- Email: [email protected]
OC-0375 A RECIPE FOR CTV MARGINS AND HOW TO COMBINE THEM WITH PTV MARGINS
S150
distributions. The most popular and least accurate tumor volume estimation approaches are based on the use of some sort of fixed threshold (such as for example 40%-75% of the tumor maximum activity, or SUV of 2.5). These thresholds are arbitrary and they are sensitive to lesion size, contrast, image noise, as well as the reconstruction algorithm and associated settings used. An alternative is the use of adaptive thresholding approaches which incorporate the background activity in the optimum threshold determination, by using manual background regions of interest drawn by the users. However, adaptive thresholding approaches need to be optimized for each scanner and acquisition protocol using uniform spheres phantom acquisitions allowing a calibration for different contrasts and lesion sizes. As such they require a-priori information and are user and system dependent. Over the last few years a number of semi- or fully automatic segmentation algorithms have been proposed. Their performance is variable depending on the algorithm and some of them depend on image pre-processing steps. A number of different validation and clinical evaluation studies have shown that some of these algorithms are capable of achieving high accuracy, with an associated robustness and reproducibility, while may be also capable to handle heterogeneous activity distributions. The advantage of such approaches is the possibility to automatically delineate complex functional volumes in a robust and reproducible fashion. One of the these methods, namely the Fuzzy Locally Adaptive Thresholding (FLAB), allows also the automatic determination of heterogeneous activity zones within the segmented 3D functional volumes, which in turn could be used in dose escalation studies. In conclusion, although some validation studies exist, more specific ones are necessary to establish the impact of these approaches within the context of radiotherapy treatment planning considering the different schemes currently under consideration for the use of biological volumes within this context. In addition, there is an urgent need for the manufacturers of PET imaging devices and image analysis platforms as well as treatment planning systems to implement these automatic approaches in order to allow their widespread use and evaluation. The objective of this presentation will be to review the current state of the art in 3D functional volume segmentation and propose the potential impact based on currently available validation and evaluation clinical studies.
PROFFERED PAPERS: PHYSICS 8: MARGINS OC-0375 A RECIPE FOR CTV MARGINS AND HOW TO COMBINE THEM WITH PTV MARGINS J. Stroom1, K. Gilhuijs2, W.E.I. Chen3, S. Vieira1, E. Moser1, J.J. Sonke3 1 Champalimaud Foundation, Radiotherapy, Lisboa, Portugal 2 UMC Utrecht, Image Sciences Institute, Amsterdam, The Netherlands 3 Netherlands Cancer Institute, Radiotherapy, Amsterdam, The Netherlands Purpose/Objective: According to the ICRU, all radiotherapy plans should contain PTV margins to account for geometric uncertainties. CTV margins should be applied separately to account for the presence of invisible microscopic disease. Some suggest that all disease should be covered in e.g. 90% of patients, but clear CTV margin recipes are lacking and it is debatable whether CTV and PTV margins should be calculated independently. The purpose of this study is to develop a combined CTV and PTV margin recipe. Materials and Methods: Geometric uncertainties can be systematic (described by standard deviation Σ) or random (described by σ). A widely accepted PTV-margin recipe is Mgeo = aΣgeo + bσgeo, with Σgeo and σgeo obtained by quadratic addition of the various components (setup error, organ motion, etc). There is consensus that systematic errors weigh heavier (a = 2 to 2.5) than random errors (b = 0.7). Based on measured microscopic pathology data from several tumour sites, we propose to describe the distribution of microscopic disease around the GTV by a standard deviation as well: Σmicro. In a computer model, a 4cm spherical GTV was treated using 39x2Gy with 5 equiangular-spaced fields. An isotropic distribution of microscopic islets was then simulated in a large group of virtual patients and the minimal margin that allows a maximal TCP drop of 1% was calculated for various Σmicro and expected mean number of islets per patient (N). A linear-quadratic TCP model was applied with a=0.27Gy-1, a/b=10Gy, and cell density 5.5e5/mm3. We assumed islet diameters of 1 to 3 mm, with the same radio-sensitivity and cell density as the GTV. This yields for the CTV margin recipe:
ESTRO 31
