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Data Science (678) Machine Learning (3036) Artificial Intelligence (4526) Colon and Rectal Cancer (81) AI for Synthetic Data (22)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/86087, first published .
3D illustration of the colon with a blue awareness ribbon, surrounded by virus-like shapes and flowers.

Evaluation of the Applicability of Synthetic Data in the Development of Colorectal Cancer Survival Prediction Models: External Validation of Advanced Machine Learning Models Based on National Cancer Data Center Data

Evaluation of the Applicability of Synthetic Data in the Development of Colorectal Cancer Survival Prediction Models: External Validation of Advanced Machine Learning Models Based on National Cancer Data Center Data

Authors of this article:

Yujeong Jang1 Author Orcid Image ;   Jae Hoon Kwon1 Author Orcid Image ;   Heeyong Kim2 Author Orcid Image ;   You-Jin Joung1, 3 Author Orcid Image ;   Junho ‍Nang4 Author Orcid Image ;   Chang Hyun Kim2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea

    2Department of Surgery, Chonnam National University Hwasun Hospital, 322 Seoyang-ro, Hwasun, Republic of Korea

    3C&R Research, Seoul, Republic of Korea

    4Clinical Trial Center, Institute for Biomedical Science, Chonnam National University Hwasun Hospital, 322, Seoyang-ro, Hwasun-eup, Hwasun-gun, Jeollanam-do, Republic of Korea

    *these authors contributed equally

    Corresponding Author:

    Junho ‍Nang, PhD


    Background: Limited data availability and privacy constraints hinder the development of robust survival prediction models for personalized treatment. Synthetic data offers a promising solution, preserving the statistical properties of real clinical data.

    Objective: This study aimed to quantitatively assess the feasibility of using synthetic data for survival prediction by evaluating model transfer performance to real-world hospital data, with a focus on model transfer strategies.

    Methods: We developed and validated colorectal cancer survival prediction models using the National Cancer Data Center (NCDC) synthetic data (30,683 patients from 3 Korean institutions) for pretraining and real hospital data (2170 patients from Hwasun Jeonnam University Hospital) for external validation. We evaluated 3 model transfer strategies—domain adaptation, zero-shot, and ensemble—using extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM). In total, 48 model configurations were tested, defined by the combination of algorithms (LightGBM and XGBoost), sampling technique (no-sampling, random undersampling [RUS], and synthetic minority oversampling technique combined with edited nearest neighbors [SMOTEENN]), model type (baseline, domain adaptation, zero-shot, and ensemble), and optimization objective (area under the precision-recall curve [AUPRC] and F1). The outcome was 7-year overall survival, evaluated using the AUPRC and Brier scores. Performance was compared against a hospital-only baseline using absolute values and deltas (ΔAUPRC and ΔBrier). Differences and corresponding 95% CIs were estimated on the held-out test set using 2000 bootstrap samples.

    Results: Zero-shot application reduced the AUPRC in most settings, and any marginal improvements observed in the remaining settings were not statistically significant. In contrast, the domain adaptation model improved AUPRC in 8/12 combinations, with 4 statistically significant gains; the best setting (XGBoost+RUS+F1 optimization) achieved AUPRC=0.5391 (Δ+0.1474; P<.001). The soft ensemble increased AUPRC in 7/12 combinations, with 3 statistically significant gains; the best setting (XGBoost+RUS+AUPRC optimization) achieved AUPRC=0.5060 (Δ+0.1258, P=.002). For calibration, Brier scores improved in most domain adaptation and ensemble combinations, with a substantial proportion reaching statistical significance.

    Conclusions: When domain adaptation using local hospital data was applied, the model pretrained on synthetic data exhibited similar performance to the hospital-only baseline across various settings. This study demonstrates the methodological utility of a model transfer approach using NCDC synthetic data in a setting with limited data sharing. At the same time, it clarifies that while synthetic data can serve as a complement to local clinical data, it is not a substitute for real-world clinical models.

    J Med Internet Res 2026;28:e86087

    doi:10.2196/86087

    Keywords

    synthetic data; domain adaptation; colorectal cancer; machine learning; extreme gradient boosting; light gradient boosting machine; survival prediction model; external validation 


    Advances in genomics and AI enable the development of personalized therapeutic strategies that integrate patients’ unique genetic information, physiological characteristics, and environmental factors [1]. Personalized therapy is advancing the precision medicine paradigm, highlighting the growing importance of predictive models for individualized treatment [2]. Reliable predictive models for patient-centered personalized treatment require access to sufficient patient data with various clinical characteristics [3]. In addition, evaluating these models through external verification is essential for their application in clinical settings [4]. However, accessing clinical data from real-world health care environments poses significant challenges. Clinical data, including patient disease information and medical records, are subject to privacy-related legal restrictions that limit their acquisition [5]. Additionally, data from a single health care organization have limited generalizability, and sharing clinical data across institutions presents technical and institutional challenges [6]. These constraints hinder model training, performance evaluation, and external validation, limiting the development of patient-specific models. Previous survival prediction model studies typically used patient data collected from medical institutions [7]. Although this approach is valuable for clinical applications owing to its direct reflection of real conditions and disease progression of patients, it is limited by insufficient external validation, as most studies rely solely on internal validation [8].

    The use of synthetic data has been proposed as a potential solution to these challenges. Synthetic data retain the variable characteristics and statistical distributions of real-world patient data while being truly nonidentifiable because the data are unrelated to any real individual [9,10]. Therefore, they are free from legal and ethical privacy constraints and can be used to generate large-scale data encompassing diverse patient population characteristics, addressing data sparsity [11]. Synthetic data can be used to proactively model and validate various medical situations that are difficult to simulate in clinical environments [12]. In addition, by applying large-scale synthetic data to the training and validation processes of survival prediction models, the model generalization performance can be improved [13]. Recent studies have reported that synthetic data can reproduce statistical properties similar to real clinical data [14,15]. Specifically, current research on synthetic data focuses on medical image or data augmentation methods that generate synthetic data from real data and combine it with the original data for training [16]. However, there are limited studies that developed predictive models using synthetic data and validated them on real medical data to evaluate the versatility and generalizability [17]. Furthermore, when external data that were not part of synthetic data generation are used for validation, domain shifts may occur owing to differences in hospital-specific patient characteristics, treatment patterns, and other factors [18]. To address the distributional differences between synthetic and real-world hospital data, we applied a domain adaptation approach. Domain adaptation is an umbrella term for a category of transfer learning strategies to mitigate performance degradation due to domain shifts [19]. In this study, we used domain adaptation as a method for transferring knowledge learned from synthetic data to real clinical data.

