Introduction: There are various open access head and neck knowledge-based planning (KBP) models available but have varying performance. This study evaluates a new open access RapidPlan (RP) model, HN-SIB-BPI, which was created using a previous best performing model as a foundation. The goal of the new model was to increase conformality and further reduce dose to organs-at-risks (OARs), including parotid glands, with the quantification of improvement evaluated with a dosimetric scorecard. The model's performance was tested in a clinic in India and compared to existing plans. Materials and Methods: A total of 18 head and neck plans treated with volumetric modulated arc therapy with Planning Target Volumes (PTVs), abutting or overlapping bilateral parotid glands, were replanned manually (TrueBeam Manual Comparator Plans [TB-MCP]) and with HN-SIB-BPI. Each patient had either two or three dose level PTVs. Plans were recreated with both C arm and ring gantry delivery systems using four arcs as specified in HN-SIB-BPI documentation. Results: C arm and O gantry plans showed similar coverage for low, intermediate, and high-risk target volumes. Both delivery systems reduced doses to brain stem, spinal cord, larynx, and bilateral parotid glands. C arm plans demonstrated mean dose reduction in left and right parotid glands 3.57 +/- 2.65 Gy (p < 0.01) and 1.60 +/- 2.13 Gy (p = 0.01), respectively, compared with TB-MCP. However, there was increased mean dose to the lips by 1.65 +/- 2.76 Gy with C arm plans (p = 0.03). Similarly, O gantry plans reduced the mean doses in left and right parotid glands by 2.80 +/- 2.84 Gy (p < 0.01) and 1.41 +/- 2.20 Gy (p = 0.01), respectively, but increased the mean dose to the lips by 1.20 +/- 2.22 Gy (p = 0.04). Conclusion: The HN-SIB-BPI open access KBP model was validated in our local Indian patient population, demonstrating significant reduction in parotid glands dose while maintaining similar target volume coverage. Applying this KBP model improved plan quality in this study. The authors plan to implement the HN-SIB-BPI model as the default model in clinical practice.

PurposeTo demonstrate the ease and feasibility that hippocampal sparing whole brain (WB) simultaneous integrated boost (HSWB-SIB) plans can be generated using knowledge-based planning and Eclipse Scripting Application Programming Interface (ESAPI) for three different modalities, HyperArc on TrueBeam (TB-HA), a coplanar beam arrangement on TrueBeam (TB-Co), and the ring-mounted Halcyon LINAC (Hal).MethodsTwelve patients with 2-14 brain metastases were retrospectively replanned for HSWB-SIB using a published HSWB RapidPlan model with modifications for the automated addition of SIB to metastases. Prescribed dose was 30 Gy to the WB planning target volume (PTV) and 50 Gy to the metastases in 10 fractions. Eclipse treatment planning system (v16.1) was used with a 6 MV-FFF beam and Acuros XB dose algorithm.ResultsThe methodology was successfully used for all modalities, generating plans in under 30 min. The plan doses were normalized to the WB PTV D95% receiving 30 Gy. Reporting values in the order of Hal, TB-Co, and TB-HA: The WB PTV received a V48 Gy of 4.58, 3.98, and 4.45 cc with statistically insignificant differences (p = 0.806). The boost PTVs received a D95% of 50.60, 50.43, and 51.13 Gy with statistically significant comparisons between TB-HA and the other two modalities (p = 0.005). The hippocampus maximum dose was 11.81, 11.51, and 11.13 Gy with no statistically significant comparisons (p = 0.105). All other oragns-at-risk (OAR) doses were clinically acceptable. The modalities were evaluated using a dosimetric scorecard, achieving average scores of 84.85%, 86.45%, and 87.39%. End-to-end testing ensured the deliverability of the HSWB-SIB plans for all modalities.ConclusionThe novel modification of the preexisting HSWB RapidPlan model with the automated inclusion of SIB objectives allows for easy, intuitive planning of complex HSWB-SIB treatments. All modalities demonstrated can be used with clinically comparable results. Other institutions are recommended to pursue and validate this HSWB-SIB technique to increase the accessibility of a single-course of high-quality treatment for patients with multiple brain lesions.

