What margins should be added to the clinical target volume in radiotherapy treatment planning for lung cancer?

Ekberg, Lars; Holmberg, Ola; Wittgren, Lena; Bjelkengren, Göran; Landberg, T.

doi:10.1016/s0167-8140(98)00046-2

Cited by 240 publications

(107 citation statements)

References 29 publications

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“…Regions close to the heart show an increase in error due to uncompensated cardiac motion (Fig. 3a and b) which may become significant for radiation therapy treatment planning in cases where tumors are attached to the aorta (32, 33). Initial results using an additional modeling term synchronized to a patient’s electrocardiogram suggest this motion can be successfully accounted for (34).…”

Section: Discussionmentioning

confidence: 99%

A Novel Fast Helical 4D-CT Acquisition Technique to Generate Low-Noise Sorting Artifact–Free Images at User-Selected Breathing Phases

Thomas

Lamb

White

et al. 2014

International Journal of Radiation Oncology*Biology*Physics

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Purpose To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts. Methods and Materials Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Results Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. Conclusions The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact–free images at a patient dose similar to or less than current 4D-CT techniques.

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Section: Discussionmentioning

confidence: 99%

A Novel Fast Helical 4D-CT Acquisition Technique to Generate Low-Noise Sorting Artifact–Free Images at User-Selected Breathing Phases

Thomas

Lamb

White

et al. 2014

International Journal of Radiation Oncology*Biology*Physics

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“…Thoracic and abdominal tumors often exhibit large respiratory motion ranges (Ekberg et al [5], Seppenwoolde et al [25], Keall et al [11]). Intrafractional target motion causes a blurring of the target dose distribution which necessitates the irradiation of additional normal tissue surrounding the tumor volume to achieve full dose coverage of the target.…”

Section: Introductionmentioning

confidence: 99%

Markerless EPID image guided dynamic multi-leaf collimator tracking for lung tumors

2013

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Compensation of target motion during the delivery of radiotherapy has the potential to improve treatment accuracy, dose conformity and sparing of healthy tissue. We implement an online image guided therapy system based on soft tissue localization (STiL) of the target from electronic portal images and treatment aperture adaptation with a dynamic multi-leaf collimator (DMLC). The treatment aperture is moved synchronously and in real-time with the tumor during the entire breathing cycle. The system is implemented and tested on a Varian TX clinical linear accelerator featuring an AS-1000 electronic portal imaging device (EPID) acquiring images at a frame rate of 12.86 Hz throughout the treatment. A position update cycle for the treatment aperture consists of 4 steps: in the first step at time t = t0 a frame is grabbed, in the second step the frame is processed with the STiL algorithm to get the tumor position at t = t0, in a third step the tumor position at t = t + δt is predicted to overcome system latencies and in the fourth step, the DMLC control software calculates the required leaf motions and applies them at time t = t + δt. The prediction model is trained before the start of the treatment with data representing the tumor motion. We analyze the system latency with a dynamic chest phantom (4D motion phantom, Washington University). We estimate the average planar position deviation between target and treatment aperture in a clinical setting by driving the phantom with several lung tumor trajectories (recorded from fiducial tracking during radiotherapy delivery to the lung). DMLC tracking for lung SBRT without fiducial markers was successfully demonstrated. The inherent system latency is found to be δt = (230 ± 11) ms for a MV portal image acquisition frame rate of 12.86 Hz. The root mean square deviation between tumor and aperture position is smaller than 1 mm. We demonstrate the feasibility of real-time markerless DMLC tracking with a standard LINAC-mounted (EPID).

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“…This may be due to smaller margins (8 mm) to account for the setup errors only for PTV_4D compared with PTV_3D (1 cm axially and 1.5 cm superior/inferior) which included both—the anticipated motion of the GTV based on population statistics and the margin for setup errors. 20 Interestingly, when the 3DCRT plan was superimposed on PTV_4D, some of the 4D_PTV was not encompassed by the 3DCRT 95% isodose line and dose coverage was more variable than 3D plans (Figure 2). This is likely due to the difference in the anatomical location between the 2 volumes caused by unpredictable trajectory of the tumor during a breathing cycle.…”

Section: Discussionmentioning

confidence: 99%

Does Motion Assessment With 4-Dimensional Computed Tomographic Imaging for Non–Small Cell Lung Cancer Radiotherapy Improve Target Volume Coverage?

