Associations of intermuscular adipose tissue and total muscle wasting score in PG-SGA with low muscle radiodensity and mass in nonmetastatic colorectal cancer: A two-center cohort study

Predicting 3-year all-cause mortality in rectal cancer patients based on body composition and machine learning

Objectives

The composition of abdominal adipose tissue and muscle mass has been strongly correlated with the prognosis of rectal cancer. This study aimed to develop and validate a machine learning (ML) predictive model for 3-year all-cause mortality after laparoscopic total mesorectal excision (LaTME).

Methods

Patients who underwent LaTME surgery between January 2018 and December 2020 were included and randomly divided into training and validation cohorts. Preoperative computed tomography (CT) image parameters and clinical characteristics were collected to establish seven ML models for predicting 3-year survival post-LaTME. The optimal model was determined based on the area under the receiver operating characteristic curve (AUROC). The SHAPley Additive exPlanations (SHAP) values were utilized to interpret the optimal model.

Results

A total of 186 patients were recruited and divided into a training cohort (70%, n = 131) and a validation cohort (30%, n = 55). In the training cohort, the AUROCs of the seven ML models ranged from 0.894 to 0.949. In the validation cohort, the AUROCs ranged from 0.727 to 0.911, with the XGBoost model demonstrating the best predictive performance: AUROC = 0.911. SHAP values revealed that subcutaneous adipose tissue index (SAI), visceral adipose tissue index (VAI), skeletal muscle density (SMD), visceral-to-subcutaneous adipose tissue ratio (VSR), and subcutaneous adipose tissue density (SAD) were the five most important variables influencing all-cause mortality post-LaTME.

Conclusion

By integrating body composition, multiple ML predictive models were developed and validated for predicting all-cause mortality after rectal cancer surgery, with the XGBoost model exhibiting the best performance.

Skeletal Muscle Index-Based Cachexia Index as a Predictor of Prognosis in Patients With Cancer: A Meta-Analysis and Systematic Review

CONTEXT

Cachexia is associated with poor survival rates. In the clinical setting, the diagnosis of cancer cachexia is challenging. The cachexia index (CXI), a new index for predicting survival time, is a promising tool for diagnosing cancer cachexia; however, its efficacy in predicting patient survival has not been validated.

OBJECTIVE

This meta-analysis and systematic review aimed to explore the CXI's prognostic value in patients with cancer.

DATA SOURCES

The PubMed, Embase, MEDLINE, and Cochrane Library databases were searched for relevant studies to determine the association between CXI findings and prognosis.

DATA EXTRACTION

The outcomes were overall survival (OS), progression-, disease-, and recurrence-free survival (PFS/DFS/RFS) rates, and the rate of complete response.

DATA ANALYSIS

The QUality In Prognostic Studies (QUIPS) tool was used to evaluate the quality of the included trials. This meta-analysis comprised 14 studies involving 2777 patients. A low CXI was associated with decreased OS (hazard ratio [HR] 2.34, 95% confidence interval [CI] 2.01-2.72; P < .001), PFS/DFS/RFS (HR 1.93, 95% CI 1.68-2.22; P < .001), and complete response (odds ratio [OR] 0.49, 95% CI 0.36-0.66; P < .001). Patients with a low CXI had a lower body mass index (mean difference [MD] -0.75, 95% CI -1.00 to 0.50; P < .001), skeletal muscle index (standardized MD -0.80, 95% CI -0.98 to -0.61; P < .001), and serum albumin level (MD -0.23, 95% CI -0.26 to -0.20; P < .001); and a higher neutrophil-lymphocyte ratio (MD 1.88, 95% CI 1.29-2.47; P < .001) and more advanced disease stages (OR 0.80, 95% CI 0.71-0.91; P = .001).

CONCLUSION

A low CXI was found to be associated with poor survival in patients with cancer. While the CXI is a promising marker for predicting cancer cachexia, further studies are required to verify its usefulness.

Sarcopenia as a Predictive Factor for Carboplatin Toxicity in Patients with Advanced Non-Small Cell Lung Cancer

Sarcopenia in cancer patients often negatively impacts various outcomes. Carboplatin, a first-line chemotherapy for non-small cell lung cancer (NSCLC), is dosed based on body weight, which doesn't account for sarcopenia. This study evaluated the association between sarcopenia and carboplatin-related toxicity in NSCLC patients. Patients with locally advanced or metastatic NSCLC treated with carboplatin were included. Toxicity events during the first two cycles of treatment were recorded. Sarcopenia was assessed using pretreatment computed tomography scans analyzed with Slice-O-Matic V4.2 software, defining sarcopenia as a skeletal muscle index (SMI) of <52.4 cm2/m2 for men and <38.5 cm2/m2 for women. Among 146 patients, 52% had sarcopenia. Hematological toxicity occurred in 71.2% of all patients and 77.6% of those with sarcopenia. The fat-free mass index (FFMI) was independently associated with hematological toxicity and dose-limiting toxicity (DLT), which was observed in 55.5% of patients. Sarcopenia significantly correlates with hematological toxicity and DLT during carboplatin treatment in NSCLC patients. Given its prevalence and noninvasive detection, further research is needed to understand its impact on treatment outcomes.

Ultrasound Muscle Evaluation for Predicting the Prognosis of Patients with Head and Neck Cancer: A Large-Scale and Multicenter Prospective Study

Head and neck cancer (HNC) is a prevalent and aggressive form of cancer with high mortality rates and significant implications for nutritional status. Accurate assessment of malnutrition in patients with HNC is crucial for optimizing treatment outcomes and improving survival rates. This study aimed to evaluate the use of ultrasound techniques for predicting nutritional status, malnutrition, and cancer outcomes in patients with HNC. A total of 494 patients with HNC were included in this cross-sectional observational study. Various tools and body composition measurements, including muscle mass and adipose tissue ultrasound evaluations, were implemented. Using regression models, we mainly found that high levels of RF-CSA (rectus femoris cross-sectional area) were associated with a decreased risk of malnutrition (as defined with GLIM criteria (OR = 0.81, 95% CI: 0.68-0.98); as defined with PG-SGA (OR = 0.78, 95% CI: 0.62-0.98)) and sarcopenia (OR = 0.64, 95% CI: 0.49-0.82) after being adjusted for age, sex, and BMI. To predict the importance of muscle mass ultrasound variables on the risk of mortality, a nomogram, a random forest, and decision tree models were conducted. RF-CSA was the most important variable under the random forest model. The obtained C-index for the nomogram was 0.704, and the Brier score was 16.8. With an RF-CSA < 2.7 (AUC of 0.653 (0.59-0.77)) as a split, the decision tree model classified up to 68% of patients as possessing a high probability of survival. According to the cut-off value of 2.7 cm2, patients with a low RF-CSA value lower than 2.7 cm2 had worse survival rates (p < 0.001). The findings of this study highlight the importance of implementing ultrasound tools, for accurate diagnoses and monitoring of malnutrition in patients with HNC. Adipose tissue ultrasound measurements were only weakly associated with malnutrition and not with sarcopenia, indicating that muscle mass is a more important indicator of overall health and nutritional status. These results have the potential to improve survival rates and quality of life by enabling early intervention and personalized nutritional management.