    In this context, this study aimed to evaluate the clinical applicability of predictive models based on synthetic data. The effectiveness of applying a model pretrained on synthetic data to actual hospital data was quantitatively evaluated. Specifically, we focused on providing empirical evidence for the clinical utility of synthetic data and identifying the conditions and limitations of its application in real health care settings.


    Data Source and Study Population

    We used 2 types of data sources for this study. Synthetic data were based on the “Colorectal Cancer Clinical Library Artificial Dataset” (the synthesized data is archived on the National Cancer Data Center [NCDC] portal) [20]. This dataset is a high-quality synthetic resource that replicates the structure and statistical characteristics of real-world medical data. It was developed based on real-world patient information collected from 3 leading Korean medical institutions specializing in cancer (Samsung Medical Center, Yonsei University Severance Hospital, and National Cancer Center) using the real-world time-series generative adversarial network (RTSGAN) technology [21]. Hospital data comprised clinical information of patients who underwent colorectal cancer surgery at the Hwasun Jeonnam University Hospital in South Korea. This dataset is derived from real patient treatment experiences and medical records, containing detailed clinical indicators and postoperative follow-up results. Study participants included in the synthetic and hospital data were selected based on the following inclusion and exclusion criteria. Inclusion criteria were as follows: (1) patients diagnosed with colorectal cancer between 2009 and 2014, and (2) patients undergoing surgery. The exclusion criterion was patients with missing survival information.

    Outcome Definition and Input Variables

    The primary outcome of this study was 7-year overall survival data. The synthetic dataset included 31 variables, while the hospital dataset contained 48 variables. In total, 12 variables common to both datasets were selected as the input variables for model training. The baseline model was intentionally restricted to 12 shared variables common to both datasets to allow for a methodologically controlled comparison between the hospital and synthetic datasets. This feature-constrained design is intended to minimize confounding factors that may arise due to differences in variable availability and information content [22,23]. The final 12 variables were sex, age, stage (0, I, II, III, or IV), adenocarcinoma histology, vascular invasion, lymph node invasion, perineural invasion, tumor location (colon, junction, or rectum), preoperative carcinoembryonic antigen (log-transformed), postoperative chemotherapy (FOLFOX, FOLFIRI, GELOX, or other), postoperative radiation therapy, and number of resected metastatic lymph nodes.

    Data Processing

    All categorical variables were encoded as one-hot. Age was used as a binary indicator by interval (<20, 20‐29, …, 80‐89, ≥90). Input variables or outcomes with missing values were excluded from the analysis, based on the principles of complete case analysis.

    Domain Adaptation

    To minimize the domain gap between synthetic and hospital data, we applied a domain adaptation technique, a form of transfer learning. Domain adaptation broadly refers to solving the problem of performance degradation caused by the difference in distribution between the source and target domains. In this study, we implemented domain adaptation through parameter transfer. It was first trained on synthetic data (source domain) and then fine-tuned on hospital data (target domain) using a low learning rate and regularization. This approach allows the model to adapt to hospital-specific characteristics using limited real-world data while retaining generalizable patterns learned from large-scale synthetic data. In this study, we did not apply feature-level alignment or adversarial domain adaptation techniques. Instead, we focused on parameter-level adaptation, which is particularly well-suited to tabular clinical data, where explicit feature alignment across domains is difficult, and privacy constraints may limit access to joint feature distributions [24-26]. Furthermore, to determine the presence of a domain gap, we performed the Kolmogorov-Smirnov test for continuous variables and chi-square (or Fisher exact test, where expected cell counts were less than 5) independence tests for categorical variables.

    Data Splitting

    In this study, synthetic data were used for pretraining and internal optimization, while hospital data were used for domain adaptation, threshold selection, probability calibration, and independent final evaluation. Therefore, the synthetic data were split into source-train or source-validation data based on the year of diagnosis, and the hospital data were sorted by the year of diagnosis and split into target-train, target-validation, or target-test using a 6:2:2 3-partition (Figure 1).

    ‎
    Figure 1. Data splitting procedure and domain adaptation procedure.

    Synthetic data were split into source-train and source-validation sets based on the year of diagnosis and were used for pretraining and internal optimization. Hospital data were chronologically divided into target-train, target-validation, and target-test sets using a 6:2:2 ratio and were used for domain adaptation, threshold selection, probability calibration, and independent final evaluation. The pretrained model was fine-tuned on target-train data through domain adaptation, with threshold selection and calibration on target-validation, and independent final evaluation on the held-out target-test set. Four model variants were evaluated: a baseline model (Hospital → Hospital), a zero-shot model (Synthetic → Hospital), a domain adaptation model (Synthetic → Hospital), and an ensemble model combining predictions from multiple models.

    Handling Class Imbalance

    According to the National Cancer Information Center, the 5-year relative survival rate for colorectal cancer is 75% [27]; however, the outcome measured in this study showed similarly high survival expected at 7 years. To address this class imbalance, sampling techniques—including no sampling, random undersampling (RUS) [28], and the synthetic minority oversampling technique combined with edited nearest neighbors (SMOTEENN) [29]—were applied to the source and target training datasets. To preserve the true prevalence during evaluation, no sampling technique was applied to the validation and test datasets [30].

    Algorithms

    We used light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost). Specifically, LightGBM offers fast learning, effective handling of imbalanced data through is_unbalance/weight, and supports efficient stopping and fine-tuning. XGBoost reliably controls overfitting and imbalanced data with L1/L2 normalization and scale_pos_weight, and is suitable for normalization fine-tuning with the xgb_model load. Furthermore, it has a high training speed to compare various combinations quickly [31,32]. Considering our settings, moderate class imbalance, domain shift, and tabular features, these 2 algorithms are natural choices.

    For domain adaptation, fine-tuning was performed by first using a model trained on synthetic data as an initial model and then training additional trees using hospital data. In this process, the previously trained tree structure was kept fixed without modification, and the adaptation to the hospital data was done by adding new trees. In order to preserve the knowledge learned from the synthetic data as much as possible and prevent overfitting due to domain shift, a low learning rate and strong L1/L2 normalization were applied in the fine-tuning phase.

