Objective:To study the dosimetric characteristics of the Avoidance Sectors function in the Eclipse planning system in the design of volumetric intensity-modulated arc radiotherapy(VMAT)plan for non-small cell lung cancer(NSCLC).Methods:The case data of 20 NSCLC patients who admitted to Hunan Provincial Cancer Hospital in 2024 were retrospectively selected,both free avoidance sectors volumetric(FASV)modulated arc therapy and avoidance sectors volumetric(ASV)modulated arc therapy were formulated for all patients.The dose parameters and the total number of machine unit(MU)of accelerator of the target area and organs at risk between two kinds of plans were compared.The correlation between the ratio(VPTV/Vlung)of planning target volume(PTV)to whole lung volume and the exposure dosimetry index of whole lung were further analyzed.Results:Comparative result between FASV plan and ASV plan indicated that the conformity index(CI)of the FASV plan was(0.83±0.07),which was better than that of the ASV plan(0.78±0.06),and the difference was significant(t=2.086,P<0.05).The V5 value(48.35±7.28)%of the whole lung in the ASV plan was significantly lower than that(57.68±6.63)%in the FASV plan(t=3.670,P<0.05).There were not statistically significant differences in the V20,V30 and mean dose(Dmean)of the whole lung and heart between two kinds of plans(P>0.05).There were not statistically significant differences in the maximum doses of the spinal cord and esophagus between two kinds of plans(P>0.05).The total MU value(444.8±78.9)of the ASV plan was less than that(518.27±70.9)of the FASV plan(t=2.682,P<0.05).The results of Pearson correlation analysis indicated that the correlative values between VPTV/Vlung and V20,V30,and Dmean of whole lung were respectively r=0.756,0.697,and 0.732 in FASV plan(P<0.05).Conclusion:For NSCLC patients,the ASV plan can effectively reduce V5 value of lung tissue,and decrease the total MU value of accelerator's irradiation.The avoidance sectors function is worthy of recommendation.In FASV plan,the VPTV/Vlung value appeared a positive correlation with whole lung V20,V30,and Dmean.It is very necessary to adopt individual management strategies for respiratory motion in reducing the irradiation range of target area.

Objective:To study the dosimetric characteristics of the Avoidance Sectors function in the Eclipse planning system in the design of volumetric intensity-modulated arc radiotherapy(VMAT)plan for non-small cell lung cancer(NSCLC).Methods:The case data of 20 NSCLC patients who admitted to Hunan Provincial Cancer Hospital in 2024 were retrospectively selected,both free avoidance sectors volumetric(FASV)modulated arc therapy and avoidance sectors volumetric(ASV)modulated arc therapy were formulated for all patients.The dose parameters and the total number of machine unit(MU)of accelerator of the target area and organs at risk between two kinds of plans were compared.The correlation between the ratio(VPTV/Vlung)of planning target volume(PTV)to whole lung volume and the exposure dosimetry index of whole lung were further analyzed.Results:Comparative result between FASV plan and ASV plan indicated that the conformity index(CI)of the FASV plan was(0.83±0.07),which was better than that of the ASV plan(0.78±0.06),and the difference was significant(t=2.086,P<0.05).The V5 value(48.35±7.28)%of the whole lung in the ASV plan was significantly lower than that(57.68±6.63)%in the FASV plan(t=3.670,P<0.05).There were not statistically significant differences in the V20,V30 and mean dose(Dmean)of the whole lung and heart between two kinds of plans(P>0.05).There were not statistically significant differences in the maximum doses of the spinal cord and esophagus between two kinds of plans(P>0.05).The total MU value(444.8±78.9)of the ASV plan was less than that(518.27±70.9)of the FASV plan(t=2.682,P<0.05).The results of Pearson correlation analysis indicated that the correlative values between VPTV/Vlung and V20,V30,and Dmean of whole lung were respectively r=0.756,0.697,and 0.732 in FASV plan(P<0.05).Conclusion:For NSCLC patients,the ASV plan can effectively reduce V5 value of lung tissue,and decrease the total MU value of accelerator's irradiation.The avoidance sectors function is worthy of recommendation.In FASV plan,the VPTV/Vlung value appeared a positive correlation with whole lung V20,V30,and Dmean.It is very necessary to adopt individual management strategies for respiratory motion in reducing the irradiation range of target area.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.