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Background: Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically implemented in-house trained DL segmentation model for breast cancer to a previously performed pilot study to assess possible differences in performance or acceptability., Material and Methods: Sixty patients with whole breast radiotherapy, with or without an indication for locoregional radiotherapy were included. Structures were qualitatively scored by radiotherapy technologists and radiation oncologists. Quantitative evaluation was performed using dice-similarity coefficient (DSC), 95th percentile of Hausdorff Distance (95%HD) and surface DSC (sDSC), and time needed for generating, checking, and correcting structures was measured., Results: Ninety-three percent of all contours in clinic were scored as clinically acceptable or usable as a starting point, comparable to 92% achieved in the pilot study. Compared to the pilot study, no significant changes in time reduction were achieved for organs at risks (OARs). For target volumes, significantly more time was needed compared to the pilot study for patients including lymph node levels 1-4, although time reduction was still 33% compared to manual segmentation. Almost all contours have better DSC and 95%HD than inter-observer variations. Only CTVn4 scored worse for both metrics, and the thyroid had a higher 95%HD value., Interpretation: The use of the DL model in clinical practice is comparable to the pilot study, showing high acceptability rates and time reduction.

Background: Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists., Methods: For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time., Results: The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90-95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model., Conclusions: Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90-95% of the AI plans clinically acceptable., (© 2022. The Author(s).)

Background: The STAR-TReC trial is an international multi-center, randomized, phase II study assessing the feasibility of short-course radiotherapy or long-course chemoradiotherapy as an alternative to total mesorectal excision surgery. A new target volume is used for both (chemo)radiotherapy arms which includes only the mesorectum. The treatment planning QA revealed substantial variation in dose to organs at risk (OAR) between centers. Therefore, the aim of this study was to determine the treatment plan variability in terms of dose to OAR and assess the effect of a national study group meeting on the quality and variability of treatment plans for mesorectum-only planning for rectal cancer., Methods: Eight centers produced 25 × 2 Gy treatment plans for five cases. The OAR were the bowel cavity, bladder and femoral heads. A study group meeting for the participating centers was organized to discuss the planning results. At the meeting, the values of the treatment plan DVH parameters were distributed among centers so that results could be compared. Subsequently, the centers were invited to perform replanning if they considered this to be necessary., Results: All treatment plans, both initial planning and replanning, fulfilled the target constraints. Dose to OAR varied considerably for the initial planning, especially for dose levels below 20 Gy, indicating that there was room for trade-offs between the defined OAR. Five centers performed replanning for all cases. One center did not perform replanning at all and two centers performed replanning on two and three cases, respectively. On average, replanning reduced the bowel cavity V20Gy by 12.6%, bowel cavity V10Gy by 22.0%, bladder V35Gy by 14.7% and bladder V10Gy by 10.8%. In 26/30 replanned cases the V10Gy of both the bowel cavity and bladder was lower, indicating an overall lower dose to these OAR instead of a different trade-off. In addition, the bowel cavity V10Gy and V20Gy showed more similarity between centers., Conclusions: Dose to OAR varied considerably between centers, especially for dose levels below 20 Gy. The study group meeting and the distribution of the initial planning results among centers resulted in lower dose to the defined OAR and reduced variability between centers after replanning., Trial Registration: The STAR-TReC trial, ClinicalTrials.gov Identifier: NCT02945566. Registered 26 October 2016, https://clinicaltrials.gov/ct2/show/NCT02945566).

Purpose: Local recurrence rates are high in patients with locally advanced NSCLC treated with 60 to 66 Gy in 2 Gy fractions. It is hypothesised that boosting volumes with high SUV on the pre-treatment FDG-PET scan potentially increases local control while maintaining acceptable toxicity levels. We compared two approaches: threshold-based dose painting by contours (DPBC) with voxel-based dose painting by numbers (DPBN)., Materials and Methods: Two dose painted plans were generated for 10 stage II/III NSCLC patients with 66 Gy at 2-Gy fractions to the entire PTV and a boost dose to the high SUV areas within the primary GTV. DPBC aims for a uniform boost dose at the volume encompassing the SUV 50%-region (GTV(boost)). DPBN aims for a linear relationship between the boost dose to a voxel and the underlying SUV. For both approaches the boost dose was escalated up to 130 Gy (in 33 fractions) or until the dose limiting constraint of an organ at risk was met., Results: For three patients (with relatively small peripheral tumours) the dose within the GTV could be boosted to 130 Gy using both strategies. For the remaining patients the boost dose was confined by a critical structure (mediastinal structures in six patients, lungs in one patient). In general the amount of large brush DPBC boosting is limited whenever the GTV(boost) is close to any serial risk organ. In contrast, small brush DPBN inherently boosts at a voxel-by-voxel basis allowing significant higher dose values to high SUV voxels more distant from the organs at risk. We found that the biological SUV gradients are reasonably congruent with the dose gradients that standard linear accelerators can deliver., Conclusions: Both large brush DPBC and sharp brush DPBN techniques can be used to considerably boost the dose to the FDG avid regions. However, significantly higher boost levels can be obtained using sharp brush DPBN although sometimes at the cost of a less increased dose to the low SUV regions., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)