Pulmonary sarcomatoid carcinoma (PSC) is a group of five rare non-small cell lung cancer subtypes. In the present study, the clinical characteristics and outcomes of patients with PSC registered in the Surveillance, Epidemiology and End Results (SEER) database were investigated. For this purpose, data for patients with PSC (n=1,723) who received their initial diagnosis between 1988 and 2016 were collected from the SEER database. Survival analysis was performed using the Kaplan-Meier curves and the log-rank test. Subsequently, multivariate analyses with the Cox proportional hazards model were used to identify significant independent predictors. A nomogram model was established to predict survival performance using the concordance index (C-index). From the total cohort, patients with pulmonary blastoma demonstrated improved 1-year overall survival (OS) rate compared with other pathological types (P<0.001). The 2-year overall survival rates of the 'only radiotherapy' cohort and the 'no specific treatment' cohort were 9.1 and 5.4% (P<0.001), respectively. Radiotherapy significantly improved the OS rate in stage I-III patients with PSC (P<0.001) when stratified by stage. After matching the propensity scores, the 'surgery combined with radiotherapy' group comprised 156 patients and the 'surgery-only' group had 247 patients (1:1.6). However, no significant differences in prognosis were found between the 2 subgroups (P= 0.052). The multivariate Cox analysis demonstrated that older age (≥76 years old), male, unmarried, pathological type, larger tumor size (≥56 mm), later tumor node metastasis stages and treatment modalities were independent prognostic factors. A nomogram model was established to predict the survival of patients with PSC. This model incorporated the seven aforementioned independent prognostic factors (C-index for survival, 0.75; 95% confidence interval, 0.74-0.76). Radiotherapy needs to considered for stage I-III patients with PSC who undergo radiation therapy without surgical resection.

Purpose. Our aim is to conduct analysis and comparison of some methods commonly used to measure the volume of hematoma, for example, slice method, voxelization method, and 3D-Slicer software method (projection method). Method. In order to validate the accuracy of the slice method, voxelization method, and 3D-Slicer method, these three methods were first applied to measure two known volumetric models, respectively. Then, a total of 198 patients diagnosed with spontaneous intracerebral hemorrhage (ICH) were recruited. The patients were split into 3 different groups based on the hematoma size: group 1: volume<10 ml (n=89), group 2: volume between 10 and 20 ml (n=59), and group 3: volume>20 ml (n=50). And the shape of the hematoma was classed into regular (round to ellipsoid) with smooth margins (n=76), irregular with frayed margins (n=85), and multilobular (n=37). The slice method, voxelization method, and 3D-Slicer method were adopted to measure the volume of hematoma, respectively, considering the nonclosed models and the models which may contain inaccurate normal information during CT scan. Moreover, the results were compared with the 3D-Slicer method for closed models. Results. There was a significant estimation error (P<0.05) using these three methods to calculate the volume of the closed hematoma model. The estimated hematoma volume was calculated to be 14.2086743±0.900559087 ml, 14.2119130±0.900851812 ml, and 14.2123825±0.900835916 ml using slice method 1, slice method 2, and the voxelization method, respectively, compared to 14.212656±0.900992371 ml using the 3D-Slicer method. The mean estimation error was -0.00398172 ml, -0.00074303 ml, and -0.00027354 ml caused by slice method 1, slice method 2, and voxelization method, respectively. There was a significant estimation error (P<0.05), applying these three methods to calculate the volume of the nonclosed hematoma model. The estimated hematoma volume was calculated to be 14.1928246±0.902210314 ml using the 3D-Slicer method. The mean estimation error was calculated to be -0.00402121 ml, -0.00078237 ml, -0.00031288 ml, and -0.01983136 ml using slice method 1, slice method 2, voxelization method, and 3D-Slicer method, respectively. Conclusions. The 3D-Slicer software method is considered as a stable and capable method of high precision for the calculation of a closed hematoma model with correct normal direction, while it would be inappropriate for the nonclosed model nor the model with incorrect normal direction. The slice method and voxelization method can be the supplement and improvement of the 3D-Slicer software method, for the purpose of achieving precision medicine.

<abstract> <p>Enhancer is a non-coding DNA fragment that can be bound with proteins to activate transcription of a gene, hence play an important role in regulating gene expression. Enhancer identification is very challenging and more complicated than other genetic factors due to their position variation and free scattering. In addition, it has been proved that genetic variation in enhancers is related to human diseases. Therefore, identification of enhancers and their strength has important biological meaning. In this paper, a novel model named iEnhancer-MFGBDT is developed to identify enhancer and their strength by fusing multiple features and gradient boosting decision tree (GBDT). Multiple features include k-mer and reverse complement k-mer nucleotide composition based on DNA sequence, and second-order moving average, normalized Moreau-Broto auto-cross correlation and Moran auto-cross correlation based on dinucleotide physical structural property matrix. Then we use GBDT to select features and perform classification successively. The accuracies reach 78.67% and 66.04% for identifying enhancers and their strength on the benchmark dataset, respectively. Compared with other models, the results show that our model is useful and effective intelligent tool to identify enhancers and their strength, of which the datasets and source codes are available at https://github.com/shengli0201/iEnhancer-MFGBDT1.</p> </abstract>