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OC-0191 PAROTID DEFORMATION DURING IMRT FOR HEAD-NECK CANCER CORRELATES WITH CLINICAL AND DOSIMETRY INFORMATION
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pretreatment T1-weighted image and markers were manually placed at distinct anatomical landmarks on the different modalities at the different time points. All images were coregistered to the primary T1weighted image using the in-house developed registration software [1]. The median ?ADC of the delineated tumour was collected at 2 and 4 weeks during CRT for both unregistered and registered images. These results were correlated to locoregional recurrence (LRR) using a Mann-Whitney U test and visualized in a PRM. Results: During follow-up, 9 patients developed an LRR (47%). If no registration was performed, no significant differences were found between patients with an LRR and no LRR at 2 weeks and 4 weeks (p=0.3 and p=0.2 respectively). After registration, patients with an LRR at follow-up had a significantly lower median ?ADC than patients who remained locoregionally controlled both at 2 weeks (-7.5% vs 22.5%; p=0.03) and at 4 weeks (-5.0% vs 35.8%; p=0.007). These results were visualized and regions of low ?ADC appear to be constant at both time points (Figure 1). OC-0191 PAROTID DEFORMATION DURING IMRT FOR HEAD-NECK CANCER CORRELATES WITH CLINICAL AND DOSIMETRY INFORMATION S. Broggi1, C. Fiorino1, E. Scalco2, M.L. Belli1, G. Sanguineti3, N. Dinapoli4, V. Valentini4, N. Di Muzio5, G. Rizzo2, G.M. Cattaneo1 1 Istituto Scientifico H.S. Raffaele, Medical Physics, Milano, Italy 3 The Johns Hopkins University, Radiotherapy, Baltimore, USA 4 Università Cattolica Roma, Radiotherapy, Roma, Italy 5 Istituto Scientifico H.S. Raffaele, Radiotherapy, Milano, Italy Purpose/Objective: The Jacobian (Jac) of the deformation field of elastic registration between images taken during RT is a measure of compression/expansion of the voxels within an organ. The Jac mean value (Jacmean) was applied to investigate possible correlations between parotid deformation and anatomical, clinical and dosimetric parameters. Materials and Methods: Data of 84 patients (168 parotids), treated with image-guided IMRT for Head-Neck cancer, were analysed. Parotid deformation was evaluated through MVCT (n=82) or KVCT (n= 86) images taken at the start and at the end of the treatment. Jac map was calculated for each parotid and Jacmean values were calculated. Several clinical, geometrical and dosimetric factors were considered, including chemotherapy, surgery, primary tumour site, age, total and daily dose, tumour (PTV) volume, initial parotid volume (IPV), overlap between PTVs (T+N/T) and parotids (OVPTV1/OVPTV2), parotid mean dose (Dmean) and some planning dose volume histogram (DVH) parameters (V10-V40). Correlation between Jacmean and these parameters was assessed through Spearman tests. Univariate and multivariate (MVA) logistic analysis were performed by considering as the end point the Jacmean smaller than the first quartile value (Q1). Parotids DVHs were stratified according to their degree of deformation, trying to assess the most predictive dose-volume combination in the low and medium dose region. Results: Based on correlation tests, OVPPTV1 (p=0.0045), OVPPTV2 (p= 0.0026), age (p= 0.0209), Dmean (p= 0.0005) and most of the DVH parameters were found as the pre-treatment variables significantly correlated with Jacmean. Similar results were found at univariate analysis by considering JacmeanQ1, V10 and V40 were found as the most predictive dosimetric parameters. Based on a ROC analysis, V10=93% and V40=36% were estimated as the best cut-off values. Parotid glands were then grouped in three different sub-groups: badDVH (V10>93% and V40>36%), medium-DVH (V10>93% and V40<36%), good-DVH (V10<93%). The risk to have Jacmean< Q1 was 39.6 % vs 19.6% vs 12 % in the three groups respectively (Figure). By including in the MVA analysis this 'DVH grouping' parameter, bad-DVH was found as the most predictive parameter for large shrinkage.
