Objective:To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC).Methods:This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models.Results:After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95% CI: 0.952-1.000) and 0.961 (95% CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95% CI: 0.893-0.959), 0.938 (95% CI: 0.876-1.000), respectively. Conclusions:The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR.

AIM: To establish a ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method to determination the plasma concentration of clozapine and compare the bioequivalence of a generic clozapine tablet with Clozaril

Lamin B1 is one of the essential members of the nuclear lamina protein family. Its main function is to maintain the integrity of nuclear skeleton, as well as to participate in the cell proliferation and aging by affecting the chromosome distribution. gene expression, and DNA damage repair. The abnormal expression of lamin B1 is related to certain diseases, including neurological diseases [e.g. neural tube defects (NDTs), adult-onset autosomal dominant leukodystrophy (ADLD)] and tumors (e.g. pancreatic cancer). It is also a potential tumor marker as well as drug target. Further research on lamin B1 will help people understand the molecular mechanism of the emergence and development of neural system diseases and tumors, and define a new future in drug target.
Cell Nucleus ; Gene Expression ; Humans ; Lamin Type B ; physiology ; Neoplasms ; Nervous System Diseases