We evaluated the efficacy of treating Kawasaki disease earlier than Day 5 of illness with a standard dose of immunoglobulin and aspirin. We performed a case-control study of patients with Kawasaki disease admitted to Princess Margaret Hospital from 1994 to 1999. Patients with pretreatment coronary aneurysm or those treated after day 10 of illness were excluded. All patients received immunoglobulin (2 g/kg) and aspirin (80-100 mg/kg/day) until fever subsided for 48 hours. Immunoglobulin retreatment was given for persistent fever 48 hours after the first dose of immunoglobulin or recrudescent fever. The case group consisted of 15 patients who received treatment earlier than day 5 of illness, and the control group consisted of 66 patients who were treated on or after day 5. Patients' sex, age, duration of posttreatment fever, need for additional immunoglobulin, and coronary artery status were noted. Treatment efficacy was assessed by the duration of posttreatment fever and the prevalence of coronary artery aneurysms. Eighty-one patients were included in this study. There were 15 patients in the case group and 66 in the control group. No significant difference was noted in age and sex between the case and control groups. Thirty-three percent (5/15) and 8% (5/66) of the case and control groups, respectively, had persistent/ recrudescent fever 48 hours after the first dose of immunoglobulin that required retreatment ( p = 0.017). Thirteen percent (2/15) and 5% (3/66) of the case and control groups, respectively, had coronary aneurysms ( p = 0.158). Treatment of Kawasaki disease before day 5 of illness was associated with persistent/recrudescent fever that required retreatment. However, there was no significant increase in the prevalence of coronary aneurysm if retreatment was given.

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Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model.

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