Antimicrobial and anti-inflammatory effects of BenTooth: A natural product blend of burdock root, persimmon leaf extracts, and quercetin on periodontal disease.

Periodontal disease represents a condition that exhibits substantial global morbidity, and is characterized by the infection and inflammation of the periodontal tissue effectuated by bacterial pathogens. The present study aimed at evaluating the therapeutic efficacy of BenTooth, an edible natural product mixture comprising burdock root extract, persimmon leaf extract and quercetin, against periodontitis both in vitro and in vivo. BenTooth was examined for antimicrobial properties and its impact on cellular responses related to inflammation and bone resorption. Its effects were also assessed in a rat model of ligature-induced periodontitis. BenTooth demonstrated potent antimicrobial activity against P. gingivalis and S. mutans. In RAW264.7 cells, it notably diminished the expression of inducible nitric oxide synthase and cyclooxygenase-2, as well as reduced interleukin-6 and tumor necrosis factor-α levels triggered by P. gingivalis-derived lipopolysaccharide. Furthermore, BenTooth inhibited osteoclastogenesis mediated by the receptor activator of nuclear factor κB ligand. In the rat model, BenTooth consumption mitigated the ligature-induced expansion in distance between the cementoenamel junction and the alveolar bone crest and bolstered the bone volume fraction. These results present BenTooth as a potential therapeutic candidate for the prevention and remediation of periodontal diseases.

Survey of Medical Applications of Federated Learning.

OBJECTIVES: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain. METHODS: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security. RESULTS: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns. CONCLUSIONS: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

Traffic Exposure and Breast Cancer Mortality by Area of Residence: Incorporating Clinical and Socioeconomic Data.

Incorporating clinical and environmental data holds promise for monitoring vulnerable populations at the community level. This spatial epidemiology study explores the link between traffic-related air pollution and breast cancer mortality in Seoul, using public socioeconomic and clinical data from Samsung Medical Center's registry (N=6,089). Traffic and socioeconomic status were collected from official sources and integrated for spatial analysis. The findings revealed a significant association between adult breast cancer mortality and districts with high road density, NO2 emissions, and family income (p<0.05). Significant spatial autocorrelation of residuals was observed (Moran's I test p<0.001).

Evaluation of the Antiviral Activity of Tabamide A and Its Structural Derivatives against Influenza Virus.

Influenza viruses cause severe endemic respiratory infections in both humans and animals worldwide. The emergence of drug-resistant viral strains requires the development of new influenza therapeutics. Tabamide A (TA0), a phenolic compound isolated from tobacco leaves, is known to have antiviral activity. We investigated whether synthetic TA0 and its derivatives exhibit anti-influenza virus activity. Analysis of structure-activity relationship revealed that two hydroxyl groups and a double bond between C7 and C8 in TA0 are crucial for maintaining its antiviral action. Among its derivatives, TA25 showed seven-fold higher activity than TA0. Administration of TA0 or TA25 effectively increased survival rate and reduced weight loss of virus-infected mice. TA25 appears to act early in the viral infection cycle by inhibiting viral mRNA synthesis on the template-negative strand. Thus, the anti-influenza virus activity of TA0 can be expanded by application of its synthetic derivatives, which may aid in the development of novel antiviral therapeutics.

Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis.

The aim of this study was to address the issue of differentiating between Mayo endoscopic subscore (MES) 0 and MES 1 using a deep learning model. A dataset of 492 ulcerative colitis (UC) patients who demonstrated MES improvement between January 2018 and December 2019 at Samsung Medical Center was utilized. Specifically, two representative images of the colon and rectum were selected from each patient, resulting in a total of 984 images for analysis. The deep learning model utilized in this study consisted of a convolutional neural network (CNN)-based encoder, with two auxiliary classifiers for the colon and rectum, as well as a final MES classifier that combined image features from both inputs. In the internal test, the model achieved an F1-score of 0.92, surpassing the performance of seven novice classifiers by an average margin of 0.11, and outperforming their consensus by 0.02. The area under the receiver operating characteristic curve (AUROC) was calculated to be 0.97 when considering MES 1 as positive, with an area under the precision-recall curve (AUPRC) of 0.98. In the external test using the Hyperkvasir dataset, the model achieved an F1-score of 0.89, AUROC of 0.86, and AUPRC of 0.97. The results demonstrate that the proposed CNN-based model, which integrates image features from both the colon and rectum, exhibits superior performance in accurately discriminating between MES 0 and MES 1 in patients with UC.

Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation.

Cancer pain is a challenging clinical problem that is encountered in the management of cancer pain. We aimed to investigate the clinical relevance of deep learning models that predict the onset of cancer pain exacerbation in hospitalized patients. We defined cancer pain exacerbation (CPE) as the pain with a numerical rating scale (NRS) score of ≥ 4. We investigated the performance of the deep learning models using the Matthews correlation coefficient (MCC) with different input lengths and time binning. All the pain records were obtained from the electronic medical records of the hematology-oncology wards in a Samsung Medical Center between July 2016 and February 2020. The model was externally validated using the holdout method with 20% of the datasets. The most common type of cancer was lung cancer (n = 745, 21.7%), and the median CPE per day was 1.01. The NRS pain records showed circadian patterns that correlated with NRS pain patterns of the previous days. The correlation of the NRS scores showed a positive association with the closeness of the NRS pattern of the day with forecast date and size of time binning. The long short-term memory-based model exhibited a good performance by demonstrating 9 times the best performance and 8 times the second-best performance among 21 different settings. The best performance was achieved with 120 h input and 12 h bin lengths (MCC: 0.4927). Our study demonstrated the possibility of predicting CPE using deep learning models, thereby suggesting that preemptive cancer pain management using deep learning could potentially improve patients' daily life.

Preliminary Attainability Assessment of Real-World Data for Answering Major Clinical Research Questions in Breast Cancer Brain Metastasis: Framework Development and Validation Study.

BACKGROUND: In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established. OBJECTIVE: We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD. METHODS: The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution's anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question. RESULTS: Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis. CONCLUSIONS: The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.

Deep learning approach to detection of colonoscopic information from unstructured reports.

BACKGROUND: Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information. METHODS: This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model. RESULTS: The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. CONCLUSIONS: This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.

Graphical user interface design to improve understanding of the patient-reported outcome symptom response.

BACKGROUND: Symptom monitoring application (SMA) has clinical benefits to cancer patients but patients experience difficulties in using it. Few studies have identified which types of graphical user interface (GUI) are preferred by cancer patients for using the SMA. METHODS: This is a cross-sectional study aimed to identify preferred GUI among cancer patients to use SMA. Total of 199 patients were asked to evaluate 8 types of GUIs combining text, icon, illustration, and colors using mixed-methods. Subgroup analyses were performed according to age and gender. RESULTS: The mean age of the patients was 57 and 42.5% was male. The most preferred GUI was "Text + Icon + Color" (mean = 4.43), followed by "Text + Icon" (mean = 4.39). Older patients (≥ 60 years) preferred "Text + Icon" than younger patients (p for interaction < 0.01). Simple and intuitive text and icons were the most useful GUI for cancer patients to use the SMA. CONCLUSION: Simple and intuitive text and icons were the most useful GUI for cancer patients to use the SMA. Researchers need to be careful when applying realistic face drawings to cancer symptom monitoring applications because they can recall negative images of cancer.

Deep-learning-based personalized prediction of absolute neutrophil count recovery and comparison with clinicians for validation.

Neutropenia and its complications are major adverse effects of cytotoxic chemotherapy. The time to recovery from neutropenia varies from patient to patient, and cannot be easily predicted even by experts. Therefore, we trained a deep learning model using data from 525 pediatric patients with solid tumors to predict the day when patients recover from severe neutropenia after high-dose chemotherapy. We validated the model with data from 99 patients and compared its performance to those of clinicians. The accuracy of the model at predicting the recovery day, with a 1-day error, was 76%; its performance was better than those of the specialist group (58.59%) and the resident group (32.33%). In addition, 80% of clinicians changed their initial predictions at least once after the model's prediction was conveyed to them. In total, 86 prediction changes (90.53%) improved the recovery day estimate.

Influenza Screening Using Patient-Generated Health Data in Post COVID-19 Pandemic.

It is very important to ensure reliable performance of deep learning model for future dataset for healthcare. This is more pronounced in the case of patient generated health data such as patient reported symptoms, which are not collected in a controlled environment. Since there has been a big difference in influenza incidence since the COVID-19 pandemic, we evaluated whether the deep learning model can maintain sufficiently robust performance against these changes. We have collected 226,655 episodes from 110,893 users since June 2020 and tested the influenza screening model, our model showed 87.02% sensitivity and 0.8670 of AUROC. The results of COVID-19 pandemic are comparable to that of before COVID-19 pandemic.

