Beyond conventional bounds: Surpassing system limits for stereotactic ablative (SAbR) lung radiotherapy using CBCT-based adaptive planning system.

PURPOSE: Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive-specific optimization tuning structures we designed and templated within Ethos to mitigate inter-planner variability in meeting RTOG metrics for both online-adaptive and offline SAbR plans. METHODS: We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with-and-without auto-generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone-beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints. RESULTS: Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non-adaptive plans, adaptive plans better meet reference plan goals (37% vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%). CONCLUSION: We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re-optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos.

Clinical experience on patient-specific quality assurance for CBCT-based online adaptive treatment plan.

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.

Evaluating machine learning enhanced intelligent-optimization-engine (IOE) performance for ethos head-and-neck (HN) plan generation.

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.

Feasibility of using ring-mounted Halcyon Linac for single-isocenter/two-lesion lung stereotactic body radiation therapy.

PURPOSE: To demonstrate the plan quality and delivery efficiency of volumetric-modulated arc therapy (VMAT) with the Halcyon Linac ring delivery system (RDS) in the treatment of single-isocenter/two-lesion lung stereotactic body radiation therapy (SBRT). MATERIALS/METHODS: Sixteen previously treated non-coplanar VMAT single-isocenter/two-lesion lung SBRT plans delivered with SBRT-dedicated C-arm TrueBeam Linac were selected. Prescribed dose was 50 Gy to each lesion over five fractions with treatment delivery every other day and AcurosXB algorithm as the final dose calculation algorithm. TrueBeam single-isocenter plans were reoptimized for Halcyon Linac with coplanar geometry. Both TrueBeam and Halcyon plans were normalized for identical combined target coverage and evaluated. Conformity indices (CIs), heterogeneity index (HI), gradient index (GI), gradient distance (GD), and D2cm were compared. The normal lung V5Gy, V10Gy, V20Gy, mean lung dose (MLD), and dose to organs at risk (OAR) were evaluated. Treatment delivery parameters, including beam-on time, were recorded. RESULTS: Halcyon plans were statistically similar to clinically delivered TrueBeam plans. No statistical differences in target conformity, dose heterogeneity, or intermediate-dose spillage were observed (all, p > 0.05). Halcyon plans, on average, demonstrated statistically insignificant reduced maximum dose to most adjacent OAR and normal lung. However, Halcyon yielded statistically significant lower maximal dose to the ribs (p = 0.041) and heart (p = 0.026), dose to 1 cc of ribs (p = 0.035) and dose to 5 cc of esophagus (p = 0.043). Plan complexity slightly increased as seen in the average increase of total monitor units, modulation factor, and beam-on time by 480, 0.48, and 2.78 min, respectively. However, the estimated overall treatment time was reduced by 2.22 min, on average. Mean dose delivery accuracy of clinical TrueBeam plans and the corresponding Halcyon plans was 98.9 ± 0.85% (range: 98.1%-100%) and 98.45 ± 0.99% (range: 97.9%-100%), respectively, demonstrating similar treatment delivery accuracy. CONCLUSION: SBRT treatment of synchronous lung lesions via single-isocenter VMAT on Halcyon RDS is feasible and dosimetrically equivalent to clinically delivered TrueBeam plans. Halcyon provides excellent plan quality and shorter overall treatment time that may improve patient compliance, reduce intrafraction movement, improve clinic efficiency, and potentially offering lung SBRT treatments for underserved patients on a Halcyon only clinic.

An Automated knowledge-based planning routine for stereotactic body radiotherapy of peripheral lung tumors via DCA-based volumetric modulated arc therapy.

