Objective To evaluate the effects of 3-dimensional conformal radiation therapy(3DCRT) in form of local control and survival of rectal cancer patients. Methods Forty-two patients with rectal cancer were irradiation by 3DCRT. They first received 40 Gy with larger field, at 1.8-2.0 Gy/f, 1 fraction qd, then followed by a boost of 24-27 Gy with reduced field, at 3.0-4.0 Gy/f, 1 fraction qod, to a total dose of 0,64-67 Gy. Results The 1-,2-,3-year survival rates were 83.3% ,64.3% and 45.2% .The 1-,2-,3-year local recurrence rates were 2.4%,11.9% and 23.9%. Conclusion Three-dimensional conformal radiotherapy is able to prolong the survival and improve the life quality of patients with rectal cancer.

Objective:To investigate the value of radiomics nomogram based on standardized pre-treatment chest enhanced CT in predicting the mutation status of epidermal growth factor receptor (EGFR) for patients with lung adenocarcinoma.Methods:A retrospective analysis was conducted on pre-treatment chest enhanced CT images and clinical data of 262 patients from the affiliated hospital of Jining Medical University with pathologically proven primary lung adenocarcinoma who received EGFR gene testing, including EGFR wild type ( n=122) and mutant type ( n=140). The patients were divided into training group ( n=183) and testing group ( n=79) according to a ratio of 7∶3 by stratified sampling method. Standardized pre-processed the images, delineated the ROI and extracted the radiomics features. Least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimension and select key features. The standardized radiomics model, clinical model and the combined model were established by Logistic Regression (LR) machine learning method. Calculated the Rad-score and drew the nomogram. ROC curve and Delong were used to evaluate and compare the predictive performance of different models. Results:23 standardized enhanced CT radiomics features and 4 clinical features were selected. The predictive performance of standardized radiomics model was better than that of non-standardized radiomics model [area under curve (AUC): 0.863 vs. 0.805, t=2.19, P<0.05]. The AUCs of the combined model and standardized radiomics model were higher than that of the clinical model (training group: 0.885, 0.863 vs. 0.774, t=3.57, 2.17, P<0.05; testing group: 0.873, 0.829 vs. 0.763, t=2.19, 2.02, P<0.05). The radiomics nomogram was built based on Rad-score, age, sex, smoking history and BMI. Conclusions:The combined model and standardized radiomics model could effectively predict the mutation status of EGFR gene in lung adenocarcinoma patients before treatment, providing valuable clinical insights.