Validation of the Korean Version of the Patient-Reported Outcomes Measurement Information System 29 Profile V2.1 among Cancer Survivors.

PURPOSE: The purpose of the study was to validate the Korean version of Patient-Reported Outcomes Measurement Information System 29 Profile v2.1 (K-PROMIS-29 V2.1) among cancer survivors. MATERIALS AND METHODS: Participants were recruited from outpatient clinics of the Comprehensive Cancer Center at the Samsung Medical Center in Seoul, South Korea, from September to October 2018. Participants completed a survey questionnaire that included the K-PROMIS-29 V2.1 and the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30). Principal component analysis and confirmatory factor analysis (CFA) and Pearson's correlations were used to evaluate the reliability and validity of the K-PROMIS-29 V2.1. RESULTS: The mean age of the study participants was 54.4 years, the mean time since diagnosis was 1.2 (±2.4) years, and 349 (87.3%) completed the entire questionnaire. The Cronbach's alpha coefficients of the seven domains in the K-PROMIS-29 V2.1 ranged from 0.81 to 0.96, indicating satisfactory internal consistency. In the CFA, the goodness-of-fit indices for the K-PROMIS-29 V2.1 were high (comparative fit index, 0.91 and standardized root-mean-squared residual, 0.06). High to moderate correlations were found between comparable subscales of the K-PROMIS-29 V2.1 and subscales of the EORTC QLQ-C30 (r=0.52-0.73). CONCLUSION: The K-PROMIS-29 V2.1 is a reliable and valid measure for assessing the health-related quality of life domains in a cancer population, thus supporting their use in studies and oncology trials.

Who is more likely to adopt and comply with the electronic patient-reported outcome measure (ePROM) mobile application? A real-world study with cancer patients undergoing active treatment.

PURPOSE: This study aims to identify factors associated with the adoption and compliance of electronic patient-reported outcome measure (ePROM) use among cancer patients in a real-world setting. METHODS: This prospective cohort study was conducted at the Samsung Medical Center in Seoul, Korea, from September 2018 to January 2019. Cancer patients aged 18 years or older who owned smartphones and who were receiving chemotherapy or radiation therapy were eligible for this study. Patients were asked to use the app to report their symptoms every 7 days for a total of 21 days (3 weeks). Logistic regression was performed to identify the factors associated with the adoption and compliance. RESULTS: Among 580 patients, 417 (71.9%) adopted the ePROM app and 159 (27.4%) out of 417 had good compliance. Patients who had greater expectations regarding the ease of use (adjusted odds ratio [aOR] 2.67, 95% CI: 1.28-5.57) and usefulness (aOR 1.69, 95% CI: 1.05-2.72) of the ePROM app were more likely to adopt the app than those who did not. Patients who had greater satisfaction with usefulness (aOR 1.89, 95% CI 1.10-3.25) were more likely to comply with using the app, but satisfaction with ease of use was not related to the compliance. CONCLUSION: While expectation regarding the ease of use and usefulness of the ePROM app was associated with the adoption of the app, satisfaction with ease of use was not related to compliance with the ePROM app. Satisfaction with usefulness was associated with the compliance of ePROM app use.

Privacy-preserving breast cancer recurrence prediction based on homomorphic encryption and secure two party computation.

Protecting patients' privacy is one of the most important tasks when developing medical artificial intelligence models since medical data is the most sensitive personal data. To overcome this privacy protection issue, diverse privacy-preserving methods have been proposed. We proposed a novel method for privacy-preserving Gated Recurrent Unit (GRU) inference model using privacy enhancing technologies including homomorphic encryption and secure two party computation. The proposed privacy-preserving GRU inference model validated on breast cancer recurrence prediction with 13,117 patients' medical data. Our method gives reliable prediction result (0.893 accuracy) compared to the normal GRU model (0.895 accuracy). Unlike other previous works, the experiment on real breast cancer data yields almost identical results for privacy-preserving and conventional cases. We also implement our algorithm to shows the realistic end-to-end encrypted breast cancer recurrence prediction.

Real-World Data from a Refractory Triple-Negative Breast Cancer Cohort Selected Using a Clinical Data Warehouse Approach.

