Predicting passing rate for VMAT validation using machine learning based on plan complexity parameters / 中华放射医学与防护杂志

Objective:To establish a prediction model using the random forest (RF) and support vector machine (SVM) algorithms to achieve the numerical and classification predictions of the gamma passing rate (GPR) for volumetric arc intensity modulation (VMAT) validation.Methods:A total of 258 patients who received VMAT radiotherapy in the 1 st Affiliated Hospital of Wenzhou Medical University from April 2019 to August 2020 were retrospectively selected for patient-specific QA measurements, including 38 patients who received VMAT radiotherapy for head and neck, and 220 patients who received VMAT radiotherapy for chest and abdomen. Thirteen complexity parameters were extracted from the patient′s VMAT plans and the GPRs for VMAT validation under the analysis criteria of 3%/3 mm and 2%/2 mm were collected. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%) , and the complexity parameters for the numerical and classification predictions were screened using the RF and minimum redundancy maximum correlation (mRMR) method, respectively. Complexity models and mixed models were established using PTV volume, subfield width, and smoothness factors based on the RF and SVM algorithms individually. The prediction performance of the established models was analyzed and compared. Results:For the validation cohort, the GPR numerical prediction errors of the complexity models based on RF and SVM under the two analysis criteria are as follows. The root-mean-square errors (RMSEs) under the analysis criterion of 3%/3 mm were 1.788% and 1.753%, respectively; the RMSEs under the analysis criterion of 2%/2 mm were 5.895% and 5.444%, respectively; the mean absolute errors (MAEs) under the analysis criterion of 3%/3 mm were 1.415% and 1.334%, respectively, and the MAEs under the analysis criteria of 2%/2 mm were 4.644% and 4.255%, respectively. For the validation cohort, the GPR numerical prediction errors of the mixed models based on RF and SVM under the two analysis criteria were as follows. The RMSEs under the analysis criterion of 3%/3 mm were 1.760% and 1.815%, respectively; the RMSEs under the analysis criterion of 2%/2 mm were 5.693% and 5.590%, respectively; the MAEs under the analysis criterion of 3%/3 mm were 1.386% and 1.319%, respectively, and the MAEs under the analysis criteria of 2%/2 mm were 4.523% and 4.310, respectively. For the validation cohort, the AUC result of the GPR classification prediction of the complexity models based on RF and SVM were 0.790 and 0.793, respectively under the analysis criterion of 3%/3 mm and were 0.763 and 0.754, respectively under the analysis criterion of 2%/2 mm. For the validation cohort, the AUC result of the GPR classification prediction of the mixed models based on RF and SVM were 0.806 and 0.859, respectively under the analysis criterion of 3%/3 mm and were 0.796 and 0.796, respectively under the analysis criterion of 2%/2 mm cohort.Conclusions:Complexity models and mixed models were developed based on the RF and SVM method. Both types of models allow for the numerical and classification predictions of the GPRs of VMAT radiotherapy plans under analysis criteria of 3%/3 mm and 2%/2 mm. The mixed models have higher prediction accuracy than the complexity models.

Comparison of ArcCheck and film verification for VMAT in the treatment of nasopharyngeal carcinoma / 中国医疗器械杂志

To compare the performance of ArcCheck and film verification for volumetric intensity modulated arc therapy (VMAT) in the treatment of nasopharyngeal carcinoma, and to study the feasibility of ArcCheck in VMAT dosimetric verification. Five patients of nasopharyngeal carcinoma treated with VMAT were enrolled in this study. Dose verification was carried out by ArcCheck and film respectively. The result showed that there were no significant differences between ArcCheck and film verification. ArcCheck software can obtain three dimensional dose distribution directly with simple operation. It is convenient for ArcCheck to be used for VMAT dosimetric verification.

Intensify modulated radiotherapy (IMRT) for nasopharyngeal carcinoma / 中华放射肿瘤学杂志

Objective To evaluate the feasibility, toxicity and clinical efficacy of simultaneous modulated accelerated radiation therapy(SMART)for nasopharyngeal carcinoma. Methods Thirty eight patients with nasopharyngeal carcinoma were treated by SMART with 2.5?Gy/fraction at gross tumor volume(GTV)to a total dose of 70?Gy and 2.0?Gy/fraction at the clinical treatment volume(CTV)to a total dose of 56?Gy in 38 days. Quantitative 99m Tc pertechnetate salivary scintigraphy was used to assess the uptake and excretion index (EI、UI) of parotid gland in order to validate the value of IMRT in parotid gland sparing. Results The mean doses delivered to the GTV and CTV were 67.2?Gy and 57.0?Gy, respectively. An average of 1% of GTV and 2% of CTV received less than 90% or 95% of the prescribed dose. The mean dose to the contralateral parotid were 23?Gy and no significant decline in EI and UI as compared with significant decline in the ipsilateral parotid by 43.6% and 26.3%(P