Background:We developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).Methods: This retrospective study included 159 patients with PDAC (118 in the primary cohort and 41 in the validation cohort) who underwent preoperative contrast-enhanced computed tomography examination between 2012 and 2015. All patients underwent surgery and lymph node status was determined. A total of 2041 radiomics features were extracted from venous phase images in the primary cohort, and optimal features were extracted to construct a radiomics signature. A combined prediction model was built by incorporating the radiomics signature and clinical characteristics selected by using multivariable logistic regression. Clinical prediction models were generated and used to evaluate both cohorts. Results: Fifteen features were selected for constructing the radiomics signature based on the primary cohort. The combined prediction model for identifying preoperative lymph node metastasis reached a better discrimination power than the clinical prediction model, with an area under the curve of 0.944 vs. 0.666 in the primary cohort, and 0.912 vs. 0.713 in the validation cohort.Conclusions: This pilot study demonstrated that a noninvasive radiomics signature extracted from contrastenhanced computed tomography imaging can be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC.
Objectives To investigated the relationship between the neutrophil-to-lymphocyte ratio (NLR) and the severity of lung injury in corona virus disease 2019 (COVID-19) patients.Methods The clinical data, laboratory examination, and chest computed tomography (CT) findings of 167 patients with confirmed COVID-19 admitted to 5 hospitals in Chongqing, China from January 2020 to February 2020 were retrospectively reviewed. According to the diagnostic criteria sixth edition of the “Diagnosis and Treatment of New Coronavirus Pneumonitis” published by the China National Health Commission, the patients were stratified by the severity of their illness to 3 groups: mild (n = 17), moderate (n = 119), or severe (n = 31).Results Differences of the NLR among the three groups and between each of the groups were significant (all p < 0.001). The NLR and CT severity score were positively correlated (r = 0.823, p < 0.001). Receiver operating characteristic (ROC) curve analysis found that NLR had diagnostic and prognostic value in COVID-19 patients with either negative or positive CT results. The area under curve (AUC) was 0.819 (95% CI: 0.729-0.910, p < 0.001), the sensitivity was 61.3%, specificity was 94.1%, and the optimal NLR cutoff value was 3.634.Conclusion NLR reflected the degree of lung injury and predicted the progression of COVID-19. NLR is a low-cost, convenient, bedside alternative to chest CT scanning to indicate the severity of lung injury in patients with COVID-19, especially in relatively underdeveloped areas.
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