Daily adaptive proton therapy – the key to innovative planning approaches for paranasal cancer treatments

Nenoff, Lena; Matter, Michael; Lindmar, Johanna Hedlund; Weber, Damien Charles; Lomax, Anthony; Albertini, Francesca

doi:10.1080/0284186x.2019.1641217

Cited by 44 publications

(60 citation statements)

References 33 publications

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“…Figure 5(c) shows the pixel-wise counts target for fluence modulation C a target ðu; 0; 0Þ (step3) as given by Eq. (6). Parts of the variance target in (b) are assigned a value of 0, and receive the unit fluence in (c).…”

Section: B2 Fluence Optimizationmentioning

confidence: 99%

“…[1][2][3][4] At the same time, low-dose, frequent and accurate imaging, ideally at the treatment site, is required to ensure a safe delivery of the therapeutic doses. 5,6 Proton therapy treatment planning requires a spatial map of the relative (to water) stopping power (RSP), which in current clinical practice is acquired through a conversion from x-ray computed tomography (CT) images. [7][8][9] X-ray CT images are typically not acquired in treatment position and not prior to every treatment fraction, in order to keep treatment time short and imaging dose low enough that they do not compromise the dose benefit of proton therapy.…”

Section: Introductionmentioning

confidence: 99%

“…The good tolerance is believed to be linked to the low dose to normal tissue when using protons for treatment . At the same time, low‐dose, frequent and accurate imaging, ideally at the treatment site, is required to ensure a safe delivery of the therapeutic doses . Proton therapy treatment planning requires a spatial map of the relative (to water) stopping power (RSP), which in current clinical practice is acquired through a conversion from x‐ray computed tomography (CT) images .…”

Section: Introductionmentioning

confidence: 99%

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An optimization algorithm for dose reduction with fluence‐modulated proton CT

et al. 2020

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Purpose: Fluence-modulated proton computed tomography (FMpCT) using pencil beam scanning aims at achieving task-specific image noise distributions by modulating the imaging proton fluence spot-by-spot based on an object-specific noise model. In this work, we present a method for fluence field optimization and investigate its performance in dose reduction for various phantoms and image variance targets. Methods: The proposed method uses Monte Carlo simulations of a proton CT (pCT) prototype scanner to estimate expected variance levels at uniform fluence. Using an iterative approach, we calculate a stack of target variance projections that are required to achieve the prescribed image variance, assuming a reconstruction using filtered backprojection. By fitting a pencil beam model to the ratio of uniform fluence variance and target variance, relative weights for each pencil beam can be calculated. The quality of the resulting fluence modulations is evaluated by scoring imaging doses and comparing them to those at uniform fluence, as well as evaluating conformity of the achieved variance with the prescription. For three different phantoms, we prescribed constant image variance as well as two regions-of-interest (ROI) imaging tasks with inhomogeneous image variance. The shape of the ROIs followed typical beam profiles for proton therapy. Results: Prescription of constant image variance resulted in a dose reduction of 8.9% for a homogeneous water phantom compared to a uniform fluence scan at equal peak variance level. For a more heterogeneous head phantom, dose reduction increased to 16.0% for the same task. Prescribing two different ROIs resulted in dose reductions between 25.7% and 40.5% outside of the ROI at equal peak variance levels inside the ROI. Imaging doses inside the ROI were increased by 9.2% to 19.2% compared to the uniform fluence scan, but can be neglected assuming that the ROI agrees with the therapeutic dose region. Agreement of resulting variance maps with the prescriptions was satisfactory. Conclusions: We developed a method for fluence field optimization based on a noise model for a real scanner used in pCT. We demonstrated that it can achieve prescribed image variance targets. A uniform fluence field was shown not to be dose optimal and dose reductions achievable with the proposed method for FMpCT were considerable, opening an interesting perspective for image guidance and adaptive therapy.

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Section: B2 Fluence Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An optimization algorithm for dose reduction with fluence‐modulated proton CT

et al. 2020

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“…For the paranasal tumor patients, artificially generated CTs were used, as described elsewhere (25), simulating 10 CTs during the treatment course (referred to as simulated CTs), each with random anatomy and setup differences. Anatomical changes were simulated by filling each pre-contoured nasal cavity independently by first overwriting with the HU of air, and then with the HU of mucus in a layerwise approach (26) with a randomly selected filling stage (25). The layer-wise approach was considered to most realistically mimic actual filling of the nasal cavities (26) and with a random filling selected for each daily delivery, a worst case scenario has been simulated.…”

Section: Patient Data and Treatment Plansmentioning

confidence: 99%

“…These also frequently occur in HN (20,21) and lung cancer patients (22,23) and can cause large dose distortions during the treatment course. Thus, regular plan adaption is advised in these areas (24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

Daily Adaptive Proton Therapy: Is it Appropriate to Use Analytical Dose Calculations for Plan Adaption?

Nenoff

Matter

Jarhall

et al. 2020

International Journal of Radiation Oncology*Biology*Physics

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Purpose: The accuracy of analytical dose calculations (ADC) and dose uncertainties resulting from anatomical changes are both limiting factors in proton therapy. For the latter, rapid plan adaption is necessary, whereas for the former, Monte Carlo (MC) approaches are instead being increasingly recommended. These however are inherently slower than analytical approaches, potentially limiting the ability to rapidly adapt plans. Here, we compare the clinical relevance of uncertainties resulting from both. Materials and Methods:Five non-small-cell lung cancer (NSCLC) patients with up to nine CTs acquired during treatment and five paranasal (HN) patients with 10 simulated anatomical changes (sinus filling), were analyzed. On the initial planning-CTs, treatment plans were optimized and calculated using an ADC and then recalculated with MC. Additionally, all plans were recalculated (non-adapted) and re-optimized (adapted), on each repeated CT using the same ADC as for the initial plan and resulting dose distributions compared.Results: When comparing analytical and MC calculations in the initial treatment plan and averaged over all patients, 94.2% (NSCLC) and 98.5% (HN) of voxels had differences <±5% and only minor differences in CTV V95 (average <2%) were observed. In contrast, when re-calculating nominal plans on the repeat (anatomically changed) CTs, CTV V95 degraded by up to 34%. Plan adaption however restored CTV V95 differences between adapted and nominal plans to <0.5%. Adapted OAR doses remained the same or improved. Conclusion:Dose degradations caused by anatomical changes are substantially larger than uncertainties introduced by the use of analytical instead of MC dose calculations. Thus, if the use of analytical calculations can enable more rapid and efficient plan adaption than MC approaches, they can, and indeed should be used for plan adaption for these patient groups.

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Proton therapy needs further technological development to fulfill the promise of becoming a superior treatment modality (compared to photon therapy)

Hyer

Ding

Rong

2021

J Applied Clin Med Phys

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Daily adaptive proton therapy – the key to innovative planning approaches for paranasal cancer treatments

Abstract: Albertini (2019) Daily adaptive proton therapy-the key to innovative planning approaches for paranasal cancer treatments,

Cited by 44 publications

References 33 publications

An optimization algorithm for dose reduction with fluence‐modulated proton CT

An optimization algorithm for dose reduction with fluence‐modulated proton CT

Daily Adaptive Proton Therapy: Is it Appropriate to Use Analytical Dose Calculations for Plan Adaption?

Proton therapy needs further technological development to fulfill the promise of becoming a superior treatment modality (compared to photon therapy)

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