Role of De-Identified Medical Imaging Data in Precision Oncology

Healthcare is shifting from population-based approaches to individualized care. Precision medicine is a novel strategy for disease treatment and prevention that takes into account variability in individual genes, environment, and lifestyle. This system disposes of the "one size fits all" principle of medicine and works to provide patients with what they specifically need. This makes it possible for healthcare workers and researchers to tailor treatment and prevention interventions to each individual patient. 

In cancer care, precision medicine aims to provide the right cancer treatment to the right patient at the right dose and time. Because progression of cancer is fueled by certain genetic mutations, addressing these mutations enables drugs to be more targeted and individualized.

Using the approach of precision medicine in oncology, researchers are able to identify individuals who might be at increased risk for cancer. This thus helps to diagnose and perform risk stratification of such patients. Further, precision medicine also helps diagnose certain cancers early, diagnose a specific type of cancer correctly, and helps to choose which cancer treatment options are best. Consequently, it also helps evaluate how well a treatment is working for a specific subgroup that is similar in response and characteristics. 

How Real-World Data (RWD) is Helping Develop Precision Medicine

In contrast to controlled trials, RWD offers practical insights from clinical settings, which is why it is so important to precision medicine. RWD offers thorough and comprehensive data from wearables, claims, electronic health records (EHRs), registries, and Omics data, among other sources. Researchers are able to create detailed patient profiles and study different populations using these datasets. This improves treatment plans by helping gain a better understanding of how the disease progresses and how specific patients respond to treatments.

The crucial aspect of utilizing RWD for precision medicine is that it helps identify biomarkers and genetic mutations for different tumors across populations and subgroups. Thus, RWD aids in enhancing the decision-making process for clinicians, as well as tailoring treatment plans and modifying treatments. All this is based on practical efficacy and safety information of treatments gathered through RWD. And combining RWD types like genetic data and pathology has the potential to drastically improve patient care for solid tumors and other cancers.

And with the availability of newer data sources like real-world imaging data (RWiD), deeper, more individualized insights into patient health and treatment effectiveness are made possible. This takes the approach of precision medicine one step further.

Role of De-Identified Medical Imaging Data in Precision Medicine 

De-identified medical imaging data is defined as imaging data from which direct identifiers, PII (personally identifiable information), have been redacted. De-identified imaging data holds rich information other than what is present in reports and electronic health records (EHRs). De-identified RWiD provides nuanced information such as shape, texture, and context with respect to the surrounding tissues. This level of information and visual details about tumors adds more meaning, which is required to carry out research and develop treatments.

Processing real-world imaging datasets (RWiD) has the potential to offer objective, quantitative, and reproducible data relevant to precision oncology. This valuable but often underrecognized data has the ability to be utilized to improve diagnostic accuracy and treatment planning. As the ability of data integration continues to expand, a combination of imaging data with genomic/pathology data offers immense opportunities for individualized medicine in oncology. These therapies are specially designed to suit the genetic profile of an individual, optimising the effectiveness of treatment and reducing side effects. RWiD is becoming essential to obtain the following insights, for accelerating research on precision medicine:

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1. Refined identification and classification of disease subpopulation 

Real-world imaging data (RWiD) collected from different demographics provides an in-depth understanding of patient subpopulations. With the help of imaging biomarkers linked to clinical information, RWiD enables the detection of differences in tumor characteristics across diverse patient groups. This further facilitates better risk stratification and prognosis predictions.

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2. Accelerated drug development 

Leveraging the combination of imaging datasets (RWiD) with other forms of genomics data & EHR is paramount for speeding up drug development. These linked datasets are used to precisely evaluate the efficacy of targeted therapies in various cancer subpopulations. This also gives end-to-end insights that enable the assessment of drug effectiveness, trial optimization, and identification of new therapeutic agents. Thus, it speeds up the development of personalized medicine.

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3. Improved clinical decision support 

Combining imaging datasets with genomics and/or clinical data enables clinicians to better understand the condition and treatment response of a patient. This comprehensive understanding can inform treatment decisions for individual patient subgroups (EGFR-mutant non-small cell lung cancer). 

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4. Training and fine-tuning of healthcare foundation models for precision medicine in oncology 

Real-world imaging data is crucial to train and fine-tune healthcare foundation models (HFM). This, in turn, increases the efficiency of models to better detect subtle variations in tumor biology as well as predict treatment responses with greater precision. By continuously fine-tuning these models with updated RWiD, AI researchers are able to build efficient HFMs that support personalized diagnosis, risk stratification, and therapy optimization.

How Segmed supports initiatives in precision medicine for oncology  

Real-world imaging data (RWiD) can revolutionize precision medicine in oncology by providing visual key insights about tumor biology. And with growing interest in the field of precision medicine, RWiD is further reinforced by emerging technologies. RWiD-driven Healthcare Foundation Models (HFM), cloud computing, and data-sharing models are leading the way for this revolution.

Segmed provides a repository of regulatory-grade RWiD that offers support for precision medicine, especially in oncology. By providing diverse and longitudinal datasets, Segmed enables researchers to develop targeted, patient-specific therapeutic strategies across various solid tumors such as non-small cell lung cancer (NSCLC), ovarian cancer, etc. 

Segmed’s RWiD enables the identification of imaging biomarkers, patterns of disease processes, and treatment response. Our standardized datasets, when combined with advanced analytics and AI/ML are used to train & fine-tune models. These collective benefits allow tailoring treatments based on patient-specific imaging and clinical characteristics, driving precision medicine to greater success and improved 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.