S1832
Physics - Dose prediction/calculation, optimisation and applications for photon and electron planning
ESTRO 2026
subsets of such a CTM based on histological data specific to the tumour type, and the treatment plan is then optimized through an expected-value optimization, to consider the setup geometric uncertainty in a probabilistic fashion [2].Five Head & Neck (H&N) cancer patient cases were analyzed. The study compared conventional PTV-based plans with probabilistic plans (created in RayStation (v2023B, RaysearchLabs)) through a probabilistic evaluation method, which involved simulating composite scenarios by sampling from the truncated PDFs of MI and setup errors. A composite scenario was realized by randomly sampling the margin of a new CTV, and a setup shift.Additionally, two other kinds of plans were created to assess the separate effects of the MI model and geometric uncertainties: in one of them only the CTM hypothesis (MI modelling) was included, and in the other one only the expected-value optimization (geometric uncertainties modelling). Everything else in such plans was performed according to conventional PTV techniques. Results: In all patient cases probabilistic planning reliably maintained target coverage while significantly improving OARs sparing compared to PTV-based plans. The evaluation using the separate-hypotheses plans showed that both the CTM and expected-value optimization contributed comparably to enhanced OARs sparing, and at times the combination of the two hypotheses provided added value to the individual ones. Conclusion: Probabilistic planning not only accounts for the probability of MI and geometric uncertainties, but it also shows maintained target coverage and improved OARs sparing. In this study, a histological dataset of 10 patients was exploited [3]; therefore, incorporating more statistically vigorous datasets and advanced MI models that account for infiltration patterns and directional tumor growth could further add to the reliability and generalizability of the probabilistic planning. References: 1) Van Herk M, Remeijer P, Rasch C, Lebesque JV. The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy. International Journal of Radiation Oncology*Biology*Physics.2) Buti G, Souris K, Barragán Montero A, Lee J, Sterpin E. Introducing a probabilistic definition of the target in a robust treatment planning framework. Physics in Medicine & Biology.3) Campbell S, Poon I, Markel D, Vena D, Higgins K, Enepekides D, et al. Evaluation of Microscopic Disease in Oral Tongue Cancer Using Whole-Mount Histopathologic Techniques: Implications for the Management of Head-and-Neck Cancers. International Journal of Radiation
noticeable in the anteroposterior direction. Conclusion:
Including the immobilisation system in the external contour is essential, regardless of the method used for assigning ED. The frameless immobilization systems showed negligible dosimetric impact on both rotational and translational misalignments, except for anteroposterior translations, which exhibited larger dose variations. These findings suggest that avoiding midline beam entry may not be necessary; however, further validation through experimental phantom irradiations is warranted to confirm their lack of clinical significance. Keywords: Beam attenuation, Dosimetric accuracy Probabilistic optimization: comparison of PTV and probabilistic treatment plans for head and neck cancer Matteo Zazzini 1,2 , Ivar Bengtsson 3 , Johan Sundström 3 , Albin Fredriksson 3 , Edmond Sterpin 1,2 1 ExpRT, KU Leuven, Leuven, Belgium. 2 MIRO, UC Louvain, Bruxelles, Belgium. 3 Research, RaySearch Laboratories, Stockholm, Sweden Purpose/Objective: Despite the PTV approach to radiotherapy treatment planning being simple and efficient [1], it consists of using fixed and symmetrical margins that neglect how tumor cells actually spread, or how likely they are to be found in surrounding tissue. Probabilistic planning could be implemented by novel target definition and optimization strategies that consider the likelihood of Microscopic Infiltration (MI) and geometric uncertainties. In addition, probabilistic planning promises to offer improved Organs At Risk (OARs) sparing by reshaping the dose distribution towards less conflicting areas when OARs and the target overlap (Figure 1). Digital Poster 1398
Material/Methods: The probabilistic framework includes creating a Clinical Target Map (CTM) and integrating it into the treatment planning system. Probabilities of MI presence are assigned as voxel-wise weights to
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