S2791
RTT - RTT contouring, target definition, and treatment planning
ESTRO 2026
Primo ž Marolt 1 , Helena Barbara Zobec Logar 2 , Barbara Š egedin 2 , Robert Hudej 1 , Manja Kobav 2 , Omar Hanuna 1 1 Department of Radiophisycs, Institute of Oncology Ljubljana, Ljubljana, Slovenia. 2 Department of Brachytherapy, Institute of Oncology Ljubljana, Ljubljana, Slovenia
25 fractions of 2 Gy. For each patient, three treatment plans were generated and compared. The model was amended according to the clinical practice of our hospital, using VMAT with one single arc. Originally the model existed of 1 dual arc. First, two experienced radiation therapists (RTTs) created a clinical treatment plan, 5 plans each fulfilling the prerequisites of the clinical protocol. Second, a DL-model generated an initial DL-plan (DL-plan). Finally the DL-plan was further manually optimized (optimized DL-plan) by the same RTTs. The generation time for the three plans was measured and subsequently compared. One radiation oncologist evaluated the target coverage and organ-at-risk doses for all three plans per patient and selected the clinically best preferred option. The radiation oncologist was blinded for the type of treatment plan. The plans were compared using a two- way ANOVA, the significance threshold was ≤ 0.05. Results: The DL model was able to generate good treatment plans. Statistically significant differences were primarily observed between the clinical plan and both DL-plans, specifically for the femoral heads (p<0.006) and planning time (p=<0.02). Notably, the optimized DL-plan resulted in an average time saving of seven minutes compared to the clinical plan. The radiation oncologist preferred the optimized DL-plan in six out of ten patients, and only one initial DL-plan was deemed clinically unacceptable, indicating high quality across all plans.
Purpose/Objective: Brachytherapy (BT) boost after external beam
radiotherapy (EBRT) is the best treatment of choice for the most advanced gynecological cancers. In modern image-guided adaptive brachytherapy (IGABT) the tumor, high-risk clinical target volume (CTV-HR), and organ at risk (OAR) delineation and treatment planning are MRI (magnetic resonance imaging) based. The newest brachytherapy applicators available on the market allow for the guidance of interstitial needles that can be implanted in conjunction with an intracavitary component. However, these standard applicators are not suitable for patients with very large tumors or unfavorable anatomy. For such cases, the 3D printing technique enables the manufacturing of custom-made applicators. The aim of the study was improving (CTV-HR) coverage with the use of custom- made 3D-printed applicators in advanced gynecological cancers without exceeding the dose constraints to the OAR. This study aimed to develop the efficacy of custom-made applicators for different gynecological cancers. Material/Methods: MRI from the preplanning brachytherapy application with an in situ standard intracavitary applicator was used for modelling of the individual applicator. Virtual needles were added to the treatment plan to achieve the best possible coverage of CTV-HR. The location of the needles in the plan was used to create a custom- made 3D printed applicator. For each patient, an optimized intracavitary + interstitial plan with a standard applicator and an optimized plan with a custom-made 3D printed applicator were created. Results: Our study confirmed that the use of custom-made applicators improved DVH parameters for CTV-HR by approximately 30–40%. The D2cc dose for the OARs was higher in most cases, but the dose did not exceed the dose constraints for the OAR. Conclusion: The involvement of the RTT dosimetrist is essential throughout the process of custom-made applicator development, encompassing preplanning, treatment planning, and 3D modeling of applicator components.The benefit of this approach could also be applied to the patients for whom the dose aim could not be reached, but the improvement of the CTV-HR DVH parameters could result in better local
Conclusion: The study concludes that an externally optimized DL- planning model can be used efficiently in a different department. Plan quality was comparable, but the use of DL-based planning significantly improved efficiency, resulting in substantial time saving, both in treatment plan creation and in DL model implementation. Keywords: Deep learning, rectal cancer
Digital Poster 3388
Custom made applicators in gynecological brachytherapy- RTT dosimetrist perspective
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