Development of a continuous multimodal data supply chain for oncology and an expandable clinical decision support system

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Methods: A real-time data supply chain was established in an academic cancer center to capture unstructured health data from various sources. For each cancer type, specific selection approaches were developed. Firstly, we predefined 817 features applicable across various cancers, along with a median of 61 histology-specific features and developed a customized Extract-Transform-Load (ETL) algorithm for each feature Standard criteria for data quality control (QC) were formulated, and a web-based computational QC platform enabling manual and automatic inspections was created. This framework has been applied to electronic health data since 2006. Findings: The data supply chain captured features from 171,128 individuals across 11 cancer types. It updates individual profiles daily and conducts QC checks, including 143 logical comparisons, processing an average of 81 cases daily. Continuous automatic and manual data QC within closed-human-in-loop systems ensured accuracy. Using the developed data warehouse, we were able to showcase survival graphs by tumor stages and demonstrate the framework's ability to expedite data collection for quick clinical hypothesis testing. A dashboard displaying patients’ cancer journeys, including landmark events, treatment progress, and longitudinal tumor tracking, was also developed. Interpretation: We have developed an automatically updated data supply chain that comprehensively synthesizes multimodal medical data and assesses data QC, aimed at directly assisting clinical decision-making for individual patients. Ongoing learning is essential, depending on the purpose of data use, and further research into its applicability in other environments is required. Health sciences/Diseases/Cancer Health sciences/Health care/Patient education Health sciences/Medical research/Outcomes research data warehouse continuous capture multimodal oncologic information electronic health record medical imaging genomic data big data Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Oncology data is multidimensional and diverse, encompassing a vast array of information such as patient characteristics, stage, tumor, and imaging data. 1 The advent of electronic medical records (EMR) and emerging data sources has caused a transformative surge in health information. 2 This data deluge often exceeds human cognitive limits for decision-making 3 and has led oncology professionals to spend more time navigating EMR than engaging with patients to seek fragmented health data from disparate sources, which exacerbates burnout. 4 Fortunately, rapid advancements in computational techniques, notably machine learning and artificial intelligence (AI), herald new possibilities for harnessing extensive and intricate medical data for individualized, data-driven care. 5 These technologies have demonstrated potential in refining imaging 6 and pathology diagnostics, 7 prognosticating clinical outcomes, optimizing radiation treatment planning, 8 and accelerating drug development. 9,10 AI has also significantly impacted foundational research in oncology. 11 However, challenges related to validation and generalizability 12 mean that the current methodologies for data management and model development fall short of the maturity required for broad-scale AI adoption. Transitioning from the present ad-hoc data aggregation and curation approach to a dynamic "metadata supply chain" is essential for providing contextualized, robust data in real-time. 13 By capturing pivotal data in real-time, this metadata supply chain can lay the groundwork for a clinical decision support system that vividly maps patient journey, potentially transforming clinical workloads. Within this framework, our objective was to present our collaborative endeavor for establishing a comprehensive data supply chain in oncology. This system seamlessly integrates clinical, genomic, and imaging data, representing a persistent, flexible, and expandable model. The infrastructure holds the potential to expedite the development of clinical decision support systems and AI applications for risk stratification, diagnosis, and treatment in oncology, paving the way for individualized patient-centered care. Methods After approval of the protocol by the Institutional Review Board of Severance Hospital (4-2021-1241), Seoul, Republic of Korea, we established a development server using a Windows-based, 12-core computer with 64 GB of memory and Serial Attached SCSI (SAS) disk drives of 100 GB and 2 TB. Operational servers, constituting High Availability (HA) systems, included a database (DB) server (2Ea) with a 10-core CPU, 128 GB memory, OS SSD 100 GB of storage/SCL 2019, DB Safer, Hiware, EMS, and Backup (DB), and a web-based server with a 12-core CPU, 64 GB memory, OS SSD 100 GB of storage/Hiware, EMS, and Backup (File). The dataflow and computational modules are illustrated in Fig. 1 . All data processing, transfer, and storage were performed within the network infrastructure of the hospital. We received authorization for access to all digital records from the EMR system and billing data from the Oncology Care System. To improve the data quality and mitigate the risks associated with erroneous or omitted data, we tailored the selection approaches for each cancer type. The selection was based on the International Classification of Diseases for Oncology (ICD) and physician-assigned ICD-M codes as well as validity criteria designated by the cancer registration program. A comprehensive breakdown of the selection methodologies for each cancer type is provided in Table 1 . Table 1 Selection methods for each cancer type Cancer Id Cancer Type DBName Criteria 01 Breast cancer YCDL_BRST (1) Cancer Registry : ICDOCd a =C50% AND available = Y AND ICDOCdM b <M9590 02 Colorectal cancer YCDL_CLRC (1) Cancer Registry: ICDOCd=(C18%, C19%, C20%) AND ICDOCdM = M81403(Adenocarcinoma) AND available = Y 03 Lung cancer YCDL_LUNG (1) Cancer Registry : ICDOCd = C34% AND available = Y AND ICDOCdM < M9590 AND ICDOCdM NOT LIKE '%/2' 04 Gastric cancer YCDL_GSTR (1) Cancer Registry : ICDOCd = C16% AND available = Y AND available = Y AND ICDOCdM < M9590 AND ICDOCdM NOT LIKE '%/2' 05 Liver cancer YCDL_LVER (1) Cancer Registry : ICDOCd = C22.0 AND available = Y AND ICDOCdM < M9590 AND ICDOCdM NOT LIKE '%/2' 06 Melanoma YCDL_MLNM (1) Cancer Registry: ICDOCdM_EngNm (pathology) LIKE '%Melanoma%' AND available = Y (2) The cancer diagnosis group = D0023(Malignant melanoma) in CAP system c . (3) There are records of '%Melanoma%', '%Malignant Spitz%' in the pathology diagnosis results. (4) There are records of '%Melanoma%', '%Malignant Spitz%' in the imaging test. (excluded '%r/o%') 07 Kidney cancer YCDL_KDNY (1) Cancer Registry : ICDOCd = C64% AND available = Y AND ICDOCdM < M9590 AND ICDOCdM NOT LIKE '%/2' 08 Prostate cancer YCDL_PRST (1) Cancer Registry : ICDOCd = C61% AND available = Y AND available = Y AND ICDOCdM < M9590 AND ICDOCdM NOT LIKE '%/2' 09 Thyroid cancer YCDL_THRD (1) Cancer Registry : ICDOCd = C73% AND available = Y AND ICDOCdM < M9590 AND ICDOCdM NOT LIKE '%/2' 10 Pancreatic cancer YCDL_PNCT (1) Cancer Registry : ICDOCd = C25% AND available = Y AND ICDOCdM < M9590 AND ICDOCdM NOT LIKE '%/2' 11 Bile duct cancer YCDL_BLDT (1) Cancer Registry : ICDOCd=(C22.1, C23.9, C24.0, C24.1, C24.8, C24.9) AND available = Y AND ICDOCdM < M9590 AND ICDOCdM NOT LIKE '%/2' a ICDOCd = ICD-O ( International Classification of Diseases for Oncology) Codes b ICDOCdM = Morphology section of the ICD-O Code c CAP system = Chemotherapy Assistance Program for ordering oncology medications Subsequently, a patient-centric data model was developed, underpinned by the patient identification numbers dispensed by the hospital information system. This served as a linchpin for linking the anonymized datasets. In the clinical data extraction stage, we developed an Extract-Transform-Load (ETL) process, which includes Natural Language Processing (NLP), for each feature (Fig. 2 ). It facilitated the daily movement of data from the DSC source DB to the YCDL target DB. The DSC DB is a reservoir containing raw medical text, (semi-)unstructured data, imaging files, next-generation sequencing (NGS) results, and Extensible Markup Language (XML) formats. In the initial phase of data processing, we tailored the database corpus from the DSC DB, optimizing the extraction and management of medical terminology, abbreviations, and recurrent misspellings (e.g., within pathology reports). Subsequently, the procured data underwent transformation through a specialized ETL algorithm designed to harmonize terminology based on assertions and the interrelationships of medical concepts. NLP was instrumental in utilizing CT and MRI interpretation counts from follow-up visits as criteria for individual selection. Parsing the raw medical text from radiological reports revealed the indications for disease recurrence and its associated patterns. The subsequent preprocessing phase employed tokenization techniques to structure the extracted data. SQL queries were harnessed to mine data from the primary DSC DB, facilitated by a data manipulation language (DML) management interface. For certain datasets requiring intricate extraction protocols, bespoke ETL strategies were devised using Python scripts crafted for each specific operation ( Supplementary Fig. 1 ). Using NGS data, Tier 1 pathogenic variants were systematically collected from the EMR. A significant portion of the procedural steps were automated, employing specialized bioinformatics tools for both processing and interpretation, as depicted in Supplementary Fig. 2 . Certain elements, including family and smoking histories, were retained in our medical record system in XML format and subsequently extracted and transferred using the ETL process. Following its development, we applied this framework to our electronic health data from its inception in 2006. The profiles were updated using electronic health records, ensuring a comprehensive view of relevant oncological components over time. The present analysis is based on data collected up to March 2022. Key constituents of these profiles included demographics, diagnoses, clinical examination reports, pathology reports, treatment histories, and encounter specifics (Tables 2 and 3 ). The structures of these individual profiles were categorized into common, cancer-specific, and index columns. The common features held universal information across multiple cancer types (e.g., age, sex, and cancer diagnosis date) and accounted for 817 features, which was nearly 80% of the total. The cancer-specific features contained data relevant only to specific cancer types (for instance, pulmonary function test in lung cancer) and comprised approximately 20% of the tables (Table 4 ). Table 2 Tables in the DSC database DB No. DB Name DB code Table No. Table Name Table Description 1 Patients PT 1 CNCR_PATINFO Patient basic information 1 Patients PT 2 CNCR_BODYINFO Body measurement information 2 Diagnosis DG 3 CNCR_DX Diagnoses relating to a hospital visits 2 Diagnosis DG 4 CNCR_CRDINFO Copayment Decreasing Policy 2 Diagnosis DG 5 CNCR_CSLT Consultant Information 3 Examination EM 6 CNCR_LAB Events relating to laboratory tests 3 Examination EM 7 CNCR_IMAGE Events relating to Imaging test 4 Pathology PH 8 CNCR_PATHOLOGY Events relating to Pathology 5 Operation OP 9 CNCR_OP Surgery 6 Treatment TX 10 CNCR_REGIMEN Chemo- therapy 6 Treatment TX 11 CNCR_RT Radiation-therapy 6 Treatment TX 12 CNCR_DRUG Medicines prescribed 6 Treatment TX 13 CNCR_PROC Procedure (included medical operation) 7 Progress TE 14 CNCR_FRM Clinical Forms 8 a Cancer registry TM 15 CNCR_TUMOR_RGT Tumor Registry (personal details and cancer diagnosis) 8 Cancer registry TM 16 CNCR_TUMOR_TRANS Tumor Registry (included cancer recurrence/metastasis) 8 Cancer registry TM 17 CNCR_TUMOR_TRC Tumor Registry (included cancer patient follow-up) 8 Cancer registry TM 18 CNCR_TUMOR_TRET Tumor Registry (included cancer treatment) a Cancer registry = database of information on cancer patients Table 3 Tables in the YCDL database and number of variables Table Category Table Name Table Description Common Breast Colorectal Lung Gastric Liver Melanoma Kidney Prostate Thyroid Prostate Bile duct Total 1 PT Patient PT_BASIC Patient Basic Information 20 20 2 PT Patient PT_PHIS Past History 39 9 12 1 2 63 3 PT Patient PT_SHIS Smoking History 29 29 4 PT Patient PT_DRNK Drinking History 33 33 5 PT Patient PT_FMHS Family History 41 41 6 