PURPOSE: To develop a knowledge-based planning (KBP) routine for stereotactic body radiotherapy (SBRT) of peripherally located early-stage non-small-cell lung cancer (NSCLC) tumors via dynamic conformal arc (DCA)-based volumetric modulated arc therapy (VMAT) using the commercially available RapidPlanTM software. This proposed technique potentially improves plan quality, reduces complexity, and minimizes interplay effect and small-field dosimetry errors associated with treatment delivery. METHODS: KBP model was developed and validated using 70 clinically treated high quality non-coplanar VMAT lung SBRT plans for training and 20 independent plans for validation. All patients were treated with 54 Gy in three treatments. Additionally, a novel k-DCA planning routine was deployed to create plans incorporating historical three-dimensional-conformal SBRT planning practices via DCA-based approach prior to VMAT optimization in an automated planning engine. Conventional KBPs and k-DCA plans were compared with clinically treated plans per RTOG-0618 requirements for target conformity, tumor dose heterogeneity, intermediate dose fall-off and organs-at-risk (OAR) sparing. Treatment planning time, treatment delivery efficiency, and accuracy were recorded. RESULTS: KBPs and k-DCA plans were similar or better than clinical plans. Average planning target volume for validation was 22.4 ± 14.1 cc (7.1-62.3 cc). KBPs and k-DCA plans provided similar conformity to clinical plans with average absolute differences of 0.01 and 0.01, respectively. Maximal doses to OAR were lowered in both KBPs and k-DCA plans. KBPs increased monitor units (MU) on average 1316 (P < 0.001) while k-DCA reduced total MU on average by 1114 (P < 0.001). This routine can create k-DCA plan in less than 30 min. Independent Monte Carlo calculation demonstrated that k-DCA plans showed better agreement with planned dose distribution. CONCLUSION: A k-DCA planning routine was developed in concurrence with a knowledge-based approach for the treatment of peripherally located lung tumors. This method minimizes plan complexity associated with model-based KBP techniques and improve plan quality and treatment planning efficiency.

Development and clinical validation of a robust knowledge-based planning model for stereotactic body radiotherapy treatment of centrally located lung tumors.

PURPOSE: To develop a robust and adaptable knowledge-based planning (KBP) model with commercially available RapidPlanTM for early stage, centrally located non-small-cell lung tumors (NSCLC) treated with stereotactic body radiotherapy (SBRT) and improve a patient's"simulation to treatment" time. METHODS: The KBP model was trained using 86 clinically treated high-quality non-coplanar volumetric modulated arc therapy (n-VMAT) lung SBRT plans with delivered prescriptions of 50 or 55 Gy in 5 fractions. Another 20 independent clinical n-VMAT plans were used for validation of the model. KBP and n-VMAT plans were compared via Radiation Therapy Oncology Group (RTOG)-0813 protocol compliance criteria for conformity (CI), gradient index (GI), maximal dose 2 cm away from the target in any direction (D2cm), dose to organs-at-risk (OAR), treatment delivery efficiency, and accuracy. KBP plans were re-optimized with larger calculation grid size (CGS) of 2.5 mm to assess feasibility of rapid adaptive re-planning. RESULTS: Knowledge-based plans were similar or better than n-VMAT plans based on a range of target coverage and OAR metrics. Planning target volume (PTV) for validation cases was 30.5 ± 19.1 cc (range 7.0-71.7 cc). KBPs provided an average CI of 1.04 ± 0.04 (0.97-1.11) vs. n-VMAT plan'saverage CI of 1.01 ± 0.04 (0.97-1.17) (P < 0.05) with slightly improved GI with KBPs (P < 0.05). D2cm was similar between the KBPs and n-VMAT plans. KBPs provided lower lung V10Gy (P = 0.003), V20Gy (P = 0.007), and mean lung dose (P < 0.001). KBPs had overall better sparing of OAR at the minimal increased of average total monitor units and beam-on time by 460 (P < 0.05) and 19.2 s, respectively. Quality assurance phantom measurement showed similar treatment delivery accuracy. Utilizing a CGS of 2.5 mm in the final optimization improved planning time (mean, 5 min) with minimal or no cost to the plan quality. CONCLUSION: The RTOG-compliant adaptable RapidPlan model for early stage SBRT treatment of centrally located lung tumors was developed. All plans met RTOG dosimetric requirements in less than 30 min of planning time, potentially offering shorter "simulation to treatment" times. OAR sparing via KBPs may permit tumorcidal dose escalation with minimal penalties. Same day adaptive re-planning is plausible with a 2.5-mm CGS optimizer setting.

