Data warehousing is the most important technology to address recent advances in precision medicine. However, a generic clinical data warehouse does not address unstructured and insufficient data. In precision medicine, it is essential to develop a platform that can collect and utilize data. Data were collected from electronic medical records, genomic sequences, tumor biopsy specimens, and national cancer control initiative databases in the National Cancer Center (NCC), Korea. Data were de-identified and stored in a safe and independent space. Unstructured clinical data were standardized and incorporated into cancer registries and linked to cancer genome sequences and tumor biopsy specimens. Finally, national cancer control initiative data from the public domain were independently organized and linked to cancer registries. We constructed a system for integrating and providing various cancer data called the Korea Cancer Big Data Platform (K-CBP). Although the K-CBP could be used for cancer research, the legal and regulatory aspects of data distribution and usage need to be addressed first. Nonetheless, the system will continue collecting data from cancer-related resources that will hopefully facilitate precision-based research.

Background/Objectives: Breast cancer is the most common cancer in women worldwide, requiring strategic efforts to reduce its mortality. This study aimed to develop a predictive classification model for breast cancer mortality using real-world data, including various clinical features. Methods: A total of 11,286 patients with breast cancer from the National Cancer Center were included in this study. The mortality rate of the total sample was approximately 6.2%. Propensity score matching was used to reduce bias. Several machine learning models, including extreme gradient boosting, were applied to 31 clinical features. To enhance model interpretability, we used the SHapley Additive exPlanations method. ML analyses were also performed on the samples, excluding patients who developed other cancers after breast cancer. Results: Among the ML models, the XGB model exhibited the highest discriminatory power, with an area under the curve of 0.8722 and a specificity of 0.9472. Key predictors of the mortality classification model included occurrence in other organs, age at diagnosis, N stage, T stage, curative radiation treatment, and Ki-67(%). Even after excluding patients who developed other cancers after breast cancer, the XGB model remained the best-performing, with an AUC of 0.8518 and a specificity of 0.9766. Additionally, the top predictors from SHAP were similar to the results for the overall sample. Conclusions: Our models provided excellent predictions of breast cancer mortality using real-world data from South Korea. Explainable artificial intelligence, such as SHAP, validated the clinical applicability and interpretability of these models.

Background: Pathology reports provide important information for accurate diagnosis of cancer and optimal treatment decision making. In particular, breast cancer has known to be the most common cancer in women worldwide. Objective: For the data extraction of breast cancer pathology reports in a single institute, we assessed the accuracy of methods between regular expression and natural language processing (NLP). Methods: A total of 1,215 breast cancer pathology reports were annotated for NLP model development. As NLP models, we considered three BERT models with specific vocabularies including BERT-basic, BioBERT, and ClinicalBERT. K-fold cross-validation was used to verify the performance of the BERT model. The results between the regular expression and the BERT model were compared using the named entity recognition (NER) techniques. Results: Among three BERT models, BioBERT was the most accurate parsing model (average performance = 0.99901) for breast cancer pathology when set to k = 5. BioBERT also had the lowest error rate for all items in the breast cancer pathology report compared to other BERT models (accuracy for all variables ≥ 0.9). Therefore, we finally selected BioBERT as the NLP model. When comparing the results of BioBERT and regular expressions using NER, we identified that BioBERT was more accurate than regular expression method, especially for some items such as intraductal component (BioBERT: 1.0, RegEx: 0.1644), lymph node (BioBERT: 0.9886, RegEx: 0.4792), and lymphovascular invasion (BioBERT: 0.9918, RegEx: 0.3759). Conclusions: Our results showed that the NLP model, BioBERT, had higher accuracy than regular expression, suggesting the importance of BioBERT in the processing of breast cancer pathology reports.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The analytical capacity and speed of next generation sequencing (NGS) technology has been improved. Lots of genetic variants correlating with various diseases have been discovered based on NGS. Therefore, applying NGS to clinical practice results in precision or personalized medicine. However, as clinical sequencing reports in electronic health records (EHRs) are not standardized, clinical decision support systems have not been fully utilized. In addition, integrating genomic data with clinical data for translational research remains a significant hurdle. Objective: To apply international standards to clinical sequencing reports and to develop a clinical research information system to integrate standardized genomic data with clinical data. Methods: The authors applied the recently published ISO/TS 20428 standard to 367 clinical sequencing reports generated by panel (91 genes) sequencing in EHR and implemented a Clinical NGS Research System (CNRS) by extending the clinical data warehouse to integrate the necessary clinical data for each patient and developed a user interface including clinical research portal, and an e-CRF (electronic-Case Report Form). Results: A single clinical sequencing report with 28 items was restructured into four database tables and 49 entities. As a result, 367 patients’ clinical sequencing data were connected with clinical data in EHR such as diagnosis, operation, or death information. This system can support development of cohort or case-control datasets as well. Conclusions: The standardized clinical sequencing data could be further applied to translational research, not only for clinical practice.

(2001). The future of the Democratic People's Republic of Korea. Journal of Contemporary Asia: Vol. 31, No. 1, pp. 104-120.

Park P, et al. J Korean Med Sci. 2026 Feb;41:e79. https://doi.org/10.3346/jkms.2026.41.e79