Evaluation of the plan delivery accuracy of intensity-modulated radiation therapy by texture analysis using fluence maps

Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors

This study aims to evaluate the specific characteristics of various multileaf collimator (MLC) position errors that are correlated with the indices using dose distribution. The dose distribution was investigated using the gamma, structural similarity, and dosiomics indices. Cases from the American Association of Physicists in Medicine Task Group 119 were planned, and systematic and random MLC position errors were simulated. The indices were obtained from distribution maps and statistically significant indices were selected. The final model was determined when all values of the area under the curve, accuracy, precision, sensitivity, and specificity were higher than 0.8 (p < 0.05). The dose-volume histogram (DVH) relative percentage difference between the error-free and error datasets was examined to investigate clinical relations. Seven multivariate predictive models were finalized. The common significant dosiomics indices (GLCM Energy and GLRLM_LRHGE) can characterize the MLC position error. In addition, the finalized logistic regression model for MLC position error prediction showed excellent performance with AUC > 0.9. Furthermore, the results of the DVH were related to dosiomics analysis in that it reflects the characteristics of the MLC position error. It was also shown that dosiomics analysis could provide important information on localized dose-distribution differences in addition to DVH information.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

Define dose field to assess the modulation complexity of intensity-modulated radiation therapy

PURPOSE

Introduce a new concept of dose field to assess the modulation complexity (MC) of intensity-modulated radiation therapy (IMRT).

METHODS

A total of 91 IMRT plans for different diseases were retrospectively retrieved randomly from treatment database. The dose field of plans were calculated and feature values such as force magnitude and diversity were defined and extracted. Correlation analysis between these feature values and execution cost, delivery accuracy of plans was performed, to verify the validity of dose field in characterizing the MC.

RESULTS

The feature values of dose field in different disease own significant differences (p < 0.001). For correlation analysis, number of control point (CP) and cumulative perimeter of CP have the highest correlation with angle entropy (0.815 and 0.848 respectively), while the correlation between number of monitor units(MU), cumulative area of CP and force, force entropy is higher than others (0.797-0.909). However, complexity of CP shape is almost irrelevant to all the dose field features. The gamma passing rate and the dose field features shows a weak negative correlation trend.

CONCLUSIONS

Dose field can be used as a tool to assess the MC of IMRT.