IntroductionThis study develops two new multi-institutional hippocampal-sparing whole-brain RapidPlan™ models (HLS-EC-WB and HMS-EC-WB) inspired by CCTG-CE.7 featuring enhanced target coverage with varying hippocampal sparing (limited and moderate).MethodsNew dosimetric scorecards were created to quantify the models’ clinical intent. The models were trained using a multi-institution dataset, and a recursive method was employed to generate consistent, high-quality plans. The models were validated using a five-case set and compared at 20- and 30-Gy prescriptions.ResultsEach model scored highest on its associated dosimetric scorecard. The new models achieved higher brain PTV prescription coverage (98%–99%) compared to the previous HSWBv2 model (95.12%), with some trade-off in hippocampal sparing.ConclusionsThree high-quality automated RapidPlan™ models for hippocampal-sparing whole brain are now available, each with a distinct dosimetric scorecard. The new models prioritize increased PTV coverage at some expense to hippocampal sparing. All models, example plans, scorecards, and scoring tools are freely available online.

PURPOSE/OBJECTIVE S:Due to manual OAR contouring challenges, various automatic contouring solutions have been introduced. Historically, common clinical auto-segmentation algorithms used were atlas-based, which required maintaining a library of self-made contours. Searching the collection was computationally intensive and could take several minutes to complete. Deep learning approaches have shown significant benefits compared to atlas-based methods in improving segmentation accuracy and efficiency in auto-segmentation algorithms. This work represents the first multi-institutional study to describe and evaluate an AI algorithm for the auto-segmentation of organs at risk (OARs) based on a deep image-to-image network (DI2IN). MATERIALS/METHODS:The AI-Rad Companion Organs RT (AIRC) algorithm (Siemens Healthineers, Erlangen, Germany) uses a two-step approach for segmentation. In the first step, the target organ region in the optimal input image is extracted using a trained deep reinforcement learning network (DRL), which is then used as input to create the contours in the second step based on DI2IN. The study was initially designed as a prospective single-center evaluation. The automated contours generated by AIRC were evaluated by three experienced board-certified radiation oncologists using a four-point scale where 4 is clinically usable and 1 requires re-contouring. After seeing favorable results in a single-center pilot study, we decided to expand the study to six additional institutions, encompassing eight additional evaluators for a total of 11 physician evaluators across seven institutions. RESULTS:One hundred and fifty-six patients and 1366 contours were prospectively evaluated. The five most commonly contoured organs were the lung (136 contours, average rating = 4.0), spinal cord (106 contours, average rating = 3.1), eye globe (80 contours, average rating = 3.9), lens (77 contours, average rating = 3.9), and optic nerve (75 contours, average rating = 4.0). The average rating per evaluator per contour was 3.6. On average, 124 contours were evaluated by each evaluator. 65% of the contours were rated as 4, and 31% were rated as 3. Only 4% of contours were rated as 1 or 2. Thirty-three organs were evaluated in the study, with 19 structures having a 3.5 or above average rating (ribs, abdominopelvic cavity, skeleton, larynx, lung, aorta, brachial plexus, lens, eye globe, glottis, heart, parotid glands, bladder, kidneys, supraglottic larynx, submandibular glands, esophagus, optic nerve, oral cavity) and the remaining organs having a rating of 3.0 or greater (female breast, proximal femur, seminal vesicles, rectum, sternum, brainstem, prostate, brain, lips, mandible, liver, optic chiasm, spinal cord, spleen). No organ had an average rating below 3. CONCLUSION:AIRC performed well with greater than 95% of contours accepted by treating physicians with no or minor edits. It supported a fully automated workflow with the potential for time savings and increased standardization with the use of AI-powered algorithms for high-quality OAR contouring.

Purpose/ObjectiveField size limitations on Halcyon and Ethos treatment machines largely preclude use of the conventional monoisocentric three-field technique for breast/chest wall and regional lymph nodes. We present an alternative, IMRT-based planning approach that facilitates treatment on Halcyon and Ethos while preserving plan quality.Materials/MethodsEight breast and regional node cases (four left-sided, four right-sided) were planned for an Ethos machine using a 15-17 field IMRT technique. Institutional plan quality metrics for CTV and PTV coverage and OAR sparing were assessed. Five plans (four right-sided, one left-sided) were also planned using a hybrid 3D multisocenter technique. CTV coverage and OAR sparing were compared to the IMRT plans. Eclipse scripting tools were developed to aid in beam placement and plan evaluation through a set of dosimetric scorecards, and both are shared publicly.ResultsOn average, the IMRT plans achieved breast CTV and PTV coverage at 50 Gy of 97.9% and 95.7%, respectively. Supraclavicular CTV and PTV coverages at 45 Gy were 100% and 95.5%. Axillary lymph node CTV and PTV coverages at 45 Gy were 100% and 97.1%, and IMN CTV coverage at 45 Gy was 99.2%. Mean ipsilateral lung V20 Gy was 19.3%, and average mean heart dose was 1.6 Gy for right-sided cases and 3.0 Gy for left-sided. In comparison to the hybrid 3D plans, IMRT plans achieved higher breast and supraclavicular CTV coverage (99.9% vs. 98.6% and 99.9% vs. 93.4%), higher IMN coverage (99.6% vs. 78.2%), and lower ipsilateral lung V20 Gy (19.6% vs. 28.2%).ConclusionInstitutional plan quality benchmarks were achieved for all eight cases using the IMRT-based planning approach. The IMRT-based planning approach offered superior conformity and OAR sparing than a competing hybrid 3D approach.

