Volumetric modulated arc therapy (VMAT) planning for head and neck cancer is a complex process. While the lowest achievable dose for each individual organ-at-risk (OAR) is unknown a priori, artificial intelligence (AI) holds promise as a tool to accurately estimate the expected dose distribution for OARs. We prospectively investigated the benefits of incorporating an AI-based decision support tool (DST) into the clinical workflow to improve OAR sparing. The DST dose prediction model was based on 276 institutional VMAT plans. Under an IRB-approved prospective trial, the physician first generated a custom OAR directive for 50 consecutive patients (physician directive, PD). The DST then estimated OAR doses (AI directive, AD). For each OAR, the treating physician used the lower directive to form a hybrid directive (HD). The final plan metrics were compared to each directive. A dose difference of 3 Gray (Gy) was considered clinically significant. Compared to the AD and PD, the HD reduced OAR dose objectives by more than 3 Gy in 22% to 75% of cases, depending on OAR. The resulting clinical plan typically met these lower constraints and achieved mean dose reductions between 4.3 and 16 Gy over the PD, and 5.6 to 9.1 Gy over the AD alone. Dose metrics achieved using the HD were significantly better than institutional historical plans for most OARs and NRG constraints for all OARs. The DST facilitated a significantly improved treatment directive across all OARs for this generalized H&N patient cohort, with neither the AD nor PD alone sufficient to optimally direct planning.

Purpose: Varian Ethos utilizes novel intelligent‐optimization‐engine (IOE) designed to automate the planning. However, this introduced a black box approach to plan optimization and challenge for planners to improve plan quality. This study aims to evaluate machine‐learning‐guided initial reference plan generation approaches for head & neck (H&N) adaptive radiotherapy (ART).Methods: Twenty previously treated patients treated on C‐arm/Ring‐mounted were retroactively re‐planned in the Ethos planning system using a fixed 18‐beam intensity‐modulated radiotherapy (IMRT) template. Clinical goals for IOE input were generated using (1) in‐house deep‐learning 3D‐dose predictor (AI‐Guided) (2) commercial knowledge‐based planning (KBP) model with universal RTOG‐based population criteria (KBP‐RTOG) and (3) an RTOG‐based constraint template only (RTOG) for in‐depth analysis of IOE sensitivity. Similar training data was utilized for both models. Plans were optimized until their respective criteria were achieved or DVH‐estimation band was satisfied. Plans were normalized such that the highest PTV dose level received 95% coverage. Target coverage, high‐impact organs‐at‐risk (OAR) and plan deliverability was assessed in comparison to clinical (benchmark) plans. Statistical significance was evaluated using a paired two‐tailed student t‐test.Results: AI‐guided plans were superior to both KBP‐RTOG and RTOG‐only plans with respect to clinical benchmark cases. Overall, OAR doses were comparable or improved with AI‐guided plans versus benchmark, while they increased with KBP‐RTOG and RTOG plans. However, all plans generally satisfied the RTOG criteria. Heterogeneity Index (HI) was on average <1.07 for all plans. Average modulation factor was 12.2 ± 1.9 (p = n.s), 13.1 ± 1.4 (p = <0.001), 11.5 ± 1.3 (p = n.s.) and 12.2 ± 1.9 for KBP‐RTOG, AI‐Guided, RTOG and benchmark plans, respectively.Conclusion: AI‐guided plans were the highest quality. Both KBP‐enabled and RTOG‐only plans are feasible approaches as clinics adopt ART workflows. Similar to constrained optimization, the IOE is sensitive to clinical input goals and we recommend comparable input to an institution's planning directive dosimetric criteria.