PT Patient PT_BDMS Body Measurement 27 27 7 DG Diagnosis DG_INFO Visit Information 27 27 8 DG Diagnosis DG_ECHI Copayment Policy 27 27 9 DG Diagnosis DG_CNCR Cancer Diagnosis 37 37 10 DG Diagnosis DG_CONS Consultant 28 28 11 EM Examination EM_LAB Laboratory Test 30 30 12 EM Examination EM_IMEX Imaging Test 28 2 2 2 2 36 13 EM Examination EM_GENE Genetic Test 28 28 14 EM Examination EM_FCLT Function Test 28 6 5 2 6 2 2 51 15 PH Pathology PH_BPSY Biopsy 29 7 13 4 13 5 5 3 7 3 4 4 97 16 PH Pathology PH_SRGC Histopathology 32 29 36 20 19 34 23 24 16 25 20 33 311 17 PH Pathology PH_IMML Immuno-histology 29 13 17 9 10 10 17 15 8 15 12 12 167 18 OP Operation OP_INFO Operation Information 28 28 19 OP Operation OP_OPNN Operation opinion 38 27 31 13 19 46 6 5 6 12 4 14 221 20 OP Operation OP_COMP Operation Complication 25 25 21 TX Treatment TX_CHTH Chemotherapy 36 36 22 TX Treatment TX_RTH Radiotherapy 41 41 23 TX Treatment TX_PRSC Drug 29 29 24 TX Treatment TX_MOPR Procedure 33 33 25 TE Follow-Up TE_MTST Follow-Up Metastasis 27 27 26 TE Follow-Up TE_RCRN Follow-Up Relapse 23 23 27 TE Follow-Up TE_DEAD Dead 25 25 Table 4 Column characteristics by cancer type Number of Common Columns (A) Number of Cancer-specific Columns (B) Number of Index Columns (C) Number of Total Columns (D) Percentage of Cancer-specific Columns (B/D) Breast 817 91 459 908 10% Colorectal 817 109 459 926 12% Lung 817 53 459 870 6% Gastric 817 61 459 878 7% Liver 817 99 459 916 11% Melanoma 817 51 459 868 6% Kidney 817 47 459 864 5% Prostate 817 38 459 855 4% Thyroid 817 63 459 880 7% Pancreatic 817 44 459 861 5% Bile duct 817 67 459 884 8% We developed a web-based computational platform for data quality control (QC) that scrutinizes potential data defects both automatically and manually on a daily basis, focusing on minimizing the role of the human component (Fig. 3 ). All data extracted and stored in the YCDL_cancer data repository were continuously evaluated and optimized to establish high-quality data outputs, adhering to standardized data and terminology. Programs for logical checks were configured to evaluate the distribution and continuity of data extracted by the SCL. Based on the QC results, the ETL code was continuously modified, thereby refining the QC logic to enhance the quality and accuracy of the automation. We examined four data quality measures (completeness, timeliness and usefulness, consistency, and accuracy) for all variables, in accordance with established data standards and pertinent aspects of data quality (Table 5 ). For instance, the logic was set such that the birth date of individuals would precede the date of the initial diagnosis. The analyses revealed that the batch processing method accurately identified erroneous data points, aligned with the established logic. Each piece of data was meticulously reviewed and optimized by a Quality Control Manager. Significant discrepancies or inaccuracies prompted an in-depth examination of the source data and respective ETL processes. Moreover, a hierarchy of data sources was established to resolve conflicts. The QC steps were continuously iterated within distinct closed-loop systems, adhered to operational ontology, and executed by independent QC personnel. This methodology gradually enhanced the accuracy of the cleansed target data with minimal intervention ( Supplementary Fig. 3 ). We assessed the completeness of each individual’s accumulated features, including fundamental characteristics such as date of birth, initial diagnosis date, age, diagnosis code (ICD), TNM and overall stages, and ICDO morphology code. Table 5 Data quality check criteria Quality Indicators Detailed Quality Indicators Diagnostic Targets Remarks Completeness Individual Completeness Columns or input values defined to exist but are Null Conditional Completeness Checking for NOT NULL constraints Structural Completeness Implementation based on the physical model designed from the schema, including data types Verified at the DB design stage Validity Code Validity Whether codes defined in the common code are used Format Validity Errors in data format Verified at the DB design stage Boolean Validity Diagnosis based on columns with Y/N, 0/1 criteria Date Validity Errors based on date formats Range Validity Diagnosis based on Min, Max, and Normal range of the column Temporal Relationship Validity Diagnosis of data that deviates from predetermined sequential relationships Consistency Referential Integrity Diagnosis of operation rules for PK (Primary Key) items Verified at the DB design stage Accuracy Logical Relationship Accuracy Data diagnosis according to logical relationships, e.g., when item A is n, item B should be at least m Derived Item Accuracy Diagnosis of derived data, e.g., whether the sum of item A and item B is equal The developed data warehouse showcased survival graphs by tumor stages and demonstrated the framework's ability to expedite data collection for quick clinical hypothesis testing. Kaplan–Meier survival graphs were generated in all cancer types according to tumor stage with 95% confidence intervals. Survival time was defined as the time interval between initial diagnosis and death or the last follow-up. To demonstrate the efficiency of our data framework as a proof-of-concept for swiftly generating and evaluating clinical hypotheses, we present a detailed chronological progression of a previously published retrospective study. The clinical question chosen by one of the authors was whether the peripheral blood neutrophil-to-lymphocyte ratio before, during, or after neoadjuvant chemoradiotherapy for locally advanced rectal cancer is associated with an increased risk of distant metastases after primary rectal cancer surgery. To underscore the capabilities of our data framework for clinical applications, we then developed a clinical decision-support system that offers (a) a longitudinal view of the complete patient journey, (b) PACS-integrated three-dimensional tumor display, and (c) summary of longitudinal changes in the form of graphs. This system was supported by a Docker-based microservice architecture and overseen by a Python-based API server for backend operations. The framework was implemented as a web application using JavaScript. This design bolsters the accessibility of the system, guarantees platform independence, and ensures that users can access services across various device types. Manual tumor segmentation data are required to use the three-dimensional tumor display with a longitudinal tumor-tracking function. If deep learning-based tumor auto-segmentation algorithms are developed, these models can be integrated into the pipeline. The PACS-integrated method enables physicians to comprehensively track changes in overall trajectory patterns over a long period, fosters an environment that better explains the disease course to patients, and facilitates communication with referring physicians. Longitudinal changes in the overall disease burden were automatically generated using the prepared manual contours and displayed as graphs. The images were de-identified; however, if another image of the same patient was transferred later, the new images were allocated the same de-identified number, facilitating tumor tracking. Results At the time of analysis, the DB contained records of the feature sets of 171,128 individuals diagnosed with 11 different cancers at a single academic cancer center between January 2006 and March 2022 (Table 6 ). For each individual, 817 essential features in the common columns and a median of 61 features (range: 38–109) in the cancer-type-specific columns were updated daily and continuously. Table 6 Number of patients added each year and, in every cohort, 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022.3 Total Breast 764 811 737 837 959 880 891 740 938 1,078 1,358 1,409 1,811 1,754 1,526 1,671 253 18,417 Colorectal 882 942 1,006 1,158 1,090 1,207 1,143 979 1,154 1,254 1,438 1,413 1,433 1,387 1,149 1,046 222 18,903 Lung 712 740 756 768 817 840 884 902 943 1,143 1,257 1,409 1,653 1,922 1,737 1,909 334 18,726 Gastric 1,829 1,815 1,944 1,865 1,914 1,964 1,836 1,696 1,899 1,915 1,949 1,918 1,862 1,522 1,267 1,433 254 28,882 Liver 657 589 656 737 679 655 621 569 589 609 657 610 616 646 565 489 112 10,056 Melanoma 49 43 73 74 87 67 58 75 86 99 113 91 108 107 123 105 24 1,382 Kidney 207 245 265 311 305 300 317 301 358 358 440 439 466 588 430 436 41 5,807 Prostate 378 529 621 755 697 760 774 713 711 779 1,007 1,105 1,195 1,432 1,289 1,152 206 14,103 Thyroid 1,535 2,372 2,841 2,748 2,783 2,619 2,909 2,615 2,037 1,792 1,975 2,206 3,058 3,201 2,805 3,575 695 41,766 Pancreas 246 254 274 298 290 313 362 324 326 442 546 455 526 605 545 631 174 6,611 Bile duct 324 330 316 340 338 418 397 331 360 409 463 472 519 453 470 423 112 6,475 Total 7,583 8,670 9,489 9,891 9,959 10,023 10,192 9,245 9,401 9,878 11,203 11,527 13,247 13,617 11,906 12,870 2,427 171,128 During the QC process, we established a comprehensive set of 143 human-driven logical comparisons, including 70 focused on identifying missing data, 41 ensuring temporal validity (e.g., the completion date of radiotherapy should coincide with or follow its initiation date), 15 pinpointing outlier data (such as age at menarche between 8 and 20 years), 13 selecting the relevant values among multiple time points, and 4 dedicated to spotting duplicated or inconsistent data. The QC logic outcomes showed consistent results across 11 different cancer types, comprising a total of 1,523 datasets. We initially set the estimated daily QC case count to 10%, which translated to approximately 81 cases per day. We generated survival graphs for each of the 11 distinct cancers in our dataset, segmented by tumor stages (Fig. 4 ). Notably, except for prostate cancer, there is a significant variation in survival rates depending on the stage of cancer; generally, higher stages are associated with lower survival rates. The efficacy of our data framework in rapidly generating and evaluating clinical hypotheses was demonstrated in a study published in 2022. Following the development of the proposal and subsequent approval from the institutional review board, researchers requested baseline data on patients, tumors, and treatments, as well as peripheral blood neutrophil and lymphocyte counts, spanning the period between the initial diagnosis and the date of primary rectal surgery in December 2020. Data abstraction for 1,386 individuals was efficiently executed using our framework, and its accuracy and reliability were confirmed by an experienced oncologist. This proficiency enabled researchers to commence a pilot analysis in January 2021, merely a month post the initial data acquisition. We successfully developed a clinical decision support system with four layouts. In the upper-left layout, shown in Fig. 5 , three selected image series are displayed alongside their corresponding three-dimensional tumor visualizations, using DICOM files of individually, manually contoured lesions ( Supplementary Video 1 , Fig. 5 a). In the middle-upper layout, the output of the longitudinal tumor tracking is in the form of a graph (Fig. 5 b). The section with the hope of predicting individual patient outcomes has been reserved for future integration of any potential model (Fig. 5 c). The lower layout presents a comprehensive overview of a patient's healthcare journey, allowing readers to intuitively understand the chronological sequence of events and progression of the patient's treatment (Fig. 5 d, Supplementary video 2 ). This offers holistic and interactive patient summaries on a graphical timeline anchored by real-time data captured within our framework. Users can easily assess patient data in a temporal context with a single click, and the depth of information can be fine-tuned using zoom features and pop-up boxes. Discussion In this study, we successfully developed a cancer-specific information technology infrastructure designed to facilitate the longitudinal collection of comprehensive health data, an accomplishment realized through extensive cross-departmental collaboration. Using this framework, we established an automated DB that continually updates the data from 171,128 individuals, each distinguished by over 800 unique features. Manually collating such an expansive array of features is challenging. To ensure data integrity, we initially implemented rigorous data quality control methods, starting with manual logic applications and subsequently transitioning to an automated management system. This approach, conducted within closed-loop systems, led to a steady enhancement in data precision. Furthermore, we