The dosimetric impact of intrafraction prostate motion and interfraction anatomical changes and the effect of beam gating and motion correction were investigated in dose-escalated linac-based SBRT. Fifty-six gated fractions were delivered using a novel electromagnetic tracking device with a 2 mm threshold. Real-time prostate motion data were incorporated into the patient’s original plan with an isocenter shift method. Delivered dose distributions were obtained by recalculating these motion-encoded plans on deformed CTs reflecting the patient’s CBCT daily anatomy. Non-gated treatments were simulated using the prostate motion data assuming that no treatment interruptions have occurred. The mean relative dose differences between delivered and planned treatments were −3.0% [−18.5–2.8] for CTV D99% and −2.6% [−17.8–1.0] for PTV D95%. The median cumulative CTV coverage with 93% of the prescribed dose was satisfactory. Urethra sparing was slightly degraded, with the maximum dose increased by only 1.0% on average, and a mean reduction in the rectum and bladder doses was seen in almost all dose metrics. Intrafraction prostate motion marginally contributed in gated treatments, while in non-gated treatments, further deteriorations in the minimum target coverage and bladder dose metrics would have occurred on average. The implemented motion management strategy and the strict patient preparation regimen, along with other treatment optimization strategies, ensured no significant degradations of dose metrics in delivered treatments.

Artificial intelligence can standardize and automatize highly demanding procedures, such as manual segmentation, especially in an anatomical site as common as the pelvis. This study investigated four automated segmentation tools on computed tomography (CT) images in female and male pelvic radiotherapy (RT) starting from simpler and well-known atlas-based methods to the most recent neural networks-based algorithms. The evaluation included quantitative, qualitative and time efficiency assessments. A mono-institutional consecutive series of 40 cervical cancer and 40 prostate cancer structure sets were retrospectively selected. After a preparatory phase, the remaining 20 testing sets per each site were auto-segmented by the atlas-based model STAPLE, a Random Forest-based model, and two Deep Learning-based tools (DL), MVision and LimbusAI. Setting manual segmentation as the Ground Truth, 200 structure sets were compared in terms of Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and Distance-to-Agreement Portion (DAP). Automated segmentation and manual correction durations were recorded. Expert clinicians performed a qualitative evaluation. In cervical cancer CTs, DL outperformed the other tools with higher quantitative metrics, qualitative scores, and shorter correction times. On the other hand, in prostate cancer CTs, the performance across all the analyzed tools was comparable in terms of both quantitative and qualitative metrics. Such discrepancy in performance outcome could be explained by the wide range of anatomical variability in cervical cancer with respect to the strict bladder and rectum filling preparation in prostate Stereotactic Body Radiation Therapy (SBRT). Decreasing segmentation times can reduce the burden of pelvic radiation therapy routine in an automated workflow.

Aim: In this study, a not yet commercially available fully-automated lexicographic optimization (LO) planning algorithm, called mCycle (Elekta AB, Stockholm, Sweden), was validated for cervical cancer.Material and methods: Twenty-four mono-institutional consecutive treatment plans (50 Gy/25 fx) delivered between November 2019 and April 2022 were retrospectively selected. The automatic re-planning was performed by mCycle, implemented in the Monaco TPS research version (v5.59.13), in which the LO and Multicriterial Optimization (MCO) are coupled with Monte Carlo calculation. mCycle optimization follows an a priori assigned priority list, the so-called Wish List (WL), representing a dialogue between the radiation oncologist and the planner, setting hard constraints and following objectives. The WL was tuned on a patient subset according to the institution’s clinical protocol to obtain an optimal plan in a single optimization. This robust WL was then used to automatically re-plan the remaining patients. Manual plans (MP) and mCycle plans (mCP) were compared in terms of dose distributions, complexity (modulation complexity score, MCS), and delivery accuracy (perpendicular diode matrices, gamma analysis-passing ratio, PR). Their clinical acceptability was assessed through the blind choice of two radiation oncologists. Finally, a global quality score index (SI) was defined to gather into a single number the plan evaluation process.Results: The WL tuning requested four patients. The 20 automated re-planning tasks took three working days. The median optimization and calculation time can be estimated at 4 h and just over 1 h per MP and mCP, respectively. The dose comparison showed a comparable organ-at-risk spare. The planning target volume coverage increased (V_95%: MP 98.0% [95.6–99.3]; mCP 99.2%[89.7–99.9], p >0.05). A significant increase has been registered in MCS (MP 0.29 [0.24–0.34]; mCP 0.26 [0.23–0.30], p 0.05). In the blind choice, all mCP results were clinically acceptable and chosen over MP in more than 75% of cases. The median SI score was 0.69 [0.41–0.84] and 0.73 [0.51–0.82] for MP and mCP, respectively (p >0.05).Conclusions: mCycle plans were comparable to clinical manual plans, more complex but accurately deliverable and registering a similar SI. Automated plans outperformed manual plans in blinded clinical choice.

