis, lipemia, jaundice interference, insufficient serum volume, missing key clinical variables, or unavailable follow-up data. Prognostic analysis was limited to ESCC patients with available tumor-node-metastasis (TNM) stage and measurable survival or progression outcomes. Patients with follow-up shorter than 6 months were retained if death or progression occurred within that period.
Baseline variables included age, sex, smoking history, alcohol consumption history, body mass index, participating center, diagnostic group, lesion type, and serum levels of CEA, SCC-Ag, and CA125. For ESCC patients, additional variables included tumor location, tumor length, histological differentiation, depth of invasion, lymph node status, TNM stage, lymphovascular invasion, initial treatment modality, recurrence, progression, survival status, and last follow-up date. Smoking history was defined as current smoking or previous regular smoking of at least 100 cigarettes during lifetime. Alcohol consumption history was defined as alcohol intake at least once per week for more than 6 months. TNM staging was determined according to the eighth edition of the American Joint Committee on Cancer/Union for International Cancer Control staging system for esophageal cancer16. Pathological slides from ESCC and benign lesion cases were independently reviewed by two senior pathologists, and any disagreements were resolved by a third pathologist.
Two trained researchers independently extracted clinical information from electronic medical records, pathology reports, laboratory information systems, treatment records, follow-up records, and death registration records using a predefined data dictionary. Discrepancies were resolved by checking the original records, with a third researcher making the final decision when needed. Tumor location was categorized as upper, middle, or lower thoracic esophagus. Histological differentiation was categorized as well, moderate, or poor differentiation. Continuous-variable units were standardized across both participating centers before analysis.
Blood sampling, serum processing, and biomarker measurement
All blood samples were collected before initial treatment, within 7 days after pathological confirmation, and at least 3 days before treatment initiation. Peripheral venous blood samples of 5–8 mL were collected between 7:00 AM and 9:00 AM after overnight fasting using serum-separation tubes. Sampling was deferred if the participant had received a blood transfusion, a large-volume albumin infusion, or an invasive procedure within 72 h before collection. Samples were allowed to clot at room temperature for 30 min, then centrifuged at 1,500 × g for 10 min. Serum was separated and aliquoted into 3–5 barcode-labeled tubes, with 300–500 µL per aliquot. Aliquots were stored at −80°C until testing, and no aliquot was subjected to more than one freeze-thaw cycle. Samples with visible hemolysis, lipemia, jaundice interference, insufficient volume, or a collection-to-freezing interval longer than 2 h were excluded.
Serum CEA, SCC-Ag, and CA125 were measured using automated chemiluminescent immunoassay systems according to the manufacturers’ instructions. Serum aliquots were thawed at 4°C before testing, gently mixed, and analyzed in a single run whenever possible. If batch testing was required, sample order was arranged to avoid systematic grouping by disease status, center, pathological stage, or follow-up outcome. Samples exceeding the analytical measurement range were diluted and retested according to the reagent instructions. Laboratory personnel were blinded to participant group, pathological stage, treatment status, and follow-up outcome.
The clinical thresholds were 5.0 ng/mL for CEA, 1.5 ng/mL for SCC-Ag, and 35 U/mL for CA125, based on local laboratory standards and manufacturers’ reference intervals. These thresholds were used to define marker elevation and high combined biomarker burden for clinicopathological association and survival-stratification analyses. For diagnostic classification, data-driven cutoffs were selected only in the training cohort using the Youden index and then applied unchanged to the external validation cohort. Instrument calibration and internal quality control were performed before testing. High- and low-level controls were included during each testing day, and testing proceeded only when quality-control results were within the accepted range.
Inter-assay and inter-center reproducibility were assessed before final model analysis using blinded bridging serum aliquots and routine quality-control materials. A bridging set of 30 blinded serum aliquots was tested across the two laboratory workflows. The inter-center coefficients of variation were 4.8% for CEA, 5.6% for SCC-Ag, and 5.1% for CA125. The corresponding intraclass correlation coefficients were 0.94 for CEA, 0.92 for SCC-Ag, and 0.93 for CA125. No center-specific assay recalibration was performed because no systematic deviation exceeded the predefined laboratory quality-control range. Final biomarker values were harmonized using unit standardization and training-cohort-based statistical standardization prior to model construction.
