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AI-driven CT analysis of 1,275 NSCLC patients at Mass General found higher muscle volume strongly predicts longer survivalespecially in those with obesity or diabeteswhile specific fat compartments benefit only metformin users. Genomic analysis revealed key links to KRAS and EGFR mutations.
Why It Matters To Your Practice
▪ AI tools can quantify nuanced body composition from standard CT scans.
▪ Muscle and fat distribution, not just BMI, inform patient prognosis.
▪ Personalised treatment strategies may be developed using these AI-derived metrics.
▪ Metformins benefits appear tied to specific adipose characteristics.
Clinical Implications
▪ Higher muscle volume is a positive prognostic marker in NSCLC.
▪ Patients with obesity or diabetes may gain more from muscle preservation interventions.
▪ Adipose tissue metrics are especially relevant for those on metformin.
▪ Body composition profiling could refine risk stratification and therapy selection.
Insights
▪ AI enables rapid, reproducible segmentation of muscle and fat on imaging.
▪ KRAS and EGFR mutation status correlates with body composition patterns.
▪ Metabolic phenotype may influence tumour genomics and treatment response.
▪ Comprehensive profiles move beyond BMI for precision oncology.
The Bottom Line
▪ AI-powered body composition analysis adds prognostic value in NSCLC.
▪ Integrating these insights could guide personalised care, particularly for patients with obesity, diabetes, or on metformin.
▪ Routine CTs offer untapped prognostic and therapeutic information.
▪ Prospective studies are needed to validate clinical integration.
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