Objective To evaluate the efficacy of three-dimensional conformal radiotherapy (3DCRT) and prognostic factors for stage Ⅲ non-small cell lung cancer (NSCLC).Methods From 2000 to 2010,474 patients with stage Ⅲ NSCLC undergoing 3DCRT were enrolled as subjects.Those patients,consisting of 382 males and 92 females,had a median age of 63 years.In those patients,211 had stage ⅢA NSCLC and 263 had stage ⅢB NSCLC;165 were treated with radiotherapy alone and 309 with chemoradiotherapy;55 were treated with conventional radiotherapy plus 3DCRT,340 with 3DCRT,and 79 with intensity-modulated radiotherapy;the median equivalent dose was 60 Gy (44-77 Gy).The Kaplan-Meier method,log-rank test,and Cox model were used for survival rate calculation,univariate analysis,and multivariate analysis,respectively.Results The follow-up rate was 96.6％.In all patients,the 1-,3-,and 5-year overall survival rates were 63.0％,24.9％,and 17.8％,respectively;the median survival time was 18 months.The univariate analysis showed that sex,age,immediate response,radiotherapy method,fractionation scheme,chemotherapy,and radiation pneumonitis (RP) were prognostic factors (P=0.004,0.001,0.000,0.007,0.004,0.009,0.049).The multivariate analysis showed that sex,age,immediate response,radiotherapy method,and RP were independent prognostic factors (P=0.006,0.000,0.000,0.003,0.048).Patients with radiation doses of 60-66 Gy had the best prognosis of all.Conclusions In patients with stage Ⅲ NSCLC undergoing 3DCRT,female patients,patients at a young age,patients with satisfactory immediate response,patients treated with full-course 3DCRT,and patients with grade 0-1 RP have better prognosis than others.3DCRT combined with chemotherapy improves survival in patients.A radiation dose of 60-66 Gy is recommended.

Objective:To explore the value of radiomics model based on intratumoral and peritumoral early dynamic contrast-enhanced (DCE) MRI for identifying benign and malignant in breast imaging reporting and data system (BI-RADS) 4 tumors.Methods:A total of 191 patients diagnosed with BI-RADS 4 breast tumors by breast MRI examination with clear pathological diagnosis from January 2016 to December 2020 in the First Affiliated Hospital of Bengbu Medical College were analyzed retrospectively, including 77 benign and 114 malignant cases, aged 23-68 (46±10) years. The one-slice image with the largest area of the lesion of the second stage DCE-MRI images was selected to outline the region of interest, and automatically conformal extrapolated by 5 mm to extract the intra-tumoral and peritumoral radiomics features. The included cases were randomly divided into training and testing cohorts in the ratio of 8∶2. The statistical and machine learning methods were used for feature dimensionality reduction and selection of optimal radiomics features, and logistic regression was used as the classifier to establish the intratumoral, peritumoral, and intratumoral combined with peritumoral radiomics models. The independent risk factors that could predict the benignity and malignancy of breast tumors were retained as clinical-radiological characteristics by univariate and multivariate logistic regression to establish a clinical-radiological model. Finally, the intratumoral and peritumoral radiomics features were combined with clinical-radiological features to develop a combined model of the three. The receiver operating curve was used to analyze the predictive performance of each model and calculate the area under the curve (AUC),the AUC was compared by DeLong test. The stability of the three-component combined diagnostic model was tested by 10-fold cross-validation, and the model was visualized by plotting nomogram and calibration curves.Results:In the training cohort, the AUC of the three-component combined model for identifying benign and malignant BI-RADS 4 breast tumors was significantly higher than that of the intratumoral radiomics model ( Z=3.38, P<0.001), the peritumoral radiomics model ( Z=4.01, P<0.001), the intratumoral combined with peritumoral radiomics model ( Z=3.11, P=0.002), and the clinical-radiological model ( Z=3.24, P=0.001). And the AUC, sensitivity, specificity, accuracy, and F1-score of the three-component combined model were 0.932, 91.2%, 86.9%, 87.0% and 0.89, respectively. In the testing cohort, the three-component combined model also had the highest AUC value (0.875), and diagnostic sensitivity, specificity, accuracy and malignancy F1-score were 95.7%, 62.5%, 76.9%, and 0.89, respectively. The AUC calculated by 10-fold cross-validation was 0.90 (0.85-0.92), and the predicted curve of the three-component combined model in the calibration curve was in good agreement with the ideal curve. Conclusion:The three-component combined diagnostic model based on the intratumoral and peritumoral radiomics features and clinical-radiological features of early DCE-MRI has good performance and stability for identifying the benign and malignant in BI-RADS 4 breast tumors, and it can provide guidance for clinical decision non-invasively.