    Model Variants

    We configured four model variants as follows:

    1. Baseline model (Hospital→Hospital): It served as the reference standard and was trained and evaluated exclusively on hospital data.
    2. Domain adaptation model (Synthetic→Hospital): This is the main model of this study, and it uses pretrained weights from synthetic data as initialization and applies domain adaptation with fine-tuning on hospital data using low learning rates and strong regularization (L1/L2).
    3. Comparator models:
      1. Zero-shot model (Synthetic→Hospital): This model is trained on synthetic data and directly applied to hospital data without any adaptation.
      2. Ensemble model (Synthetic→Hospital): This model combines 3 components through weighted ensemble—a pretrained model from synthetic data, a fine-tuned model on hospital data, and a model trained from scratch on hospital data.

    Optimization Objective

    Each model was optimized for 2 objectives—area under the precision-recall curve (AUPRC) and F1-scores. On the target validation set, hyperparameter tuning, early stopping, and threshold selection were performed by maximizing each metric.

    Model Evaluation

    In this study, 48 combinations were tested as follows: algorithm (LightGBM, XGBoost)×sampling technique (no-sampling, RUS, SMOTEENN)×model (baseline, domain adaptation, zero-shot, ensemble)×optimization objective (AUPRC, F1).

    The performances of the domain adaptation and baseline models were compared in the primary analysis. To objectively evaluate the domain adaptation effect, zero-shot and ensemble models were included as comparators. The main evaluation metrics are the AUPRC and the Brier score; for each experimental combination, we determined absolute values on the test set and changes relative to the baseline (ΔAUPRC and ΔBrier score). The Δ values presented in this study are not the arithmetic differences between point estimates. They represent the mean difference in performance between models, estimated by bootstrapping the test set 2000 times. This approach was adopted to provide more robust estimates of performance differences. The 95% CIs were derived from the 2.5th and 97.5th percentiles of the bootstrap distribution, and 2-sided P values were obtained as twice the minimum of the proportion of bootstrap resamples with a difference of zero or less and the proportion with a difference of zero or greater. The AUPRC was chosen to measure discriminative performance in the presence of class imbalance. In situations of class imbalance, the precision-recall approach further highlights the limitations of receiver operating characteristic–based evaluation [33]. The Brier score was included to assess whether the predicted probability matches the observed outcome. Models with similar discriminative power can have significant differences in probability accuracy and real-world interpretability; therefore, a comprehensive evaluation of clinical prediction models should consider both discriminative power and degree of calibration [34]. The AUROC, precision, recall, F1, F2, accuracy, Matthews correlation coefficient, and specificity were reported as secondary metrics.

    Software and Statistical Analysis

    Data preprocessing and extraction were performed using SAS (version 9.4; SAS Institute), while all subsequent analyses were conducted in Python (version 3.8.10; Python Software Foundation) within a controlled computational environment ensuring reproducibility. All analyses in this study were performed in a controlled computational environment with fixed software versions, an object-oriented pipeline structure, and systematic logging to ensure reproducible experiments for multiple model combinations [35].

    Data management:

    • SAS 9.4: Used for initial data extraction, cleaning, and preprocessing of hospital clinical data. DATA manipulation and PROC procedures were performed using DATA steps and quality control SQL.

    Python environment:

    • Machine Learning: scikit-learn (1.2.0), XGBoost (1.7.3), and LightGBM (3.3.5)
    • Data Processing: pandas (1.5.3), numpy (1.24.2)
    • Statistical Analysis: scipy (1.10.1), statsmodels (0.13.5)
    • Sampling Techniques: imbalanced-learn (0.10.1)
    • Visualization: matplotlib (3.6.3), seaborn (0.12.2)
    • Performance Metrics: scikit-learn, custom implementation for medical-specific metrics

    Ethical Considerations

    This retrospective study was approved by the Institutional Review Board of Chonnam National University Hwasun Hospital (IRB file CNUHH-2024‐113). The ethics committee of Chonnam National University Hwasun Hospital waived the requirement for informed consent because the study involved a retrospective review of anonymized medical data. All data used in the analysis were fully deidentified before access by the research team, and no personally identifiable information was available. Data were stored on secure, access-restricted systems to ensure privacy and confidentiality. No financial or other compensation was provided to participants, as this study involved secondary analysis of existing clinical data. The manuscript and all supplementary materials do not contain any images or information that could lead to the identification of individual participants.


    Cohort

    In this study, synthetic data comprising 161,410 records—54,311 from the National Cancer Center, 52,767 from Samsung Medical Center, and 54,332 from Yonsei University Medical Center—were used for pretraining. During preprocessing, 44,106 records were excluded due to missing values in essential covariates. After applying selection and exclusion criteria and data preprocessing, the final dataset for survival model prediction included 30,683 patients. The external validation dataset from the Hwasun Jeonnam National University Hospital included 5490 surgical patient records, of which 2170 were used in the analysis. A flowchart of the participant selection process is illustrated in Figure 2.

    ‎
    Figure 2. Flowchart of participant selection.

    Domain Shift Assessment

    Distribution comparison of the 2 datasets (Table 1) showed statistically significant differences for all variables (P<.05). For patient characteristics, the hospital data showed higher age (median 67, IQR 59-74 y vs median 63, IQR 55-70 y; P<.001) and a slight but statistically significant difference in sex (P=.04). The prevalence of adenocarcinoma (histology) was significantly higher in the hospital data than in the synthetic data (97.2% vs 55.6%, P<.001), and perineural invasion (29.4% vs 0.7%, P<.001) and vascular invasion (19.2% vs 9.4%, P<.001) were higher in hospital data. Stage distribution differed significantly (P<.001), with a higher proportion of stage III patients in the hospital cohort, which comprised predominantly high-risk patients. A clear domain shift was observed between synthetic and hospital data, necessitating the use of domain adaptation techniques to ensure model performance in real-world settings.