PURPOSE: Triple-negative breast cancer (TNBC) is well known for its aggressive course and poor prognosis. In this study, we sought to investigate clinical, demographic, and pathologic characteristics and treatment outcomes of patients with refractory, metastatic TNBC selected by a clinical data warehouse (CDW) approach. PATIENTS AND METHODS: Data were extracted from the real-time breast cancer registry integrated into the Data Analytics and Research Window for Integrated Knowledge C (DARWIN-C), the CDW of Samsung Medical Center. Between January 1997 and December 2019, a TNBC cohort was searched for in the breast cancer registry, which includes records from more than 40,000 patients. Among them, cases of pathologically confirmed metastatic TNBC (mTNBC) were selected as the cohort group (n = 451). The extracted data from the registry via the CDW platform included clinical, pathological, laboratory, and chemotherapy information. Refractory TNBC was defined as confirmed distant metastasis within one year after adjuvant treatment. RESULTS: This study comprised a total of 451 patients with mTNBC, including 69 patients with de novo mTNBC, 131 patients in the nonrefractory TNBC group with confirmed stage IV disease after one year of adjuvant treatment, and 251 patients with refractory mTNBC, whose disease recurred as stage IV within one year after completing adjuvant treatment. The refractory mTNBC cohort was composed of patients with disease that recurred at stage IV after surgery (refractory mTNBC after surgery) (n = 207) and patients in whom metastasis was confirmed during neoadjuvant chemotherapy (unresectable TNBC due to progression during neoadjuvant chemotherapy) (n = 44). Patients in the refractory mTNBC group were younger than those in the nonrefractory group (median age 46 vs. 51 years; p < 0.001). Considering the pathological findings, the refractory group had a greater proportion of cases with Ki-67 ≥ 3+ than did the nonrefractory group (71% vs. 47%; p = 0.004). During a median 8.4 years of follow-up, the overall survival was 24.8 months in the nonrefractory mTNBC group and 14.3 months in the refractory mTNBC group (p < 0.001), and the median progression-free survival periods were 6.2 months and 4.2 months, respectively (p < 0.001). The median disease-free survival period was 30.1 months in the nonrefractory mTNBC group and only 7.6 months in the refractory mTNBC group. Factors related to metastatic sites affecting overall survival were liver metastasis at diagnosis (p < 0.001) and leptomeningeal involvement (p = 0.001). CONCLUSIONS: We revealed that patients with refractory mTNBC had a much poorer prognosis among all mTNBC cases and described the characteristics of this patient group.

Developing a Dementia Platform Databank Using Multiple Existing Cohorts.

This study was conducted as a pilot project to evaluate the feasibility of building an integrate dementia platform converging preexisting dementia cohorts from several variable levels. The following four cohorts were used to develop this pilot platform: 1) Clinical Research Center for Dementia of South Korea (CREDOS), 2) Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's disease (K-BASE), 3) Environmental Pollution-induced Neurological Effects (EPINEF) study, and 4) a prospective registry in Dementia Platform Korea project (DPKR). A total of 29916 patients were included in the platform with 348 integrated variables. Among participants, 13.9%, 31.5%, and 44.2% of patients had normal cognition, mild cognitive impairment, and dementia, respectively. The mean age was 72.4 years. Females accounted for 65.7% of all patients. Those with college or higher education and those without problems in reading or writing accounted for 12.3% and 46.8%, respectively. Marital status, cohabitation, family history of Parkinson's disease, smoking and drinking status, physical activity, sleep status, and nutrition status had rates of missing information of 50% or more. Although individual cohorts were of the same domain and of high quality, we found there were several barriers to integrating individual cohorts, including variability in study variables and measurements. Although many researchers are trying to combine pre-existing cohorts, the process of integrating past data has not been easy. Therefore, it is necessary to establish a protocol with considerations for data integration at the cohort establishment stage.

Survival in untreated hepatocellular carcinoma: A national cohort study.