Automation and integration of a novel restricted single-isocenter stereotactic body radiotherapy (a-RESIST) method for synchronous two lung lesions.

Synchronous treatment of two lung lesions using a single-isocenter volumetric modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) plan can decrease treatment time and reduce the impact of intrafraction motion. However, alignment of both lesions on a single cone beam CT (CBCT) can prove difficult and may lead to setup errors and unacceptable target coverage loss. A Restricted Single-Isocenter Stereotactic Body Radiotherapy (RESIST) method was created to minimize setup uncertainties and provide treatment delivery flexibility. RESIST utilizes a single-isocenter placed at patient's midline and allows both lesions to be planned separately but treated in the same session. Herein is described a process of automation of this novel RESIST method. Automation of RESIST significantly reduced treatment planning time while maintaining the benefits of RESIST. To demonstrate feasibility, ten patients with two lung lesions previously treated with a single-isocenter clinical VMAT plan were replanned manually with RESIST (m-RESIST) and with automated RESIST (a-RESIST). a-RESIST method automatically sets isocenter, creates beam geometry, chooses appropriate dose calculation algorithms, and performs VMAT optimization using an in-house trained knowledge-based planning model for lung SBRT. Both m-RESIST and a-RESIST showed lower dose to normal tissues compared to manually planned clinical VMAT although a-RESIST provided slightly inferior, but still clinically acceptable, dose conformity and gradient indices. However, a-RESIST significantly reduced the treatment planning time to less than 20 min and provided a higher dose to the lung tumors. The a-RESIST method provides guidance for inexperienced planners by standardizing beam geometry and plan optimization using DVH estimates. It produces clinically acceptable two lesions VMAT lung SBRT plans efficiently. We have further validated a-RESIST on phantom measurement and independent pretreatment dose verification of another four selected 2-lesions lung SBRT patients and implemented clinically. Further development of a-RESIST for more than two lung lesions and refining this approach for extracranial oligometastastic abdominal/pelvic SBRT, including development of automated simulated collision detection algorithm, merits future investigation.

Clinical validation of ring-mounted halcyon linac for lung SBRT: comparison to SBRT-dedicated C-arm linac treatments.

Stereotactic body radiotherapy (SBRT) of lung tumors via the ring-mounted Halcyon Linac, a fast kilovoltage cone beam CT-guided treatment with coplanar geometry, a single energy 6MV flattening filter free (FFF) beam and volumetric modulated arc therapy (VMAT) is a fast, safe, and feasible treatment modality for selected lung cancer patients. Four-dimensional (4D) CT-based treatment plans were generated using advanced AcurosXB algorithm with heterogeneity corrections using an SBRT board and Halcyon couch insert. Halcyon VMAT-SBRT plans with stacked and staggered multileaf collimators produced highly conformal radiosurgical dose distribution to the target, lower intermediate dose spillage, and similar dose to adjacent organs at risks (OARs) compared to SBRT-dedicated highly conformal clinical noncoplanar Truebeam VMAT plans following the RTOG-0813 requirements. Due to low monitor units per fraction and less multileaf collimator (MLC) modulation, the Halcyon VMAT plan can deliver lung SBRT fractions with an overall treatment time of less than 15 min (for 50 Gy in five fractions), significantly improving patient comfort and clinic workflow. Higher pass rates of quality assurance results demonstrate a more accurate treatment delivery on Halcyon. We have implemented Halcyon for lung SBRT treatment in our clinic. We suggest others use Halcyon for lung SBRT treatments using abdominal compression or 4D CT-based treatment planning, thus expanding the access of curative ultra-hypofractionated treatments to other centers with only a Halcyon Linac. Clinical follow-up results for patients treated on Halcyon Linac with lung SBRT is ongoing.

Fast generation of lung SBRT plans with a knowledge-based planning model on ring-mounted Halcyon Linac.

