ABSTRACT
Objective@#To design and implement an automatic independent check software (AutoReview) for the radiotherapy treatment plan and improve the efficiency of independent check.@*Methods@#The patient′s electronic treatment plan chart in the record and verify (R&V) system database accessed by the SQL language was read and parsed. Based on the treatment plan information obtained from the chart, AutoReview extracted all parameters related to the treatment plan from the treatment planning system and the R&V system, and saved the structured data into a local database. For different check items, corresponding check logic was designed and the program was written to realize the automatic independent check. In total, 664 clinical treatment plans for one month were selected as a sample to evaluate the clinical application effect.@*Results@#Compared with manual check, AutoReview improved the check efficiency by nearly 60 times and the problem detection rate was elevated by 19.2%. In the two-year clinical application, the software operation was stable, and 14509 treatment plans were automatically checked and identified 588 cases with problems, accounting for 4.05%.@*Conclusion@#AutoReview can effectively improve the efficiency and accuracy of independent check in radiotherapy treatment plan.
ABSTRACT
Independent check is one of the key measures of quality control (QC) in radiotherapy and is essential for the assurance of radiotherapy accuracy.In recent years,with the application of computer-aided system,the automaticity of independent check,as well as its accuracy and efficiency,increases.Meanwhile,QC has evolved from a device-centered to a process-centered program.Therefore,independent check has been increasingly systematic and specific.This review gives a brief summary of the implementation and development of independent check.
ABSTRACT
Objective To use clustering analysis to help physicians detect abnormal parameters in radiotherapy treatment plans and improve the efficiency of plan verification. Methods From 2010 to 2015, 835 breast cancer treatment plans for using 4?field hybrid intensity?modulated radiotherapy from MOSAIQ were collectted. Fractional dose, beam angle, and monitor unit were used as featured parameters of a treatment plan to generate a dataset. The K?means clustering algorithm based on principal component analysis was used to perform a clustering analysis of the dataset and divide the dataset into different clusters. The outliers of clusters were automatically detected based on the distance threshold. The outlier?contained treatment plans were manually verified by physicians to determine the accuracy of clustering analysis in detection of abnormal plans. Results In the clustering analysis, the sample space composed by parameters of treatment plans for breast cancer was divided into 4 clusters, 3 of which had outliers detected. In the targeted treatment plans, 3 plans became outliers because of special target volume and the other 4 plans needed improvement. Conclusions Clustering analysis is effective to help physicians to independently verify treatment plans.