• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Saturday, August 2, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Health

Hollings Researchers Demonstrate How Natural Language Processing Enhances Medical Practice

Bioengineer by Bioengineer
August 1, 2025
in Health
Reading Time: 4 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

blank

In recent years, the intersection of healthcare and artificial intelligence has ushered in transformative approaches to patient care, and the latest advancement from researchers at the MUSC Hollings Cancer Center exemplifies this shift. Pioneered by Jihad Obeid, M.D., and Mario Fugal, Ph.D., their team has developed a cutting-edge natural language processing (NLP) model designed to decode and classify complex medical narratives within patient records. This breakthrough specifically targets the challenges of identifying the primary cancer diagnosis in patients undergoing stereotactic radiosurgery (SRS) for brain metastases—a critical factor in tailoring effective therapeutic strategies.

Brain metastases, secondary tumors originating from cancers elsewhere in the body such as the lung, breast, skin, kidney, or digestive tract, pose intricate clinical dilemmas. The brain’s delicate architecture necessitates precision in radiation therapy, particularly with SRS, which delivers a concentrated dose in a one-time session. However, the efficacy and safety of SRS rely heavily on understanding the tumor’s lineage. Some cancers, like those rooted in lung tissue, exhibit high radiosensitivity and respond favorably to lower radiation doses, while others, including renal cancers, demonstrate resistance, demanding alternative dosing and treatment regimens. Accurately pinpointing the origin of brain metastases is therefore paramount to minimizing collateral damage and optimizing patient outcomes.

Historically, clinicians have grappled with the unstructured and often inconsistent format of medical records, especially when vital information is buried within extensive free-text clinical notes. Despite the existence of standardized coding systems such as the International Classification of Diseases (ICD), these codes frequently fall short in capturing the nuanced details necessary for specialized cancer treatments. ICD codes tend to be too broad, failing to distinguish between subtypes or the precise anatomical location of the primary tumor, which are essential variables in personalized treatment planning.

.adsslot_9wYfhQOdyM{ width:728px !important; height:90px !important; }
@media (max-width:1199px) { .adsslot_9wYfhQOdyM{ width:468px !important; height:60px !important; } }
@media (max-width:767px) { .adsslot_9wYfhQOdyM{ width:320px !important; height:50px !important; } }

ADVERTISEMENT

The MUSC research team circumvented this bottleneck by leveraging NLP, a sub-discipline of artificial intelligence focused on enabling machines to interpret human language. By training an algorithm to recognize semantic patterns, keywords, and contextual clues embedded in clinical notes, the model discerns specific cancer types and subtypes with unprecedented accuracy. For instance, terms like “ductal” signal breast cancer, whereas “melanoma” indicates skin cancer. This semantic precision allows for a more detailed and patient-specific cancer classification beyond the capabilities of conventional coding.

This NLP model was rigorously evaluated using a vast dataset comprising over 82,000 radiation oncology notes from the electronic health records (EHRs) of more than 1,400 patients treated with SRS for brain metastases. The performance of the NLP system was benchmarked against ground truth annotations manually verified by expert reviewers, confirming its ability to extract primary cancer diagnoses with over 90% accuracy overall. Remarkably, for prevalent cancers such as those of the lung, breast, and skin, the model’s classification accuracy soared to nearly 97%, including the precise identification of lung cancer subtypes—an achievement beyond the purview of ICD coding.

One of the compelling facets of this development is the model’s operational simplicity and scalability. Unlike more computationally intensive AI innovations, this approach does not demand expansive datasets or heavy resource investment. Importantly, it avoids the ethical and privacy concerns often associated with complex generative AI systems, positioning it as an immediately deployable tool for a wide range of healthcare settings, including those with limited infrastructural capacity.

The clinical implications of integrating such a model are profound. By automating the extraction of relevant diagnostic information from unstructured physician notes, the technology expedites the data availability that oncologists need for timely decision-making. This acceleration can significantly reduce the latency between diagnosis and treatment, thereby enhancing patient outcomes. Furthermore, systematically captured, high-fidelity data can underpin more robust research studies and clinical trials, fostering a cycle of continuous improvement in cancer care.