ESTRO 31
Conclusions The pattern of deformation may be well predicted by a model including the planned DVH and the age of the patients. In particular, a bad DVH (high value of V10 and V40) seems the most important predictor of parotid deformation. OC-0192 MRIGRT: CHARACTERIZATION OF THE COMPOSITE MR IMAGE DISTORTION FIELD ASSOCIATED WITH ORGAN MOTION T. Stanescu1, D.A. Jaffray1 1 Princess Margaret Hospital, Radiation Physics PMH, Toronto, Canada Purpose/Objective: To quantify the dynamic behavior of the 3D MR image distortion field associated with organ motion and its implications for MR-only radiotherapy planning and patient setup verification. Materials and Methods: Organ motion is expressed in terms of the relative change in shape and spatial location of the organ volume and neighboring structures. This leads to correlated variations in the way the anatomical topography interacts with the local MR image distortion landscape (e.g., liver/lung during breathing). The composite MR image distortion field experienced by the imaged anatomy consists of a) system-related distortions due to B0 inhomogeneities and gradient nonlinearities, and b) patient-induced distortions which are mainly caused by tissue magnetic susceptibility gradients. During organ motion the system and patient-induced distortions manifest differently. The system distortion field is static whereas the susceptibility field is dynamic, following closely any changes in the local anatomy (e.g., diaphragm during breathing). Using a full-bore phantom, we measured the 3D system distortion fields corresponding to the imaging volume of an 1.5 T Siemens Espree and 3T Philips Intera scanners. The susceptibility-induced distortion field were simulated via numerical simulations. The 3D composite distortion field was generated by combining the system and susceptibility fields into the same system of coordinates. Organ motion (e.g., liver) was captured by rapid sampling of the targeted volume via 2D cine-MR performed in the main orthogonal planes. Subsequently, the composite distortion field was applied to the cine images as well as to the corresponding volumetric image dataset to investigate the implications for planning and verification. This study was performed in the context of a clinical workflow designed for MRIgRT systems, i.e. MR-linac. Results: We measured and validated the tools required to generate the 3D MR composite distortion field for any anatomical site (e.g., liver lung). To showcase the proposed methodology we studied the case of MR-only planning of liver SBRT. We quantified the impact of different types of distortions on the PTV/ITV margins required for the planning process. The errors associated with patient setup verification were also quantified considering daily MR dynamic imaging prior to each treatment fraction. In particular for liver, the small targets embedded into the targeted soft tissue are affected only by the 3D system distortion field, however the large targets located near the diaphragm experience both geometric and signal intensity artifacts caused by the entire composite distortion field. The impact of geometric distortions can be mitigated by a selective choice of imaging parameters and by implementing efficient and robust image distortion correction methods. Conclusions: The aggregated contribution of various types of MR image distortions was quantified in conjunction with organ motion. The loss of spatial accuracy and its implications were investigated in the context of MR-only planning and verification for MRIgRT.
pretreatment T1-weighted image and markers were manually placed at distinct anatomical landmarks on the different modalities at the different time points. All images were coregistered to the primary T1weighted image using the in-house developed registration software [1]. The median ?ADC of the delineated tumour was collected at 2 and 4 weeks during CRT for both unregistered and registered images. These results were correlated to locoregional recurrence (LRR) using a Mann-Whitney U test and visualized in a PRM. Results: During follow-up, 9 patients developed an LRR (47%). If no registration was performed, no significant differences were found between patients with an LRR and no LRR at 2 weeks and 4 weeks (p=0.3 and p=0.2 respectively). After registration, patients with an LRR at follow-up had a significantly lower median ?ADC than patients who remained locoregionally controlled both at 2 weeks (-7.5% vs 22.5%; p=0.03) and at 4 weeks (-5.0% vs 35.8%; p=0.007). These results were visualized and regions of low ?ADC appear to be constant at both time points (Figure 1). OC-0191 PAROTID DEFORMATION DURING IMRT FOR HEAD-NECK CANCER CORRELATES WITH CLINICAL AND DOSIMETRY INFORMATION S. Broggi1, C. Fiorino1, E. Scalco2, M.L. Belli1, G. Sanguineti3, N. Dinapoli4, V. Valentini4, N. Di Muzio5, G. Rizzo2, G.M. Cattaneo1 1 Istituto Scientifico H.S. Raffaele, Medical Physics, Milano, Italy 3 The Johns Hopkins University, Radiotherapy, Baltimore, USA 4 Università Cattolica Roma, Radiotherapy, Roma, Italy 5 Istituto Scientifico H.S. Raffaele, Radiotherapy, Milano, Italy Purpose/Objective: The Jacobian (Jac) of the deformation field of elastic registration between images taken during RT is a measure of compression/expansion of the voxels within an organ. The Jac mean value (Jacmean) was applied to investigate possible correlations between parotid deformation and anatomical, clinical and dosimetric parameters. Materials