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

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.

A voxel-based diffusion kurtosis imaging study of whole-brain in alcohol dependent patients / 中华放射学杂志

Objective To study the changes of micro structure of white matter and gray matter in alcohol dependent patients by using diffusion kurtosis imaging(DKI) based on the method of voxel-based analysis.Methods Thirty alcohol dependent individuals and twenty healthy control volunteers,matched in gender, age, handedness and education, were enrolled as the alcohol dependent group and control group from September 2016 to June 2017.Michigan alcoholism screening test(MAST)was done for all subjects.All the subjects underwent DKI data acquisition by MR scanning. The relevant parameters were obtained by DKE software, including fractional anisotropy(FA), mean diffusivity(MD), axial diffusivity(AD), radial diffusivity(RD), mean kurtosis(MK), axial kurtosis(AK), radial kurtosis(RK), FA of kurtosis(FAK). Independent sample t test was performed to evaluate the significant difference of DKI parameters of two groups,meanwhile,the correlation analysis was conducted in DKI parameter values of different brain regions and MAST scores. Results Compared with the healthy control group, the FA value, MK value and RK value were decreased while the RD value was increased in alcohol dependence group, and there was significant difference between the two groups respectively(P<0.001). There was no significant difference between the two groups in the other parameters (AD, MD, AK, FAK). Compared with the healthy control group,the FA values of left lingual gyrus(164 voxels,t=-5.582)and left hippocampus(38 voxels,t=-3.664) increased;the MK value of left cerebellum posterior lobe(71 voxels,t=-4.004)reduced;the RK value of left cerebellum posterior lobe(67 voxels, t=-4.174), left middle cingulum(32 voxels, t=-3.925), left superior parietal gyrus(36 voxels,t=-3.812)reduced;and the RD value of the left inferior parietal gyrus(31 voxels,t=3.731)increased in alcohol dependence group.There was no correlation between MAST score and the value of DKI parameters. Conclusions There are dominant areas of brain injury in patients with alcohol dependence. The DKI parameters can reflect the changes of the whole brain microstructure of alcohol dependent patients and provide imaging basis in the diagnosis of alcohol dependence.