MLC tracking for lung SABR reduces planning target volumes and dose to organs at risk

Caillet, Vincent; Keall, Paul J.; Colvill, Emma; Hardcastle, Nicholas; O’Brien, Ricky; Szymura, Kathryn; Booth, Jeremy

doi:10.1016/j.radonc.2017.06.016

Cited by 31 publications

(35 citation statements)

References 31 publications

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“…In the case of MLC tracking, there are also rotational parts in the changes of the beam path present, as well as an influence of the dependency of the dose depth profile on the distance to the central axis. Until now, MLC tracking was mainly used for prostate cancer, but the application for lung cancer is topic of current research . If it is used for DTT treatment of the lung, the chosen reference phase can also influence the applied dose to OARs and the impact should be investigated.…”

Section: Discussionmentioning

confidence: 99%

“…Until now, MLC tracking was mainly used for prostate cancer, 28,29 but the application for lung cancer is topic of current research. 30,31 If it is used for DTT treatment of the lung, the chosen reference phase can also influence the applied dose to OARs and the impact should be investigated. For MLC tracking, the calculation would be possible within modern treatment planning systems since only a change in the MLC position is necessary and there would be no need for external processing of the image data (i.e., the image rotation).…”

Section: Discussionmentioning

confidence: 99%

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Choosing a reference phase for a dynamic tumor tracking treatment: A new degree of freedom?

et al. 2019

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Purpose With the introduction of dynamic tumor tracking in radiotherapy, it is possible to irradiate moving targets with minimal safety margins. However, most dynamic tumor tracking techniques rely on changing the beam geometry by, for example, adapting the multileaf collimator (MLC) positions or rotating the LINAC head. These changes are relative to a reference position which is determined by a specific breathing phase. Since these changes in the beam path also influence the delivered dose, choosing a different reference position based on a different breathing phase impacts the applied dose to the patient. This work investigates the influence of choosing different reference breathing phases on the dose distribution. Methods The Vero system tracks the moving target by performing a pan and tilt rotation of the LINAC head. For 13 patients, the target position was extracted from every phase of a four‐dimensional computed tomography (4DCT) and the pan and tilt values were determined with respect to three different reference phases. These reference phases were inspiration, expiration, and the midventilation. For all reference phases, a 4D dose calculation was performed on the 4DCT regarding the respective pan and tilt values. Furthermore, the applied dose to the target and surrounding organs at risk was calculated. To accumulate the dose distribution, weights from the actual patient breathing motion were determined. The weights were calculated from the breathing motions from different days to investigate the impact of daily variations in the breathing motion onto the accumulated dose distribution. All obtained values were then compared to the static treatment plan. Results The mean and maximum doses applied to the target or surrounding organs at risk show no general behavior depending on the different reference phases. Nevertheless, for some patients, large differences (approx. 30%) in the applied dose to certain organs at risk could be observed, whereas the applied dose to the target shows no dependency on the different reference phases. However, the mean target dose is in all cases approx. 1.5% below the reference value from the static treatment plan. Conclusion Although no general dependency of the applied dose on the selected reference phase could be found, the choice of the reference phase can have great impact on the organ at risk dose for some patients. Thus, the choice of the reference phase used for patient positioning should be considered during treatment planning since it can be seen as a new degree of freedom of a treatment based on tracking.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Choosing a reference phase for a dynamic tumor tracking treatment: A new degree of freedom?

et al. 2019

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“…It is often impossible to perform surgical resection because of the wide range of tumour metastases or poor patient health at the time of diagnosis [3]. The success of radiation therapy relies on the accurate irradiation of the tumour region while carefully protecting normal tissues, especially in the case of stereotactic ablative body radiotherapy (SABR) [4,5] to avoid the complications that arise during hypofractionation therapy [6][7][8][9]. Therefore, when developing a treatment plan, it is necessary to segment and adequately protect the organs at risk (OARs).…”

Section: Introductionmentioning

confidence: 99%

Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques

et al. 2018

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“…High-precision radiotherapy requires detection of the 3D position at high temporal resolution (sampling rate > 1 Hz). Localization of 3D target position during beam irradiation is used for the dose-of-the-day calculations [ 2 ], planning target volume verification [ 3 ], and real-time tumor tracking [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of training periods for the estimation model of three-dimensional target positions using an external respiratory surrogate

et al. 2018

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BackgroundDuring therapeutic beam irradiation, an unvisualized three-dimensional (3D) target position should be estimated using an external surrogate with an estimation model. Training periods for the developed model with no additional imaging during beam irradiation were optimized using clinical data.MethodsDual-source 4D-CBCT projection data for 20 lung cancer patients were used for validation. Each patient underwent one to three scans. The actual target positions of each scan were equally divided into two equal parts: one for the modeling and the other for the validating session. A quadratic target position estimation equation was constructed during the modeling session. Various training periods for the session—i.e., modeling periods (TM)—were employed: TM ∈ {5,10,15,25,35} [s]. First, the equation was used to estimate target positions in the validating session of the same scan (intra-scan estimations). Second, the equation was then used to estimate target positions in the validating session of another temporally different scan (inter-scan estimations). The baseline drift of the surrogate and target between scans was corrected. Various training periods for the baseline drift correction—i.e., correction periods (TCs)—were employed: TC ∈ {5,10,15; TC ≤ TM} [s]. Evaluations were conducted with and without the correction. The difference between the actual and estimated target positions was evaluated by the root-mean-square error (RMSE).ResultsThe range of mean respiratory period and 3D motion amplitude of the target was 2.4–13.0 s and 2.8–34.2 mm, respectively. On intra-scan estimation, the median 3D RMSE was within 1.5–2.1 mm, supported by previous studies. On inter-scan estimation, median elapsed time between scans was 10.1 min. All TMs exhibited 75th percentile 3D RMSEs of 5.0–6.4 mm due to baseline drift of the surrogate and the target. After the correction, those for each TMs fell by 1.4–2.3 mm. The median 3D RMSE for both the 10-s TM and the TC period was 2.4 mm, which plateaued when the two training periods exceeded 10 s.ConclusionsA widely-applicable estimation model for the 3D target positions during beam irradiation was developed. The optimal TM and TC for the model were both 10 s, to allow for more than one respiratory cycle.Trial registrationUMIN000014825. Registered: 11 August 2014.Electronic supplementary materialThe online version of this article (10.1186/s13014-018-1019-9) contains supplementary material, which is available to authorized users.

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MLC tracking for lung SABR reduces planning target volumes and dose to organs at risk

Cited by 31 publications

References 31 publications

Choosing a reference phase for a dynamic tumor tracking treatment: A new degree of freedom?

Choosing a reference phase for a dynamic tumor tracking treatment: A new degree of freedom?

Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques

Optimization of training periods for the estimation model of three-dimensional target positions using an external respiratory surrogate

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