validated the caliber of our automatically harvested data by assessing survival rates in relation to cancer stage. Of practical significance, our system highlights the potential for real-time capture of disease state and treatment data, exemplified by a proof-of-concept for rapid clinical hypothesis testing and offering a holistic view of a patient's journey with a single click. This not only alleviates the clinical burden but also optimizes the research workflow. Understanding the crucial role of a reliable automatic data supply chain, several research groups have collaboratively developed frameworks to capture and transport oncologic data. 14,15 Morin et al. 14 introduced MEDomics, an information technology infrastructure that integrates seamlessly with multiple EHR DBs to ensure uniform data collection. Their research amassed data from nearly 175,000 patients with cancer at the University of California, San Francisco, between 2010 and 2019. Employing rule-based selection techniques, they identified individuals with high-quality data, narrowing them down to 3,782 breast cancer and 2,054 lung cancer cases. Lower-quality data were more prevalent among individuals located further away from the institution, a trend associated with increased mortality rates. Jung et al. 15 showcased ROOT, an auto-updating data warehouse that consolidates comprehensive clinical data of 67,617 individuals diagnosed with head and neck, thoracic, and esophageal cancers at the Samsung Medical Center in Korea between 2008 and 2020. These endeavors underscore the importance of data governance and active participation of all stakeholders. Considering geographic disparities and practice variations, in-house development might be best positioned to cater to the specific needs of end users. Building an automated data warehouse using oncology EMR data poses inherent challenges because of the varying degrees of data completeness, inconsistencies, and conflicting or evolving records. 16 In this context, a nationwide initiative was launched to create a comprehensive cancer data library aimed at standardizing terminology and classification within our country. Concurrently, institutional efforts aimed to gather extensive feedback and integrate preexisting registries from diverse cancer groups. The recent proposal of Operational Ontology for Oncology (O3) seeks to achieve multi-institutional and multi-stakeholder consensus, lowering the barriers for collaborative information aggregation. 17 Our next task involves identifying differences and similarities between our defined features and the variables proposed by O3, and if possible, updating the necessary parts. To manage the vast variability of data sources and types, we devised algorithms that harness structured data from diverse origins and process unstructured content using ETL procedures. ETL operations present unique challenges, especially when dealing with components presenting multiple ETL-related complications. Collaboration with team members well-versed in treatment workflows and medical informatics, combined with close cooperation with the IT department, was pivotal in understanding the system functionality and nuances of data interpretation. Both data governance and ethical deliberation are instrumental in ensuring data security and patient privacy. In the absence of formalized frameworks, challenges may arise in query fulfillment and data management. 18 However, our data supply chain addresses this issue through an end-to-end workflow for data quality assurance, ensuring continual evaluation and improvement. Conflicting, missing, or incorrect data were identified through human-driven logical comparisons and rectified by making logical corrections or adjusting the algorithms. Since its implementation, the quality assurance workflow has been continuously refined, accumulating data checks across multiple cycles. This iterative process enhances data quality and reduces the need for human intervention. Engagement with various groups familiar with the data sources and limitations is essential. The YCDL framework has numerous potential clinical and research applications. Although limited data have evaluated clinically relevant outcomes in oncology care, emerging evidence suggests that clinical decision support systems using EMR data can positively impact care quality. 19 A recent randomized controlled trial by Hong et al. 20 demonstrated accurate triaging of patients with cancer and reduced acute care rates using an EMR-based machine learning algorithm. Coombs et al. 21 showed that a proposed machine learning tool using real-world EMR data could identify patients with cancer at risk for a 60-day emergency department visit. Another potential application is the generation and rapid testing of clinical hypotheses, as suggested by Morin et al., 14 which would not have been feasible using traditional data approaches. The YCDL enabled the collection of a vast amount of data, including laboratory results and patient features, thereby facilitating the first pilot analysis. Additionally, automatic flagging of eligible patients for clinical trials shows promise. 21 The real-time metadata supply chain can automatically display patient histories, thereby eliminating the need for physicians to navigate through numerous pages. With advancements in systemic drugs, patients with stage IV cancer now live longer and have complex treatment histories. 22 A quick overview of a patient’s cancer journey allows physicians to efficiently characterize both the disease and the individual, potentially reducing burnout and ensuring quality care. 23 Commercial clinical decision support software such as NAVIFY Oncology Hub, 24 Syapse, 25 and Flatiron Assist, 26 are undergoing evaluation for integration into the EMR system to provide a comprehensive view of a patient's journey. With emerging local therapies, 27 AI can play a significant role in detecting and segmenting normal tissues and tumors, 28 as well as tracking lesions over time in relation to treatment. 29 However, further research on tumor auto-segmentation is warranted. This study has several limitations that should be considered when interpreting our results. First, our method represents the experience of a single institution, and large-scale adjustments may be necessary for implementation elsewhere. Second, the data supply chain approach is designed as an expandable infrastructure that accommodates updated ontologies and evolving demands. Establishing a strong leadership in data governance, implementing sharing agreements, and promoting open science practices are essential for a robust metadata supply chain. This requires dedicated departments to ensure job security. Collaborative efforts such as workshops and knowledge transfers promote an understanding of the benefits offered by the metadata supply chain and AI technologies. Future work will incorporate additional cancer types such as brain tumors and rare malignancies. Once the ETL process is finalized, we aim to make it publicly accessible. Our hospital primarily diagnoses and follows up with patients within our institution; however, inter-hospital data sharing may become necessary in certain cases. Finally, the current framework only captures survival and recurrence data despite the growing recognition of the importance of quality of life and toxicity profiles as critical outcomes. In conclusion, this study emphasizes the importance of leveraging computational methods and real-time data supply chains to address the challenges posed by the overwhelming volume of health information in oncology. Our collaborative endeavor to construct a robust data framework not only demonstrates its potential to enhance personalized care, facilitate AI applications, and refine clinical decision-making, but also serves as the cornerstone for acquiring comprehensive health data over time. To promote the widespread adoption of data supply chains and AI technologies, it is imperative to emphasize strong data governance, embrace open science practices, and foster collaborative efforts within the healthcare community. Declarations Authors and contributors JSC, JSK, and SJS designed the research. ESP and JEC collected the data. SJS verified the raw data. ESP, JSK, and SJS developed ETL and CDSS. JSC, ESP, JEC, JSL, JSK, and SJS analyzed the results. JSC wrote the manuscript, ESP and SJS critically revised the manuscript, and all authors provided feedback. All authors had full access to all the data in the study and read and approved the final manuscript.. Competing interests: All authors declare no financial or non-financial competing interests Acknowledgments: Portions of the content of this paper were presented at the 2023 CARO-COMP Joint Scientific Meeting (September 22, 2023, Montreal, Canada) and the Practical Big Data Workshop 2023 (May 19, 2023, Ann Arbor, MI). This study was funded by the Big data Center at the National Cancer Center of Korea (grant number: 2020-data-we08).The funder played no role in study design, data collection, data analysis, data interpretation, or writing of this manuscript. Data availability: The Yonsei University Health System (YUHS) inaugurated the Severance Data Portal (SDP), a comprehensive medical big data platform, on 2 May 2023 (available at: https://sobig.yuhs.ac/portal ). The SDP provides an accessible portal tailored for the research community, with a focus on medical investigations. It is supported by a 'Data Lake' search portal that empowers researchers to locate and harness extensive data sets aligned with their specific research goals. In the forthcoming expansion phase, YUHS intends to enhance the platform through the integration of pioneering digital medical imaging information systems, such as Picture Archiving and Communication Systems (PACS), along with digital pathology data and genomic analysis datasets. Access to the SDP is governed by stringent policies devised to safeguard patient confidentiality and to ensure adherence to all pertinent legal and ethical standards. Researchers aiming to utilize the SDP must submit an access application specifying the proposed data usage, which is then subjected to a thorough review process to ensure compliance with established data governance criteria. References 1. Figueiredo, E. B. d., Dametto, M., Rosa, F. d. F. & Bonacin, R. A Multidimensional Framework for Semantic Electronic Health Records in Oncology Domain. In 2021 IEEE 30th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) 165–170 (2021). 2. Heart, T., Ben-Assuli, O. & Shabtai, I. A review of PHR, EMR and EHR integration: A more personalized healthcare and public health policy. Health Policy and Technology 6, 20–25 (2017). 3. Abernethy, A. P. et al. Rapid-learning system for cancer care. J Clin Oncol 28, 4268–4274 (2010). 4. Shanafelt, T. D. et al. Relationship Between Clerical Burden and Characteristics of the Electronic Environment With Physician Burnout and Professional Satisfaction. Mayo Clinic Proceedings 91, 836–848 (2016). 5. Davenport, T. & Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc J 6, 94–98 (2019). 6. Huang, S. C. et al. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digit Med 3, 136 (2020). 7. Cui, M. & Zhang, D. Y. Artificial intelligence and computational pathology. Lab Invest 101, 412–422 (2021). 8. Huynh, E. et al. Artificial intelligence in radiation oncology. Nat Rev Clin Oncol 17, 771–781 (2020). 9. Perez-Lopez, R., Reis-Filho, J. S. & Kather, J. N. A framework for artificial intelligence in cancer research and precision oncology. NPJ Precis Oncol 7, 43 (2023). 10. Shreve, J. T., Khanani, S. A. & Haddad, T. C. Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations. Am Soc Clin Oncol Educ Book 42, 1–10 (2022). 11. Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023). 12. Ramspek, C. L. et al. External validation of prognostic models: what, why, how, when and where? Clin Kidney J 14, 49–58 (2021). 13. Chung, C. & Jaffray, D. A. Cancer Needs a Robust "Metadata Supply Chain" to Realize the Promise of Artificial Intelligence. Cancer Res 81, 5810–5812 (2021). 14. Morin, O. et al. An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nat Cancer 2, 709–722 (2021). 15. Jung, H. A. et al. Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system. Transl Lung Cancer Res 10, 3865–3874 (2021). 16. Kanas, G. et al. Use of electronic medical records in oncology outcomes research. Clinicoecon Outcomes Res 2, 1–14 (2010). 17. Mayo, C. S. et al. Operational Ontology for Oncology (O3) - A Professional Society Based, Multi-Stakeholder, Consensus Driven Informatics Standard Supporting Clinical and Research use of "Real -World" Data from Patients Treated for Cancer: Operational Ontology for Radiation Oncology. Int J Radiat Oncol Biol Phys https://doi.org/10.1016/j.ijrobp.2023.05.033 (2023). (2023). 