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The Impact of Body Composition on Cancer Prognosis

Subcutaneous adiposity is an independent predictor of mortality in cancer patients. Additionally, sarcopenia—the loss of skeletal muscle mass—is strongly associated with poor prognosis across a wide variety of malignancies. These findings underscore the critical need for efficient and reliable measurement of muscle mass and subcutaneous adipose tissue volume in cancer prognosis.



The Challenges of Traditional Body Composition Analysis

Traditional, manual approaches to body composition analysis are not only time-consuming but often fall short in accuracy. Most studies have relied on measuring a single transverse CT slice, a method that has been proven to be unreliable, as demonstrated by Ying et al. (2024). This outdated approach lacks the precision needed for comprehensive patient assessments.


Ying et al. (2021) further highlighted the clinical value of AI-based 3D assessment of muscle volume in CT scans, emphasizing the potential of automated tools in delivering more accurate and detailed data. The shift to 3D analysis not only enhances the precision of body composition assessments but also provides deeper insights into patient health.


Introducing Body Composition AI

To address these challenges, SliceVault has developed the Body Composition AI tool, designed for the 3D assessment of muscle volume, subcutaneous adipose tissue, and visceral adipose tissue. This advanced AI solution overcomes the limitations of traditional methods, providing a more accurate and comprehensive analysis of body composition.




Revolutionizing Clinical Trials with Prognostic Biomarkers

With our Body Composition AI tool, clinical trials involving CT scans can automatically incorporate prognostic biomarkers. This automation enhances the efficiency of trials while ensuring that critical prognostic data—vital for understanding and predicting patient outcomes—is consistently and accurately included.


By leveraging the power of AI for body composition analysis, SliceVault is taking significant strides in improving prognosis and treatment planning for cancer patients. Our AI tools are designed to integrate seamlessly into clinical trials, ensuring that valuable biomarkers are reliably measured and utilized in patient care.


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