Looking forward, the MUSC team is extending this NLP framework to address other pressing clinical challenges, such as early detection of radiation necrosis—a serious, albeit rare, inflammatory side effect marked by brain swelling following radiation therapy. Identifying patients at heightened risk for such complications can enable preemptive interventions or adjustments to treatment protocols, mitigating harm and improving quality of life.

Moreover, the adaptability of the NLP model holds promise for integration with multimodal healthcare data streams. Combining unstructured clinical narratives with imaging data, laboratory results, or genomic information could yield richer, multidimensional insights into cancer biology and patient prognosis. This multidisciplinary data fusion represents the vanguard of precision oncology and offers a roadmap toward truly personalized medicine.

At its core, this research embodies a broader paradigm shift within healthcare: repurposing electronic health records from static repositories to dynamic, analyzable datasets capable of informing real-time clinical decisions. By harnessing AI-driven tools like NLP, clinicians can transcend the limitations of current documentation formats, transforming the vast expanse of textual data into actionable knowledge that benefits both patients and providers.

As cancer treatments grow increasingly sophisticated and individualized, tools that bridge the gap between raw clinical documentation and precise medical understanding will become indispensable. The MUSC Hollings Cancer Center’s NLP model demonstrates how targeted AI applications can catalyze this transformation, ensuring that technological advances translate directly into improved patient care without adding to the burdens shouldered by healthcare professionals.

Subject of Research: People

Article Title: Classifying Stereotactic Radiosurgery Patients by Primary Diagnosis Using Natural Language Processing

News Publication Date: 13-Jun-2025

Web References:
https://ascopubs.org/doi/10.1200/CCI-24-00268
https://hollingscancercenter.musc.edu/

References:
Jihad Obeid, M.D., Mario Fugal, Ph.D., et al. “Classifying Stereotactic Radiosurgery Patients by Primary Diagnosis Using Natural Language Processing.” JCO Clinical Cancer Informatics, 13 June 2025.

Image Credits:
Medical University of South Carolina / Photo by Clif Rhodes

Keywords: Cancer, Brain cancer, Artificial intelligence, Natural language processing

Tags: artificial intelligence in medical practicebrain metastases diagnosis challengescancer treatment optimization strategiesenhancing patient outcomes with AIidentifying primary cancer originsimproving therapeutic strategies for cancerMUSC Hollings Cancer Center researchnatural language processing in healthcareNLP applications in oncologypatient record analysis using NLPprecision radiation therapy techniquesstereotactic radiosurgery advancements

Share12Tweet8Share2ShareShareShare2

Related Posts

Gut γδ T17 Cells Drive Brain Inflammation via STING

Gut γδ T17 Cells Drive Brain Inflammation via STING

August 2, 2025
blank

Agent-Based Framework for Assessing Environmental Exposures

August 2, 2025

MARCO Drives Myeloid Suppressor Cell Differentiation, Immunity

August 2, 2025

Personalized ML Wearable Enhances Impaired Arm Function

August 2, 2025

POPULAR NEWS

  • Blind to the Burn

    Overlooked Dangers: Debunking Common Myths About Skin Cancer Risk in the U.S.

    60 shares
    Share 24 Tweet 15
  • Dr. Miriam Merad Honored with French Knighthood for Groundbreaking Contributions to Science and Medicine

    46 shares
    Share 18 Tweet 12
  • Study Reveals Beta-HPV Directly Causes Skin Cancer in Immunocompromised Individuals

    38 shares
    Share 15 Tweet 10
  • Neuropsychiatric Risks Linked to COVID-19 Revealed

    36 shares
    Share 14 Tweet 9

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Gut γδ T17 Cells Drive Brain Inflammation via STING

Agent-Based Framework for Assessing Environmental Exposures

MARCO Drives Myeloid Suppressor Cell Differentiation, Immunity

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.