Radiation therapy is one of the main treatment methods for cancer. Machine learning can be used in all aspects of clinical practice in radiation therapy, including clinical decision support, automatic segmentation of target volumes, prediction of treatment efficacy and side effects. Despite the challenges of lacking structured data and poor interpretability of models, the application of machine learning in radiotherapy will become increasingly profound and extensive. This review contains three aspects: introduction of machine learning, the clinical application of machine learning in radiotherapy, challenges and solutions.

Objective To investigate the relationship of serum ferritin(SF) and acute coronary syndrome(ACS) .Methods ACS group(n= 110) was divided into unstable angina(UA) subgroup(n= 46) ,non-ST-segment elevation myocardial infarction (NSTEMI) subgroup(n=30) and ST-segment elevation myocardial infarction(STEMI) subgroup(n=34) according to clinical da-ta ,coronary angiography and electrocardiogram examination .The normal coronary patients provided as control group(n=42) .The levels of serum ferritin ,hs-CRP and serum lipids were determined and the differences of SF among the groups were analyzed .The correlation of hs-CRP ,TG ,LDL-C ,HDL-C ,TC and SF were also analyzed .Results Compared with control group ,the level of SF in UA ,NSTEMI ,STEMI were significantly increased(P<0 .05) .Comparison in the levels of SF in NSTEMI ,STEMI group and UA group had significant difference(P<0 .05) .Correlation analysis indicated that the level of SF were positively correlated with hs-CRP ,TG and negatively associated with HDL-C .Conclusion The level of SF significantly increased in patients with ACS and has a positive relationship with hs-CRP and the disorder of lipid metablism .SF helps in assessing the clinical classification and risk strati-fication in patients with ACS .