Combined biomarker definition and model construction
The combined serum biomarker panel consisted of CEA, SCC-Ag, and CA125. For clinicopathological association analysis and Kaplan-Meier survival stratification, each marker was classified as elevated or non-elevated according to the predefined clinical threshold. High combined biomarker burden was defined as the elevation of at least two of the three markers. For diagnostic and prognostic modeling, the three biomarkers were retained as continuous variables, transformed using the natural logarithm, and standardized using the mean and standard deviation from the training cohort. The same transformation parameters were applied to the external validation cohort.
The standardized log-transformed biomarkers were calculated using the following training-cohort parameters:
zlnCEA = (lnCEA - 1.42)/0.71
zlnSCC - Ag = (lnSCC - Ag - 0.28)/0.57
zlnCA125 = (lnCA125 - 3.05)/0.62
The combined diagnostic panel was constructed using multivariable logistic regression in the training cohort. The diagnostic linear predictor was calculated as follows:
LPdiagnostic = -0.46 + 0.59 × zlnCEA + 1.05 × zlnSCC - Ag+0.42 × zlnCA125
The individual predicted probability of ESCC was calculated as follows:
PESCC = 1/1+exp(-LPdiagnostic)
The optimal diagnostic cutoff for the combined panel was 0.57, selected in the training cohort using the Youden index and applied unchanged in external validation.
The prognostic biomarker score was constructed using Cox proportional hazards modeling. The TNM-stage-alone model was used as the comparator for anatomical staging. The biomarker model used combined biomarker burden. The clinicopathological model used age, tumor length, histological differentiation, pT stage, lymph node status, and initial treatment modality. The integrated model added a high combined biomarker burden to the clinicopathological model. For OS, the integrated prognostic linear predictor was calculated as follows:
LPOS = 0.33 × age ≥65 years +0.36 × tumorlength ≥5cm + 0.25 × poordifferentiation + 0.31 × pT3
- 4stage + 0.66 × lymphnodemetastasis
-0.58 × radicalsurgery - basedtreatment
+0.81 × highcombinedbiomarkerburden
For PFS, the integrated prognostic linear predictor was calculated as follows:
LPPFS = 0.27 × age ≥ 65years + 0.31 × tumorlength ≥ 5cm + 0.24 × poordifferentiation +0.25 × pT3
-4stage + 0.55 × lymphnodemetastasis
- 0.46 × radicalsurgery - basedtreatment
+0.71 × highcombinedbiomarkerburden
All binary variables in the prognostic equations were coded as 1 when present and 0 when absent. All coefficients, standardization parameters, and cutoffs were estimated in the training cohort and applied directly to the external validation cohort without re-optimization.
Diagnostic and prognostic endpoints
Pathological diagnosis was used as the diagnostic reference standard. The primary diagnostic endpoint was discrimination between ESCC and non-ESCC controls, including benign esophageal disease and healthy controls. Secondary diagnostic endpoints included discrimination between ESCC and benign esophageal disease, ESCC and healthy controls, and early-stage ESCC and non-malignant controls. Early-stage ESCC was defined as TNM stage I–II disease. Diagnostic performance was evaluated using receiver operating characteristic curves, area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, precision-recall curves, calibration curves, Brier scores, calibration intercepts, calibration slopes, and decision curve analysis.
Prognostic analysis was conducted only in ESCC patients. The primary prognostic endpoint was OS, defined as the interval from the date of first definitive antitumor treatment to death from any cause. The secondary prognostic endpoint was PFS, defined as the interval from first treatment to documented disease progression, recurrence, or death. For patients who underwent radical surgery, local recurrence, distant metastasis, or tumor-related death was counted as a PFS event when no residual lesion was present after surgery. For patients receiving definitive chemoradiotherapy or palliative treatment, progression was determined according to imaging findings, endoscopic assessment, and clinical evaluation.
Follow-up data were obtained through outpatient visits, hospitalization records, community follow-up records, referral records, and telephone contact. Patients were followed every 3 months during the first 2 years after treatment, every 6 months from years 3 to 5, and annually thereafter. Patients without death, recurrence, or progression by the final follow-up date were censored at their last confirmed contact. Outcome events were independently reviewed by two investigators. When event dates differed between sources, imaging reports, discharge records, endoscopic reports, referral documents, and death registration records were checked to determine the final event date. The follow-up cutoff date was December 31, 2025, for both participating centers.
