We investigated the feasibility of measuring daily fiducial length on cone-beam computed tomography (CBCT) images to assess the variation in daily tumor motion for pancreatic SBRT. Motion data for fifty pancreatic SBR...We investigated the feasibility of measuring daily fiducial length on cone-beam computed tomography (CBCT) images to assess the variation in daily tumor motion for pancreatic SBRT. Motion data for fifty pancreatic SBRT patients with fiducials were analyzed retrospectively to determine the tumor motion statistics. We also performed a phantom study which involved motion analysis of three gold fiducials placed around a solid target inside the Quasar Phantom as a function of variable tumor motion and breathing period. The end-exhalation CT-50 images were compared with the CBCT images acquired prior to treatment delivery on a TrueBeam STx linear accelerator. Sinusoidal tumor motion and patients’ breathing files acquired from a Varian-RPM system were used to simulate patients’ breathing patterns. The fiducial length was measured to determine its correlation with tumor motion. Patient tumor motions along the superior-inferior (SI), anterior-posterior (AP), and left-right (LR) directions were found to be 0.7 ± 0.4 cm, 0.2 ± 0.3 cm, and 0.1 ± 0.2 cm, respectively. Average breathing period was 4.3 ± 0.8 seconds. For sinusoidal and patients’ breathing patterns, a significant correlation was observed between the fiducial length and tumor motions with R2 of 0.99. However, fiducial length was found to be independent of the variation in breathing periods. This work suggests that measuring the fiducial length on daily CBCT images could provide quantitative daily tumor motion for fiducial-based pancreatic SBRT. A timely decision to modify the motion management strategy could be made prior to daily treatment delivery.展开更多
Objective To evaluate the value of texture features derived from intravoxel incoherent motion(IVIM) parameters for differentiating pancreatic neuroendocrine tumor(pNET) from pancreatic adenocarcinoma(PAC).Methods Eigh...Objective To evaluate the value of texture features derived from intravoxel incoherent motion(IVIM) parameters for differentiating pancreatic neuroendocrine tumor(pNET) from pancreatic adenocarcinoma(PAC).Methods Eighteen patients with pNET and 32 patients with PAC were retrospectively enrolled in this study. All patients underwent diffusion-weighted imaging with 10 b values used(from 0 to 800 s/mm2). Based on IVIM model, perfusion-related parameters including perfusion fraction(f), fast component of diffusion(Dfast) and true diffusion parameter slow component of diffusion(Dslow) were calculated on a voxel-by-voxel basis and reorganized into gray-encoded parametric maps. The mean value of each IVIM parameter and texture features [Angular Second Moment(ASM), Inverse Difference Moment(IDM), Correlation, Contrast and Entropy] values of IVIM parameters were measured. Independent sample t-test or Mann-Whitney U test were performed for the betweengroup comparison of quantitative data. Regression model was established by using binary logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to evaluate the diagnostic efficiency.Results The mean f value of the pNET group were significantly higher than that of the PAC group(27.0% vs. 19.0%, P = 0.001), while the mean values of Dfast and Dslow showed no significant differences between the two groups. All texture features(ASM, IDM, Correlation, Contrast and Entropy) of each IVIM parameter showed significant differences between the pNET and PAC groups(P = 0.000-0.043). Binary logistic regression analysis showed that texture ASM of Dfast and texture Correlation of Dslow were considered as the specific imaging variables for the differential diagnosis of pNET and PAC. ROC analysis revealed that multiple texture features presented better diagnostic performance than IVIM parameters(AUC 0.849-0.899 vs. 0.526-0.776), and texture ASM of Dfast combined with Correlation of Dslow in the model of logistic regression had largest area under ROC curve for distinguishing pNET from PAC(AUC 0.934, cutoff 0.378, sensitivity 0.889, specificity 0.854). Conclusion Texture analysis of IVIM parameters could be an effective and noninvasive tool to differentiate pNET from PAC.展开更多
文摘We investigated the feasibility of measuring daily fiducial length on cone-beam computed tomography (CBCT) images to assess the variation in daily tumor motion for pancreatic SBRT. Motion data for fifty pancreatic SBRT patients with fiducials were analyzed retrospectively to determine the tumor motion statistics. We also performed a phantom study which involved motion analysis of three gold fiducials placed around a solid target inside the Quasar Phantom as a function of variable tumor motion and breathing period. The end-exhalation CT-50 images were compared with the CBCT images acquired prior to treatment delivery on a TrueBeam STx linear accelerator. Sinusoidal tumor motion and patients’ breathing files acquired from a Varian-RPM system were used to simulate patients’ breathing patterns. The fiducial length was measured to determine its correlation with tumor motion. Patient tumor motions along the superior-inferior (SI), anterior-posterior (AP), and left-right (LR) directions were found to be 0.7 ± 0.4 cm, 0.2 ± 0.3 cm, and 0.1 ± 0.2 cm, respectively. Average breathing period was 4.3 ± 0.8 seconds. For sinusoidal and patients’ breathing patterns, a significant correlation was observed between the fiducial length and tumor motions with R2 of 0.99. However, fiducial length was found to be independent of the variation in breathing periods. This work suggests that measuring the fiducial length on daily CBCT images could provide quantitative daily tumor motion for fiducial-based pancreatic SBRT. A timely decision to modify the motion management strategy could be made prior to daily treatment delivery.
文摘Objective To evaluate the value of texture features derived from intravoxel incoherent motion(IVIM) parameters for differentiating pancreatic neuroendocrine tumor(pNET) from pancreatic adenocarcinoma(PAC).Methods Eighteen patients with pNET and 32 patients with PAC were retrospectively enrolled in this study. All patients underwent diffusion-weighted imaging with 10 b values used(from 0 to 800 s/mm2). Based on IVIM model, perfusion-related parameters including perfusion fraction(f), fast component of diffusion(Dfast) and true diffusion parameter slow component of diffusion(Dslow) were calculated on a voxel-by-voxel basis and reorganized into gray-encoded parametric maps. The mean value of each IVIM parameter and texture features [Angular Second Moment(ASM), Inverse Difference Moment(IDM), Correlation, Contrast and Entropy] values of IVIM parameters were measured. Independent sample t-test or Mann-Whitney U test were performed for the betweengroup comparison of quantitative data. Regression model was established by using binary logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to evaluate the diagnostic efficiency.Results The mean f value of the pNET group were significantly higher than that of the PAC group(27.0% vs. 19.0%, P = 0.001), while the mean values of Dfast and Dslow showed no significant differences between the two groups. All texture features(ASM, IDM, Correlation, Contrast and Entropy) of each IVIM parameter showed significant differences between the pNET and PAC groups(P = 0.000-0.043). Binary logistic regression analysis showed that texture ASM of Dfast and texture Correlation of Dslow were considered as the specific imaging variables for the differential diagnosis of pNET and PAC. ROC analysis revealed that multiple texture features presented better diagnostic performance than IVIM parameters(AUC 0.849-0.899 vs. 0.526-0.776), and texture ASM of Dfast combined with Correlation of Dslow in the model of logistic regression had largest area under ROC curve for distinguishing pNET from PAC(AUC 0.934, cutoff 0.378, sensitivity 0.889, specificity 0.854). Conclusion Texture analysis of IVIM parameters could be an effective and noninvasive tool to differentiate pNET from PAC.
