Empowering Chinese Clinical Research on the Global Stage: Big Data Fuels Physicians' Scientific Ambitions
As an indispensable and critical driver for hospitals to achieve disciplinary development and enhance research capabilities, clinical research not only enables clinicians to gain a deeper understanding of disease progression and discover new therapeutic approaches, but also directly reflects the strength of national innovation in the pharmaceutical sector, thereby garnering sustained attention.
However, for clinicians, the tedious nature of daily clinical work consumes nearly all their time. When they identify issues in clinical practice and wish to conduct research, they often find themselves stretched thin: they lack the time to organize and analyze medical record data, let alone perform research analyses or write manuscripts. This disconnect between clinical practice and scientific research has long severely hindered technological innovation and the translation of scientific achievements into practical applications.
Fortunately, this dilemma is gradually being broken by medical big data.
“Completed this paper in less than three months and won the ASCO Merit Award!”
On December 15, 2018, at the year-end summary conference of the CSCO Youth Committee, a doctor was greatly surprised to learn about Professor Liang Wenhua from the First Affiliated Hospital of Guangzhou Medical University/National Clinical Research Center for Respiratory Disease, who had won the prestigious “Merit Award” at ASCO 2018 for his research on “The Relationship Between Driver Genes in Non-Small Cell Lung Cancer and Biomarkers of Sensitivity to Chemotherapy/PD-(L)1 Blockade Therapy.” “I thought this type of study would take at least a year, given the enormous amount of data required.”
According to the report, supported by LinkDoc Technology’s medical big data platform and based on high-quality, multi-dimensional data, the study completed the entire workflow—including project initiation, study design, and patient data inclusion—within just three months. It evaluated the relationship between driver gene subtyping and predictive indicators of efficacy for chemotherapy and immunotherapy. The results demonstrated the association between driver gene mutations and conventional biomarkers of antitumor drug sensitivity, potentially providing strong support for selecting optimal salvage treatment regimens for patients who have developed resistance to targeted therapy.
In fact, at the 2018 ASCO Annual Meeting alone, nine papers resulting from research conducted in collaboration with LinkDoc explored answers to challenges in clinical practice from various perspectives. Among these, a Chinese real-world multicenter observational study led by Professor Liu Yang of the Chinese PLA General Hospital provided a comprehensive comparison of postoperative clinical outcomes and safety between Ivor-Lewis (IL) and McKeown (Mc) procedures in minimally invasive esophagectomy. The results indicated that patients undergoing the Mc procedure had better recurrence rates and survival outcomes than those undergoing the IL procedure, particularly among patients with stage T3 mid-thoracic esophageal cancer. However, the incidence of postoperative complications was lower with the IL procedure than with the Mc procedure. This research attracted extensive attention from domestic and international researchers, as its use of multicenter data and an enrollment of 1,862 patients set a world record in the field of esophageal cancer research.
Furthermore, at several top international academic conferences in 2018, including ESMO Asia and SABCS, authoritative Chinese medical institutions such as the Cancer Hospital Affiliated to Xiangya School of Medicine, Central South University; the National Clinical Research Center for Respiratory Diseases; and the First Affiliated Hospital of Guangzhou Medical University, with the assistance of LinkDoc, leveraged big data to conduct real-world studies, providing significant reference value for the selection of patient treatment regimens.
At the 2018 San Antonio Breast Cancer Symposium (SABCS) held in the United States one month ago, a study titled “A Single-Center Real-World Observational Study on Clinical Treatment and Prognosis of Chinese Breast Cancer Patients with Brain Metastases Using Big Data” was presented. This research was conducted jointly by the Department of Breast Oncology at the Affiliated Tumor Hospital of Xiangya School of Medicine, Central South University, Hunan Cancer Hospital, and LinkDoc Technology. By analyzing clinical evidence from breast cancer patients with brain metastases treated between 2012 and 2017, including clinical characteristics, patient management, and survival outcomes, the study found that factors such as tumor biological characteristics have predictive value for patient survival. These findings have significant implications for clinical treatment and prognosis.
At ESMO Asia 2018, a large-scale analysis titled “Association Between Pre-existing Conditions and Driver Gene Profiles/Immune Contexture in Non-Small Cell Lung Cancer” was released. This study was conducted collaboratively by the team from the State Key Laboratory of Respiratory Disease, the National Clinical Research Center for Respiratory Disease, and The First Affiliated Hospital of Guangzhou Medical University, in partnership with LinkDoc Technology. It was reported that driver gene profiles and the immune microenvironment, such as PD-L1 expression, have significantly redefined the treatment landscape for non-small cell lung cancer (NSCLC). By investigating whether these NSCLC subtypes are associated with certain pre-existing conditions (such as common comorbidities and family history), this study provides clues for understanding the etiology of NSCLC with distinct molecular characteristics.
It is evident that collaborating with medical big data platforms to leverage big data for end-to-end empowerment of scientific research, thereby enhancing research efficiency and quality, has increasingly become a trend.
When discussing his experience in leveraging big data for scientific research, Professor Liang Wenhua from the First Affiliated Hospital of Guangzhou Medical University noted that big data has become increasingly important in clinical research. Built upon large sample sizes, it reveals clinical patterns and helps eliminate certain confounding factors, which distinguishes it from previous single-center or small-sample datasets. “In addition to large sample size, big data must also contain substantial information, meaning both the breadth and depth of the data are crucial. Data depth refers to the amount of information available for analysis, which in turn depends on the construction of data models—a process that requires close collaboration and refinement with clinical experts.”
Indeed, with the application of big data technology in the healthcare industry, a cross-boundary revolution is underway in the field of medical health.
By efficiently and accurately structuring diverse data types—including imaging data, medical records, laboratory and diagnostic test results, and treatment costs—medically valuable information can be extracted from vast volumes of medical data, and data mining can be leveraged to address challenges in clinical practice. Medical big data platforms specializing in high-quality data, such as LinkDoc Technology, have already made big data processing more convenient, rapid, and clinically relevant. By empowering data, these platforms provide services and support to hospital administrators and clinical researchers, while continuously exploring new pathways, technologies, and methodologies to better serve clinical diagnosis and treatment practices, thereby yielding numerous scientific research achievements.
Currently, Zero Kr Tech has been collecting and analyzing real-world data in oncology fields such as lung cancer, breast cancer, esophageal cancer, and liver cancer, with the participation of nearly 100 hospitals across China. This process has yielded many findings that surpass previous understanding and has also identified numerous issues in clinical diagnosis and treatment. It can be said that big data analytics from the real world have brought many fresh insights, even challenging traditional perceptions.
After experiencing the big data technology of Lingke, Professor Liu Yunpeng from the First Affiliated Hospital of China Medical University is full of hope for the future. “Medical big data is ‘encountering’ clinical practice. I believe that in the near future, medical big data may completely change the landscape of medical practice. Under the traditional medical model, we already have the hardware and software conditions for electronic informatization and have accumulated a large amount of data. In the future, through the effective combination of artificial intelligence and big data to assist doctors in scientific research, we will promote the development of medicine into a new era.”