Statistical analysis
Statistical analysis was performed using R 4.3.2 and SPSS 27.0. Continuous variables were summarized as mean ± standard deviation or median (interquartile range), depending on the distribution. Categorical variables were summarized as counts and percentages. Between-group comparisons were performed using the independent-samples t-test, Mann–Whitney U test, one-way analysis of variance, Kruskal–Wallis test, chi-square test, or Fisher’s exact test, as appropriate. Missing data were assessed before model construction. Participants missing key diagnostic status, pretreatment biomarker values, TNM stage, or survival/progression outcome data were excluded according to the predefined eligibility criteria. Non-key covariates with missingness below 5.0% were handled using multiple imputation by chained equations with 20 imputed datasets and 20 iterations17. Imputation models included diagnostic group, center, age, sex, smoking history, alcohol use, biomarker values, TNM stage, treatment modality, OS status, PFS status, and follow-up time. Complete-case analyses were performed as sensitivity analyses.
The diagnostic model was developed in the training cohort. Receiver operating characteristic curves were used to calculate area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and F1 score. Areas under the curve were compared using the DeLong test18. Precision-recall curves were used as a supplementary diagnostic evaluation. Calibration was assessed using calibration curves, Brier scores, calibration intercepts, and calibration slopes. Clinical utility was evaluated using decision curve analysis19. Internal validation was performed using 1,000 bootstrap resamples to estimate optimism-corrected discrimination and calibration. External validation was performed by applying the training-cohort equation, standardization parameters, and cutoff directly to Center B without refitting.
For prognostic analysis, Kaplan–Meier curves were generated to compare OS and PFS between biomarker-burden groups, and the log-rank test was used to assess differences in survival. Cox proportional hazards regression was used to estimate hazard ratios and 95% confidence intervals. Variables with clinical relevance and variables associated with outcomes in univariable analysis were considered for multivariable models. Model performance was evaluated using Harrell’s C-index, time-dependent receiver operating characteristic curves, calibration curves, Brier scores, and decision curve analysis20. Prognostic calibration was assessed at 1, 3, and 5 years. The proportional hazards assumption was assessed using Schoenfeld residuals and log-minus-log survival plots21. The global Schoenfeld test P values were 0.42 for the OS model and 0.37 for the PFS model. Multicollinearity was assessed using variance inflation factors before final model interpretation, and all candidate variables in the final diagnostic and prognostic models had variance inflation factors below 2.1. Incremental prognostic value beyond TNM stage and conventional clinicopathological models was assessed using net reclassification improvement and integrated discrimination improvement. Model parameters estimated in the training cohort were applied to the external validation cohort without refitting22. All tests were two-sided, and P < 0.05 was considered statistically significant.
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Participant flow and assay reproducibility
A total of 326 individuals were screened, and 49 were excluded: 12 had received antitumor treatment before blood sampling, 8 had another primary malignancy, 9 had severe infection, autoimmune disease, or uncontrolled comorbidities likely to affect serum tumor markers, 11 had hemolysis, lipemia, jaundice interference, insufficient serum volume, or prolonged processing interval, and 9 had missing key clinical, pathological, or follow-up...
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This two-center study developed and externally validated a combined serum biomarker panel integrating CEA, SCC-Ag, and CA125 for diagnostic and prognostic assessment in ESCC. The combined panel outperformed each individual biomarker in distinguishing ESCC from non-malignant controls and retained acceptable discrimination in the external validation cohort. This finding is consistent with previous ESCC studies showing that serum tumor markers can provide clinically relevant diagnostic and prognostic information, although s...
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The authors have nothing to disclose.
The authors thank the staff of the Department of Laboratory Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, and Nanhu Community Health Service Center, Nanjing, for their support in sample collection, laboratory testing, and data management.