18. Khare, R. et al. Design and Refinement of a Data Quality Assessment Workflow for a Large Pediatric Research Network. EGEMS (Wash DC) 7, 36 (2019). 19. Pawloski, P. A., Brooks, G. A., Nielsen, M. E. & Olson-Bullis, B. A. A Systematic Review of Clinical Decision Support Systems for Clinical Oncology Practice. J Natl Compr Canc Netw 17, 331–338 (2019). 20. Hong, J. C. et al. System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation. J Clin Oncol 38, 3652–3661 (2020). 21. Coombs, L. et al. A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology. NPJ Digit Med 5, 117 (2022). 22. Colicchio, T. K., Cimino, J. J. & Del Fiol, G. Unintended Consequences of Nationwide Electronic Health Record Adoption: Challenges and Opportunities in the Post-Meaningful Use Era. J Med Internet Res 21, e13313 (2019). 23. Pivovarov, R. & Elhadad, N. Automated methods for the summarization of electronic health records. J Am Med Inform Assoc 22, 938–947 (2015). 24. Goh, E. et al. Remote evaluation of NAVIFY Oncology Hub using clinical simulation. Journal of Clinical Oncology 41, e13622-e13622 (2023). 25. Hirsch, J., Ford, J. M., Nadauld, L. & Hsu, A. Design and implementation of an informatics infrastructure for actionable precision oncology. Journal of Clinical Oncology 33, e17521-e17521 (2015). 26. Maniago, R. et al. Implementation of an EHR-embedded decision support tool in community oncology practices. Journal of Clinical Oncology 39, 274–274 (2021). 27. Liu, W., Bahig, H. & Palma, D. A. Oligometastases: Emerging Evidence. J Clin Oncol 40, 4250–4260 (2022). 28. Primakov, S. P. et al. Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nat Commun 13, 3423 (2022). 29. Cai, J. et al. Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies. arXiv 2012.04872 (2020). Additional Declarations (Not answered) Supplementary Files Supplementaryfigures.docx Supplementaryvideo1.mp4 Supplementary video 1 Supplementaryvideo2.mp4 Supplementary video 2 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 24 Apr, 2024 Review # 4 received at journal 04 Apr, 2024 Reviewer # 4 agreed at journal 26 Mar, 2024 Review # 2 received at journal 16 Feb, 2024 Reviewer # 3 agreed at journal 13 Feb, 2024 Reviewer # 2 agreed at journal 06 Feb, 2024 Reviewer # 1 agreed at journal 05 Feb, 2024 Reviewers invited by journal 02 Feb, 2024 Editor assigned by journal 15 Jan, 2024 Submission checks completed at journal 15 Jan, 2024 First submitted to journal 14 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3864430","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":270731532,"identity":"4afdbaf0-739b-45d6-a248-3bd441862353","order_by":0,"name":"Sang Joon Shin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYHCC5IcfKmwYGCRI0JJmLHEmjTQtDBK8bYdJ0CLff+CBgcSZ84kbbrc/YPhRQ4QWgxsJCQ8KKm4nbrhzxoCx5xgxWiQYEoC2ALXcyGFg4G0gzmEJQL+cA2pJf8D4lxgtDAcSQFoOALUkGDATZQvQL6BATjaeeSPH4LAMMX6R7z8Diko72b4b6Q8fviEmxBgYeBJApCPISQeI0sDAwA5WaE+k6lEwCkbBKBiJAADdkT8bRnrSXAAAAABJRU5ErkJggg==","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Sang","middleName":"Joon","lastName":"Shin","suffix":""},{"id":270731533,"identity":"f05cce88-c5a3-4c2e-b233-2b0a1f517d32","order_by":1,"name":"Jee Suk Chang","email":"","orcid":"https://orcid.org/0000-0001-7685-3382","institution":"Cancer Center, Yonsei University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jee","middleName":"Suk","lastName":"Chang","suffix":""},{"id":270731534,"identity":"572476dd-130c-42eb-b726-f7a6177e1af7","order_by":2,"name":"Eun Sil Baek","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Eun","middleName":"Sil","lastName":"Baek","suffix":""},{"id":270731535,"identity":"47119e5a-3fed-42a8-beef-576f2bfda657","order_by":3,"name":"Jeong Eun Choi","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jeong","middleName":"Eun","lastName":"Choi","suffix":""},{"id":270731536,"identity":"e87b5352-1331-49f3-bcc7-076b1363ad91","order_by":4,"name":"Joon Seok Lim","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Joon","middleName":"Seok","lastName":"Lim","suffix":""},{"id":270731537,"identity":"0ff55a68-4555-46d7-abc8-dce94f40fc11","order_by":5,"name":"Jin Sung Kim","email":"","orcid":"","institution":"Yonsei Cancer Center, Yonsei University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Sung","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2024-01-14 21:25:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3864430/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3864430/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50749046,"identity":"cd723daa-508b-428b-9462-0545e3d45e64","added_by":"auto","created_at":"2024-02-06 17:22:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99194,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the YCDL framework: From electronic health data to the data supply chain\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/3e1861cab82ee38e49f3e6f5.png"},{"id":50750418,"identity":"a85a67cb-8ae2-4fd6-be8a-1e449f1f389c","added_by":"auto","created_at":"2024-02-06 17:30:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203786,"visible":true,"origin":"","legend":"\u003cp\u003eThe Extract-Transform-Load (ETL) process within the YCDL framework\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/fd91862a116df8b417a1a10d.png"},{"id":50750419,"identity":"001048e2-a13f-4ff9-86ef-038307fd8db7","added_by":"auto","created_at":"2024-02-06 17:30:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74273,"visible":true,"origin":"","legend":"\u003cp\u003eQuality management of data in the YCDL framework\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/2688ebd18ac1d7cf640f5997.png"},{"id":50749049,"identity":"be4c8848-fa39-4d57-9b82-f93065dadf2d","added_by":"auto","created_at":"2024-02-06 17:22:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":411615,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for patients with cancer in YCDL target data, stratified by tumor stage \u003cstrong\u003e(a)\u003c/strong\u003e breast cancer, \u003cstrong\u003e(b)\u003c/strong\u003e colorectal cancer, \u003cstrong\u003e(c)\u003c/strong\u003elung cancer, \u003cstrong\u003e(d)\u003c/strong\u003e stomach cancer, \u003cstrong\u003e(e) \u003c/strong\u003eliver cancer, \u003cstrong\u003e(f)\u003c/strong\u003e melanoma, \u003cstrong\u003e(g)\u003c/strong\u003e kidney cancer, \u003cstrong\u003e(h)\u003c/strong\u003e prostate cancer, \u003cstrong\u003e(i)\u003c/strong\u003e thyroid cancer, \u003cstrong\u003e(j)\u003c/strong\u003e pancreatic cancer, and \u003cstrong\u003e(k)\u003c/strong\u003e biliary tract cancer.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/64b9072e4f85be478c4db336.png"},{"id":50749048,"identity":"cd9c5ffc-0e0d-48d6-be96-9972a48c2d2c","added_by":"auto","created_at":"2024-02-06 17:22:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":250343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed clinical decision support system with four layouts\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003eThree-dimensional display of overall disease burden, with individual lesions contoured manually or automatically in advance \u003cstrong\u003e(b)\u003c/strong\u003e Longitudinal tumor tracking output in the form of a graph \u003cstrong\u003e(c)\u003c/strong\u003e Section displaying survival curves for assessing and predicting individual patient outcomes by integrating any potential model \u003cstrong\u003e(d)\u003c/strong\u003e Comprehensive overview of a patient's cancer journey including treatment history, follow-up, and disease status\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/24080cbf2f99f0232ab4de2d.png"},{"id":50751458,"identity":"0546d1c6-0730-4f9a-bec1-adb70b3f6fd4","added_by":"auto","created_at":"2024-02-06 17:38:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/025b9a5b-5985-4c84-8a42-3c4e91ffc1be.pdf"},{"id":50749051,"identity":"bbe30f8c-96a4-4f17-9491-4ead25422cf4","added_by":"auto","created_at":"2024-02-06 17:22:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":449719,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/6d9b011309668af3eb549a68.docx"},{"id":50750420,"identity":"d6bbcde1-6937-4b49-8c6c-9cc9db1407ad","added_by":"auto","created_at":"2024-02-06 17:30:28","extension":"mp4","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":902589,"visible":true,"origin":"","legend":"Supplementary video 1","description":"","filename":"Supplementaryvideo1.mp4","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/997a4d5a2e2df1e796f34439.mp4"},{"id":50749053,"identity":"12052ba6-8d6e-40bf-87fb-2c955657f61f","added_by":"auto","created_at":"2024-02-06 17:22:28","extension":"mp4","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":6288935,"visible":true,"origin":"","legend":"Supplementary video 2","description":"","filename":"Supplementaryvideo2.mp4","url":"https://assets-eu.researchsquare.com/files/rs-3864430/v1/0ce471a52ab0d53f6c3a958e.mp4"}],"financialInterests":"(Not answered)","formattedTitle":"Development of a continuous multimodal data supply chain for oncology and an expandable clinical decision support system","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOncology data is multidimensional and diverse, encompassing a vast array of information such as patient characteristics, stage, tumor, and imaging data.\u003csup\u003e1\u003c/sup\u003e The advent of electronic medical records (EMR) and emerging data sources has caused a transformative surge in health information.\u003csup\u003e2\u003c/sup\u003e This data deluge often exceeds human cognitive limits for decision-making\u003csup\u003e3\u003c/sup\u003e and has led oncology professionals to spend more time navigating EMR than engaging with patients to seek fragmented health data from disparate sources, which exacerbates burnout.\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFortunately, rapid advancements in computational techniques, notably machine learning and artificial intelligence (AI), herald new possibilities for harnessing extensive and intricate medical data for individualized, data-driven care.\u003csup\u003e5\u003c/sup\u003e These technologies have demonstrated potential in refining imaging\u003csup\u003e6\u003c/sup\u003e and pathology diagnostics,\u003csup\u003e7\u003c/sup\u003e prognosticating clinical outcomes, optimizing radiation treatment planning,\u003csup\u003e8\u003c/sup\u003e and accelerating drug development.\u003csup\u003e9,10\u003c/sup\u003e AI has also significantly impacted foundational research in oncology.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, challenges related to validation and generalizability\u003csup\u003e12\u003c/sup\u003e mean that the current methodologies for data management and model development fall short of the maturity required for broad-scale AI adoption. Transitioning from the present ad-hoc data aggregation and curation approach to a dynamic \"metadata supply chain\" is essential for providing contextualized, robust data in real-time.\u003csup\u003e13\u003c/sup\u003e By capturing pivotal data in real-time, this metadata supply chain can lay the groundwork for a clinical decision support system that vividly maps patient journey, potentially transforming clinical workloads.\u003c/p\u003e \u003cp\u003eWithin this framework, our objective was to present our collaborative endeavor for establishing a comprehensive data supply chain in oncology. This system seamlessly integrates clinical, genomic, and imaging data, representing a persistent, flexible, and expandable model. The infrastructure holds the potential to expedite the development of clinical decision support systems and AI applications for risk stratification, diagnosis, and treatment in oncology, paving the way for individualized patient-centered care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAfter approval of the protocol by the Institutional Review Board of Severance Hospital (4-2021-1241), Seoul, Republic of Korea, we established a development server using a Windows-based, 12-core computer with 64 GB of memory and Serial Attached SCSI (SAS) disk drives of 100 GB and 2 TB. Operational servers, constituting High Availability (HA) systems, included a database (DB) server (2Ea) with a 10-core CPU, 128 GB memory, OS SSD 100 GB of storage/SCL 2019, DB Safer, Hiware, EMS, and Backup (DB), and a web-based server with a 12-core CPU, 64 GB memory, OS SSD 100 GB of storage/Hiware, EMS, and Backup (File).