PURPOSE: To demonstrate fast treatment planning feasibility of stereotactic body radiation therapy (SBRT) for centrally located lung tumors on Halcyon Linac via a previously validated knowledge-based planning (KBP) model to support offline adaptive radiotherapy. MATERIALS/METHODS: Twenty previously treated non-coplanar volumetric-modulated arc therapy (VMAT) lung SBRT plans (c-Truebeam) on SBRT-dedicated C-arm Truebeam Linac were selected. Patients received 50 Gy in five fractions. c-Truebeam plans were re-optimized for Halcyon manually (m-Halcyon) and with KBP model (k-Halcyon). Both m-Halcyon and k-Halcyon plans were normalized for identical or better target coverage than clinical c-Truebeam plans and compared for target conformity, dose heterogeneity, dose fall-off, and dose tolerances to the organs-at-risk (OAR). Treatment delivery parameters and planning times were evaluated. RESULTS: k-Halcyon plans were dosimetrically similar or better than m-Halcyon and c-Truebeam plans. k-Halcyon and m-Halcyon plan comparisons are presented with respect to c-Truebeam. Differences in conformity index were statistically insignificant in k-Halcyon and on average 0.02 higher (p = 0.04) in m-Halcyon plans. Gradient index was on average 0.43 (p = 0.006) lower and 0.27 (p = 0.02) higher for k-Halcyon and m-Halcyon, respectively. Maximal dose 2 cm away in any direction from target was statistically insignificant. k-Halcyon increased maximal target dose on average by 2.9 Gy (p < 0.001). Mean lung dose was on average reduced by 0.10 Gy (p = 0.004) in k-Halcyon and increased by 0.14 Gy (p < 0.001) in m-Halcyon plans. k-Halcyon plans lowered bronchial tree dose on average by 1.2 Gy. Beam-on-time (BOT) was increased by 2.85 and 1.67 min, on average for k-Halcyon and m-Halcyon, respectively. k-Halcyon plans were generated in under 30 min compared to estimated dedicated 180 ± 30 min for m-Halcyon or c-Truebeam plan. CONCLUSION: k-Halcyon plans were generated in under 30 min with excellent plan quality. This adaptable KBP model supports high-volume clinics in the expansion or transfer of lung SBRT patients to Halcyon.

Prostate SBRT using O-Ring Halcyon Linac - Plan quality, delivery efficiency, and accuracy.

Cone beam CT-guided prostate stereotactic body radiotherapy (SBRT) treatment on the recently installed novel O-ring coplanar geometry Halcyon Linac with a single energy 6MV-flattening filter free (FFF) beam and volumetric modulated arc therapy (VMAT) is a fast, safe, and feasible treatment modality for early stage low- and intermediate-risk prostate cancer patients. Following the RTOG-0938 compliance criteria and utilizing two-full arc geometry, VMAT prostate SBRT plans were generated for ten consecutive patients using advanced Acuros-based algorithm for heterogeneity corrections with Halcyon couch insert. Halcyon VMAT plans with the stacked and staggered multileaf collimators (MLC) produced highly conformal SBRT dose distributions to the prostate, lower intermediate dose spillage and similar dose to adjacent organs-at-risks (OARs) compared to SBRT-dedicated Truebeam VMAT plans. Due to lower monitor units per fraction and less MLC modulation through the target, the Halcyon VMAT plan can deliver prostate SBRT fractions in and overall treatment time of less than 10 minutes (for 36.25 Gy in five fractions), significantly improving patient compliance and clinic workflow. Pretreatment quality assurance results were similar to Truebeam VMAT plans. We have implemented Halcyon Linac for prostate SBRT treatment in our institution. We recommend that others use Halcyon for prostate SBRT treatments to expand the access of curative hypofractionated treatments to other clinics only equipped with a Halcyon Linac. Clinical follow-up results for patients who underwent prostate SBRT treatment on our Halcyon Linac is underway.

A novel and clinically useful dynamic conformal arc (DCA)-based VMAT planning technique for lung SBRT.