Purpose: The autosegmentation algorithm of Siemens Healthineers version VA 30 (AASH) (Siemens Healthineers, Erlangen, Germany) was trained and developed in the male pelvis, with no published data on its usability in the female pelvis. This is the first multiinstitutional study to describe and evaluate an artificial intelligence algorithm for autosegmentation of the pelvic nodal region by gender. Methods and Materials: We retrospectively evaluated AASH pelvic nodal autosegmentation in both male and female patients treated at our network of institutions. The automated pelvic nodal contours generated by AASH were evaluated by 1 board -certified radiation oncologist. A 4 -point scale was used for each nodal region contour: a score of 4 is clinically usable with minimal edits; a score of 3 requires minor edits (missing nodal contour region, cutting through vessels, or including bowel loops) in 3 or fewer computed tomography slices; a score of 2 requires major edits, as previously defined but in 4 or more computed tomography slices; and a score of 1 requires complete recontouring of the region. Pelvic nodal regions included the right and left side of the common iliac, external iliac, internal iliac, obturator, and midline presacral nodes. In addition, patients were graded based on their lowest nodal contour score. Statistical analysis was performed using Fisher exact tests and Yates -corrected x2 tests. Results: Fifty-two female and 51 male patients were included in the study, representing a total of 468 and 447 pelvic nodal regions, respectively. Ninety-six percent and 99% of contours required minor edits at most (score of 3 or 4) for female and male patients, respectively (P = .004 using Fisher exact test; P = .007 using Yates correction). No nodal regions had a statistically significant difference in scores between female and male patients. The percentage of patients requiring no more than minor edits was 87% (45 patients) and 92% (47 patients) for female and male patients, respectively (P = .53 using Fisher exact test; P = .55 using Yates correction). Conclusions: AASH pelvic nodal autosegmentation performed very well in both male and female pelvic nodal regions, although with better male pelvic nodal autosegmentation. As autosegmentation becomes more widespread, it may be important to have equal representation from all sexes in training and validation of autosegmentation algorithms. (c) 2023 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

To develop a Knowledge Based Planning (KBP) model for creating quantifiably high quality VMAT treatment plans in a single click for head and neck cases treated Simultaneous Integrated Boost (SIB) with bilateral parotid involvement (BPI) where both parotids are near, abutting or partially overlapping target volume. Eclipse RapidPlan and the publicly available PlanScorecard tool were used to assess existing Head and Neck RapidPlan models on two representative cases. The best performer was used as a foundation model to assist in creating new initial training set doses from previously treated cases. Those initial 27 cases were first replanned using only the selected foundation model, then further improved based on manual replanning, informed by dosimetric scorecard assessment. A new, initial model was trained from those 27 foundation model created cases that had been manually improved. Then, that initial model was used to replan those cases again, resulting in higher scores. Additional cases were also replanned using the initial model along with some manual changes to the optimization objectives to increase the score. This resulted in a total of 66 cases from which the final, released, HN-SIB-BPI was trained. A 27 case subset of the full training set was replanned and rescored at each phase of the process with a 260 total point 3-target scorecard. The average score increased: 210.5 foundation model; 226.96 manually improved plans; 230.1 initial model; 231.7 HN-SIB-BPI. On the same 27 case subset, mean ipsilateral and contralateral parotid dose decreased by 1.05Gy and 1.58Gy respectively from the foundation model to HN-SIB-BP. Eight external cases were created from HN-SIB-BPI with dosimetric scorecard validation on Halcyon (3-PTV:221.38/260; 2-PTV:196.1/228.5) and TrueBeam (3-PTV:222.01/260; 2-PTV:202.24/228.5). A specific clinical intent (ie: max parotid sparing) can be articulated in a comprehensive and precise manner by creating a dosimetric scorecard with individual metrics points assigned to each OAR and target metric reflecting their relative importance. This process improved the KBP model (HN-SIB-BPI) in several quantifiable ways including further sparing of parotid dose. All results and tools in this work are shared publicly.