    Table 1. Demographic and clinical characteristics of patients with colorectal cancer using synthetic and hospital-based data.
    VariablesSynthetic data (n=30,683)Hospital data (n=2170)P valuea
    7-year overall survival, n (%)<.001
    No1943 (6.3)431 (19.9)
    Yes28,740 (93.7)1739 (80.1)
    Sex, n (%).04
    Male18,279 (59.6)1341 (61.8)
    Female12,404 (40.4)829 (38.2)
    Age (y), median (IQR)63 (55‐70)67 (59‐74)<.001
    Stage, n (%)<.001
    0587 (1.9)82 (3.8)
    I9928 (32.4)530 (24.4)
    II10,599 (34.5)747 (34.4)
    III6995 (22.8)789 (36.4)
    IV2574 (8.4)22 (1.0)
    Adenocarcinoma (histology), n (%)<.001
    Non-adenocarcinoma13,627 (44.4)60 (2.8)
    Adenocarcinoma17,056 (55.6)2110 (97.2)
    Vascular invasion, n (%)<.001
    No27,791 (90.6)1753 (80.8)
    Yes2892 (9.4)417 (19.2)
    Lymph node invasion, n (%)<.001
    No22,501 (73.3)1742 (82.3)
    Yes8182 (26.7)428 (19.7)
    Perineural invasion, n (%)<.001
    No30,474 (99.3)1531 (70.6)
    Yes209 (0.7)639 (29.4)
    Tumor location, n (%)<.001
    Colon18,777 (61.2)1404 (64.7)
    Rectum8869 (28.9)681 (31.4)
    Junction3037 (9.9)106 (4.9)
    Number of resected metastatic lymph nodes, median (IQR)0.0 (0.0‐0.0)0.0 (0.0‐1.0)<.001
    Preoperative CEAb level (log), median (IQR)0.00 (0.0‐0.4)1.4 (1.1‐1.9)<.001
    Postoperative chemotherapy, n (%)<.001
    FOLFOX4631 (15.1)308 (14.2)
    FOLFIRI302 (1.0)5 (0.2)
    GELOX662 (2.1)114 (5.3)
    Other2138 (6.9)1155 (53.2)
    None22,950 (74.8)588 (27.1)
    Postoperative radiation therapy, n (%)<.001
    No30,153 (98.3)2088 (96.2)
    Yes530 (1.7)82 (3.8)

    aP value was calculated using the Kolmogorov-Smirnov test for continuous variables and the chi-square test or Fisher exact test for categorical variables.

    bCEA: carcinoembryonic antigen.

    Data Splitting

    The synthetic data were split into source training (20,520 patients) and source validation (10,163 patients). Hospital data were split into target training (1386 patients), target validation (409 patients), and target tests (375 patients).

    Synthetic data were obtained from 3 Korean institutions (161,410 records in total) and reduced to 30,683 patients after applying selection, exclusion, and preprocessing steps for model pretraining. External validation was performed using a dataset from the Hwasun Jeonnam National University Hospital, which included surgical patient data (5490 records), of which 2170 patients were included in the final analysis.

    Model Performance

    The experimental results are presented in Tables 2-5. Detailed performance results for all model combinations optimized using AUPRC and F1-score are provided in Multimedia Appendix 1 and Multimedia Appendix 2, respectively. The AUPRC for models trained on synthetic data and evaluated on hospital data ranged from 0.2706 to 0.5391—domain adaptation (0.4124‐0.5391), zero-shot (0.2706‐0.4171), and ensemble (0.3919‐0.5161). In comparison, the hospital-trained baseline model showed an AUPRC of 0.3576‐0.4663. The low absolute performance of AUPRC is likely the result of a complex interplay of various factors, including moderate class imbalance, a limited feature set, and domain differences between synthetic and hospital data [36]. Therefore, we used changes relative to the baseline to evaluate the net improvement effect of external validation.

    Table 2. Performance of colorectal cancer survival prediction models optimized for AUPRCa, evaluated using AUPRC and ΔAUPRC on the external hospital validation cohort.
    Algorithm, sampling, and modelAUPRCΔAUPRCb (95% CI)P valuec
    LightGBMd
    No sampling
    Baseline0.4025—e—
    Domain adaptation0.4336+0.0347 (−0.0583 to +0.1346).48
    Zero-shot0.2706−0.1326 (−0.2378 to −0.0299).02
    Ensemble0.4064+0.0089 (−0.0636 to +0.0809).80
    RUSf
    Baseline0.3576——
    Domain adaptation0.4931+0.1334 (+0.0489 to +0.2168).001g
    Zero-shot0.3339−0.0293 (−0.1435 to +0.0828).59
    Ensemble0.4605+0.1012 (+0.0314 to +0.1778).004g
    SMOTEENNh
    Baseline0.4361——
    Domain adaptation0.4711+0.0298 (−0.0684 to +0.1237).55
    Zero-shot0.3328−0.1094 (−0.2340 to +0.0124).09
    Ensemble0.4674+0.0247 (−0.0530 to +0.1017).53
    XGBoosti
    No sampling
    Baseline0.4555——
    Domain adaptation0.4781+0.0180 (−0.0591 to +0.0994).66
    Zero-shot0.3597−0.0990 (−0.2158 to +0.0179).097
    Ensemble0.4524−0.0093 (−0.0763 to +0.0539).79
    RUS
    Baseline0.3729——
    Domain adaptation0.5061+0.1263 (+0.0387 to +0.2044).004g
    Zero-shot0.4008+0.0222 (−0.0914 to +0.1291).66
    Ensemble0.5060+0.1258 (+0.0478 to +0.2034).002g
    SMOTEENN
    Baseline0.4484——
    Domain adaptation0.4295−0.0246 (−0.1321 to +0.0810).64
    Zero-shot0.3896−0.0657 (−0.1859 to +0.0550).27
    Ensemble0.4661+0.0111 (−0.0875 to +0.1004).82

    aAUPRC: area under the precision-recall curve.

    bΔ values represent the mean difference estimated from 2000 bootstrap resamples of the test set, not the arithmetic difference between point estimates. 95% CIs were derived from the bootstrap distribution.

    cTwo-sided P values were derived from the bootstrap distribution.

    dLightGBM: light gradient boosting machine.

    eNot applicable.

    fRUS: random undersampling.

    gValues with improved ΔAUPRC direction and P<.05.

    hSMOTEENN: synthetic minority oversampling technique combined with edited nearest neighbors.

    iXGBoost: extreme gradient boosting.

    Table 3. Calibration performance of colorectal cancer survival prediction models optimized for AUPRCa, evaluated using Brier score and ΔBrier on the external hospital validation cohort.
    ModelBrierΔBrierb (95% CI)P valuec
    LightGBMd
    No sampling
    Baseline0.1514—e—
    Domain adaptation0.1421−0.0094 (−0.0217 to +0.0032).13
    Zero-shot0.1529+0.0016 (−0.0151 to +0.0190).87
    Ensemble0.1402−0.0112 (−0.0211 to −0.0011).03f
    RUSg
    Baseline0.2018——
    Domain adaptation0.1977−0.0041 (−0.0105 to +0.0024).21
    Zero-shot0.2287+0.0271 (+0.0170 to +0.0368)<.001
    Ensemble0.1887−0.0132 (−0.0192 to −0.0068)<.001f
    SMOTEENNh
    Baseline0.1958——
    Domain adaptation0.1594−0.0369 (−0.0625 to −0.0120).006f
    Zero-shot0.1824−0.0132 (−0.0438 to +0.0160).397
    Ensemble0.1529−0.0429 (−0.0669 to −0.0216)<.001f
    XGBoosti
    No sampling
    Baseline0.1495——
    Domain adaptation0.1588+0.0092 (−0.0026 to +0.0206).13
    Zero-shot0.1841+0.0346 (+0.0183 to +0.0506)<.001f
    Ensemble0.1573+0.0078 (−0.0014 to +0.0166).09
    RUS
    Baseline0.1999——
    Domain adaptation0.1511−0.0487 (−0.0611 to −0.0358)<.001f
    Zero-shot0.1856−0.0144 (−0.0272 to −0.0003).05f
    Ensemble0.1535−0.0464 [−0.0556 to −0.0369)<.001f
    SMOTEENN
    Baseline0.1930——
    Domain adaptation0.1658−0.0273 (−0.0441 to −0.0102).001f
    Zero-shot0.1675−0.0252 (−0.0454 to −0.0042).02f
    Ensemble0.1517−0.0415 (−0.0551 to −0.0267)<.001f

