Discover how real-world imaging datasets are transforming clinical trials, enhancing insights, efficiency, and decision-making.

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Introduction

Clinical trials are the reference standard for generating evidence that validates the safety and efficacy of new treatments, devices, or therapies. They provide the definitive proof required for regulatory approval, ensuring an intervention is both effective and safe before it reaches the target population. However, the economics behind these trials are complex, with costs often exceeding hundreds of millions of dollars due to rigorous protocols, lengthy timelines, and high regulatory demands. Despite these investments, trials have a chance of failing, resulting in losses and delays in bringing the treatment to the population. 

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Utilizing real-world data (RWD) for this process can address the challenges of clinical trials offering insights that can improve efficiency and reduce cost. RWD helps ensure the identification of the right population for recruitment into trials, creating a cost-effective, multi-win scenario. The use of RWD adds perspective to clinical trial design by offering a granular view of patient journey and treatment experience in the real-world while also considerably shortening the timeline.

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Building on the advantages of RWD, real-world imaging datasets (RWiD) offer an additional layer of valuable insights for clinical trials. These datasets provide detailed visual insights into disease progression, treatment response, while also giving a broader and more generalizable view of patient outcomes.

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RWiD offers a robust foundation for evidence generation as it enhances trial design, supports patient stratification and enables identification of imaging biomarkers. This not only improves the strength of clinical evidence but also helps accelerate the discovery and validation of innovative treatments.

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Value of imaging in Clinical Trials and Disease Management in the real world setting

Access to an extensive volume of imaging data offers tremendous advantages for clinical research and disease management in the real world. Imaging provides critical insights across the entire therapeutic journey - from diagnosis to treatment evaluation enabling precise patient stratification and outcome assessment. Advanced imaging techniques enable the detection of metabolic changes before structural changes crucial for monitoring disease progression. Imaging also allows for longitudinal monitoring of patients repeatedly over a period of time, particularly important in chronic conditions that include cancer and neurodegenerative diseases ensuring continuous assessment of treatment efficacy in trials. Multi-modal approaches that integrate imaging with genomic and proteomic data offer a more comprehensive view of disease mechanisms, supporting the identification of predictive biomarkers and personalized treatment strategies to optimize trial success. By providing objective, reproducible, quantitative real-time data, imaging supports identification of imaging biomarkers, refines clinical trial protocol through robust evidence generation and helps in providing personalized treatments ultimately driving innovation, efficiency and higher success rates in clinical trials and disease management. 

Value of RWiD in trials 

RWiD have the potential to transform clinical trials with rich, diverse, and longitudinal data that enhance patient selection, improve trial efficiency, and enable data-driven decision-making. By providing insights about real-world patient populations, imaging biomarkers, and disease progression, RWiD helps identify the right patients for trials, ensuring cohorts more accurately reflect the target population. These datasets support evidence generation, reducing trial timelines and costs and ultimately enhancing clinical trials.

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1. Clinical trial design and optimization

RWiD by providing insights about disease types and stages, and identifying the right biomarkers enables researchers to develop more precise inclusion and exclusion criteria. This ensures that the right patient population is chosen for conducting the trials, thereby increasing the possibility of obtaining meaningful results.

Additionally, RWiD also facilitates patient stratification by allowing division of population into subgroups based on imaging-derived biomarkers, disease progression patterns, and treatment response prediction. 

The other critical advantage of RWiD is its ability to capture data from diverse and underrepresented patient populations. Traditional clinical trials often suffer from limited demographic diversity, which can lead to skewed results and a lack of generalizability. RWiD gathered across multiple regions, healthcare systems, and patient demographics offers a broader, more representative sample. This diversity enables researchers to know how therapies work across different age groups, ethnic backgrounds, and varying levels of disease severity.

RWiD also play a crucial role in establishing objective endpoints for clinical trials by providing precise, quantitative measurements that reduce variability and enhance the statistical power of the trial.

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2. External control arm 

Real-world imaging datasets are instrumental in developing external control arms playing a major role in making clinical trials more efficient and patient-centric by reducing the need for large placebo or control groups, while still ensuring a rigorous evaluation of a treatment's efficacy and safety. Leveraging longitudinal patient datasets as external control arms can be impactful in advancing treatments for chronic diseases. These datasets enable researchers to analyze alternatives in drug administration and assess treatment efficacy over time, providing robust comparative insights without relying heavily on traditional control groups. This approach not only accelerates trial timelines but also minimizes the burden on patients, fostering more ethical and innovative trial designs.

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3. Leverage AI/ML models

Raw imaging datasets enable the training and application using AI models to uncover patterns and gain deeper insights. With reduced human error and automation in analysis, AI/ML models significantly enhance the efficiency, precision, and scalability of imaging-driven research. Furthermore, these tools enable predictive analytics, facilitating personalized treatment plans tailored to individual patients and optimizing clinical trial design by identifying key imaging-based endpoints.

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Segmed’s role in supporting Clinical Trials 

RWiD has established itself as a cornerstone of modern clinical trials, representing a transformative resource in clinical trial design and optimization. By providing non-invasive, objective, and reproducible data, imaging datasets enhance the accuracy of trial outcomes, facilitates stratification of patient population, identification of imaging biomarkers and improves diagnosis and staging of disease ultimately enhancing design of trials, empowering precision medicine, and facilitating in-depth analysis for more informed decision-making. 

At Segmed we provide high-quality, regulatory-grade RWiD and multimodal longitudinal datasets that are specifically designed to aid in clinical trial design and optimization. These regulatory-grade, tokenized datasets are integrated with other types of data like EHR data, expert annotations, and digital pathology images, ensuring high-quality insights for your research across disease areas such as oncology, neurology, and cardiology.

Connect with us to discover how our high-quality, diverse, tokenized imaging datasets can drive a wide range of research and development efforts, advancing healthcare innovation.