For medical practice administrators, owners, and IT managers in the United States, understanding how this data shapes cancer care is important.
\nMultimodal RWD includes clinical records, genomic data, pathology images, treatment outcomes, and other patient information. When all this data is combined and analyzed using advanced artificial intelligence (AI) techniques, it can help healthcare providers make better decisions.
\nThis article looks at the role of multimodal RWD in changing oncology practices and shows how AI and workflow automation help support this change.<\/p>\n
Multimodal RWD means different types of patient data collected from regular clinical settings. This includes electronic health records (EHRs), genomic sequencing, imaging data, pathology slides, and biochemical markers.
\nUnlike data from traditional clinical trials, which are controlled and limited, real-world data shows how patients actually experience treatments and their outcomes.
\nCaris Life Sciences, a company focused on personalized cancer care, has built one of the largest databases. It combines molecular, clinical, and claims data.
\nTheir database has over 580,000 clinico-genomic profiles from more than 57 tumor types and more than 4.45 million digitized pathology slides.
\nThis combined approach helps describe tumors better and gives doctors a fuller view of each patient\u2019s condition.<\/p>\n
Multimodal RWD helps research aimed at customizing cancer treatments for individual patients.
\nTraditional clinical trials often study large groups of patients with similar conditions.
\nBut precision oncology knows cancer is different even in the same tumor type. Patients may have different molecular causes, reactions to treatment, and outcomes.
\nReal-world data lets researchers study these smaller groups in more detail.
\nFor example, Tempus is a company that uses AI to join clinical, molecular, and behavioral data. They work to find new treatment targets and find where care is missing.
\nMore than 65% of Academic Medical Centers and over 50% of oncologists in the US use Tempus\u2019s platform.
\nAI models can analyze large patient groups to find new biomarkers that show which patients will respond to certain treatments.
\nTempus created an Immune Profile Score (IPS) biomarker that helps find patients who might benefit from immune checkpoint inhibitors.
\nThis shows how multimodal data can meet clinical needs not addressed before.<\/p>\n
One key use of multimodal RWD is to improve how clinical trials are designed and how drugs are developed.
\nWith advances in molecular and computational biology, oncology trials are moving away from strict drug-centered designs.
\nMany trials now allow multiple treatment options and change while running based on patient responses.
\nThis method makes trials more efficient and lets more patients qualify.
\nPeople who might not have met traditional trial rules may now join if their biomarkers or genetic profiles suggest they could benefit.
\nTempus has identified over 30,000 patients who might be eligible for trials using AI analysis of multimodal data.
\nFlatiron Health uses AI to create “digital twins”\u2014virtual patient models that show how treatments might work.
\nThis method improves how accurate trials are, speeds up recruitment, and cuts costs.
\nBy mixing real-world data with traditional trial data, Flatiron helps deliver more personalized care and find new treatments.<\/p>\n
Multi-omics data includes genomics, transcriptomics, proteomics, and metabolomics, giving a deeper view of tumors.
\nThe McGill Centre for Translational Research in Cancer collects this kind of data and mixes it with real-world patient data from over 11,000 cancer cases across 16 Canadian centers.
\nThis provides a big data set for research focusing on precise cancer treatment.
\nAstraZeneca is working on using computational pathology to improve cancer diagnosis.
\nTheir Quantitative Continuous Scoring (QCS) platform uses deep learning to study tissue samples at the single-cell level.
\nThis helps detect biomarkers and sort patients better.
\nThese technologies study how tumor cells interact with the immune system and other nearby cells, which is important for making combined therapies.
\nAlso, circulating tumor DNA (ctDNA) tests help detect mutations without invasive procedures and monitor tumor changes during treatment.
\nThis real-world method helps doctors adjust treatments quickly and improve results.<\/p>\n
Multimodal RWD has potential but also faces challenges.
\nData quality, privacy concerns, and differences in data sources are main problems for healthcare providers.
\nWork is ongoing to build standardized data storage and rules for data security and sharing without risking patient privacy.
\nAuthors like K. Verkerk and E.E. Voest say that creating special RWD databases can help extend or improve drug uses and confirm biomarkers for custom therapies.
\nAlso, combining real-world data with AI analytics can speed up government decisions and conditional drug approvals, helping patients get access to new treatments faster.<\/p>\n
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