Mmicro = ƒ(N)*Σmicro. Since the distribution of microscopic disease is systematic and independent of geometric uncertainties, they add quadratically, yielding for a general GTV-to-PTV margin recipe, MGTV-PTV = a×SQRT(Σgeo2 + (ƒ(N)/a)2(Σmicro)2) + bσgeo. This was validated by the computer model through simultaneous simulation of microscopic and geometric uncertainties for various combinations of Σgeo, σgeo, Σmicro, and N. Results: Our simulations in a relevant range of uncertainties (Σgeo, σgeo = 1 to 6 mm, Σmicro = 1 to 11 mm and N = 1 to 100), indicate that ƒ(N) can be given by: ƒ(N) = 1.3+0.78log(N). With e.g. N=5, Mmicro = 1.84*Σmicro implying that, geometrically, coverage of all islets for 72% of patients is sufficient. Filling in all the different factors found in our simulations (a=2.1 and b=0.6) would yield for MGTV-PTV: MGTV-PTV = 2.1SQRT(Σgeo2 + ((0.65+0.39log(N))*Σmicro)2) + 0.6σgeo. The average differences between the simultaneous simulations and the above recipe were 0.3 ± 1.8 mm (1SD). Calculating Mgeo and Mmicro separately and adding them linearly would add on average 4.5 mm, determining Mgeo by requiring coverage in 90% of patients adds another unnecessary 3 mm. This indicates that the ICRU approach will overestimate PTVs. Conclusions: A general recipe for GTV-to-PTV margins is proposed, which shows that CTV and PTV margins should not be calculated separately. OC-0376 IMPACT OF 4DCT IMAGE QUALITY ON ITV MARGINS FOR RADIOTHERAPY SIMULATION: HELICAL VS. VOLUMETRIC 4DCT C. Coolens1, B. Driscoll1, J. Bracken1, D.A. Jaffray1 1 Princess Margaret Hospital, Department of Medical Physics, Toronto, Canada Purpose/Objective: 4D Computed Tomography (4DCT) is a powerful imaging technique for radiation oncology, providing images of moving targets and organs-at-risk for contouring and radiation treatment planning. Conventional (i.e. helical) 4DCT exploits the repetitive nature of breathing to provide an estimate of motion; however, it has limitations due to binning artifacts and irregular breathing frequencies in realistic patient breathing patterns. The aim is to evaluate the accuracy and image quality of a volumetric, 'true 4D' CT approach using a 320-slice CT scanner to overcome these limitations, wherein entire image volumes are acquired dynamically without couch movement. Materials and Methods: Volumetric 4DCT with a 320-slice CT was performed and characterized using an in-house, programmable respiratory motion phantom containing multiple geometric and morphological 'tumor' objects over a range of regular and irregular patient breathing traces from 3D fluoroscopy data. This was compared to the helical 4DCT approach, here referred to as '3.5DCT', on a 16slice CT using bellows to capture an external breathing signal for image sorting. The accuracy of volumetric capture and breathing displacement were evaluated and compared with the ground truth values and with the results reported using 3.5DCT methods. A motion model was investigated to validate the number of motion samples needed to obtain accurate motion probability density functions (PDF). The impact of 4D image quality on this accuracy was then investigated. Dose measurements using volumetric and conventional scan techniques were also performed and compared. Results
distributions. The most popular and least accurate tumor volume estimation approaches are based on the use of some sort of fixed threshold (such as for example 40%-75% of the tumor maximum activity, or SUV of 2.5). These thresholds are arbitrary and they are sensitive to lesion size, contrast, image noise, as well as the reconstruction algorithm and associated settings used. An alternative is the use of adaptive thresholding approaches which incorporate the background activity in the optimum threshold determination, by using manual background regions of interest drawn by the users. However, adaptive thresholding approaches need to be optimized for each scanner and acquisition protocol using uniform spheres phantom acquisitions allowing a calibration for different contrasts and lesion sizes. As such they require a-priori information and are user and system dependent. Over the last few years a number of semi- or fully automatic segmentation algorithms have been proposed. Their performance is variable depending on the algorithm and some of them depend on image pre-processing steps. A number of different validation and clinical evaluation studies have shown that some of these algorithms are capable of achieving high accuracy, with an associated robustness and reproducibility, while may be also capable to handle heterogeneous activity distributions. The advantage of such approaches is the possibility to automatically delineate complex functional volumes in a robust and reproducible fashion. One of the these methods, namely the Fuzzy Locally Adaptive Thresholding (FLAB), allows also the automatic determination of heterogeneous activity zones within the segmented 3D functional volumes, which in turn could be used in dose escalation studies. In conclusion, although some validation studies exist, more specific ones are necessary to establish the impact of these approaches within the context of radiotherapy treatment planning considering the different schemes currently under consideration for the use of biological volumes within this context. In addition, there is an urgent need for the manufacturers of PET imaging devices and image analysis platforms as well as treatment planning systems to implement these automatic approaches in order to allow their widespread use and evaluation. The objective of this presentation will be to review the current state of the art in 3D functional volume segmentation and propose the potential impact based on currently available validation and evaluation clinical studies.