PURPOSE: Volumetric modulated arc therapy (VMAT) is gaining popularity for stereotactic treatment of lung lesions for medically inoperable patients. Due to multiple beamlets in delivery of highly modulated VMAT plans, there are dose delivery uncertainties associated with small-field dosimetry error and interplay effects with small lesions. We describe and compare a clinically useful dynamic conformal arc (DCA)-based VMAT (d-VMAT) technique for lung SBRT using flattening filter free (FFF) beams to minimize these effects. MATERIALS AND METHODS: Ten solitary early-stage I-II non-small-cell lung cancer (NSCLC) patients were treated with a single dose of 30 Gy using 3-6 non-coplanar VMAT arcs (clinical VMAT) with 6X-FFF beams in our clinic. These clinically treated plans were re-optimized using a novel d-VMAT planning technique. For comparison, d-VMAT plans were recalculated using DCA with user-controlled field aperture shape before VMAT optimization. Identical beam geometry, dose calculation algorithm, grid size, and planning objectives were used. The clinical VMAT and d-VMAT plans were compared via RTOG-0915 protocol compliances for conformity, gradient indices, and dose to organs at risk (OAR). Additionally, treatment delivery efficiency and accuracy were recorded. RESULTS: All plans met RTOG-0915 requirements. Comparing with clinical VMAT, d-VMAT plans gave similar target coverage with better target conformity, tighter radiosurgical dose distribution with lower gradient indices, and dose to OAR. Lower total number of monitor units and small beam modulation factor reduced beam-on time by 1.75 min (P < 0.001), on average (maximum up to 2.52 min). Beam delivery accuracy was improved by 2%, on average (P < 0.05) and maximum up to 6% in some cases for d-VMAT plans. CONCLUSION: This simple d-VMAT technique provided excellent plan quality, reduced intermediate dose-spillage, and dose to OAR while providing faster treatment delivery by significantly reducing beam-on time. This novel treatment planning approach will improve patient compliance along with potentially reducing intrafraction motion error. Moreover, with less MLC modulation through the target, d-VMAT could potentially minimize small-field dosimetry errors and MLC interplay effects. If available, d-VMAT planning approach is recommended for future clinical lung SBRT plan optimization.

Mitigating Risks in Cone Beam Computed Tomography Guided Online Adaptive Radiation Therapy: A Preventative Reference Planning Review Approach.

Purpose: Online adaptive radiation therapy (oART) treatment planning requires evaluating the temporal robustness of reference plans and anticipating the potential changes during treatment courses that may even lead to risks unique to the adaptive workflow. This study conducted a risk analysis of the cone beam computed tomography guided adaptive workflow and is the first to assess an adaptive-specific reference planning review that mitigates risk in the planning process to prevent events and treatment deficiencies during adaptation. Methods and Materials: A quality management team of medical physicists, residents, physicians, and radiation therapists performed a fault tree analysis and failure mode and effects analysis. Fault trees were created for under/overdosing targets and treatment deficiencies and assisted in identifying failure modes for the failure mode and effects analysis. Treatment deficiency was defined as a nonideal oART plan resulting in treatment with a lower quality plan (either oART or scheduled plan), treatment delay, or canceling treatment for the day. A reference planning checklist was created to catch failure modes before reaching the patient. Risk priority numbers (RPNs = severity * detectability * occurrence) were scored with and without the reference planning checklist to quantify risk mitigation. A root cause analysis was conducted for an event where an adaptive plan failed to generate. Results: The reference planning checklist (with items covering patient background, contouring/planning robustness for anatomy variability, and machine limitations) reduced the RPN for all failure modes. Only 1 failure mode with an RPN > 150 occurred with the reference planning checklist compared with 29 failure modes without, including 14 adaptive-specific failure modes. Contouring, planning, setup, scheduling, and documentation errors were identified during the fault tree analysis. Twenty-nine of 70 errors were adaptive-specific. The reference planning checklist could address 23 of 33 errors for over- or underdosing and 28 of 37 errors for treatment deficiency. The root cause analysis highlighted the need to check the setup prior to adaptive plan delivery and the time-out checklist. Conclusions: The reference planning checklist improved the detection of the failure modes and improved the quality and robustness of the plans produced for oART. It is ideally performed before the physician plan review to prevent last-minute replan (before or after first adaptive treatment) and delay of patient start. The checklist presented can be modified based on failures specific to individual clinics and used at various planning steps based on available resources.