    aAUPRC: area under the precision-recall curve.

    bΔ values represent the mean difference estimated from 2000 bootstrap resamples of the test set, not the arithmetic difference between point estimates. 95% CIs were derived from the bootstrap distribution.

    cTwo-sided P values were derived from the bootstrap distribution.

    dLightGBM: light gradient boosting machine.

    eNot applicable.

    fValues with improved ΔBrier direction and P<.05.

    gRUS: random undersampling.

    hSMOTEENN: synthetic minority oversampling technique combined with edited nearest neighbors.

    iXGBoost: extreme gradient boosting.

    Table 4. Performance of colorectal cancer survival prediction models optimized for F1-score, evaluated using AUPRCa and ΔAUPRC on the external hospital validation cohort.
    ModelAUPRCΔAUPRCb (95% CI)P valuec
    LightGBMd
    No sampling
    Baseline0.4339—e—
    Domain adaptation0.4124−0.0258 (−0.1424 to +0.0899).66
    Zero-shot0.2765−0.1571 (−0.2735 to −0.0395).006
    Ensemble0.4179−0.0209 (−0.1223 to +0.0773).72
    RUSf
    Baseline0.3816——
    Domain adaptation0.4881+0.0996 (+0.0295 to +0.1709).008g
    Zero-shot0.3596−0.0294 (−0.1447 to +0.0911).63
    Ensemble0.3919+0.0706 (−0.0253 to +0.1575).15
    SMOTEENNh
    Baseline0.4205——
    Domain adaptation0.4363+0.0170 (−0.0785 to +0.1161).74
    Zero-shot0.3122−0.1049 (−0.2131 to +0.0037).06
    Ensemble0.4460−0.0299 (−0.1314 to +0.0699).54
    XGBoosti
    No sampling
    Baseline0.4642——
    Domain adaptation0.4510−0.0104 (−0.0890 to +0.0704).81
    Zero-shot0.3405−0.1222 (−0.2305 to ‐0.0162).03
    Ensemble0.4452−0.0193 (−0.0912 to +0.0495).61
    RUS
    Baseline0.3876——
    Domain adaptation0.5391+0.1474 (+0.0523 to +0.2387)<.001g
    Zero-shot0.4171+0.0290 (−0.0856 to +0.1355).59
    Ensemble0.5161+0.1253 (+0.0433 to +0.2063).005g
    SMOTEENN
    Baseline0.4663——
    Domain adaptation0.4524−0.0154 (−0.1114 to +0.0772).74
    Zero-shot0.3763−0.0917 (−0.2093 to +0.0200).12
    Ensemble0.4591−0.0088 (−0.0983 to +0.0777).88

    aAUPRC: area under the precision-recall curve.

    bΔ values represent the mean difference estimated from 2000 bootstrap resamples of the test set, not the arithmetic difference between point estimates. 95% CIs were derived from the bootstrap distribution.

    cTwo-sided P values were derived from the bootstrap distribution.

    dLightGBM: light gradient boosting machine.

    eNot applicable.

    fRUS: random undersampling.

    gValues with improved ΔAUPRC direction and P<.05.

    hSMOTEENN: synthetic minority oversampling technique combined with edited nearest neighbors.

    iXGBoost: extreme gradient boosting.

    Table 5. Calibration performance of colorectal cancer survival prediction models optimized for F1-score, evaluated using Brier score and ΔBrier on the external hospital validation cohort.
    Algorithm, sampling, and modelBrierΔBriera (95% CI)P valueb
    LightGBMc
    No sampling
    Baseline0.1389—d —
    Domain adaptation0.1371−0.0019 (−0.0159 to +0.0125).799
    Zero-shot0.1480+0.0090 (−0.0103 to +0.0290).39
    Ensemble0.1433+0.0043 (−0.0083 to +0.0160).49
    RUSe
    Baseline0.1929— —
    Domain adaptation0.1516−0.0411 (−0.0577 to −0.0251)<.001f
    Zero-shot0.2151+0.0226 (−0.0032 to +0.0469).08
    Ensemble0.1969+0.0041 (−0.0195 to +0.0256).71
    SMOTEENNg
    Baseline0.1951— —
    Domain adaptation0.1569−0.0384 (−0.0634 to −0.0139)<.001f
    Zero-shot0.1791−0.0159 (−0.0458 to +0.0134).29
    Ensemble0.1710−0.0241 (−0.0516 to +0.0008).06
    XGBoosth
    No sampling
    Baseline0.1787— —
    Domain adaptation0.1517−0.0270 (−0.0394 to −0.0142)<.001f
    Zero-shot0.1893+0.0107 (−0.0020 to +0.0236).10
    Ensemble0.1653−0.0134 (−0.0217 to −0.0051).003f
    RUS
    Baseline0.2080— —
    Domain adaptation0.1461−0.0618 (−0.0749 to −0.0484)<.001f
    Zero-shot0.1989−0.0092 (−0.0181 to +0.0004).06
    Ensemble0.1680−0.0401 (−0.0479 to −0.0320)<.001f
    SMOTEENN
    Baseline0.1798— —
    Domain adaptation0.1566−0.0233 (−0.0441 to −0.0023).03f
    Zero-shot0.1674−0.0124 (−0.0388 to +0.0141).35
    Ensemble0.1511−0.0287 (−0.0473 to −0.0098).002f

    aΔ values represent the mean difference estimated from 2000 bootstrap resamples of the test set, not the arithmetic difference between point estimates. 95% CIs were derived from the bootstrap distribution.

    bTwo-sided P values were derived from the bootstrap distribution.

    cLightGBM: light gradient boosting machine.

    dNot applicable.

    eRUS: random undersampling.

    fValues with improved ΔBrier direction and P<.05.

    gSMOTEENN: synthetic minority oversampling technique combined with edited nearest neighbors.

    hXGBoost: extreme gradient boosting.