and Methods: Data of 84 patients (168 parotids), treated with image-guided IMRT for Head-Neck cancer, were analysed. Parotid deformation was evaluated through MVCT (n=82) or KVCT (n= 86) images taken at the start and at the end of the treatment. Jac map was calculated for each parotid and Jacmean values were calculated. Several clinical, geometrical and dosimetric factors were considered, including chemotherapy, surgery, primary tumour site, age, total and daily dose, tumour (PTV) volume, initial parotid volume (IPV), overlap between PTVs (T+N/T) and parotids (OVPTV1/OVPTV2), parotid mean dose (Dmean) and some planning dose volume histogram (DVH) parameters (V10-V40). Correlation between Jacmean and these parameters was assessed through Spearman tests. Univariate and multivariate (MVA) logistic analysis were performed by considering as the end point the Jacmean smaller than the first quartile value (Q1). Parotids DVHs were stratified according to their degree of deformation, trying to assess the most predictive dose-volume combination in the low and medium dose region. Results: Based on correlation tests, OVPPTV1 (p=0.0045), OVPPTV2 (p= 0.0026), age (p= 0.0209), Dmean (p= 0.0005) and most of the DVH parameters were found as the pre-treatment variables significantly correlated with Jacmean. Similar results were found at univariate analysis by considering JacmeanQ1, V10 and V40 were found as the most predictive dosimetric parameters. Based on a ROC analysis, V10=93% and V40=36% were estimated as the best cut-off values. Parotid glands were then grouped in three different sub-groups: badDVH (V10>93% and V40>36%), medium-DVH (V10>93% and V40<36%), good-DVH (V10<93%). The risk to have Jacmean< Q1 was 39.6 % vs 19.6% vs 12 % in the three groups respectively (Figure). By including in the MVA analysis this 'DVH grouping' parameter, bad-DVH was found as the most predictive parameter for large shrinkage.
ESTRO 31
Conclusions The pattern of deformation may be well predicted by a model including the planned DVH and the age of the patients. In particular, a bad DVH (high value of V10 and V40) seems the most important predictor of parotid deformation. OC-0192 MRIGRT: CHARACTERIZATION OF THE COMPOSITE MR IMAGE DISTORTION FIELD ASSOCIATED WITH ORGAN MOTION T. Stanescu1, D.A. Jaffray1 1 Princess Margaret Hospital, Radiation Physics PMH, Toronto, Canada Purpose/Objective: To quantify the dynamic behavior of the 3D MR image distortion field associated with organ motion and its implications for MR-only radiotherapy planning and patient setup verification. Materials and Methods: Organ motion is expressed in terms of the relative change in shape and spatial location of the organ volume and neighboring structures. This leads to correlated variations in the way the anatomical topography interacts with the local MR image distortion landscape (e.g., liver/lung during breathing). The composite MR image distortion field experienced by the imaged anatomy consists of a) system-related distortions due to B0 inhomogeneities and gradient nonlinearities, and b) patient-induced distortions which are mainly caused by tissue magnetic susceptibility gradients. During organ motion the system and patient-induced distortions manifest differently. The system distortion field is static whereas the susceptibility field is dynamic, following closely any changes in the local anatomy (e.g., diaphragm during breathing). Using a full-bore phantom, we measured the 3D system distortion fields corresponding to the imaging volume of an 1.5 T Siemens Espree and 3T Philips Intera scanners. The susceptibility-induced distortion field were simulated via numerical simulations. The 3D composite distortion field was generated by combining the system and susceptibility fields into the same system of coordinates. Organ motion (e.g., liver) was captured by rapid sampling of the targeted volume via 2D cine-MR performed in the main orthogonal planes. Subsequently, the composite distortion field was applied to the cine images as well as to the corresponding volumetric image dataset to investigate the implications for planning and verification. This study was performed in the context of a clinical workflow designed for MRIgRT systems, i.e. MR-linac. Results: We measured and validated the tools required to generate the 3D MR composite distortion field for any anatomical site (e.g., liver lung). To showcase the proposed methodology we studied the case of MR-only planning of liver SBRT. We quantified the impact of different types of distortions on the PTV/ITV margins required for the planning process. The errors associated with patient setup verification were also quantified considering daily MR dynamic imaging prior to each treatment fraction. In particular for liver, the small targets embedded into the targeted soft tissue are affected only by the 3D system distortion field, however the large targets located near the diaphragm experience both geometric and signal intensity artifacts caused by the entire composite distortion field. The impact of geometric distortions can be mitigated by a selective choice of imaging parameters and by implementing efficient and robust image distortion correction methods. Conclusions: The aggregated contribution of various types of MR image distortions was quantified in conjunction with organ motion. The loss of spatial accuracy and its implications were investigated in the context of MR-only planning and verification for MRIgRT.