Purpose: A scoring mechanism called the scorecard that objectively quantifies the dosimetric plan quality of pancreas stereotactic body radiation therapy treatment plans is introduced.Methods and Materials: A retrospective analysis of patients with pancreatic ductal adenocarcinoma receiving stereotactic body radiation therapy at our institution between November 2019 and November 2020 was performed. Ten patients were identified. All patients were treated to 36 Gy in 5 fractions, and organs at risk (OARs) were constrained based on Alliance A021501. The scorecard awarded points for OAR doses lower than those cited in Alliance A021501. A team of 3 treatment planners and 2 radiation oncologists, including a physician resident without plan optimization experience, discussed the relative importance of the goals of the treatment plan and added additional metrics for OARs and plan quality indexes to create a more rigorous scoring mechanism. The scorecard for this study consisted of 42 metrics, each with a unique piecewise linear scoring function which is summed to calculate the total score (maximum possible score of 365). The scorecard-guided plan, the planning and optimization for which were done exclusively by the physician resident with no prior plan optimization experience, was compared with the clinical plan, the planning and optimization for which were done by expert dosimetrists, using the Sign test.Results: Scorecard-guided plans had, on average, higher total scores than those clinically delivered for each patient, averaging 280.1 for plans clinically delivered and 311.7 for plans made using the scorecard (P = .003). Additionally, for most metrics, the average score of each metric across all 10 patients was higher for scorecard-guided plans than for clinically delivered plans. The scorecard guided the Conclusions: A scorecard tool can help clarify the goals of a treatment plan and provide an objective method for comparing the results of different plans. Our study suggests that a completely novice treatment planner can use a scorecard to create treatment plans with enhanced coverage, conformality, and improved OAR sparing, which may have significant effects on both tumor control and toxicity. These tools, including the scorecard used in this study, have been made freely available.& COPY; 2023 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monitor inter- or intra-fraction anatomical variations of the target and organs-at-risk (OARs). Ethos™ (Varian Medical Systems, Palo Alto, CA), a cone beam computed tomography (CBCT) based radiotherapy treatment system that uses artificial intelligence (AI) and machine learning to perform ART, was introduced in 2020. Since then, numerous studies have been done to examine the potential benefits of Ethos™ CBCT-guided ART compared to non-adaptive radiotherapy. This review will explore the current trends of Ethos™, including improved CBCT image quality, a feasible clinical workflow, daily automated contouring and treatment planning, and motion management. Nevertheless, evidence of clinical improvements with the use of Ethos™ are limited and is currently under investigation via clinical trials.

Purpose: The Adaptive Radiation Therapy Individualized Approach-Cervix clinical trial uses predefined clinical directive templates (CDTs) combined with RapidPlan dose-volume histogram estimations (DVHe) to guide plan optimization in the Ethos treatment planning system. The dosimetric scorecard is a scoring tool that quantifies improvements in plan quality after physicians have precisely expressed their complete clinical intent. To our knowledge, this is the first study to use the dosimetric scorecard tool to tune an Ethos CDT to improve resulting plan quality. Methods and Materials: Iterative replanning was used to modify the draft CDT (CDT-1) in Ethos 1.1 to generate a new CDT (CDT-2) that maximized the clinical consensus scorecard's total score compared with CDT-1. CDT-2 was established, and resulting plans were compared with and without a DVHe. Additional fixed field intensity modulated radiation therapy beam geometries were compared between CDT-1 and CDT-2, both with DVHe. After obtaining favorable results when comparing CDT-1 versus CDT-2 for 2 test cases, 10 additional cases were retrospectively identified and tested. Results: CDT-2 reduced organ at risk doses without compromising planning target volume coverage in the initial test cases. When combined with DVHe, CDT-2 marginally outperformed CDT-1. Plan quality further improved with a 19-field geometry. In the expanded analysis, CDT-2 achieved higher scores than CDT-1 in most cases, with the 19-field approach showing superiority. Optimization and calculation time increased by 1.9 minutes, monitor unit (MU)/field decreased by 44.4, whereas beam-on time increased by 2.8 minutes when increasing fields to 19 from 9. Reoptimization with Ethos 1.1 Maintenance Release 1 resulted in decreased MU and minimal score changes. Conclusions: The scorecard is an effective tool to adjust an Ethos CDT to improve the average calculated plan quality. It also allowed for easy evaluation of the dosimetric impact of other planning parameters (beam arrangements and use of DVHe) to identify the best approach. Using a finely tuned CDT is expected to improve planning efficiency and decrease intrainstitutional plan quality variability, benefiting cone beam computed tomography–guided adaptive radiation therapy.

