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Rationale:Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disease identified with diffusion-weighted imaging (DWI) high-intensity signal in magnetic resonance imaging (MRI). The disappearance of the abnormal signal is extremely rare.Patient concerns:We present the 2 cases of patients, both of them were suffering from heterogeneous symptoms. We followed up one of them for 7 years with MRI, the other accepted comprehensive MRI inspections.Diagnoses:DWI high-intensity signal were observed along the corticomedullary junction in MRI plan scan of heads of 2 patients. For patient 1, the hyperintensities in DWI and fluid-attenuated inversion recovery (FLAIR) images in the occipital lobe disappeared 5 years after onset. Based on the biopsy, patient 1 and 2 were diagnosed as NIID.Interventions:There have not effective medication and prevention for NIID. Patient 1 and 2 received symptomatic treatment.Outcomes:Up until now, the patients are alive but the disease is progressing.Lessons:DWI high-intensity signal is a strong clue for the diagnosis of NIID, but the rare case of the disappearance of it may lead to misdiagnosis.

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Palliative care might be beneficial to heart failure. However, the results remain controversial. We conducted a systematic review and meta-analysis to explore the effect of palliative care on heart failure.PubMed, Embase, Web of Science, EBSCO, and Cochrane library databases were systematically searched. Randomized controlled trials (RCTs) assessing the effect of palliative care versus usual care on heart failure were included. Two investigators independently searched articles, extracted data, and assessed the quality of included studies. The primary outcome was readmission. Meta-analysis was performed using random-effect model.Five RCTs involving 545 patients were included in the meta-analysis. Overall, compared with control intervention, palliative care intervention was found to significantly reduce the readmission [Std. mean difference = 0.79; 95% confidence intervals (CI) = 0.23 to 1.35; P = 0.006], Edmonton Symptom Assessment Scale (ESAS) (Std. mean difference = -2.5; 95% CI = -4.39 to -0.62; P = 0.009), and PHQ-9 (Std. mean difference = -1.16; 95% CI = -1.73 to -0.58; P < 0.005), as well as improve heart failure questionnaire (Std. mean difference = 4.46; 95% CI = 3.44 to 5.47; P < 0.005), but had no influence on mortality (RR = 1.54; 95% CI = 0.80 to 2.96; P = 0.19) and quality of life questionnaire (Std. mean difference = 1.81; 95% CI = -0.14 to 3.77; P = 0.07).Compared with control intervention, palliative care intervention was found to significantly reduce readmission, ESAS, PHQ-9, and improve heart failure questionnaire, but showed no influence on mortality and quality of life questionnaire in patients with heart failure.

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.