ABSTRACT
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.
ABSTRACT
Objective To determine the optimal electron beam energy at different field size through a Monte Carlo-based simulation of the therapy head of Varian X 6 MV linear accelerator so as to study the influence of radial intensity on depth dose.Methods Firstly,keeping the radial intensity unchanged for the field of interest while changing electron beam energy,compassion was carried out of calculated percentage depth doses between measured values.Thus,the optimal energy was identified for this field size.Then,the obtained energy was set the optimal value to study the radial intensity influence on the depth doses.Results The optimal electron energy for 4 cm ×4 cm,10 cm × 10 cm,20 cm × 20 cm and 30 cm × 30 cm field sizes was 5.9,6.0,6.3 and 6.4 MeV respectively.Changes in radial intensities resulted in negligible changes in percentage depth doses for4 cm ×4-cm and 10 cm × 10 cm fields,but led to observable discrepancy for 20 cm × 20 cm and 30 cm × 30 cm fields.Conclusions The optimal electron energies for different field sizes are slightly different.Change in radial intensity distribution has significant influence on the depth dose for large field.To improve simulation accuracy,the field size needs to be taken into consideration in determining the electron beam energy and radial intensity distribution.