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[Preprint]. 2024 Mar 4:rs.3.rs-3957125. [Version 1] doi: 10.21203/rs.3.rs-3957125/v1

Figure 1. Predicting Low Pectoralis Muscle Area.

Figure 1

Areas under the receiver operating characteristic curves (AUROC) of our 3 random forest classification models built to predict low pectoralis muscle area (PMA). Five clinical measures were used in the clinical-only model: age, gender, pack years, height, and weight. Eight feature selected biomarkers for predicting the development of low PMA were used in the biomarker-only model: Histone acetyltransferase type B catalytic subunit (Hat1), Secreted protein acidic and rich in cysteine (SPARC), Lymphotoxin alpha 1/ beta 2 (Lymphotoxin a1/b2), Growth/differentiation factor 15 (GDF15), Cell adhesion molecule-related/down-regulated by oncogenes (CDON), Neurexophilin-1 (NXPH1), Vascular cell adhesion protein 1 (VCAM-1), and EGF-containing fibulin-like extracellular matrix protein 1 (EFEMP1). The combined model used predictors from both the clinical-only and biomarker-only models. The combined model and the clinical-only model were significantly different (P = 0.032). The combined model and the biomarker-only model were not significantly different (P = 0.78). The clinical-only model and the biomarker-only model were not significantly different (P = 0.09).