Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method

Hall, David C.; Trofimov, Alexei V.; Winey, B.; Liebsch, Norbert J.; Paganetti, Harald

doi:10.1016/j.ijrobp.2017.01.236

Cited by 18 publications

(10 citation statements)

References 19 publications

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“…Automated IMPT planning is in its infancy, however, Hall et al constructed a geometric knowledge-based model to predict patient-specific improvements using protons, over other modalities, for clival chordoma patients. Their model was based upon the correlation between dose and the distance-to-target, and used to predict feasible OAR DVHs for new patients [21]. Hennings et al developed a tool to automatically pre-calculate feasible planning solutions for uveal melanomas [22].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an Automated Proton Planning Solution

Delaney¹,

Verbakel²,

Lindberg³

et al. 2018

Cureus

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PurposeIntensity-modulated proton therapy (IMPT) treatments are increasing, however, treatment planning remains complex and prone to variability. RapidPlanTMPT (Varian Medical Systems, Palo Alto, California, USA) is a pre-clinical, proton-specific, automated knowledge-based planning solution which could reduce variability and increase efficiency. It uses a library of previous IMPT treatment plans to generate a model which can predict organ-at-risk (OAR) dose for new patients, and guide IMPT optimization. This study details and evaluates RapidPlanTMPT.MethodsIMPT treatment plans for 50 head-and-neck cancer patients populated the model-library. The model was then used to create knowledge-based plans (KBPs) for 10 evaluation-patients. Model quality and accuracy were evaluated using model-provided OAR regression plots and examining the difference between predicted and achieved KBP mean dose. KBP quality was assessed through comparison with respective manual IMPT plans on the basis of boost/elective planning target volume (PTVB/PTVE) homogeneity and OAR sparing. The time to create KBPs was recorded.ResultsModel quality was good, with an average R2 of 0.85 between dosimetric and geometric features. The model showed high predictive accuracy with differences of <3 Gy between predicted and achieved OAR mean doses for 88/109 OARs. On average, KBPs were comparable to manual IMPT plans with differences of <0.6% in homogeneity. Only 2 of 109 OARs in KBPs had a mean dose >3 Gy more than the manual plan. On average, dose-volume histogram (DVH) predictions required 0.7 minutes while KBP optimization and dose calculation required 4.1 minutes (a ‘continue optimization’ phase, if required, took an additional 2.8 minutes, on average).ConclusionsRapidPlanTMPT demonstrated efficiency and consistency and IMPT KBPs were comparable to manual plans. Because worse OAR sparing in a KBP was not always associated with geometric-outlier warnings, manual plan checks remain important. Such an automated planning solution could also assist in clinical trial quality assurance and overcome the learning curve associated with IMPT.

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Section: Discussionmentioning

confidence: 99%

Evaluation of an Automated Proton Planning Solution

Delaney¹,

Verbakel²,

Lindberg³

et al. 2018

Cureus

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“…Due to lack of proton derived radiobiological parameters, researchers continue to use photon-derived NTCP models for proton therapy. 12,14,25 Recently, Blanchard et al 24 validated photon-derived NTCP models that can be used to select head and neck patients for proton treatment.…”

Section: F | Patient-specific Quality Assurance (Qa) Analysismentioning

confidence: 99%

Radiobiological and dosimetric impact of RayStation pencil beam and Monte Carlo algorithms on intensity‐modulated proton therapy breast cancer plans