\u003c/p\u003e \u003cp\u003eThe dataflow and computational modules are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All data processing, transfer, and storage were performed within the network infrastructure of the hospital. We received authorization for access to all digital records from the EMR system and billing data from the Oncology Care System. To improve the data quality and mitigate the risks associated with erroneous or omitted data, we tailored the selection approaches for each cancer type. The selection was based on the International Classification of Diseases for Oncology (ICD) and physician-assigned ICD-M codes as well as validity criteria designated by the cancer registration program. A comprehensive breakdown of the selection methodologies for each cancer type is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection methods for each cancer type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDBName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_BRST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd\u003csup\u003ea\u003c/sup\u003e=C50% AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u003csup\u003eb\u003c/sup\u003e \u0026lt;M9590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_CLRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry: ICDOCd=(C18%, C19%, C20%) AND ICDOCdM\u0026thinsp;=\u0026thinsp;M81403(Adenocarcinoma) AND available\u0026thinsp;=\u0026thinsp;Y\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_LUNG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd\u0026thinsp;=\u0026thinsp;C34% AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u0026thinsp;\u0026lt;\u0026thinsp;M9590 AND ICDOCdM NOT LIKE '%/2'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_GSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd\u0026thinsp;=\u0026thinsp;C16% AND available\u0026thinsp;=\u0026thinsp;Y AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u0026thinsp;\u0026lt;\u0026thinsp;M9590 AND ICDOCdM NOT LIKE '%/2'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiver cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_LVER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd\u0026thinsp;=\u0026thinsp;C22.0 AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u0026thinsp;\u0026lt;\u0026thinsp;M9590 AND ICDOCdM NOT LIKE '%/2'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_MLNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry: ICDOCdM_EngNm (pathology) LIKE '%Melanoma%' AND available\u0026thinsp;=\u0026thinsp;Y\u003c/p\u003e \u003cp\u003e(2) The cancer diagnosis group\u0026thinsp;=\u0026thinsp;D0023(Malignant melanoma) in CAP system\u003csup\u003ec\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(3) There are records of '%Melanoma%', '%Malignant Spitz%' in the pathology diagnosis results.\u003c/p\u003e \u003cp\u003e(4) There are records of '%Melanoma%', '%Malignant Spitz%' in the imaging test. (excluded '%r/o%')\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_KDNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd\u0026thinsp;=\u0026thinsp;C64% AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u0026thinsp;\u0026lt;\u0026thinsp;M9590 AND ICDOCdM NOT LIKE '%/2'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_PRST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd\u0026thinsp;=\u0026thinsp;C61% AND available\u0026thinsp;=\u0026thinsp;Y AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u0026thinsp;\u0026lt;\u0026thinsp;M9590 AND ICDOCdM NOT LIKE '%/2'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThyroid cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_THRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd\u0026thinsp;=\u0026thinsp;C73% AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u0026thinsp;\u0026lt;\u0026thinsp;M9590 AND ICDOCdM NOT LIKE '%/2'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_PNCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd\u0026thinsp;=\u0026thinsp;C25% AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u0026thinsp;\u0026lt;\u0026thinsp;M9590 AND ICDOCdM NOT LIKE '%/2'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBile duct cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYCDL_BLDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1) Cancer Registry : ICDOCd=(C22.1, C23.9, C24.0, C24.1, C24.8, C24.9) AND available\u0026thinsp;=\u0026thinsp;Y AND ICDOCdM\u0026thinsp;\u0026lt;\u0026thinsp;M9590 AND ICDOCdM NOT LIKE '%/2'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eICDOCd = ICD-O ( International Classification of Diseases for Oncology) Codes\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003eICDOCdM = Morphology section of the ICD-O Code\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ec\u003c/sup\u003eCAP system\u0026thinsp;=\u0026thinsp;Chemotherapy Assistance Program for ordering oncology medications\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubsequently, a patient-centric data model was developed, underpinned by the patient identification numbers dispensed by the hospital information system. This served as a linchpin for linking the anonymized datasets. In the clinical data extraction stage, we developed an Extract-Transform-Load (ETL) process, which includes Natural Language Processing (NLP), for each feature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It facilitated the daily movement of data from the DSC source DB to the YCDL target DB. The DSC DB is a reservoir containing raw medical text, (semi-)unstructured data, imaging files, next-generation sequencing (NGS) results, and Extensible Markup Language (XML) formats.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the initial phase of data processing, we tailored the database corpus from the DSC DB, optimizing the extraction and management of medical terminology, abbreviations, and recurrent misspellings (e.g., within pathology reports). Subsequently, the procured data underwent transformation through a specialized ETL algorithm designed to harmonize terminology based on assertions and the interrelationships of medical concepts. NLP was instrumental in utilizing CT and MRI interpretation counts from follow-up visits as criteria for individual selection. Parsing the raw medical text from radiological reports revealed the indications for disease recurrence and its associated patterns. The subsequent preprocessing phase employed tokenization techniques to structure the extracted data. SQL queries were harnessed to mine data from the primary DSC DB, facilitated by a data manipulation language (DML) management interface. For certain datasets requiring intricate extraction protocols, bespoke ETL strategies were devised using Python scripts crafted for each specific operation (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eUsing NGS data, Tier 1 pathogenic variants were systematically collected from the EMR. A significant portion of the procedural steps were automated, employing specialized bioinformatics tools for both processing and interpretation, as depicted in \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e. Certain elements, including family and smoking histories, were retained in our medical record system in XML format and subsequently extracted and transferred using the ETL process.\u003c/p\u003e \u003cp\u003eFollowing its development, we applied this framework to our electronic health data from its inception in 2006. The profiles were updated using electronic health records, ensuring a comprehensive view of relevant oncological components over time. The present analysis is based on data collected up to March 2022. Key constituents of these profiles included demographics, diagnoses, clinical examination reports, pathology reports, treatment histories, and encounter specifics (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The structures of these individual profiles were categorized into common, cancer-specific, and index columns. The common features held universal information across multiple cancer types (e.g., age, sex, and cancer diagnosis date) and accounted for 817 features, which was nearly 80% of the total. The cancer-specific features contained data relevant only to specific cancer types (for instance, pulmonary function test in lung cancer) and comprised approximately 20% of the tables (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTables in the DSC database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDB Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDB code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTable Description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_PATINFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePatient basic information\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_BODYINFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody measurement information\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_DX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDiagnoses relating to a hospital visits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_CRDINFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCopayment Decreasing Policy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_CSLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConsultant Information\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExamination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_LAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEvents relating to laboratory tests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExamination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_IMAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEvents relating to Imaging test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_PATHOLOGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEvents relating to Pathology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_OP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_REGIMEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChemo- therapy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_RT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRadiation-therapy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_DRUG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedicines prescribed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_PROC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProcedure (included medical operation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProgress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_FRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClinical Forms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eCancer registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_TUMOR_RGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTumor Registry (personal details and cancer diagnosis)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_TUMOR_TRANS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTumor Registry (included cancer recurrence/metastasis)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_TUMOR_TRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTumor Registry (included cancer patient follow-up)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNCR_TUMOR_TRET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTumor Registry (included cancer treatment)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eCancer registry\u0026thinsp;=\u0026thinsp;database of information on cancer patients\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTables in the YCDL database and number of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTable Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTable Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCommon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eColorectal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGastric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eKidney\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eProstate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eProstate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eBile duct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT_BASIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatient Basic Information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT_PHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePast History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT_SHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT_DRNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDrinking History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT_FMHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFamily History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT_BDMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBody Measurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDG_INFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVisit Information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDG_ECHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCopayment Policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDG_CNCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCancer Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDG_CONS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConsultant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExamination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEM_LAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLaboratory Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExamination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEM_IMEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImaging Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExamination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEM_GENE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenetic Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExamination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEM_FCLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFunction Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePH_BPSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBiopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePH_SRGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePH_IMML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImmuno-histology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOP_INFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOperation Information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOP_OPNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOperation opinion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOP_COMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOperation Complication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTX_CHTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTX_RTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTX_PRSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTX_MOPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProcedure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFollow-Up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTE_MTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFollow-Up Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFollow-Up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTE_RCRN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFollow-Up Relapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFollow-Up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTE_DEAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eColumn characteristics by cancer type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Common Columns (A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Cancer-specific Columns (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Index Columns (C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of Total Columns (D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage of Cancer-specific Columns (B/D)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBile duct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe developed a web-based computational platform for data quality control (QC) that scrutinizes potential data defects both automatically and manually on a daily basis, focusing on minimizing the role of the human component (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All data extracted and stored in the YCDL_cancer data repository were continuously evaluated and optimized to establish high-quality data outputs, adhering to standardized data and terminology. Programs for logical checks were configured to evaluate the distribution and continuity of data extracted by the SCL. Based on the QC results, the ETL code was continuously modified, thereby refining the QC logic to enhance the quality and accuracy of the automation. We examined four data quality measures (completeness, timeliness and usefulness, consistency, and accuracy) for all variables, in accordance with established data standards and pertinent aspects of data quality (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For instance, the logic was set such that the birth date of individuals would precede the date of the initial diagnosis. The analyses revealed that the batch processing method accurately identified erroneous data points, aligned with the established logic. Each piece of data was meticulously reviewed and optimized by a Quality Control Manager. Significant discrepancies or inaccuracies prompted an in-depth examination of the source data and respective ETL processes. Moreover, a hierarchy of data sources was established to resolve conflicts. The QC steps were continuously iterated within distinct closed-loop systems, adhered to operational ontology, and executed by independent QC personnel. This methodology gradually enhanced the accuracy of the cleansed target data with minimal intervention (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). We assessed the completeness of each individual\u0026rsquo;s accumulated features, including fundamental characteristics such as date of birth, initial diagnosis date, age, diagnosis code (ICD), TNM and overall stages, and ICDO morphology code.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData quality check criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDetailed Quality Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnostic Targets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eCompleteness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndividual Completeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColumns or input values defined to exist but are Null\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditional Completeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChecking for NOT NULL constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStructural Completeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImplementation based on the physical model designed from the schema,\u003c/p\u003e \u003cp\u003eincluding data types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVerified at the DB design stage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eValidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode Validity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhether codes defined in the common code are used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormat Validity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eErrors in data format\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVerified at the DB design stage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoolean Validity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis based on columns with Y/N, 0/1 criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate Validity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eErrors based on date formats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange Validity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis based on Min, Max, and Normal range of the column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemporal Relationship Validity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis of data that deviates from predetermined sequential relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsistency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReferential Integrity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis of operation rules for PK (Primary Key) items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVerified at the DB design stage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAccuracy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogical Relationship Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData diagnosis according to logical relationships,\u003c/p\u003e \u003cp\u003ee.g., when item A is n, item B should be at least m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDerived Item Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis of derived data, e.g., whether the sum of item A and item B is equal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe developed data warehouse showcased survival graphs by tumor stages and demonstrated the framework's ability to expedite data collection for quick clinical hypothesis testing. Kaplan\u0026ndash;Meier survival graphs were generated in all cancer types according to tumor stage with 95% confidence intervals. Survival time was defined as the time interval between initial diagnosis and death or the last follow-up. To demonstrate the efficiency of our data framework as a proof-of-concept for swiftly generating and evaluating clinical hypotheses, we present a detailed chronological progression of a previously published retrospective study. The clinical question chosen by one of the authors was whether the peripheral blood neutrophil-to-lymphocyte ratio before, during, or after neoadjuvant chemoradiotherapy for locally advanced rectal cancer is associated with an increased risk of distant metastases after primary rectal cancer surgery.\u003c/p\u003e \u003cp\u003eTo underscore the capabilities of our data framework for clinical applications, we then developed a clinical decision-support system that offers (a) a longitudinal view of the complete patient journey, (b) PACS-integrated three-dimensional tumor display, and (c) summary of longitudinal changes in the form of graphs. This system was supported by a Docker-based microservice architecture and overseen by a Python-based API server for backend operations. The framework was implemented as a web application using JavaScript. This design bolsters the accessibility of the system, guarantees platform independence, and ensures that users can access services across various device types. Manual tumor segmentation data are required to use the three-dimensional tumor display with a longitudinal tumor-tracking function. If deep learning-based tumor auto-segmentation algorithms are developed, these models can be integrated into the pipeline. The PACS-integrated method enables physicians to comprehensively track changes in overall trajectory patterns over a long period, fosters an environment that better explains the disease course to patients, and facilitates communication with referring physicians. Longitudinal changes in the overall disease burden were automatically generated using the prepared manual contours and displayed as graphs. The images were de-identified; however, if another image of the same patient was transferred later, the new images were allocated the same de-identified number, facilitating tumor tracking.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAt the time of analysis, the DB contained records of the feature sets of 171,128 individuals diagnosed with 11 different cancers at a single academic cancer center between January 2006 and March 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For each individual, 817 essential features in the common columns and a median of 61 features (range: 38\u0026ndash;109) in the cancer-type-specific columns were updated daily and continuously.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of patients added each year and, in every cohort,\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e2022.