Whole-brain radiotherapy has been the standard palliative treatment for patients with brain metastases due to its effectiveness, availability, and ease of administration. Recent clinical trials have shown that limiting radiation dose to the hippocampus is associated with decreased cognitive toxicity. In this study, we updated an existing Knowledge Based Planning model to further reduce dose to the hippocampus and improve other dosimetric plan quality characteristics. Forty-two clinical cases were contoured according to guidelines. A new dosimetric scorecard was created as an objective measure for plan quality. The new Hippocampal Sparing Whole Brain Version 2 (HSWBv2) model adopted a complex recursive training process and was validated with five additional cases. HSWBv2 treatment plans were generated on the Varian Halcyon TM and TrueBeam TM systems and compared against plans generated from the existing (HSWBv1) model released in 2016. On the Halcyon TM platform, 42 cases were re-planned. Hippocampal D 100% from HSWBv2 and HSWBv1 models had an average dose of 5.75 Gy and 6.46 Gy, respectively ( p < 0.001). HSWBv2 model also achieved a hippocampal D mean of 7.49 Gy, vs 8.10 Gy in HSWBv1 model ( p < 0.001). Hippocampal D 0.03CC from HSWBv2 model was 9.86 Gy, in contrast to 10.57 Gy in HSWBv1 ( p < 0.001). For PTV_30 0 0, D 98% and D 2% from HSWBv2 model were 28.27 Gy and 31.81 Gy, respectively, compared to 28.08 Gy ( p = 0.020) and 32.66 Gy from HSWBv1 ( p < 0.001). Among several other dosimetric quality improvements, there was a significant reduction in PTV_30 0 0 V 105% from 35.35% (HSWBv1) to 6.44% (HSWBv2) ( p < 0.001). On 5 additional validation cases, dosimetric improvements were also observed on TrueBeam TM . In comparison to published data, the HSWBv2 model achieved higher quality hippocampal avoidance whole brain radiation therapy treatment plans through further reductions in hippocampal dose while improving target coverage and dose conformity/homogeneity. HSWBv2 model is shared publicly. (c) 2022 The Authors. Published by Elsevier Inc. on behalf of American Association of Medical Dosimetrists. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Whole-brain radiotherapy has been the standard palliative treatment for patients with brain metastases due to its effectiveness, availability, and ease of administration. Recent clinical trials have shown that limiting radiation dose to the hippocampus is associated with decreased cognitive toxicity. In this study, we updated an existing Knowledge Based Planning model to further reduce dose to the hippocampus and improve other dosimetric plan quality characteristics. Forty-two clinical cases were contoured according to guidelines. A new dosimetric scorecard was created as an objective measure for plan quality. The new Hippocampal Sparing Whole Brain Version 2 (HSWBv2) model adopted a complex recursive training process and was validated with five additional cases. HSWBv2 treatment plans were generated on the Varian HalcyonTM and TrueBeamTM systems and compared against plans generated from the existing (HSWBv1) model released in 2016. On the HalcyonTM platform, 42 cases were re-planned. Hippocampal D100% from HSWBv2 and HSWBv1 models had an average dose of 5.75 Gy and 6.46 Gy, respectively (p < 0.001). HSWBv2 model also achieved a hippocampal Dmean of 7.49 Gy, vs 8.10 Gy in HSWBv1 model (p < 0.001). Hippocampal D0.03CC from HSWBv2 model was 9.86 Gy, in contrast to 10.57 Gy in HSWBv1 (p < 0.001). For PTV_3000, D98% and D2% from HSWBv2 model were 28.27 Gy and 31.81 Gy, respectively, compared to 28.08 Gy (p = 0.020) and 32.66 Gy from HSWBv1 (p < 0.001). Among several other dosimetric quality improvements, there was a significant reduction in PTV_3000 V105% from 35.35% (HSWBv1) to 6.44% (HSWBv2) (p < 0.001). On 5 additional validation cases, dosimetric improvements were also observed on TrueBeamTM. In comparison to published data, the HSWBv2 model achieved higher quality hippocampal avoidance whole brain radiation therapy treatment plans through further reductions in hippocampal dose while improving target coverage and dose conformity/homogeneity. HSWBv2 model is shared publicly.