RESUMO
Objective:To investigate the value of a preoperatively MRI-based deep learning (DL) radiomics machine learning model to distinguish low-grade and high-grade soft tissue sarcomas (STS).Methods:From November 2007 to May 2019, 151 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled as training sets, and 131 patients in the Affiliated Hospital of Shandong First Medical University and the Third Hospital of Hebei Medical University were enrolled as external validation sets. According to the French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) system, 161 patients with FNCLCC grades Ⅰ and Ⅱ were defined as low-grade and 121 patients with grade Ⅲ were defined as high-grade. The hand-crafted radiomic (HCR) and DL radiomic features of the lesions were extracted respectively. Based on HCR features, DL features, and HCR-DL combined features, respectively, three machine-learning models were established by decision tree, logistic regression, and support vector machine (SVM) classifiers. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each machine learning model and choose the best one. The univariate and multivariate logistic regression were used to establish a clinical-imaging factors model based on demographics and MRI findings. The nomogram was established by combining the optimal radiomics model and the clinical-imaging model. The AUC was used to evaluate the performance of each model and the DeLong test was used for comparison of AUC between every two models. The Kaplan-Meier survival curve and log-rank test were used to evaluate the performance of the optimal machine learning model in the risk stratification of progression free survival (PFS) in STS patients.Results:The SVM radiomics model based on HCR-DL combined features had the optimal predicting power with AUC values of 0.931(95%CI 0.889-0.973) in the training set and 0.951 (95%CI 0.904-0.997) in the validation set. The AUC values of the clinical-imaging model were 0.795 (95%CI 0.724-0.867) and 0.615 (95%CI 0.510-0.720), and of the nomogram was 0.875 (95%CI 0.818-0.932) and 0.786 (95%CI 0.701-0.872) in the training and validation sets, respectively. In validation set, the performance of SVM radiomics model was better than those of the nomogram and clinical-imaging models ( Z=3.16, 6.07; P=0.002,<0.001). Using the optimal radiomics model, there was statistically significant in PFS between the high and low risk groups of STS patients (training sets: χ2=43.50, P<0.001; validation sets: χ2=70.50, P<0.001). Conclusion:Preoperative MRI-based DL radiomics machine learning model has accurate prediction performance in differentiating the histopathological grading of STS. The SVM radiomics model based on HCR-DL combined features has the optimal predicting power and was expected to undergo risk stratification of prognosis in STS patients.
RESUMO
ObjectiveTo construct ompW- and ompW+ mutants of Salmonella paratyphi A with λRed system,and then study the function of the gene preliminarily.Methods Homologous regions were amplified from the genome Salmonella paratyphi A 50973,and then connect with kana fragment from plasmid pET22b-kan to construct a recombinant vector.The resultant fragments were amplified and transferred into 50973 with the help of λRed system after its concentration.Then the ompW- mutant was obtained PCR identification.Connect the recombinase expression plasmid pACU184 with full fragment of ompW regulatory region and coding region,then transfer the connection product into the mutant,the ompW+ mutant was obtained after double digest identification.Full cells of the wild,ompW- and ompW+ mutants were samples for SDS-PAGE and Western blot to detect the expression of protein OmpW.Biochemical identification of wild strain and mutant strains was conducted,so did the growth curves of the wild and the ompW- mutant.Choose BALB/c mice as a model to determine median lethal dose LD50 of the wild and mutant strains in order to observe the correlation between ompW gene and bacterial virulence.ResultsompW gene was knocked out in Salmonella paratyphi A 50973,also the ompW+ mutant was constructed; The wild and ompW+ mutant express the protein OmpW,while the ompW- mutant lost the protein.Each of the wild and mutant strains was Salmonella paratyphi A,and no obvious difference could be observed for their growth curves.LD50 for each strain was also similar.Conclusion The ompW gene has no correlation with the virulence in S.paratyphi A 50973,but the contribution of the mutants made an important foundation for the further study of functions of the gene ompW in Salmonella paratyphi.