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBreast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1,754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1,526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1,671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e18,417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eColorectal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1,387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1,149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e18,903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1,922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1,737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e18,726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGastric\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1,696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1,899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1,522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1,267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e28,882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e10,056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMelanoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e1,382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKidney\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e5,807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProstate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1,152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e14,103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThyroid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2,615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2,206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e3,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3,575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e41,766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePancreas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e6,611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBile duct\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e6,475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9,245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9,401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9,878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11,203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e11,527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e13,247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e13,617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e11,906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e12,870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e2,427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e171,128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring the QC process, we established a comprehensive set of 143 human-driven logical comparisons, including 70 focused on identifying missing data, 41 ensuring temporal validity (e.g., the completion date of radiotherapy should coincide with or follow its initiation date), 15 pinpointing outlier data (such as age at menarche between 8 and 20 years), 13 selecting the relevant values among multiple time points, and 4 dedicated to spotting duplicated or inconsistent data. The QC logic outcomes showed consistent results across 11 different cancer types, comprising a total of 1,523 datasets. We initially set the estimated daily QC case count to 10%, which translated to approximately 81 cases per day.\u003c/p\u003e \u003cp\u003eWe generated survival graphs for each of the 11 distinct cancers in our dataset, segmented by tumor stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, except for prostate cancer, there is a significant variation in survival rates depending on the stage of cancer; generally, higher stages are associated with lower survival rates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe efficacy of our data framework in rapidly generating and evaluating clinical hypotheses was demonstrated in a study published in 2022. Following the development of the proposal and subsequent approval from the institutional review board, researchers requested baseline data on patients, tumors, and treatments, as well as peripheral blood neutrophil and lymphocyte counts, spanning the period between the initial diagnosis and the date of primary rectal surgery in December 2020. Data abstraction for 1,386 individuals was efficiently executed using our framework, and its accuracy and reliability were confirmed by an experienced oncologist. This proficiency enabled researchers to commence a pilot analysis in January 2021, merely a month post the initial data acquisition.\u003c/p\u003e \u003cp\u003eWe successfully developed a clinical decision support system with four layouts. In the upper-left layout, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, three selected image series are displayed alongside their corresponding three-dimensional tumor visualizations, using DICOM files of individually, manually contoured lesions (\u003cb\u003eSupplementary Video 1\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In the middle-upper layout, the output of the longitudinal tumor tracking is in the form of a graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The section with the hope of predicting individual patient outcomes has been reserved for future integration of any potential model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The lower layout presents a comprehensive overview of a patient's healthcare journey, allowing readers to intuitively understand the chronological sequence of events and progression of the patient's treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, \u003cb\u003eSupplementary video 2\u003c/b\u003e). This offers holistic and interactive patient summaries on a graphical timeline anchored by real-time data captured within our framework. Users can easily assess patient data in a temporal context with a single click, and the depth of information can be fine-tuned using zoom features and pop-up boxes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we successfully developed a cancer-specific information technology infrastructure designed to facilitate the longitudinal collection of comprehensive health data, an accomplishment realized through extensive cross-departmental collaboration. Using this framework, we established an automated DB that continually updates the data from 171,128 individuals, each distinguished by over 800 unique features. Manually collating such an expansive array of features is challenging. To ensure data integrity, we initially implemented rigorous data quality control methods, starting with manual logic applications and subsequently transitioning to an automated management system. This approach, conducted within closed-loop systems, led to a steady enhancement in data precision. Furthermore, we validated the caliber of our automatically harvested data by assessing survival rates in relation to cancer stage. Of practical significance, our system highlights the potential for real-time capture of disease state and treatment data, exemplified by a proof-of-concept for rapid clinical hypothesis testing and offering a holistic view of a patient's journey with a single click. This not only alleviates the clinical burden but also optimizes the research workflow.\u003c/p\u003e \u003cp\u003eUnderstanding the crucial role of a reliable automatic data supply chain, several research groups have collaboratively developed frameworks to capture and transport oncologic data.\u003csup\u003e14,15\u003c/sup\u003e Morin et al.\u003csup\u003e14\u003c/sup\u003e introduced MEDomics, an information technology infrastructure that integrates seamlessly with multiple EHR DBs to ensure uniform data collection. Their research amassed data from nearly 175,000 patients with cancer at the University of California, San Francisco, between 2010 and 2019. Employing rule-based selection techniques, they identified individuals with high-quality data, narrowing them down to 3,782 breast cancer and 2,054 lung cancer cases. Lower-quality data were more prevalent among individuals located further away from the institution, a trend associated with increased mortality rates. Jung et al.\u003csup\u003e15\u003c/sup\u003e showcased ROOT, an auto-updating data warehouse that consolidates comprehensive clinical data of 67,617 individuals diagnosed with head and neck, thoracic, and esophageal cancers at the Samsung Medical Center in Korea between 2008 and 2020. These endeavors underscore the importance of data governance and active participation of all stakeholders. Considering geographic disparities and practice variations, in-house development might be best positioned to cater to the specific needs of end users.\u003c/p\u003e \u003cp\u003eBuilding an automated data warehouse using oncology EMR data poses inherent challenges because of the varying degrees of data completeness, inconsistencies, and conflicting or evolving records.\u003csup\u003e16\u003c/sup\u003e In this context, a nationwide initiative was launched to create a comprehensive cancer data library aimed at standardizing terminology and classification within our country. Concurrently, institutional efforts aimed to gather extensive feedback and integrate preexisting registries from diverse cancer groups. The recent proposal of Operational Ontology for Oncology (O3) seeks to achieve multi-institutional and multi-stakeholder consensus, lowering the barriers for collaborative information aggregation.\u003csup\u003e17\u003c/sup\u003e Our next task involves identifying differences and similarities between our defined features and the variables proposed by O3, and if possible, updating the necessary parts. To manage the vast variability of data sources and types, we devised algorithms that harness structured data from diverse origins and process unstructured content using ETL procedures. ETL operations present unique challenges, especially when dealing with components presenting multiple ETL-related complications. Collaboration with team members well-versed in treatment workflows and medical informatics, combined with close cooperation with the IT department, was pivotal in understanding the system functionality and nuances of data interpretation. Both data governance and ethical deliberation are instrumental in ensuring data security and patient privacy.\u003c/p\u003e \u003cp\u003eIn the absence of formalized frameworks, challenges may arise in query fulfillment and data management.\u003csup\u003e18\u003c/sup\u003e However, our data supply chain addresses this issue through an end-to-end workflow for data quality assurance, ensuring continual evaluation and improvement. Conflicting, missing, or incorrect data were identified through human-driven logical comparisons and rectified by making logical corrections or adjusting the algorithms. Since its implementation, the quality assurance workflow has been continuously refined, accumulating data checks across multiple cycles. This iterative process enhances data quality and reduces the need for human intervention. Engagement with various groups familiar with the data sources and limitations is essential.\u003c/p\u003e \u003cp\u003eThe YCDL framework has numerous potential clinical and research applications. Although limited data have evaluated clinically relevant outcomes in oncology care, emerging evidence suggests that clinical decision support systems using EMR data can positively impact care quality.\u003csup\u003e19\u003c/sup\u003e A recent randomized controlled trial by Hong et al.\u003csup\u003e20\u003c/sup\u003e demonstrated accurate triaging of patients with cancer and reduced acute care rates using an EMR-based machine learning algorithm. Coombs et al.\u003csup\u003e21\u003c/sup\u003e showed that a proposed machine learning tool using real-world EMR data could identify patients with cancer at risk for a 60-day emergency department visit. Another potential application is the generation and rapid testing of clinical hypotheses, as suggested by Morin et al.,\u003csup\u003e14\u003c/sup\u003e which would not have been feasible using traditional data approaches. The YCDL enabled the collection of a vast amount of data, including laboratory results and patient features, thereby facilitating the first pilot analysis. Additionally, automatic flagging of eligible patients for clinical trials shows promise.\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe real-time metadata supply chain can automatically display patient histories, thereby eliminating the need for physicians to navigate through numerous pages. With advancements in systemic drugs, patients with stage IV cancer now live longer and have complex treatment histories.\u003csup\u003e22\u003c/sup\u003e A quick overview of a patient\u0026rsquo;s cancer journey allows physicians to efficiently characterize both the disease and the individual, potentially reducing burnout and ensuring quality care.\u003csup\u003e23\u003c/sup\u003e Commercial clinical decision support software such as NAVIFY Oncology Hub,\u003csup\u003e24\u003c/sup\u003e Syapse,\u003csup\u003e25\u003c/sup\u003e and Flatiron Assist,\u003csup\u003e26\u003c/sup\u003e are undergoing evaluation for integration into the EMR system to provide a comprehensive view of a patient's journey. With emerging local therapies,\u003csup\u003e27\u003c/sup\u003e AI can play a significant role in detecting and segmenting normal tissues and tumors,\u003csup\u003e28\u003c/sup\u003e as well as tracking lesions over time in relation to treatment.\u003csup\u003e29\u003c/sup\u003e However, further research on tumor auto-segmentation is warranted.