This study aimed to analyze the proportion, characteristics and prognosis of untreated hepatocellular carcinoma (HCC) patients in a large representative nationwide study. A cohort study was conducted using the National Health Insurance Service (NHIS) database in Korea. A total of 63,668 newly-diagnosed HCC patients between January 2008 and December 2013 were analyzed. Patients were categorized into treatment group and no treatment group using claim codes after HCC diagnosis. The proportion of untreated HCC patients was 27.6%, decreasing from 33.4% in 2008 to 24.8% in 2013. Compared to treated patients, untreated patients were more likely to be older (P < 0.001), female (P < 0.01), to have a distant SEER stage (P < 0.001), severe liver disease (P < 0.001), and lower income (P < 0.001). The fully-adjusted hazard ratio for all-cause mortality comparing untreated to treated patients was 3.11 (95% CI, 3.04-3.18). The risk of mortality was higher for untreated patients in all pre-defined subgroups, including those with distant SEER stage and those with severe liver disease. About one fourth of newly diagnosed HCC patients did not receive any HCC-specific treatment. Untreated patients showed higher risk of mortality compared to treated patients in all subgroups. Further studies are needed to identify obstacles for HCC treatment and to improve treatment rates.

Detection of Hepatocellular Carcinoma in Contrast-Enhanced Magnetic Resonance Imaging Using Deep Learning Classifier: A Multi-Center Retrospective Study.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and a leading cause of cancer-related death worldwide. We propose a fully automated deep learning model to detect HCC using hepatobiliary phase magnetic resonance images from 549 patients who underwent surgical resection. Our model used a fine-tuned convolutional neural network and achieved 87% sensitivity and 93% specificity for the detection of HCCs with an external validation data set (54 patients). We also confirmed whether the lesion detected by our deep learning model is a true lesion using a class activation map.

Implementing Structured Clinical Templates at a Single Tertiary Hospital: Survey Study.

BACKGROUND: Electronic health record (EHR) systems have been widely adopted in hospitals. However, since current EHRs mainly focus on lowering the number of paper documents used, they have suffered from poor search function and reusability capabilities. To overcome these drawbacks, structured clinical templates have been proposed; however, they are not widely used owing to the inconvenience of data entry. OBJECTIVE: This study aims to verify the usability of structured templates by comparing data entry times. METHODS: A Korean tertiary hospital has implemented structured clinical templates with the modeling of clinical contents for the last 6 years. As a result, 1238 clinical content models (ie, body measurements, vital signs, and allergies) have been developed and 492 models for 13 clinical templates, including pathology reports, were applied to EHRs for clinical practice. Then, to verify the usability of the structured templates, data entry times from free-texts and four structured pathology report templates were compared using 4391 entries from structured data entry (SDE) log data and 4265 entries from free-text log data. In addition, a paper-based survey and a focus group interview were conducted with 23 participants from three different groups, including EHR developers, pathology transcriptionists, and clinical data extraction team members. RESULTS: Based on the analysis of time required for data entry, in most cases, beginner users of the structured clinical templates required at most 70.18% more time for data entry. However, as users became accustomed to the templates, they were able to enter data more quickly than via free-text entry: at least 1 minute and 23 seconds (16.8%) up to 5 minutes and 42 seconds (27.6%). Interestingly, well-designed thyroid cancer pathology reports required 14.54% less data entry time from the beginning of the SDE implementation. In the interviews and survey, we confirmed that most of the interviewees agreed on the need for structured templates. However, they were skeptical about structuring all the items included in the templates. CONCLUSIONS: The increase in initial elapsed time led users to hold a negative opinion of SDE, despite its benefits. To overcome these obstacles, it is necessary to structure the clinical templates for optimum use. In addition, user experience in terms of ease of data entry must be considered as an essential aspect in the development of structured clinical templates.

Next-Generation Sequencing-Based Cancer Panel Data Conversion Using International Standards to Implement a Clinical Next-Generation Sequencing Research System: Single-Institution Study.

BACKGROUND: The analytical capacity and speed of next-generation sequencing (NGS) technology have been improved. Many genetic variants associated with various diseases have been discovered using 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 structured according to recommended standards, clinical decision support systems have not been fully utilized. In addition, integrating genomic data with clinical data for translational research remains a great challenge. 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: We applied the recently published ISO/TS 20428 standard to 367 clinical sequencing reports generated by panel (91 genes) sequencing in EHRs and implemented a clinical NGS research system by extending the clinical data warehouse to integrate the necessary clinical data for each patient. We also developed a user interface with a clinical research portal and an NGS result viewer. 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 EHRs, such as diagnosis, surgery, and death information. This system can support the development of cohort or case-control datasets as well. CONCLUSIONS: The standardized clinical sequencing data are not only for clinical practice and could be further applied to translational research.