RESUMEN
Objective:To effectively quantify and evaluate the quality of different deformation registration algorithms, in order to enhance the possibility of implementing deformation registration in clinical practice.Methods:The Jacobian determinant mean (JDM) is proposed based on the Jacobian determinant (JD) of displacement vector field (DVF), and the Jacobian determinant error (DJDE) is introduced by incorporating the JD of the inverse DVF. The optical flow method (OF-DIR) and fast demons method with elastic regularization (FD-DIR) were tested on nasopharyngeal and lung cancer datasets. Finally, JDM and DJDE with the Jacobian determinant negative percentage (JDNP), inverse consistency error (ICE) and normalized mean square error (NMSE) were used to evaluate the registration algorithms and compare the differences evaluation indicators in different tumor images and different algorithms, and the receiver operating curve (ROC) was analyzed in evaluation.Results:In lung cancer, OF-DIR outperformed FD-DIR in terms of JDM, NMSE, DJDE and ICE, and the difference was statistically significant( z = -2.24, -4.84, t = 4.01, 6.54, P<0.05). In nasopharyngeal carcinoma, DJDE, ICE and NMSE of OF-DIR were superior to FD-DIR, and the difference was statistically significant ( t = 4.46, -7.49, z = -2.22, P<0.05), but there was no significant difference in JDM ( P>0.05). In lung cancer and nasopharyngeal carcinoma, JDNP of OF-DIR was worse than that of FD-DIR, and the difference was statistically significant ( z = -4.29, -4.02, P<0.01). In addition, DJDE is more specific and sensitive on ROC curve (AUC=0.77), and has different performance result for tumor images at different sites. Conclusions:The JDM and DJDE evaluation metrics proposed are effective for deformation registration algorithms. OF-DIR is suitable for both lung cancer and nasopharyngeal carcinoma, while the influence of organ motion on the registration effect should be considered when using FD-DIR.
RESUMEN
Objective:To analyze the dosimetric differences of volumetric modulated arc therapy (VMAT) plans for lung cancer caused by different dose calculation algorithms and radiation field settings and thus to provide a reference for designing clinical VMAT plans for lung cancer.Methods:This study randomly selected 20 patients with lung cancer and divided them into four groups of VMAT plans, namely, a group adopting two fields and two arcs based on the AAA algorithm (2F2A_AAA), a group employing two fields and two arcs based on the AXB algorithm (2F2A_AXB), a group using two fields and two arcs based on the MC algorithm (2F2A_MC), and a group adopting one field and two arcs based on the MC algorithm (1F2A_MC). Then, this study evaluated the target coverage, high-dose control, dose homogeneity index (HI), conformity index (CI), and organs at risk (OARs) of the plans using different algorithms and radiation field settings.Results:The planning target volume (PTV) results of two fields combined with two arcs (2F2A) of three groups using different algorithms are as follows. 2F2A_MC achieved better results in both D1% and V 95% (the relative volume of the target volume surrounded by 95% of the prescribed dose) of planning gross target volume (PGTV) than 2F2A_AAA (D1%: t=-2.44, P=0.03; V95%:z=-2.04, P=0.04) and 2F2A_AXB (D1%: t=2.34, P=0.03; z=-3.21, P < 0.01). 2F2A_AXB outperformed 2F2A_AAA ( z=-3.66, P < 0.01) and was comparable to 2F2A_MC in terms of the CI of PGTV. Regarding OARs, 2F2A_AXB and 2F2A_MC decreased the V5 Gy of the whole lung by 0.68% ( z=-2.69, P=0.01) and 3.05% ( z=-3.52, P < 0.01), respectively compared to 2F2A_AAA. 2F2A_AXB achieved a whole-lung Dmean of 1776.44 cGy, which was superior to that of 2F2A_MC ( t=2.67, P=0.02) and 2F2A_AAA ( t=8.62, P < 0.01). Compared to 2F2A_AAA and 2F2A_MC, 2F2A_AXB decreased the V20 Gy of Body_5 mm by 1.45% ( z=-3.88, P < 0.01) and 2.01% ( z=-3.66, P < 0.01), respectively. The results of the two groups with different field settings showed that 1F2A_MC was superior to 2F2A_MC in both the CI of PTV1 and the HI of PTV2 (CI: t=2.61, P=0.02; HI: z=-2.20, P=0.03). Moreover, 1F2A_MC increased the Dmean of the whole lung by 26.29 cGy compared to 2F2A_MC ( t=2.28, P=0.04). Conclusions:Regarding the design of VMAT plans for lung cancer, the MC algorithm is suitable for the target priority and the AXB algorithm is suitable for the OAR priority. When only the MC algorithm is available, it is recommended to choose 1F2A in the case of target priority and select 2F2A in the case of OAR priority.
RESUMEN
Objective:To standardize the naming of organ at risk (OAR) and target area during cervical cancer radiotherapy based on AAPM TG-263.Methods:After self-programming of Matlab software to implement the reading and resolution of radiotherapy structure files, the naming of each substructure was automatically output, recorded and restored. After naming all substructures, the structure names were classified by keywords. According to TG-263, a standard naming conversion table of OAR and target area was developed, and the classified structure names were standardized through procedures. Finally, the standardized named radiotherapy structure files were output and imported into the treatment planning system (TPS).Results:The radiation structure of 144 patients with cervical cancer was successfully transformed and displayed correctly in TPS. Before the transformation, the naming of OAR and target area lacked of uniform norms and standards, and the naming of the same structure significantly differed. After the transformation, 43 naming methods of OAR and 74 naming methods of the target area were unified into 20 and 8 naming methods, which were more convenient for staff understanding and communication.Conclusion:The standardization of cervical cancer radiotherapy structure naming can reduce the inconsistency of naming and provide reference for the standardized naming of pelvic tumors.
RESUMEN
Objective:Based on the AAPM TG-263, a Content-Based Standardizing Nomenclatures (CBSN) was proposed to explore the feasibility of its standardization verification for organs at risk (OAR) of nasopharyngeal carcinoma (NPC).Methods:The radiotherapy structure files of 855 patients with nasopharyngeal carcinoma (NPC) receiving intensity-modulated radiotherapy (IMRT) from 2017 to 2019(15 of whom showed clinical anomalous structures) were retrospectively collected and processed. The Matlab self-developed software was used to obtain the image position, geometric features, first-order gray histogram, and the Gray-level Co-occurrence Matrix′s texture features of the OAR contour outlined by the doctor to establish the CBSN Location Verification model and CBSN Knowledge Library. Fisher discriminant analysis was employed to establish a CBSN OAR classification model, which was evaluated using self-validation, cross-validation, and external validation, respectively.Results:99%(69/70) of the simulated anomalous structures were outside the 90% reference range of the CBSN Knowledge Library and the characteristic parameters significantly differed among different OARs (all P<0.001). The accuracy rates of self-validation, cross-validation and external verification of the CBSN OAR classification model were 92.1%, 92.0% and 91.8%, respectively. Fourteen cases of clinical abnormal structures were successfully detected by CBSN with an accuracy rate of 93%(14/15). In the simulation test, the accuracy of the left and right location verification reached 100%, such as detecting the right eye lens named Len_L. Conclusion:CBSN can be used for OAR verification of NPC, providing reference for multi-center cooperation and standardized radiotherapy of NPC patients.