    The domain adaptation model showed ΔAUPRC>0 in 8 out of 12 combinations, with significant improvement over the baseline model observed in 4 combinations. The largest improvement was observed in the XGBoost+RUS+F1 optimization setting (ΔAUPRC=+0.1474, 95% CI +0.0523 to +0.2387; P<.001; AUPRC=0.5391). For calibration performance, 11 out of 12 combinations showed ΔBrier score<0, with 8 of these demonstrating statistically significant improvement. Thus, the predicted rates of the domain adaptation model matched the actual occurrence rates, even in the external hospital distribution.

    The ensemble model, serving as a comparator group, showed ΔAUPRC>0 in 7 out of 12 combinations, with statistically significant improvement over the baseline model observed in 3 combinations. The largest improvement was observed in the XGBoost+RUS+AUPRC optimization (ΔAUPRC=+0.1258, 95% CI +0.0478 to +0.2034; P=.002; AUPRC=0.5060). Calibration performance showed ΔBrier score<0 in 9 out of 12 combinations, with 8 being statistically significant, demonstrating results similar to those of the domain adaptation model.

    In contrast, the zero-shot model consistently showed poor discrimination performance. Only 2 of 12 combinations exhibited ΔAUPRC>0, and none of these improvements were statistically significant. The remaining 10 combinations showed no improvement, including 3 with statistically significant degradation. For the ΔBrier score, 6 out of 12 combinations showed performance decline, with 2 combinations being significant.

    Although absolute performance was constrained by the limited number of shared features, certain model combinations significantly outperformed the baseline. The performance of the zero-shot model was limited and inconsistent, and there was no statistically significant improvement. In some combinations, performance actually dropped significantly. In contrast, models that directly applied domain adaptation (domain adaptation models) or models using domain adaptation (ensemble models) showed performance improvements across various combinations. Statistically significant performance improvements were observed in some settings. This confirms the clinical value of synthetic data when the distribution of target data is leveraged.


    Principal Findings

    This study evaluated the feasibility and limitations of survival prediction models using specific synthetic data (NCDC synthetic dataset) in a privacy-constrained environment. After pretraining the model using large-scale synthetic data, we applied domain adaptation using single-center clinical data. The results showed that domain adaptation using real clinical data can partially compensate for the performance observed in the synthetic data-based zero-shot model used in this study. This suggests that synthetic data alone cannot replace real clinical data in the setting of this study, but when combined with limited real data, it can be used as a supplementary resource to improve model transfer performance.

    Comparison With Previous Work

    Previous studies have mainly focused on domain adaptation between multicenter real-world clinical data, and relatively few studies have evaluated the transfer performance to real-world clinical data after using synthetic data as the main training resource. This study differs from previous studies in that we used generated and publicly available NCDC synthetic data for pretraining and applied a strategy to calibrate it with single-institution data. As reported in previous studies, synthetic data may not sufficiently reproduce the distribution of rare or clinically important variables depending on how they are generated. We show empirically in an external validation setting that this limitation can lead to distributional differences between synthetic data and real-world hospital data, which can manifest as poor zero-shot model performance. This demonstrates the potential of synthetic data-based pretraining learning strategies, as well as the limitations due to data quality and domain differences, with concrete examples.

    Limitations

    This study had several limitations. First, there are limitations regarding the quality and distributional representativeness of the synthetic data used in this study. Significant discrepancies were observed in the prevalence of perineural invasion (PNI) between the NCDC synthetic data and hospital data. PNI is a well-established prognostic factor in predicting cancer survival. In addition, the synthetic dataset used in this study is a publicly available dataset, and a significant number of records were excluded from the analysis due to missing essential covariates. The NCDC synthetic data is RTSGAN-based and publicly available, and the distributional skew and missingness issues are consistent with the nature of RTSGAN-based synthetic data, where the goal is to preserve global statistical distribution. Previous studies have repeatedly reported underrepresentation of rare or clinically important variables as a limitation of synthetic data generation models [37]. It has also been reported that missingness in synthetic data may not be a newly generated problem, but rather a reflection of missingness mechanisms in the original clinical data or structural limitations of the generation model [38,39]. In this study, we did not artificially correct for these distributional skewnesses and missingnesses but rather performed the analysis while reflecting the realistic characteristics of the available synthetic data. Distributional differences in key prognostic factors and sample loss due to missingness may have partially contributed to the poorer predictive performance observed in the zero-shot transition setting. Additionally, differences in the distribution of major clinical variables were also observed between the synthetic dataset and the hospital dataset. In particular, there was a significant difference in the distribution of histological subtypes. While 97.2% of the hospital cohort was classified as adenocarcinoma, only 55.6% of the synthetic cohort was classified as adenocarcinoma, with the remainder consisting of nonadenocarcinoma subtypes, such as mucinous carcinoma and carcinoid tumors. Differences were also observed in postoperative chemotherapy patterns. These included regimen changes, combination or sequential therapies, and institution-specific treatment patterns not predefined, reflecting heterogeneous real-world treatment practices. These inconsistencies may have contributed to the observed performance differences and limitations in transferability between datasets. Second, the results of this study do not represent a direct performance comparison with an optimized clinical prediction model that uses all clinical variables included in the hospital data. The hospital data–based baseline model used in this study is a feature-constrained baseline model for comparison that is limited to 12 common variables to maintain commonality with synthetic data. Therefore, the performance improvement of domain adaptation models observed in this study should be interpreted as a validation of the methodological efficiency of synthetic data-based pretraining learning and model transfer strategies under the same input constraints, not as a claim of clinical superiority over local models that use all clinical information. Third, the external validation of this study was conducted using data from a single health care organization, which does not sufficiently validate its applicability to a variety of health care settings. Multicenter validation is needed to confirm the transferability and domain adaptation of synthetic data in settings with different institutional sizes, geographic characteristics, and patient populations. In addition, while the approximately 80:20 survival-to-death ratio observed for 7-year survival prediction is not an extreme level of class imbalance, the limited number of variables and domain differences between synthetic and hospital data, combined with the single-institution validation environment, may have formed an upper bound on model performance. Considering these characteristics, we chose AUPRC as the main evaluation metric to more appropriately reflect the discriminative performance of the model in an unbalanced clinical event prediction environment, while simultaneously using the Brier score to assess the degree of calibration of the prediction probability, which is important in clinical risk prediction. However, despite this choice of metrics, the absolute performance of the models remained in a limited range. In fact, the absolute performance of the model based on synthetic data ranged from an AUPRC of 0.3070 to 0.5165 and an F1-score of 0.3391 to 0.5132, while the baseline model using only hospital data had a similarly wide performance range with an AUPRC of 0.3975 to 0.458 and an F1-score of 0.4231 to 0.4751. This suggests that the performance limitations observed in this study may be the result of a combination of structural constraints of single-site validation, variable limitations, and domain differences, rather than simple class imbalance. Fourth, while age can be used as a continuous variable, we binned it in this study to increase distributional stability and mitigate nonlinear effects between synthetic and hospital data. This approach was intended to simplify interactions between variables in the tree-based model and reduce sensitivity to distributional differences between domains. However, this can be interpreted as a limitation of this study in that some continuous information may be lost.

