AI-Assisted Body Composition Assessment Using CT Imaging in Colorectal Cancer Patients: Predictive Capacity for Sarcopenia and Malnutrition Diagnosis.

(1) Background: The assessment of muscle mass is crucial in the nutritional evaluation of patients with colorectal cancer (CRC), as decreased muscle mass is linked to increased complications and poorer prognosis. This study aims to evaluate the utility of AI-assisted L3 CT for assessing body composition and determining low muscle mass using both the Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition and the European Working Group on Sarcopenia in Older People (EWGSOP2) criteria for sarcopenia in CRC patients prior to surgery. Additionally, we aim to establish cutoff points for muscle mass in men and women and propose their application in these diagnostic frameworks. (2) Methods: This retrospective observational study included CRC patients assessed by the Endocrinology and Nutrition services of the Regional University Hospitals of Malaga, Virgen de la Victoria of Malaga, and Vall d'Hebrón of Barcelona from October 2018 to July 2023. A morphofunctional assessment, including anthropometry, bioimpedance analysis (BIA), and handgrip strength, was conducted to apply the GLIM criteria for malnutrition and the EWGSOP2 criteria for sarcopenia. Body composition evaluation was performed through AI-assisted analysis of CT images at the L3 level. ROC analysis was used to determine the predictive capacity of variables derived from the CT analysis regarding the diagnosis of low muscle mass and to describe cutoff points. (3) Results: A total of 586 patients were enrolled, with a mean age of 68.4 ± 10.2 years. Using the GLIM criteria, 245 patients (41.8%) were diagnosed with malnutrition. Applying the EWGSOP2 criteria, 56 patients (9.6%) were diagnosed with sarcopenia. ROC curve analysis for the skeletal muscle index (SMI) showed a strong discriminative capacity of muscle area to detect low fat-free mass index (FFMI) (AUC = 0.82, 95% CI 0.77-0.87, p < 0.001). The identified SMI cutoff for diagnosing low FFMI was 32.75 cm2/m2 (Sn 77%, Sp 64.3%; AUC = 0.79, 95% CI 0.70-0.87, p < 0.001) in women, and 39.9 cm2/m2 (Sn 77%, Sp 72.7%; AUC = 0.85, 95% CI 0.80-0.90, p < 0.001) in men. Additionally, skeletal muscle area (SMA) showed good discriminative capacity for detecting low appendicular skeletal muscle mass (ASMM) (AUC = 0.71, 95% CI 0.65-0.76, p < 0.001). The identified SMA cutoff points for diagnosing low ASMM were 83.2 cm2 (Sn 76.7%, Sp 55.3%; AUC = 0.77, 95% CI 0.69-0.84, p < 0.001) in women and 112.6 cm2 (Sn 82.3%, Sp 58.6%; AUC = 0.79, 95% CI 0.74-0.85, p < 0.001) in men. (4) Conclusions: AI-assisted body composition assessment using CT is a valuable tool in the morphofunctional evaluation of patients with colorectal cancer prior to surgery. CT provides quantitative data on muscle mass for the application of the GLIM criteria for malnutrition and the EWGSOP2 criteria for sarcopenia, with specific cutoff points established for diagnostic use.

Exploratory Assessment of Nutritional Evaluation Tools as Predictors of Complications and Sarcopenia in Patients with Colorectal Cancer.

BACKGROUND: Patients with colorectal cancer (CRC) are largely malnourished, which decreases overall survival and treatment efficacy and increases mortality rates. We hypothesize that angle phase might be associated with the risk of sarcopenia as well as cancer complications in patients with CRC. The inclusion of various nutritional status indicators and clinical cancer outcomes can result in significant variability. Therefore, the objective of this study was to perform an exploratory analysis of nutritional evaluation tools used to assess body composition and muscle quality in patients with CRC, in order to predict cancer complications and survival rate. METHODS: A total of 127 patients with CRC were included in this study. Bioelectrical impedance analysis and body composition were performed, which we used to obtain phase angle (PhA) values. Muscle function was assessed by hand-grip strength (HGS) and muscle quality and adipose tissue depot were performed using ultrasound techniques. RESULTS: This study showed that there were significant differences in body composition between females and males, as well as in muscle quantity and quality. PhA was highly correlated with quadriceps rectus femoris of cross-sectional area (RF-CSA), circumference of quadriceps rectus femoris (RF-CIR), superficial subcutaneous abdominal fat (S-SAT), as well as HGS (p < 0.05). PhA was also correlated with water content in females, and with muscle mass and quality in males (p < 0.05). Specifically, we found that PhA was a good predictor for cancer complications in women and the risk of sarcopenia in men. In the linear model controlled for age and body mass index (BMI), high PhA value was associated with a decreased risk of complications in females (Odds Ratio (OR) = 0.15, 95% CI: 0.03-0.81, p < 0.05). High PhA value was associated with a decreased risk of sarcopenia in males (OR = 0.42, 95% CI: 0.19-0.95, p < 0.05). In addition, Receiving Operating Characteristics (ROC) curve analysis showed that PhA had a good diagnostic accuracy for detecting cancer complications in females (Area under curve (AUC) = 0.894, 95% CI: 0.88-0.89, p < 0.05) and the risk of sarcopenia in males (AUC = 0.959, 95% CI: 0.91-0.92, p < 0.05). CONCLUSIONS: PhA can accurately predict oncological complications in women and sarcopenia in men. These differences are relevant to understanding the nutritional status of patients with CRC and their personalized nutritional treatment.