Multimodal AI-driven Biomarker for Early Detection of Cancer Cachexia.

Cancer cachexia, a multifactorial metabolic syndrome characterized by severe muscle wasting and weight loss, contributes to poor outcomes across various cancer types but lacks a standardized, generalizable biomarker for early detection. We present a multimodal AI-based biomarker trained on real-world clinical, radiologic, laboratory, and unstructured clinical note data, leveraging foundation models and large language models (LLMs) to identify cachexia at the time of cancer diagnosis. Prediction accuracy improved with each added modality: 77% using clinical variables alone, 81% with added laboratory data, and 85% with structured symptom features extracted from clinical notes. Incorporating embeddings from clinical text and CT images further improved accuracy to 92%. The framework also demonstrated prognostic utility, improving survival prediction as data modalities were integrated. Designed for real-world clinical deployment, the framework accommodates missing modalities without requiring imputation or case exclusion, supporting scalability across diverse oncology settings. Unlike prior models trained on curated datasets, our approach utilizes standard-of-care clinical data, facilitating integration into oncology workflows. In contrast to fixed-threshold composite indices such as the cachexia index (CXI), the model generates patient-specific predictions, enabling adaptable, cancer-agnostic performance. To enhance clinical reliability and safety, the framework incorporates uncertainty estimation to flag low-confidence cases for expert review. This work advances a clinically applicable, scalable, and trustworthy AI-driven decision support tool for early cachexia detection and personalized oncology care.