Using a knowledge-based planning solution to select patients for proton therapy

Delaney, Alexander R.; Dahele, Max; Tol, Jim P.; Kuijper, I.T.; Slotman, Ben J.; Verbakel, Wilko F.A.R.

doi:10.1016/j.radonc.2017.03.020

Cited by 38 publications

(48 citation statements)

References 18 publications

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“…An example is the difference between the dose distribution used for patient selection and that in the final deliverable treatment plan. These differences can occur if predicted organ-at-risk DVHs, are used for patient selection, which was proposed by Delaney et al [22]. In our study, we used fully generated treatment plans for patient selection to minimize the dose uncertainty.…”

Section: Discussionmentioning

confidence: 99%

“…Another source of uncertainty is the difference between planned and actually delivered doses [7]. Those differences are not exactly known but are estimated to be in the order of a few Gy, however, there is evidence for larger differences as well [16,22]. Part of the dose uncertainties are already included in the models' uncertainty as the models have been built from real clinical data.…”

Section: Dose Uncertaintymentioning

confidence: 99%

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Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

et al. 2017

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Section: Discussionmentioning

confidence: 99%

Section: Dose Uncertaintymentioning

confidence: 99%

Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

et al. 2017

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“…1 The NTCP model-based approach uses normal tissue effect thresholds to categorise patients into those deemed likely to benefit from PBT, those who will not and a selection of patients where there is uncertainty for whom RCTs would be used to compare the two treatment modalities. 65,77,78 Countries offering PT are conducting or planning clinical trials. 15,72 However, a waning of clinical trial initiation was noted by Odei et al 79 in 2016 with funding barriers one cited cause.…”

Section: What Evidence Is Needed?mentioning

confidence: 99%

Particle therapy tumour outcomes: An updated systematic review

Hwang

Gorayski

et al. 2020

J Med Imag Rad Onc

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Particle therapy (PT) offers the potential for reduced normal tissue damage as well as escalation of target dose, thereby enhancing the therapeutic ratio in radiation therapy. Reflecting the building momentum of PT use worldwide, construction has recently commenced for The Australian Bragg Centre for Proton Therapy and Research in Adelaidethe first PT centre in Australia. This systematic review aims to update the clinical evidence base for PT, both proton beam and carbon ion therapy. The purpose is to inform clinical decision-making for referral of patients to PT centres in Australia as they become operational and overseas in the interim. Three major databases were searched by two independent researchers, and evidence quality was classified according to the National Health and Medical Research Council evidence hierarchy. One hundred and thirty-six studies were included, two-thirds related to proton beam therapy alone. PT at the very least provides equivalent tumour outcomes compared to photon controls with the possibility of improved control in the case of carbon ion therapy. There is suggestion of reduced morbidities in a range of tumour sites, supporting the predictions from dosimetric modelling and the wide international acceptance of PT for specific indications based on this. Though promising, this needs to be counterbalanced by the overall low quality of evidence found, with 90% of studies of level IV (case series) evidence. Prospective comparative clinical trials, supplemented by database-derived outcome information, preferably conducted within international and national networks, are strongly recommended as PT is introduced into Australasia.

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“…We previously illustrated the principle of using the photon-RapidPlan solution to automatically create IMPT treatment plans [ 15 ]. Since then, we have collaborated on the development of a prototype knowledge-based IMPT (proton-specific) planning solution, RapidPlanPT, in which the physical characteristics of proton beams have been appropriately modeled.…”

Section: Introductionmentioning

confidence: 99%

Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study

Delaney

Dong

Mascia

et al. 2018

Cancers

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Background: Radiotherapy treatment planning is increasingly automated and knowledge-based planning has been shown to match and sometimes improve upon manual clinical plans, with increased consistency and efficiency. In this study, we benchmarked a novel prototype knowledge-based intensity-modulated proton therapy (IMPT) planning solution, against three international proton centers. Methods: A model library was constructed, comprising 50 head and neck cancer (HNC) manual IMPT plans from a single center. Three external-centers each provided seven manual benchmark IMPT plans. A knowledge-based plan (KBP) using a standard beam arrangement for each patient was compared with the benchmark plan on the basis of planning target volume (PTV) coverage and homogeneity and mean organ-at-risk (OAR) dose. Results: PTV coverage and homogeneity of KBPs and benchmark plans were comparable. KBP mean OAR dose was lower in 32/54, 45/48 and 38/53 OARs from center-A, -B and -C, with 23/32, 38/45 and 23/38 being >2 Gy improvements, respectively. In isolated cases the standard beam arrangement or an OAR not being included in the model or being contoured differently, led to higher individual KBP OAR doses. Generating a KBP typically required <10 min. Conclusions: A knowledge-based IMPT planning solution using a single-center model could efficiently generate plans of comparable quality to manual HNC IMPT plans from centers with differing planning aims. Occasional higher KBP OAR doses highlight the need for beam angle optimization and manual review of KBPs. The solution furthermore demonstrated the potential for robust optimization.

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Using a knowledge-based planning solution to select patients for proton therapy

Cited by 38 publications

References 18 publications

Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

Particle therapy tumour outcomes: An updated systematic review

Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study

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