In silico evaluation and feasibility of near margin-less head and neck daily adaptive radiotherapy.

OBJECTIVE: We explore the potential dosimetric benefits of reducing treatment volumes through daily adaptive radiation therapy for head and neck cancer (HNC) patients using the Ethos system/Intelligent Optimizer Engine (IOE). We hypothesize reducing treatment volumes afforded by daily adaption will significantly reduce the dose to adjacent organs at risk. We also explore the capability of the Ethos IOE to accommodate this highly conformal approach in HNC radiation therapy. METHODS: Ten HNC patients from a phase II trial were chosen, and their cone-beam CT (CBCT) scans were uploaded to the adaptive RT (ART) emulator. A new initial reference plan was generated using both a 1 mm and 5 mm planning target volume (PTV) expansion. Daily adaptive ART plans (1 mm) were simulated from the clinical CBCT taken every fifth fraction. Additionally, using physician-modified ART contours the larger 5 mm plan was recalculated on this recontoured on daily anatomy. Changes in target and OAR contours were measured using Dice coefficients as a surrogate of clinician effort. PTV coverage and organ-at-risk (OAR) doses were statistically compared, and the robustness of each ART plan was evaluated at fractions 5 and 35 to observe if OAR doses were within 3 Gy of pre-plan. RESULTS: This study involved six patients with oropharynx and four with larynx cancer, totaling 70 adaptive fractions. The primary and nodal gross tumor volumes (GTV) required the most adjustments, with median Dice scores of 0.88 (range: 0.80-0.93) and 0.83 (range: 0.66-0.91), respectively. For the 5th and 35th fraction plans, 80 % of structures met robustness criteria (quartile 1-3: 67-100 % and 70-90 %). Adaptive planning improved median PTV V100% coverage for doses of 70 Gy (96 % vs. 95.6 %), 66.5 Gy (98.5 % vs. 76.5 %), and 63 Gy (98.9 % vs. 74.9 %) (p < 0.03). Implementing ART with total volume reduction yielded median dose reductions of 7-12 Gy to key organs-at-risk (OARs) like submandibular glands, parotids, oral cavity, and constrictors (p < 0.05). CONCLUSIONS: The IOE enables feasible daily ART treatments with reduced margins while enhancing target coverage and reducing OAR doses for HNC patients. A phase II trial recently finished accrual and forthcoming analysis will determine if these dosimetric improvements correlate with improved patient-reported outcomes.

Multimodal radiotherapy dose prediction using a multi-task deep learning model.

BACKGROUND: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt-60-based systems and linear accelerators with C-arm, O-ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality-specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient-specific basis. However, manually generating treatment plans for each modality across every patient is time-consuming and clinically impractical. PURPOSE: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning-based convolutional neural networks. The baseline network performs a single-task (ST), predicting dose for a single modality. Our proposed multi-task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient-specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient-specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. METHODS: The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two-tailed Wilcoxon signed-rank test. RESULTS: Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [-0.0865, -0.0712]). CONCLUSION: Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre-treatment decision-making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site-agnostic dose prediction model, enhancing its generalizability and applicability.

Impact of manual contour editing on plan quality for online adaptive radiotherapy for head and neck cancer.

