Artificial Intelligence Enables Early Prediction of Wasting Syndrome in Cancer Patients

A cutting-edge AI system has been developed to identify cancer patients at risk of developing cachexia, a severe and often fatal wasting syndrome. This advancement could significantly improve patient outcomes by enabling earlier intervention and tailored treatment strategies.

Cachexia affects approximately 20% of all cancer-related deaths and is characterized by systemic inflammation, muscle loss, and significant weight reduction. Despite its high prevalence, early detection remains a challenge because current methods rely heavily on clinical observation, weight loss thresholds, and indirect biomarkers, which can be subjective and often detect the syndrome too late.

The new AI approach leverages imaging scans and clinical data to predict the likelihood of cachexia. During the study, researchers trained the AI to analyze CT scans to evaluate muscle mass, alongside other patient information such as demographics, weight, height, and cancer stage. When combined with lab results and clinical notes, the AI achieved an accuracy rate of up to 85% in predicting cachexia, outperforming existing diagnostic methods.

This predictive ability also correlates with survival odds in patients with pancreatic, colon, and ovarian cancers. The AI’s assessments of skeletal muscle showed a median discrepancy of just about 2.5% compared to expert radiologists, indicating high reliability.

Detecting cachexia early can facilitate lifestyle adjustments and pharmacological treatments that may slow muscle wasting, improve metabolic health, and enhance quality of life. The findings, presented at the American Association for Cancer Research’s annual meeting in Chicago, highlight the potential of AI to transform cancer care by enabling precise and timely interventions.

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