Real-World Imaging Datasets to Enhance Precision Medicine

Healthcare delivery is evolving from population medicine to precision medicine. Precision medicine means to customize the treatment to a subpopulation who differ in disease susceptibility, process, progression or response to a medication. Based on factors such as genetics, lifestyle, environment, etc., the treatment is tailored to the individual. The main aim is to provide the right medicine for the right individual. This ensures that individuals get medications that work best for them i.e. better efficacy and fewer side effects.

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How precision medicine is being practiced currently

At present, precision medicine is majorly driven by genomic data, electronic health records (EHRs), and biomarkers. By analyzing and integrating these datasets, researchers identify patterns and correlations in the disease processes and treatment response. Advancements in big data and AI/ML enable for analysis of vast amounts of datasets, to obtain targeted therapies.

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How real-world data (RWD) is helping develop precision medicine

RWD is playing a crucial role in precision medicine, by providing real world insights from clinical settings, as compared to controlled trials. RWD provides comprehensive and complete information from registries, EHRs, Omics data, wearables, claims etc. These datasets allow researchers to study diverse populations and develop comprehensive patient profiles. This provides a better understanding of the disease progression and response to treatments, helping to refine treatment strategies.

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How real-world imaging datasets (RWiD) can enhance precision medicine

Imaging data contains a lot of valuable information, which is typically not captured in reports and EHRs. Reports lack granular details such as shape, texture, relationship with surrounding tissue, and other detailed nuances which provide a broader context useful for research. Reports also may have qualitative issues, along with inter-observer variability, leading to subjective biases. Analyzing raw real-world imaging datasets (RWiD) can provide objective, quantifiable, and reproducible information which is vital for precision medicine. This rich, usually untapped information can enhance diagnostic accuracy and treatment planning. With the growth in Radiomics, it is becoming easier to evaluate and integrate imaging data to obtain the following insights, for accelerating research on precision medicine:

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1) Improved identification of disease subpopulation

Real world imaging datasets (RWiD) collected from different demographics, help with in-depth understanding of patient subpopulations and their disease processes along with treatment response. This is a crucial first step in research, development and delivery of precision medicine.

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2) Accelerating drug development 

Pharmaceutical companies can integrate imaging datasets (RWiD) with other types of omics data & EHR. This provides comprehensive insights that help to evaluate drug efficacy, optimize trials, and discover new therapeutic agents, thereby accelerating the development of precision medicines.

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3) Improved clinical decision support 

By integrating imaging datasets with genomic and clinical data, healthcare professionals can gain a comprehensive and better understanding of a patient’s condition. AI powered analysis can further support in highlighting abnormalities, predicting disease progressions and identifying the right treatment.

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4) Training and validation of AI models

Raw medical images can be used to train and validate AI models. Real-world imaging datasets provide diverse, high-volume data to enhance AI's ability to detect and classify diseases with greater precision.

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Challenges with using real-world imaging datasets

Despite its potential, leveraging real-world imaging datasets for precision medicine presents several challenges:

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• Data Standardization: Medical imaging data is obtained from various healthcare systems, which use different formats and protocols. This makes it tedious and a difficult process for standardizing them into a common usable format.

• Data Privacy & Security: Imaging datasets contain sensitive patient information that needs to be redacted. There are chances of losing vital data from the images while redacting patient identifiers and health information, leading to bias and/or wrong outcomes.

• Integration with Other Data Sources: Integrating imaging datasets with other types such as genomic and clinical data is complex due to differences in data structure and storage systems.

• Computational and Storage Costs: Storage and analysis of high-resolution medical images demands significant resources and computational power, making it a costly affair.

• Bias and Representativeness: Existing imaging datasets may lack diversity, leading to biases in AI models that may impact patient outcomes across different patient populations.

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Conclusion

RWiD has the potential to revolutionize precision medicine by providing critical insights that complement genomic and clinical data. While challenges do exist, increasing interest & research along with advancements in technologies such as AI, cloud computing, & data-sharing frameworks are paving the way. As the field continues to evolve, harnessing RWiD will be key to delivering truly personalized and precise healthcare solutions.

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How Segmed can support your precision medicine research

Segmed, with its repository of regulatory grade RWiD, offers unparalleled support for precision medicine research. By providing diverse and longitudinal datasets, Segmed can enable researchers to develop targeted, patient-specific therapeutic strategies across various medical domains. Segmed’s datasets can aid in both clinical decision-making and drug discovery & development research through:
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• Biomarker Discovery & Validation: Segmed’s curated datasets support in identifying and validating imaging biomarkers. This enables personalized treatment decisions in oncology, cardiology, neurology, and other fields.
• Insights on patient subpopulations: Segmed’s annotated datasets help in identifying different patient populations, through identification of patterns of disease processes and treatment response.
• Enhancing Clinical Trials: Segmed offers pre-curated datasets to optimize patient recruitment and stratification, ensuring trials are more efficient and representative.
• Personalized Treatment Insights: Our standardized datasets can be utilized with advanced analytics and AI/ML to train & validate models that can tailor treatments based on patient-specific imaging and clinical characteristics.
• Real-World Evidence Generation: Segmed’s regulatory grade datasets can be used for regulatory submissions and post-market surveillance, for indication expansion, and other statutory approvals.

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Segmed has a team of subject matter experts (SMEs) with expertise in clinical, medical and technology fields that provides end-to-end support, ensuring that datasets are curated and tailored to specific research needs. This allows for seamless integration of complex data types, enabling researchers to focus on actionable insights rather than data preparation. Additionally, Segmed’s tokenization of datasets ensure data privacy and security while assisting in longitudinal integrity and completeness necessary for studying patient-specific responses to treatments.

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By combining cutting-edge data solutions with expert guidance, Segmed facilitates breakthroughs in precision medicine research, supporting everything from early diagnosis and risk assessment to treatment optimization and real-world evidence generation.

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With Segmed’s expertise and advanced data solutions, researchers and clinicians can unlock new frontiers in precision medicine, driving innovation and improving patient outcomes.

‍Connect with us to explore how Segmed’s offerings align with your research goals and to learn more about our work in personalized healthcare solutions.