    Future Work

    Future work should extend analyses using public synthetic data, such as this study, to more systematically evaluate the applicability and limitations of synthetic data-based pretraining learning and model transfer strategies using real-world clinical data. In particular, the impact of distributional mismatches or missing characteristics of clinically important rare variables on model performance should be quantitatively analyzed, and data usage and transfer strategies that can mitigate these issues should be further explored. Furthermore, by introducing a quality assessment framework that considers how the missingness mechanisms of the original clinical data are reflected in the synthetic data, it is expected that the information loss that may occur when using synthetic data can be interpreted more clearly. Furthermore, external validation using multicenter real-world data will validate the generalizability of the synthetic data-based transfer learning approach presented in this study, and studies extending it to other diseases and prediction tasks will contribute to a broader examination of the conditional use of synthetic data.

    Conclusion

    In this study, we define NCDC synthetic data not as a stand-alone learning resource, but as an auxiliary resource for pretraining learning that can be used in a privacy-preserving environment and demonstrate that it can improve prediction performance when combined with real-world clinical data through domain adaptation. In the setting of this study, the results clearly highlight the limitations of synthetic data-based zero-shot models, while confirming that limited real-world data plays a key role in model transfer. These results suggest that synthetic data are not a substitute for independent clinical application but can be used as a complement to increase methodological efficiency when combined with real-world clinical data with limited sample size and variable composition. This supports that the purpose of this study was not to replace real-world clinical decision-making, but to evaluate the methodological feasibility of synthetic data-based transfer learning strategies. Recent studies have emphasized that reproducibility and computational efficiency are also important quality criteria for medical AI research involving large-scale synthetic data and iterative model evaluation [35]. From this perspective, the fixed software environment and systematic experimental pipeline design of this study can be interpreted as a foundation for ensuring the sustainability and reproducibility of future synthetic data-driven clinical AI research.

    Acknowledgments

    We thank the National Cancer Data Center (NCDC) for supporting synthetic data access and the clinical team at Hwasun Jeonnam University Hospital for preparing the external validation cohort. We are also grateful for the patients and clinical staff who made this study possible. Additionally, we would like to thank Editage (www.editage.co.kr) for English language editing.

    Funding

    This research was supported by the Korea-Hwasun Oncology Precision Biomedicine & Experimental Trials Project, a research and development project commissioned by the Ministry of Health and Welfare of the Republic of Korea (grant RS-2025-00433283), and by the Development of an AI-Driven Platform for Clinical Validation in Senior Healthcare Focused on Degenerative Brain Diseases through the Korea Planning and Evaluation Institute of Industrial Technology (grant RS-2024-00433283).

    Data Availability

    The National Cancer Data Center datasets generated and/or analyzed during this study are available in the National Cancer Data Center portal [40]. The Hwasun Jeonnam University Hospital dataset is not publicly available; however, access may be granted to qualified researchers upon reasonable request following Chonnam National University Hwasun Hospital Data Access Committee approval and completion of a data-use agreement.

    The full analysis code used in this study has been published on GitHub [41].

    Authors' Contributions

    YJ conceived the study, performed the analyses, and drafted the manuscript. YJ and JHK developed the study methodology. CHK and JN conceived, led, and supervised the study, and contributed equally as corresponding authors. JN and JHK reviewed and revised the study design and the manuscript draft. HK and YJJ managed the data. HK collected and curated the hospital data. YJJ performed additional curation and preprocessing of the collected data. All authors read and approved the final manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Detailed performance results of colorectal cancer survival prediction models under AUPRC-based optimization across algorithms, sampling strategies, and model types.

    DOCX File, 24 KB

    Multimedia Appendix 2

    Detailed performance results of colorectal cancer survival prediction models under F1-score–based optimization across algorithms, sampling strategies, and model types.