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| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| Access CEA Calibrators | Beckman Coulter | 33205 | Calibration for serum CEA assay |
| Access CEA Quality Control | Beckman Coulter | 33029 | Daily internal quality control for serum CEA assay |
| Access CEA Reagent Pack | Beckman Coulter | 33200 | Serum CEA measurement on the UniCel DxI 800 Access Immunoassay System |
| Adjustable micropipette, 100–1000 µL | Eppendorf | 3123000063 | Serum aliquoting after centrifugation |
| ARCHITECT CA 125 II Calibrators | Abbott Diagnostics | 02K4502 | Calibration for serum CA125 assay |
| ARCHITECT CA 125 II Controls | Abbott Diagnostics | 02K4511 | Daily internal quality control for serum CA125 assay |
| ARCHITECT CA 125 II Reagent Kit | Abbott Diagnostics | 02K4529 | Serum CA125 measurement on the ARCHITECT i2000SR Immunoassay Analyzer |
| ARCHITECT i2000SR Immunoassay Analyzer | Abbott Diagnostics | i2000SR | Automated chemiluminescent microparticle immunoassay platform for SCC-Ag and CA125 testing |
| ARCHITECT SCC Calibrator | Abbott Diagnostics | 8D1801 | Calibration for serum SCC-Ag assay |
| ARCHITECT SCC Controls | Abbott Diagnostics | 8D1810 | Daily internal quality control for serum SCC-Ag assay |
| ARCHITECT SCC Reagent Kit | Abbott Diagnostics | 8D1828 | Serum SCC-Ag measurement on the ARCHITECT i2000SR Immunoassay Analyzer |
| Barcode label printer | Brady | BMP51 | Barcode labeling of serum aliquots and cryogenic vials |
| Blood collection needle | BD Biosciences | 367286 | Peripheral venous blood collection |
| Cryogenic storage box | Corning | 431131 | Organized storage of frozen serum aliquots |
| Cryogenic vial, 2.0 mL, external thread, self-standing | Corning | 430659 | Barcode-labeled serum aliquot storage at −80 °C |
| Data extraction form and predefined data dictionary | Study team | Not applicable | Standardized extraction of clinical, pathological, laboratory, and follow-up variables |
| dcurves package | R package | Version 0.4.0 | Decision curve analysis and clinical net-benefit estimation |
| Electronic medical record system | Participating hospitals | Not applicable | Extraction of demographic, clinical, treatment, and follow-up data |
| Filtered pipette tips, 100–1000 µL | Eppendorf | 30073594 | Contamination-controlled serum aliquoting |
| ggplot2 package | R package | Version 3.5.1 | Statistical visualization and figure preparation |
| IBM SPSS Statistics | IBM | Version 27.0 | Baseline comparisons and supplementary statistical checking |
| Laboratory information management system | Participating hospital laboratory | Not applicable | Retrieval and linkage of serum biomarker test records |
| mice package | R package | Version 3.16.0 | Multiple imputation by chained equations |
| nricens package | R package | Version 1.6 | Net reclassification improvement analysis |
| Pathology reporting system | Participating hospitals | Not applicable | Confirmation of ESCC diagnosis, benign pathology, tumor features, and staging-related variables |
| pROC package | R package | Version 1.18.5 | Receiver operating characteristic curve analysis and DeLong comparison |
| R statistical software | R Foundation for Statistical Computing | Version 4.3.2 | Statistical analysis, model construction, validation, and figure generation |
| Refrigerated centrifuge | Eppendorf | 5810 R | Serum separation by centrifugation at 1,500 x g for 10 min |
| rms package | R package | Version 6.8-1 | Calibration analysis, nomogram construction, and regression model support |
| Serum-separation blood collection tube, 5 mL | BD Biosciences | 367955 | Collection of fasting peripheral venous blood for serum biomarker testing |
| survival package | R package | Version 3.5-7 | Cox proportional hazards regression and survival analysis |
| survminer package | R package | Version 0.4.9 | Kaplan–Meier curve visualization |
| Telephone follow-up record form | Study team | Not applicable | Collection and verification of survival, recurrence, progression, and last-contact information |
| timeROC package | R package | Version 0.4 | Time-dependent receiver operating characteristic curve analysis |
| Ultra-low temperature freezer | Thermo Fisher Scientific | TSX60086A | Storage of serum aliquots at −80 °C |
| UniCel DxI 800 Access Immunoassay System | Beckman Coulter | DxI 800 | Automated chemiluminescent immunoassay platform for serum CEA testing |
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