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting our results. First, our method represents the experience of a single institution, and large-scale adjustments may be necessary for implementation elsewhere. Second, the data supply chain approach is designed as an expandable infrastructure that accommodates updated ontologies and evolving demands. Establishing a strong leadership in data governance, implementing sharing agreements, and promoting open science practices are essential for a robust metadata supply chain. This requires dedicated departments to ensure job security. Collaborative efforts such as workshops and knowledge transfers promote an understanding of the benefits offered by the metadata supply chain and AI technologies. Future work will incorporate additional cancer types such as brain tumors and rare malignancies. Once the ETL process is finalized, we aim to make it publicly accessible. Our hospital primarily diagnoses and follows up with patients within our institution; however, inter-hospital data sharing may become necessary in certain cases. Finally, the current framework only captures survival and recurrence data despite the growing recognition of the importance of quality of life and toxicity profiles as critical outcomes.\u003c/p\u003e \u003cp\u003eIn conclusion, this study emphasizes the importance of leveraging computational methods and real-time data supply chains to address the challenges posed by the overwhelming volume of health information in oncology. Our collaborative endeavor to construct a robust data framework not only demonstrates its potential to enhance personalized care, facilitate AI applications, and refine clinical decision-making, but also serves as the cornerstone for acquiring comprehensive health data over time. To promote the widespread adoption of data supply chains and AI technologies, it is imperative to emphasize strong data governance, embrace open science practices, and foster collaborative efforts within the healthcare community.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eAuthors and contributors\u003c/strong\u003e \u003cp\u003eJSC, JSK, and SJS designed the research. ESP and JEC collected the data. SJS verified the raw data. ESP, JSK, and SJS developed ETL and CDSS. JSC, ESP, JEC, JSL, JSK, and SJS analyzed the results. JSC wrote the manuscript, ESP and SJS critically revised the manuscript, and all authors provided feedback. All authors had full access to all the data in the study and read and approved the final manuscript..\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eAll authors declare no financial or non-financial competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003ePortions of the content of this paper were presented at the 2023 CARO-COMP Joint Scientific Meeting (September 22, 2023, Montreal, Canada) and the Practical Big Data Workshop 2023 (May 19, 2023, Ann Arbor, MI).\u003c/p\u003e \u003cp\u003eThis study was funded by the Big data Center at the National Cancer Center of Korea (grant number: 2020-data-we08).The funder played no role in study design, data collection, data analysis, data interpretation, or writing of this manuscript.\u003c/p\u003e\u003ch2\u003eData availability:\u003c/h2\u003e \u003cp\u003eThe Yonsei University Health System (YUHS) inaugurated the Severance Data Portal (SDP), a comprehensive medical big data platform, on 2 May 2023 (available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sobig.yuhs.ac/portal\u003c/span\u003e\u003cspan address=\"https://sobig.yuhs.ac/portal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The SDP provides an accessible portal tailored for the research community, with a focus on medical investigations. It is supported by a 'Data Lake' search portal that empowers researchers to locate and harness extensive data sets aligned with their specific research goals. In the forthcoming expansion phase, YUHS intends to enhance the platform through the integration of pioneering digital medical imaging information systems, such as Picture Archiving and Communication Systems (PACS), along with digital pathology data and genomic analysis datasets. Access to the SDP is governed by stringent policies devised to safeguard patient confidentiality and to ensure adherence to all pertinent legal and ethical standards. Researchers aiming to utilize the SDP must submit an access application specifying the proposed data usage, which is then subjected to a thorough review process to ensure compliance with established data governance criteria.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Figueiredo, E. B. d., Dametto, M., Rosa, F. d. F. \u0026amp; Bonacin, R. A Multidimensional Framework for Semantic Electronic Health Records in Oncology Domain. In \u003cem\u003e2021 IEEE 30th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)\u003c/em\u003e 165\u0026ndash;170 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2. Heart, T., Ben-Assuli, O. \u0026amp; Shabtai, I. 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A framework for artificial intelligence in cancer research and precision oncology. \u003cem\u003eNPJ Precis Oncol\u003c/em\u003e 7, 43 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e10. Shreve, J. T., Khanani, S. A. \u0026amp; Haddad, T. C. Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations. \u003cem\u003eAm Soc Clin Oncol Educ Book\u003c/em\u003e 42, 1\u0026ndash;10 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e11. Moor, M. et al. Foundation models for generalist medical artificial intelligence. \u003cem\u003eNature\u003c/em\u003e 616, 259\u0026ndash;265 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e12. Ramspek, C. L. et al. External validation of prognostic models: what, why, how, when and where? \u003cem\u003eClin Kidney J\u003c/em\u003e 14, 49\u0026ndash;58 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e13. 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Use of electronic medical records in oncology outcomes research. \u003cem\u003eClinicoecon Outcomes Res\u003c/em\u003e 2, 1\u0026ndash;14 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e17. Mayo, C. S. et al. Operational Ontology for Oncology (O3) - A Professional Society Based, Multi-Stakeholder, Consensus Driven Informatics Standard Supporting Clinical and Research use of \"Real -World\" Data from Patients Treated for Cancer: Operational Ontology for Radiation Oncology. \u003cem\u003eInt J Radiat Oncol Biol Phys\u003c/em\u003e https://doi.org/10.1016/j.ijrobp.2023.05.033 (2023). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e18. Khare, R. et al. Design and Refinement of a Data Quality Assessment Workflow for a Large Pediatric Research Network. \u003cem\u003eEGEMS (Wash DC)\u003c/em\u003e 7, 36 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e19. Pawloski, P. A., Brooks, G. A., Nielsen, M. 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Unintended Consequences of Nationwide Electronic Health Record Adoption: Challenges and Opportunities in the Post-Meaningful Use Era. \u003cem\u003eJ Med Internet Res\u003c/em\u003e 21, e13313 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e23. Pivovarov, R. \u0026amp; Elhadad, N. Automated methods for the summarization of electronic health records. \u003cem\u003eJ Am Med Inform Assoc\u003c/em\u003e 22, 938\u0026ndash;947 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e24. Goh, E. et al. Remote evaluation of NAVIFY Oncology Hub using clinical simulation. \u003cem\u003eJournal of Clinical Oncology\u003c/em\u003e 41, e13622-e13622 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e25. Hirsch, J., Ford, J. M., Nadauld, L. \u0026amp; Hsu, A. Design and implementation of an informatics infrastructure for actionable precision oncology. \u003cem\u003eJournal of Clinical Oncology\u003c/em\u003e 33, e17521-e17521 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e26. Maniago, R. et al. Implementation of an EHR-embedded decision support tool in community oncology practices. \u003cem\u003eJournal of Clinical Oncology\u003c/em\u003e 39, 274\u0026ndash;274 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e27. Liu, W., Bahig, H. \u0026amp; Palma, D. A. Oligometastases: Emerging Evidence. \u003cem\u003eJ Clin Oncol\u003c/em\u003e 40, 4250\u0026ndash;4260 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e28. Primakov, S. P. et al. Automated detection and segmentation of non-small cell lung cancer computed tomography images. \u003cem\u003eNat Commun\u003c/em\u003e 13, 3423 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e29. Cai, J. et al. Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies. \u003cem\u003earXiv\u003c/em\u003e 2012.04872 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"data warehouse; continuous capture, multimodal oncologic information, electronic health record, medical imaging, genomic data, big data ","lastPublishedDoi":"10.21203/rs.3.rs-3864430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3864430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThis study presents an inter-departmental collaboration to create a comprehensive oncology data framework and clinical decision support system, integrating clinical, genomic, and imaging data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A real-time data supply chain was established in an academic cancer center to capture unstructured health data from various sources. For each cancer type, specific selection approaches were developed. Firstly, we predefined 817 features applicable across various cancers, along with a median of 61 histology-specific features and developed a customized Extract-Transform-Load (ETL) algorithm for each feature Standard criteria for data quality control (QC) were formulated, and a web-based computational QC platform enabling manual and automatic inspections was created. This framework has been applied to electronic health data since 2006.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003e The data supply chain captured features from 171,128 individuals across 11 cancer types. It updates individual profiles daily and conducts QC checks, including 143 logical comparisons, processing an average of 81 cases daily. Continuous automatic and manual data QC within closed-human-in-loop systems ensured accuracy. Using the developed data warehouse, we were able to showcase survival graphs by tumor stages and demonstrate the framework's ability to expedite data collection for quick clinical hypothesis testing. A dashboard displaying patients’ cancer journeys, including landmark events, treatment progress, and longitudinal tumor tracking, was also developed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation:\u003c/strong\u003e We have developed an automatically updated data supply chain that comprehensively synthesizes multimodal medical data and assesses data QC, aimed at directly assisting clinical decision-making for individual patients. Ongoing learning is essential, depending on the purpose of data use, and further research into its applicability in other environments is required.\u003c/p\u003e","manuscriptTitle":"Development of a continuous multimodal data supply chain for oncology and an expandable clinical decision support system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 17:22:23","doi":"10.21203/rs.3.rs-3864430/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-04-24T04:53:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-04-05T01:23:50+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-03-26T13:22:29+00:00","index":4,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-02-16T10:35:25+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-02-13T22:17:46+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-02-06T08:41:27+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-02-05T18:25:09+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-02-02T21:29:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-16T01:42:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-15T10:05:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Digital Medicine","date":"2024-01-14T21:23:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49614210-0962-4b71-b4e6-5caded45d25e","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":28544554,"name":"Health sciences/Diseases/Cancer"},{"id":28544555,"name":"Health sciences/Health care/Patient education"},{"id":28544556,"name":"Health sciences/Medical research/Outcomes research"}],"tags":[],"updatedAt":"2024-12-20T08:17:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 17:22:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3864430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3864430","identity":"rs-3864430","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}