Objective To compare the dosimetric effects of micro-multileaf collimator (MLC)(2 mm leaf width) and conventional MLC (10 mm leaf width) on inverse intensity-modulated radiotherapy(IMRT) in intracranial stereotactic radiosurgery(SRS). Methods In view of the fact that the micro-MLC has a small open field,30 patients with intracranial tumor with a<10 cm diameter were enrolled in this study. Their inverse dynamic IMRT plans were established using conventional MLC (conventional group) and micro-MLC (micro group) with the same other conditions. The radiation doses to the target volume and the organs at risk (OAR) were compared between the two groups with t test. Results Compared with the conventional group, the micro group had a significantly better dose distribution in the target volume (P=0.019). However, there were no significant differences in D98,D95,D50,and D3between the two groups (P=0.774,0.650,0.170,0.080). The micro group had a 58.7% lower mean homogeneity index and a 20.1% higher mean conformity index than the conventional group (P=0.000). The micro group had significantly lower radiation doses to OAR than the conventional group (P=0.044). The mean Dmeanand Dmaxof the brain stem in the micro group were 10.0% and 8.2%,respectively,lower than those in the conventional group (P=0.768,0.753). The mean Dmeanand Dmax of the right eye and left eye in the micro group were 16.5%,19.3%,21.4%,and 13.4%,respectively,lower than those in the conventional group (P=0.572,0.775 and 0.734,0.630). The mean Dmaxof the left lens, right lens, left optic nerve, right optic nerve, and optic chiasm in the micro group were 50.4%, 24.1%, 38.5%, 27.8%, and 5.7%, respectively, lower than those in the conventional group (P=0.172,0.467, 0.521,0.740,0.899). The PRV100,PRV50,and PRV25of the normal tissue in the micro group were no more than those in the conventional group(P=0.839,0.832,0.972). Conclusions In inverse IMRT in intracranial SRS,micro-MLC is better than conventional MLC because it can improve CI of the target volume and reduce the radiation doses to OAR.

Background::Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.Methods::The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.Results::The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.Conclusions::We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.