Ahmed

Venkataraman

Johnson

et al. 2017

Clin Med Insights Oncol

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Introduction:Modern radiotherapy with 4-dimensional computed tomographic (4D-CT) image acquisition for non–small cell lung cancer (NSCLC) captures respiratory-mediated tumor motion to provide more accurate target delineation. This study compares conventional 3-dimensional (3D) conformal radiotherapy (3DCRT) plans generated with standard helical free-breathing CT (FBCT) with plans generated on 4D-CT contoured volumes to determine whether target volume coverage is affected.Materials and methods:Fifteen patients with stage I to IV NSCLC were enrolled in the study. Free-breathing CT and 4D-CT data sets were acquired at the same simulation session and with the same immobilization. Gross tumor volume (GTV) for primary and/or nodal disease was contoured on FBCT (GTV_3D). The 3DCRT plans were obtained, and the patients were treated according to our institution’s standard protocol using FBCT imaging. Gross tumor volume was contoured on 4D-CT for primary and/or nodal disease on all 10 respiratory phases and merged to create internal gross tumor volume (IGTV)_4D. Clinical target volume margin was 5 mm in both plans, whereas planning tumor volume (PTV) expansion was 1 cm axially and 1.5 cm superior/inferior for FBCT-based plans to incorporate setup errors and an estimate of respiratory-mediated tumor motion vs 8 mm isotropic margin for setup error only in all 4D-CT plans. The 3DCRT plans generated from the FBCT scan were copied on the 4D-CT data set with the same beam parameters. GTV_3D, IGTV_4D, PTV, and dose volume histogram from both data sets were analyzed and compared. Dice coefficient evaluated PTV similarity between FBCT and 4D-CT data sets.Results:In total, 14 of the 15 patients were analyzed. One patient was excluded as there was no measurable GTV. Mean GTV_3D was 115.3 cm3 and mean IGTV_4D was 152.5 cm3 (P = .001). Mean PTV_3D was 530.0 cm3 and PTV_4D was 499.8 cm3 (P = .40). Both gross primary and nodal disease analyzed separately were larger on 4D compared with FBCT. D95 (95% isodose line) covered 98% of PTV_3D and 88% of PTV_4D (P = .003). Mean dice coefficient of PTV_3D and PTV_4D was 84%. Mean lung V20 was 24.0% for the 3D-based plans and 22.7% for the 4D-based plans (P = .057). Mean heart V40 was 12.1% for the 3D-based plans and 12.7% for the 4D-based plans (P = .53). Mean spinal cord Dmax was 2517 and 2435 cGy for 3D-based and 4D-based plans, respectively (P = .019). Mean esophageal dose was 1580 and 1435 cGy for 3D and 4D plans, respectively (P = .13).Conclusions:IGTV_4D was significantly larger than GTV_3D for both primary and nodal disease combined or separately. Mean PTV_3D was larger than PTV_4D, but the difference was not statistically significant. The PTV_4D coverage with 95% isodose line was inferior, indicating the importance of incorporating the true size and shape of the target volume. Relatively less dose was delivered to spinal cord and esophagus with plans based on 4D data set. Dice coefficient analysis for degree of similarity revealed that 16% of PTVs from both data sets did not overlap, indi...

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What margins should be added to the clinical target volume in radiotherapy treatment planning for lung cancer?

Cited by 240 publications

References 29 publications

A Novel Fast Helical 4D-CT Acquisition Technique to Generate Low-Noise Sorting Artifact–Free Images at User-Selected Breathing Phases

A Novel Fast Helical 4D-CT Acquisition Technique to Generate Low-Noise Sorting Artifact–Free Images at User-Selected Breathing Phases

Markerless EPID image guided dynamic multi-leaf collimator tracking for lung tumors

Does Motion Assessment With 4-Dimensional Computed Tomographic Imaging for Non–Small Cell Lung Cancer Radiotherapy Improve Target Volume Coverage?

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