Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.
Objective To investigate the correlation between the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), systemic immune inflammation index (SII) and clinicopathological characteristics and prognosis in patients with gastrointestinal stromal tumor (GIST). Methods The clinicopathological data and blood routine results of 101 patients with GIST who were treated surgically in the General Hospital Western Theater Command PLA from December 2014 to December 2018 were collected retrospectively, samples were obtained to calculate NLR, PLR and SII. The optimal cutoff value of NLR, PLR and SII were evaluated by receiver operating characteristic (ROC) curve. The Chi-square test and t-test were used to analyze the relationship between NLR, PLR, SII and clinicopathological characteristics of GIST. The Kaplan-Meier plots and the log-rank test were used to analyze the influence factors affecting the recurrence-free survival (RFS) of patients with GIST. Multivariate Cox regression analyses was used to identify the independent influence factors affecting the RFS of patients with GIST. Results The preoperative peripheral blood NLR, PLR and SII of patients with GIST were correlated with the tumor site, tumor diameter and modified NIH risk stratification (P<0.05), but not with the mitotic count of tumor cells (P>0.05). Kaplan-Meier plots and log-rank test showed that NLR, PLR, SII, surgical method, tumor site, tumor diameter, mitosis rate and modified NIH risk stratification were the influential factors of RFS in with GIST. The multivariate Cox regression analysis revealed that postoperative whether to accept regular imatinib adjuvant therapy (HR=32.876, P<0.001), modified NIH risk stratification (HR=129.182, P<0.001), and PLR (HR=5.719, P=0.028) were independent influence factors affecting the RFS of patients with GIST. Conclusions Preoperative peripheral blood PLR, NLR, and SII are correlated with clinicopathological characteristics such as the tumor location, tumor diameter and modified NIH risk stratification, and are the influencing factors of postoperative RFS in patients with GIST. PLR is an independent predictor of RFS in patients with GIST.