Artificial Intelligence

<p>In a groundbreaking development, London scientists at Imperial College have introduced the world’s first “virtual biopsy” technology, employing Artificial Intelligence (<a href="https://www.cryptopolitan.com/the-power-of-ai-in-reshaping-prostate-cancer-treatment/">AI</a>) to diagnose lung cancer with unprecedented accuracy. This innovative approach aims to revolutionize cancer diagnosis and treatment by providing swift and dependable predictions about patient outcomes, potentially sparing them from uncomfortable and invasive biopsy procedures.</p> 
<h2 class="wp-block-heading" id="pioneering-ai-technology">Pioneering AI technology</h2> 
<p>The research, led by a team at Imperial College, utilized an AI tool to predict lung cancer progression in hundreds of patients. This AI-powered innovation, dubbed tissue-metabolomic-radiomic-CT (TMR-CT), merges data from CT scans with metabolic profiling, offering detailed insights into the chemistry of tumor cells and their evolutionary trajectory.&nbsp;</p> 
<p>By analyzing this comprehensive dataset, the AI model effectively classifies lung cancer types and provides reliable prognostic information, outperforming traditional diagnostic methods such as CT scans and clinical assessments.</p> 
<p>The study, conducted in collaboration with University Hospital Reina Sofia in Córdoba, Spain, and healthcare institutions in the UK, demonstrated the efficiency and dependability of the TMR-CT technology. Initial trials involving 48 lung cancer patients showed promising results, prompting further testing on a larger cohort of 723 patients. The findings revealed that the AI-based approach accurately classified lung cancer and delivered dependable predictions about patient outcomes, surpassing conventional diagnostic techniques.</p> 
<h2 class="wp-block-heading" id="transforming-cancer-diagnosis-and-treatment">Transforming Cancer diagnosis and treatment</h2> 
<p>Lung cancer, the leading cause of cancer-related deaths in the UK, claims approximately 35,000 lives annually. With the potential to enhance diagnostic accuracy and streamline treatment decisions, the TMR-CT technology holds significant promise for improving patient outcomes. The researchers envision integrating this AI tool into commercial medical imaging scanners, enabling widespread adoption and transforming diagnostic and treatment protocols for lung cancer and potentially other challenging-to-biopsy cancers such as brain, ovarian, and endometrial cancers.</p> 
<p>Senior author Professor Eric Aboagye, from Imperial’s Department of Surgery and Cancer, emphasized the transformative potential of the TMR-CT method, particularly in countries with high lung cancer prevalence like the UK. By leveraging CT scans to gain a deeper understanding of tissue and tumor chemical composition, this technology could revolutionize cancer diagnosis and treatment paradigms, offering new avenues for personalized medicine and improved patient care.</p> 
<p>Marc Boubnovski Martell, the study’s first author, and an Imperial PhD candidate, highlighted the significance of the developed system, which integrates CT scans with the chemical makeup of tumors and normal lung tissue. This integration facilitates accurate classification of lung cancer types and provides reliable predictions about patient outcomes, empowering clinicians to make informed treatment decisions and optimize patient care.</p> 
<p>The pioneering work by London scientists at Imperial College represents a significant milestone in cancer diagnosis and treatment. By harnessing the power of AI and integrating advanced imaging techniques with metabolic profiling, the TMR-CT technology offers a non-invasive and reliable alternative to traditional biopsies, potentially transforming the landscape of cancer care. With further validation and refinement, this innovative approach promises to improve patient outcomes and reduce the burden of cancer worldwide.</p>

Breakthrough in Cancer Diagnosis: London Scientists Develop AI-Powered “Virtual Biopsy”

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Ďalšie články zverejnené 22. 2. 2024

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