Identifying sarcopenia in advanced non‐small cell lung cancer patients using skeletal muscle CT radiomics and machine learning

Abstract

Background Sarcopenia has been confirmed as a poor prognostic indicator of lung cancer. However, the lack of abdominal computed tomography (CT) hindered the application to assess the status of sarcopenia. The purpose of this study was to assess the ability of chest CT radiomics combined with machine learning classifiers to identify sarcopenia in advanced non‐small cell lung cancer (NSCLC) patients. Methods This study retrospectively analyzed CT images of 99 patients with NSCLC. Skeletal muscle radiomics were extracted from a single axial slice of the chest CT scan at the 12th thoracic vertebrae level. In total, 854 radiomic and clinical features were obtained from each patient. Feature selection was conducted with FeatureSelector module, optimal key features were fed into the lightGBM classifier for model construction, and Bayesian optimization was adopted to tune hyperparameters. The model's performance was evaluated by specificity, sensitivity, accuracy, precision, F1‐score, Matthew's correlation coefficient (MCC), Cohen's kappa coefficient (Kappa), and AUC. Results A total of 40 patients were found to have sarcopenia. Five optimal features were selected. In the base lightGBM model, the specificity, sensitivity, accuracy, precision, F1‐score, AUC, MCC, Kappa of validation set were 0.889, 0.750, 0.833, 0.818, 0.783, 0.819, 0.649, 0.648, respectively. After Bayesian hyperparameter tuning, the optimized lightGBM model achieved better prediction performance, and the corresponding values were 0.944, 0.833, 0.900, 0.909, 0.870, 0.889, 0.791, 0.789, respectively. Conclusions Chest CT‐based radiomics has the potential to identify sarcopenia in NSCLC patients with the lightGBM classifier, and the optimal lightGBM model via Bayesian hyperparameter tuning demonstrated better performance. Key points Significant findings of the study Our study demonstrates that chest CT‐based radiomics combined with lightGBM classifier has the ability to identify sarcopenia in NSCLC patients. What this study adds Skeletal muscle radiomics would be a potential biomarker for sarcopenia identity in NSCLC patients.

Sarcopenia: imaging assessment and clinical application

“…The data generated by software may be independent to the interobserver variability in sarcopenia quantification. Dong et al analysed CT images of 99 patients with advanced non-small cell lung cancer, with 40 of them having been identified as sarcopenic using skeletal muscle CSA at the L3 vertebral level; the authors extracted 854 radiomic and clinical features from the skeletal muscle area at the 12th thoracic vertebra level and five optimal features were selected [ 98 ]. An automated muscle measurement on a single-slice chest CT at T12 vertebral level was used by Lenchik et al to determine a relationship between muscle measurement with the rate of survival in a large patient population of 6.803 men and 4.558 women.…”

Prediction of Renal Damage in Children with Henoch-Schönlein Purpura Based on Machine Learning

“…Missing values may cause confusion in model tting, and the output values are unreliable, and features with missing values exceeding 60% contain little availability information, so they are deleted directly. Because some features in the data have outliers, in order to avoid affecting the overall effect of the model, the remaining missing values are lled with median [10] .…”

A deep look into radiomics

“…It is also relevant to mention the use of Artificial Intelligence and radiomics in sarcopenia evaluation [82]. In particular, a recent study revealed that chest CT radiomics combined with machine learning classifiers allows to identify sarcopenia in advanced non-small cell lung cancer patients, by using skeletal muscle radiomics as a potential biomarker for sarcopenia identity [83]. The last application field that is worth mentioning is the gastrointestinal application.…”