PROFFERED PAPERS: PHYSICS 8: MARGINS OC-0375 A RECIPE FOR CTV MARGINS AND HOW TO COMBINE THEM WITH PTV MARGINS J. Stroom1, K. Gilhuijs2, W.E.I. Chen3, S. Vieira1, E. Moser1, J.J. Sonke3 1 Champalimaud Foundation, Radiotherapy, Lisboa, Portugal 2 UMC Utrecht, Image Sciences Institute, Amsterdam, The Netherlands 3 Netherlands Cancer Institute, Radiotherapy, Amsterdam, The Netherlands Purpose/Objective: According to the ICRU, all radiotherapy plans should contain PTV margins to account for geometric uncertainties. CTV margins should be applied separately to account for the presence of invisible microscopic disease. Some suggest that all disease should be covered in e.g. 90% of patients, but clear CTV margin recipes are lacking and it is debatable whether CTV and PTV margins should be calculated independently. The purpose of this study is to develop a combined CTV and PTV margin recipe. Materials and Methods: Geometric uncertainties can be systematic (described by standard deviation Σ) or random (described by σ). A widely accepted PTV-margin recipe is Mgeo = aΣgeo + bσgeo, with Σgeo and σgeo obtained by quadratic addition of the various components (setup error, organ motion, etc). There is consensus that systematic errors weigh heavier (a = 2 to 2.5) than random errors (b = 0.7). Based on measured microscopic pathology data from several tumour sites, we propose to describe the distribution of microscopic disease around the GTV by a standard deviation as well: Σmicro. In a computer model, a 4cm spherical GTV was treated using 39x2Gy with 5 equiangular-spaced fields. An isotropic distribution of microscopic islets was then simulated in a large group of virtual patients and the minimal margin that allows a maximal TCP drop of 1% was calculated for various Σmicro and expected mean number of islets per patient (N). A linear-quadratic TCP model was applied with a=0.27Gy-1, a/b=10Gy, and cell density 5.5e5/mm3. We assumed islet diameters of 1 to 3 mm, with the same radio-sensitivity and cell density as the GTV. This yields for the CTV margin recipe:
ESTRO 31
Mmicro = ƒ(N)*Σmicro. Since the distribution of microscopic disease is systematic and independent of geometric uncertainties, they add quadratically, yielding for a general GTV-to-PTV margin recipe, MGTV-PTV = a×SQRT(Σgeo2 + (ƒ(N)/a)2(Σmicro)2) + bσgeo. This was validated by the computer model through simultaneous simulation of microscopic and geometric uncertainties for various combinations of Σgeo, σgeo, Σmicro, and N. Results: Our simulations in a relevant range of uncertainties (Σgeo, σgeo = 1 to 6 mm, Σmicro = 1 to 11 mm and N = 1 to 100), indicate that ƒ(N) can be given by: ƒ(N) = 1.3+0.78log(N). With e.g. N=5, Mmicro = 1.84*Σmicro implying that, geometrically, coverage of all islets for 72% of patients is sufficient. Filling in all the different factors found in our simulations (a=2.1 and b=0.6) would yield for MGTV-PTV: MGTV-PTV = 2.1SQRT(Σgeo2 + ((0.65+0.39log(N))*Σmicro)2) + 0.6σgeo. The average differences between the simultaneous simulations and the above recipe were 0.3 ± 1.8 mm (1SD). Calculating Mgeo and Mmicro separately and adding them linearly would add on average 4.5 mm, determining Mgeo by requiring coverage in 90% of patients adds another unnecessary 3 mm. This indicates that the ICRU approach will overestimate PTVs. Conclusions: A general recipe for GTV-to-PTV margins is proposed, which shows that CTV and PTV margins should not be calculated separately. OC-0376 IMPACT OF 4DCT IMAGE QUALITY ON ITV MARGINS FOR RADIOTHERAPY SIMULATION: HELICAL VS. VOLUMETRIC 4DCT C. Coolens1, B. Driscoll1, J. Bracken1, D.A. Jaffray1 1 Princess Margaret Hospital, Department of Medical Physics, Toronto, Canada Purpose/Objective: 4D Computed Tomography (4DCT) is a powerful imaging technique for radiation oncology, providing images of moving targets and organs-at-risk for contouring and radiation treatment planning. Conventional (i.e. helical) 4DCT exploits the repetitive nature of breathing to provide an estimate of motion; however, it has limitations due to binning artifacts and irregular breathing frequencies in realistic patient breathing patterns. The aim is to evaluate the accuracy and image quality of a volumetric, 'true 4D' CT approach using a 320-slice CT scanner to overcome these limitations, wherein entire image volumes are acquired dynamically without couch movement. Materials and Methods: Volumetric 4DCT with a 320-slice CT was performed and characterized using an in-house, programmable respiratory motion phantom containing multiple geometric and morphological 'tumor' objects over a range of regular and irregular patient breathing traces from 3D fluoroscopy data. This was compared to the helical 4DCT approach, here referred to as '3.5DCT', on a 16slice CT using bellows to capture an external breathing signal for image sorting. The accuracy of volumetric capture and breathing displacement were evaluated and compared with the ground truth values and with the results reported using 3.5DCT methods. A motion model was investigated to validate the number of motion samples needed to obtain accurate motion probability density functions (PDF). The impact of 4D image quality on this accuracy was then investigated. Dose measurements using volumetric and conventional scan techniques were also performed and compared. Results