Rana

Greco

Samuel

et al. 2019

J Applied Clin Med Phys

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RayStation treatment planning system employs pencil beam (PB) and Monte Carlo (MC) algorithms for proton dose calculations. The purpose of this study is to evaluate the radiobiological and dosimetric impact of RayStation PB and MC algorithms on the intensity-modulated proton therapy (IMPT) breast plans. Methods: The current study included ten breast cancer patients, and each patient was treated with 1-2 proton beams to the whole breast/chestwall (CW) and regional lymph nodes in 28 fractions for a total dose of 50.4 Gy relative biological effectiveness (RBE). A total clinical target volume (CTV_Total) was generated by combining individual CTVs: AxI, AxII, AxIII, CW, IMN, and SCVN. All beams in the study were treated with a range shifter (7.5 cm water equivalent thickness). For each patient, three sets of plans were generated: (a) PB optimization followed by PB dose calculation (PB-PB), (b) PB optimization followed by MC dose calculation (PB-MC), and (c) MC optimization followed by MC dose calculation (MC-MC). For a given patient, each plan was robustly optimized on the CTVs with same parameters and objectives. Treatment plans were evaluated using dosimetric and radiobiological indices (equivalent uniform dose (EUD), tumor control probability (TCP), and normal tissue complication probability (NTCP)). Results: The results are averaged over ten breast cancer patients. In comparison to PB-PB plans, PB-MC plans showed a reduction in CTV target dose by 5.3% for D 99% and 4.1% for D 95% , as well as a reduction in TCP by 1.5-2.1%. Similarly, PB overestimated the EUD of target volumes by 1.8─3.2 Gy(RBE). In contrast, MC-MC plans achieved similar dosimetric and radiobiological (EUD and TCP) results as the ones in PB-PB plans. A selection of one dose calculation algorithm over another did not produce any noticeable differences in the NTCP of the heart, lung, and skin. Conclusion: If MC is more accurate than PB as reported in the literature, dosimetric and radiobiological results from the current study suggest that PB overestimates the target dose, EUD, and TCP for IMPT breast cancer treatment. The overestimation of dosimetric and radiobiological results of the target volume by PB needs to be further interpreted in terms of clinical outcome.

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“…(To the best of our knowledge, this desired water‐equivalent margin expansion is not yet available in any commercial treatment planning system so far.) Further improvement may involve various machine learning approaches (e.g., Ref …”

Section: Discussionmentioning

confidence: 99%

Contour‐based lung dose prediction for breast proton therapy

Zeng

Sine

Mah

2018

J Applied Clin Med Phys

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PurposeThis study evaluates the feasibility of lung dose prediction based on target contour and patient anatomy for breast patients treated with proton therapy.MethodsFifty‐two randomly selected patients were included in the cohort, who were treated to 50.4–66.4 Gy(RBE) to the left (36), right (15), or bilateral (1) breast with uniform scanning (32) or pencil beam scanning (20). Anterior‐oblique beams were used for each patient. The prescription doses were all scaled to 50.4 Gy(RBE) for the current analysis. Isotropic expansions of the planning target volume of various margins m were retrospectively generated and compared with isodose volumes in the ipsilateral lung. The fractional volume V of each expansion contour within the ipsilateral lung was compared with dose–volume data of clinical plans to establish the relationship between the margin m and dose D for the ipsilateral lung such that V D = V(m). This relationship enables prediction of dose–volume VD from V(m), which could be derived from contours before any plan is generated, providing a goal of plan quality. Lung V 20 Gy( RBE ) and V 5 Gy( RBE ) were considered for this pilot study, while the results could be generalized to other dose levels and/or other organs.ResultsThe actual V 20 Gy( RBE ) ranged from 6% to 23%. No statistically significant difference in V 20 Gy( RBE ) was found between breast irradiation and chest wall irradiation (P = 0.8) or between left‐side and right‐side treatment (P = 0.9). It was found that V(1.1 cm) predicted V 20 Gy( RBE ) to within 5% root‐mean‐square deviation (RMSD) and V(2.2 cm) predicted V 5 Gy( RBE ) to within 6% RMSD.ConclusionA contour‐based model was established to predict dose to ipsilateral lung in breast treatment. Clinically relevant accuracy was demonstrated. This model facilitates dose prediction before treatment planning. It could serve as a guide toward realistic clinical goals in the planning stage.

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Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method

Cited by 18 publications

References 19 publications

Evaluation of an Automated Proton Planning Solution

Evaluation of an Automated Proton Planning Solution

Radiobiological and dosimetric impact of RayStation pencil beam and Monte Carlo algorithms on intensity‐modulated proton therapy breast cancer plans

Contour‐based lung dose prediction for breast proton therapy

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