Aim: To validate a fully-automated lexicographic optimization-planning system (mCycle, Elekta) for single-(SL) and multiple-(ML, up to 4 metastases) lesions in intracranial stereotactic radiosurgery (SRS, 21 Gy, single fraction). Methods: A pre-determined priority list, Wish-List (WL), represents a dialogue between planner and clinician, establishing strict constraints and pursuing objectives. In order to satisfy the clinical protocol without manual intervention, four patients were required to tweak and fine-tune each WL (SLp, MLp) for coplanar arcs. Thirtyfive testing plans (20 SLp, 15 MLp) were automatically re-planned (mCP). Automatic and manual plans were compared including dose constraints, conformality, modulation complexity score (MCS), delivery time, and local gamma analysis (2%/2 mm). To ensure plan clinical acceptability, two radiation oncologists conducted an independent blind plan choice. Results: Each WL-tuning took 3 days. Estimated median manual plans and mCP calculation time were 8 and 3 h, respectively. Significant increases in SLp and MLp target coverage and conformity were registered. mCP showed a not significant and clinically acceptable higher median brain V12 Gy. SLp registered a -5.8% MU decrease with comparable median delivery time (MP 2.0 min, mCP 1.9 min) while MLp showed a +9.8% MU increase and longer delivery time (MP 3.5 min, mCP 4.4 min). mCP MCS resulted significantly higher without affecting gamma passing rates. At blind choice, mCP were preferred in the majority of cases. Conclusions: Lexicographic optimization produced acceptable SRS plans with coplanar arcs significantly reducing the overall planning time in cases with up to 4 brain metastases. These planning improvements suggest further investigations by setting high-quality non-coplanar arc plans as a reference.

Myeloablative Total Body Irradiation (TBI) used in our Institution, as part of the conditioning scheme for haematopoietic stem cell transplantation, is an extended-distance supine technique that has been implemented using a 15 MV LINAC beam, lead lung compensators, PMMA, and water bolus to improve homogeneity. This study reviews in-vivo dosimetry (IVD) over 10 years of treatments, assessing the technique's robustness, accuracy, and efficiency. A 2-lateral opposite fields plan was calculated from planning CT with validated Oncentra TPS (Elekta AB, Sweden). Monitor units (MUs), lung compensators shape and thickness were calculated to deliver the prescription dose (12 Gy in 6 bi-daily fractions or 9.9 Gy in 3 daily fractions) to the patient's abdomen midline at the umbilical level, maintaining lung dose within ±5 % range of prescription. Data from 103 patients, of which more than 87 % were pediatric, were retrieved and analyzed for a total of 537 treatment fractions. The impact of IVD omission was evaluated, supposing doing it only once or in the first two fractions, if necessary. Median ΔMU from planned was -1.2 %. Median percentage dose deviation from prescription in 6 anatomical regions was below 2 %. IVD omission could have resulted in an increase of 7 patients registering at least one anatomical region outside the ±5 % dose range at the end of treatment. It is possible to confirm the implemented technique's robustness and accuracy in delivering the prescribed dose under IVD monitoring. Nevertheless, this technique and associated IVD are time-consuming and IVD omission could be assessed with limited drawbacks.