PURPOSE: Online adaptive radiotherapy (oART) has high resource costs especially for head-and-neck (H&N) cancer, which requires recontouring complex targets and numerous organs-at-risk (OARs). ART systems provide auto-contours to help, we aim to explore the optimal level of editing automatic contours to maintain plan quality in a cone-beam-computed-tomography (CBCT)-based oART system for H&N. In this system influencer OAR contours are generated and reviewed first, which then drives the auto-contouring of the remaining OARs and targets. METHODS AND MATERIALS: Three-hundred-and-forty-nine adapted fractions of forty-four H&N patients were retrospectively analyzed, with physician-edited OARs and targets. These contours and associated online adapted plans served as the gold standard for comparison. We simulated three contour editing workflows: (1) no editing of contours, (2) only editing the influencers, (3) editing the influencers and targets. The geometric difference was quantified with Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). The dosimetric differences in target coverage and OAR doses were calculated between the gold standard and these three simulated workflows. RESULTS: Workflow 1 resulted in significantly inferior contour quality for all OARs (mean DSC 0.85±0.17 and HD95 3.10±5.80mm), dosimetric data was hence not calculated for workflow 1. In workflow (2), the frequency of physician editing targets and remaining OARs were 80.8%-95.7% and 2.3% (brachial plexus)-67.7% (oral cavity) respectively, where the OAR differences were geometrically minor (mean DSC>0.95 with std≤0.09). However, due to the unedited target contours of workflow 2 (mean DSC 0.86-0.92 and mean HD95 2.56-3.30mm versus the ground-truth targets), plans were inadequate with insufficient coverage. In workflow (3) when both targets and influencers were edited (non-influencer OARs were unedited), over 95.5% of the adapted plans achieved the patient-specific dosimetry goals. CONCLUSION: The CBCT-based H&N oART workflow can be meaningfully accelerated by only editing the influencers and targets while omitting the remaining OARs without compromising the quality of the adaptive plans.

Clinical evaluation of photon optimizer (PO) MLC algorithm for stereotactic, single-dose of VMAT lung SBRT.

Recently implemented photon optimizer (PO) MLC optimization algorithm is mandatory for RapidPlan modeling in Eclipse. This report quantifies and compares the dosimetry and treatment delivery parameters of PO vs its predecessor progressive resolution optimizer (PRO) algorithm for a single-dose of volumetric modulated arc therapy (VMAT) lung stereotactic body radiation therapy (SBRT). Clinical SBRT treatment plans for 12 early-stage non-small-cell lung cancer patients receiving 30 Gy in 1 fraction using PRO-VMAT were re-optimized using the PO-VMAT MLC algorithm with identical planning parameters and objectives. Average planning target volume derived from the 4D CT scans was 13.6 ± 12.0 cc (range: 4.3 to 41.1 cc) Patients were treated with 6 MV flattening filter free beam using Acuros-based calculations and 2.5 mm calculation grid-size (CGS). Both treatment plans were normalized to receive same target coverage and identical CGS to isolate effects of MLC positioning optimizers. Original PRO and re-optimized PO plans were compared via RTOG-0915 protocol compliance criteria for target conformity, gradient indices, dose to organs at risks and delivery efficiency. Additionally, PO-VMAT plans with a 1.25 mm CGS were evaluated. Both plans met RTOG protocol requirements. Conformity indices showed no statistical difference between PO 2.5 mm CGS and PRO 2.5 mm CGS plans. Gradient index (p = 0.03), maximum dose to 2 cm away from planning target volume in any direction (D2cm) (p < 0.05), and gradient distance (p < 0.05) presented statistically significant differences for both plans with 2.5 mm CGS. Some organs at risks showed statistically significant differences for both plans calculated with 2.5 mm CGS; however, no clinically significant dose differences were observed between the plans. Beam modulation factor was statistically significant for both PO 1.25 mm CGS (p = 0.001) and PO 2.5 mm CGS (p < 0.001) compared to clinical PRO 2.5 mm CGS plans. PO-VMAT plans provided decreased beam-on time by an average of 0.2 ± 0.1 minutes (up to 1.0 minutes) with PO 2.5 mm and 1.2 ± 0.39 minutes (maximum up to 3.22 minutes) with PO 1.25 mm plans compared to PRO 2.5 mm plans. PO-VMAT single-dose of VMAT lung SBRT plans showed slightly increased intermediate-dose spillage but boasted overall similar plan quality with less beam modulation and hence shorter beam-on time. However, PO 1.25 mm CGS had less intermediate-dose spillage and analogous plan quality compared to clinical PRO-VMAT plans with no additional cost of plan optimization. Further investigation into peripheral targets with PO-MLC algorithm is warranted. This study indicates that PO 1.25 mm CGS plans can be used for RapidPlan modeling for a single dose of lung SBRT patients. PO-MLC 1.25 mm algorithm is recommended for future clinical single-dose lung SBRT plan optimization.