Purpose: Ethos CBCT‐based adaptive radiotherapy (ART) system can generate an online adaptive plan by re‐optimizing the initial reference plan based on the patient anatomy at the treatment. The optimization process is fully automated without any room for human intervention. Due to the change in anatomy, the ART plan can be significantly different from the initial plan in terms of plan parameters such as the aperture shapes and number of monitor units (MUs). In this study, we investigated the feasibility of using calculation‐based patient specific QA for ART plans in conjunction with measurement‐based and calculation‐based QA for initial plans to establish an action level for the online ART patient‐specific QA.Methods: A cohort of 98 cases treated on CBCT‐based ART system were collected for this study. We performed measurement‐based QA using ArcCheck and calculation‐based QA using Mobius for both the initial plan and the ART plan for analysis. For online the ART plan, Mobius calculation was conducted prior to the delivery, while ArcCheck measurement was delivered on the same day after the treatment. We first investigated the modulation factors (MFs) and MU numbers of the initial plans and ART plans, respectively. The γ passing rates of initial and ART plan QA were analyzed. Then action limits were derived for QA calculation and measurement for both initial and online ART plans, respectively, from 30 randomly selected patient cases, and were evaluated using the other 68 patient cases.Results: The difference in MF between initial plan and ART‐plan was 12.9% ± 12.7% which demonstrates their significant difference in plan parameters. Based on the patient QA results, pre‐treatment calculation and measurement results are generally well aligned with ArcCheck measurement results for online ART plans, illustrating their feasibility as an indicator of failure in online ART QA measurements. Furthermore, using 30 randomly selected patient cases, the γ analysis action limit derived for initial plans and ART plans are 89.6% and 90.4% in ArcCheck QA (2%/2 mm) and are 92.4% and 93.6% in Mobius QA(3%/2 mm), respectively. According to the calculated action limits, the ArcCheck measurements for all the initial and ART plans passed QA successfully while the Mobius calculation action limits flagged seven and four failure cases respectively for initial plans and ART plans, respectively.Conclusion: An ART plan can be substantially different from the initial plan, and therefore a separate session of ART plan QA is needed to ensure treatment safety and quality. The pre‐treatment QA calculation via Mobius can serve as a reliable indicator of failure in online ART plan QA. However, given that Ethos ART system is still relatively new, ArcCheck measurement of initial plan is still in practice. It may be skipped as we gain more experience and have better understanding of the system.

Background: The utility of adaptive radiotherapy (ART) for head and neck squamous cell carcinoma (HNSCC) remains poorly defined. Daily ART (DART) promises both anatomic adaptation and planning target volume (PTV) reduction. In this prospective trial using cone-beam computed tomography-based ART, patients with HNSCC undergoing definitive radiotherapy (RT) or chemoradiotherapy (CRT) were randomly assigned to DART with reduced PTV margins or no ART with standard margins (image-guided RT [IGRT]). Methods: Eligibility criteria included a diagnosis of oropharynx, larynx, or hypopharynx HNSCC receiving definitive radiotherapy. All individuals received involved nodal radiotherapy per previous institutional study. The PTV margins were 1 mm (2 mm craniocaudal) vs 5 mm in the DART and IGRT arms, respectively. The primary endpoint was patient-reported xerostomia at 1 year, assessed with the Xerostomia Questionnaire (XQ). Results: Fifty patients were enrolled (26 IGRT, 24 DART) between March 2022 and June 2023. The cohort consisted of 38 oropharynx and 12 larynx/hypopharynx patients. The mean ipsilateral parotid gland, ipsilateral and contralateral submandibular gland doses were significantly lower with DART. There was significantly less acute dermatitis in the DART arm (Grade 0/1/2 0%/69%/31% vs 17%/75%/8% DART, P = .01) but no significant difference in any patient-reported outcome at 1 year. The adjusted difference in XQ score at 1 year was 10.0 (95% CI = −4.7 to 24.7, P = .58). Conclusion: Online DART for HNSCC is oncologically sound and improved acute toxicity profiles, but it did not reduce patient-reported xerostomia, the primary endpoint. Additional evidence is needed to understand the potential benefits and limitations of this paradigm, including its cost-effectiveness. Trial registration: clinicaltrials.gov identifier, NCT04883281