    DOCX File, 24 KB
    1. Johnson KB, Wei WQ, Weeraratne D, et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci. Jan 2021;14(1):86-93. [CrossRef] [Medline]
    2. Billheimer D, Gerner EW, McLaren CE, LaFleur B. Combined benefit of prediction and treatment: a criterion for evaluating clinical prediction models. Cancer Inform. 2014;13(Suppl 2):93-103. [CrossRef] [Medline]
    3. Cruz-Correia R, Ferreira D, Bacelar G, Marques P, Maranhão P. Personalised medicine challenges: quality of data. Int J Data Sci Anal. Nov 2018;6(3):251-259. [CrossRef]
    4. Vale-Silva LA, Rohr K. Long-term cancer survival prediction using multimodal deep learning. Sci Rep. Jun 29, 2021;11(1):13505. [CrossRef] [Medline]
    5. Kim R, Shinn J, Kim HS. Using medical big data for clinical research and legal considerations for the protection of personal information: the double-edged sword. CPP. Jan 2024;6(1):8-16. [CrossRef]
    6. Shah SM, Khan RA. Secondary use of electronic health record: opportunities and challenges. IEEE Access. 2020;8:136947-136965. [CrossRef]
    7. Abbasi AF, Asim MN, Ahmed S, Vollmer S, Dengel A. Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases. Front Artif Intell. 2024;7:1428501. [CrossRef] [Medline]
    8. Huang Y, Li J, Li M, Aparasu RR. Application of machine learning in predicting survival outcomes involving real-world data: a scoping review. BMC Med Res Methodol. Nov 13, 2023;23(1):268. [CrossRef] [Medline]
    9. Giuffrè M, Shung DL. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ Digit Med. Oct 9, 2023;6(1):186. [CrossRef] [Medline]
    10. Arora A, Wagner SK, Carpenter R, Jena R, Keane PA. The urgent need to accelerate synthetic data privacy frameworks for medical research. Lancet Digit Health. Feb 2025;7(2):e157-e160. [CrossRef] [Medline]
    11. Liu Y, Acharya UR, Tan JH. Preserving privacy in healthcare: a systematic review of deep learning approaches for synthetic data generation. Comput Methods Programs Biomed. Mar 2025;260:108571. [CrossRef] [Medline]
    12. Chen J, Chun D, Patel M, Chiang E, James J. The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures. BMC Med Inform Decis Mak. Mar 14, 2019;19(1):44. [CrossRef] [Medline]
    13. Rankin D, Black M, Bond R, Wallace J, Mulvenna M, Epelde G. Reliability of supervised machine learning using synthetic data in health care: model to preserve privacy for data sharing. JMIR Med Inform. Jul 20, 2020;8(7):e18910. [CrossRef] [Medline]
    14. Qian Z, Callender T, Cebere B, Janes SM, Navani N, van der Schaar M. Synthetic data for privacy-preserving clinical risk prediction. Sci Rep. Oct 27, 2024;14(1):25676. [CrossRef] [Medline]
    15. Zhou Y, Chia MA, Wagner SK, et al. A foundation model for generalizable disease detection from retinal images. Nature. Oct 2023;622(7981):156-163. [CrossRef] [Medline]
    16. Pezoulas VC, Zaridis DI, Mylona E, et al. Synthetic data generation methods in healthcare: a review on open-source tools and methods. Comput Struct Biotechnol J. Dec 2024;23:2892-2910. [CrossRef] [Medline]
    17. Gonzales A, Guruswamy G, Smith SR. Synthetic data in health care: a narrative review. PLOS Digit Health. Jan 2023;2(1):e0000082. [CrossRef] [Medline]
    18. Yang J, Soltan AAS, Clifton DA. Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening. NPJ Digit Med. Jun 7, 2022;5(1):69. [CrossRef] [Medline]
    19. Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data. Dec 2016;3(1):9. [CrossRef]
    20. National Cancer Data Center. URL: https://www.cancerdata.re.kr/main/publicData/publicDataList [Accessed 2026-05-26]
    21. Kim J, Choo H, Shin SY, Song KD. Synthesis and quality assessment of combined time-series and static medical data using a real-world time-series generative adversarial network. Sci Rep. Aug 17, 2024;14(1):19064. [CrossRef] [Medline]
    22. Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW. A theory of learning from different domains. Mach Learn. May 2010;79(1-2):151-175. [CrossRef]
    23. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation. Ewha Med J. Jul 2025;48(3):e48. [CrossRef] [Medline]
    24. Zhang X, Li S, Cheng Z, Callcut R, Petzold L. Domain adaptation for trauma mortality prediction in EHRs with feature disparity. Presented at: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); Dec 9-12, 2021. [CrossRef] [Medline]
    25. Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning. Proc IEEE. 2019;109(1):43-76. [CrossRef]
    26. Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345-1359. [CrossRef]
    27. Major cancer mortality rate. National Cancer Information Center. URL: https://www.cancer.go.kr/lay1/program/S1T211C214/cancer/view.do?cancer_seq=3797&menu_seq=3816 [Accessed 2026-05-26]
    28. Bagui S, Li K. Resampling imbalanced data for network intrusion detection datasets. J Big Data. Dec 2021;8(1):6. [CrossRef]
    29. Matharaarachchi S, Domaratzki M, Muthukumarana S. Enhancing SMOTE for imbalanced data with abnormal minority instances. Machine Learning with Applications. Dec 2024;18:100597. [CrossRef]
    30. Dubey R, Zhou J, Wang Y, Thompson PM, Ye J. Analysis of sampling techniques for imbalanced data: an n=648 ADNI study. Neuroimage. Feb 15, 2014;87:220-241. [CrossRef] [Medline]
    31. Ke G, Meng Q, Finley T, et al. LightGBM: a highly efficient gradient boosting decision tree. Presented at: NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems; Dec 4-9, 2017. URL: https://dl.acm.org/doi/10.5555/3294996.3295074 [Accessed 2026-06-25]
    32. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Presented at: KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Aug 13-17, 2016:785-794; San Francisco, California, United States. [CrossRef]
    33. Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. 2015;10(3):e0118432. [CrossRef] [Medline]
    34. Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA. Oct 10, 2017;318(14):1377-1384. [CrossRef] [Medline]
    35. Jean-Quartier C, Tscheppe N, Millonig S, et al. Reproducibility and environmental efficiency of metabolomics cancer modeling. Applied Sciences. 2026;16(2):588. [CrossRef]
    36. Guo LL, Pfohl SR, Fries J, et al. Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Sci Rep. Feb 17, 2022;12(1):2726. [CrossRef] [Medline]
    37. Prieto G, Rodríguez JA, Fullaondo A. Enhancing prediction of short linear protein motifs with Wregex 3.0. Comput Struct Biotechnol J. Dec 2024;23:2978-2984. [CrossRef] [Medline]
    38. Beaulieu-Jones BK, Wu ZS, Williams C, et al. Privacy-preserving generative deep neural networks support clinical data sharing. Circ Cardiovasc Qual Outcomes. Jul 2019;12(7):e005122. [CrossRef] [Medline]
    39. Chen RJ, Lu MY, Chen TY, Williamson DFK, Mahmood F. Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng. Jun 2021;5(6):493-497. [CrossRef] [Medline]
    40. Colorectal cancer multi-institutional clinical library synthetic dataset. National Cancer Data Center. URL: https://www.cancerdata.re.kr/main/publicData/publicDataView [Accessed 2026-07-06]
    41. YujeongJang/colorectal-survival-domain-adaptation. Github. URL: https://github.com/YujeongJang/colorectal-survival-domain-adaptation [Accessed 2026-05-26]

    ‎
    AUPRC: area under the precision-recall curve
    LightGBM: light gradient boosting machine
    NCDC: National Cancer Data Center
    PNI: perineural invasion
    RTSGAN: real-world time-series generative adversarial network
    RUS: random undersampling
    SMOTEENN: synthetic minority oversampling technique combined with edited nearest neighbors
    XGBoost: extreme gradient boosting

    Edited by Andrew Coristine; submitted 20.Oct.2025; peer-reviewed by Andreas Holzinger, Bin Xie; final revised version received 22.May.2026; accepted 25.May.2026; published 07.Jul.2026.

    Copyright

    © Yujeong Jang, Jae Hoon Kwon, Heeyong Kim, You-Jin Joung, Junho ‍Nang, Chang Hyun Kim. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 7.Jul.2026.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.