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.

On the feasibility of improved target coverage without compromising organs at risk using online adaptive stereotactic partial breast irradiation (A-SPBI).

PURPOSE: Describe an early-adopting institution's experience with online adaptive radiation for stereotactic partial breast irradiation. METHODS AND MATERIALS: Retrospective review of 22 women treated between May 2021 and March 2022 with adaptive stereotactic partial breast irradiation. A total of 106 of 110 fractions were evaluated for dosimetric changes in target coverage and organ-at-risk (OAR) dose. Patient set up with stereotactic wooden frame and adapted per fraction. Treatment and planning times were collected prospectively by radiation therapists. RESULTS: Scheduled PTV30 Gy was <95% in 72.1% and <90% in 38.5% of fractions, and both PTV and CTV coverage were improved significantly after adaption, and 83.7% of fractions were delivered as adapted per physician choice. There was no difference in OAR coverage. Average adaptive treatment planning took 15 min and average time-on-couch was 34.4 min. CONCLUSIONS: Adaptive stereotactic breast irradiation resulted in improved target coverage with equivalent dosing to OARs in an efficient and tolerated treatment time. Improved target coverage allowed for decreased PTV margins compared to prior trial protocols that may improve acute and late toxicities.

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.

Boosting radiotherapy dose calculation accuracy with deep learning.

In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly caused by inhomogeneities and electronic disequilibrium, is highly correlated with the dose distribution and the underlying anatomical tissue density. We hypothesize that a conversion scheme can be established to boost low-accuracy doses to high-accuracy, using intensity information obtained from computed tomography (CT) images. A deep learning-driven framework was developed to test the hypothesis by converting between two commercially available dose calculation methods: Anisotropic analytic algorithm (AAA) and Acuros XB (AXB). A hierarchically dense U-Net model was developed to boost the accuracy of AAA dose toward the AXB level. The network contained multiple layers of varying feature sizes to learn their dose differences, in relationship to CT, both locally and globally. Anisotropic analytic algorithm and AXB doses were calculated in pairs for 120 lung radiotherapy plans covering various treatment techniques, beam energies, tumor locations, and dose levels. For each case, the CT and the AAA dose were used as the input and the AXB dose as the "ground-truth" output, to train and test the model. The mean squared errors (MSEs) and gamma passing rates (2 mm/2% & 1 mm/1%) were calculated between the boosted AAA doses and the "ground-truth" AXB doses. The boosted AAA doses demonstrated substantially improved match to the "ground-truth" AXB doses, with average (± s.d.) gamma passing rate (1 mm/1%) 97.6% (±2.4%) compared to 87.8% (±9.0%) of the original AAA doses. The corresponding average MSE was 0.11(±0.05) vs 0.31(±0.21). Deep learning is able to capture the differences between dose calculation algorithms to boost the low-accuracy algorithms. By combining a less accurate dose calculation algorithm with a trained deep learning model, dose calculation can potentially achieve both high accuracy and efficiency.

Modeling Elekta VersaHD using the Varian Eclipse treatment planning system for photon beams: A single-institution experience.

The aim of this study was to report a single-institution experience and commissioning data for Elekta VersaHD linear accelerators (LINACs) for photon beams in the Eclipse treatment planning system (TPS). Two VersaHD LINACs equipped with 160-leaf collimators were commissioned. For each energy, the percent-depth-dose (PDD) curves, beam profiles, output factors, leaf transmission factors and dosimetric leaf gaps (DLGs) were acquired in accordance with the AAPM task group reports No. 45 and No. 106 and the vendor-supplied documents. The measured data were imported into Eclipse TPS to build a VersaHD beam model. The model was validated by creating treatment plans spanning over the full-spectrum of treatment sites and techniques used in our clinic. The quality assurance measurements were performed using MatriXX, ionization chamber, and radiochromic film. The DLG values were iteratively adjusted to optimize the agreement between planned and measured doses. Mobius, an independent LINAC logfile-based quality assurance tool, was also commissioned both for routine intensity-modulated radiation therapy (IMRT) QA and as a secondary check for the Eclipse VersaHD model. The Eclipse-generated VersaHD model was in excellent agreement with the measured PDD curves and beam profiles. The measured leaf transmission factors were less than 0.5% for all energies. The model validation study yielded absolute point dose agreement between ionization chamber measurements and Eclipse within ±4% for all cases. The comparison between Mobius and Eclipse, and between Mobius and ionization chamber measurements lead to absolute point dose agreement within ±5%. The corresponding 3D dose distributions evaluated with 3%global/2mm gamma criteria resulted in larger than 90% passing rates for all plans. The Eclipse TPS can model VersaHD LINACs with clinically acceptable accuracy. The model validation study and comparisons with Mobius demonstrated that the modeling of VersaHD in Eclipse necessitates further improvement to provide dosimetric accuracy on par with Varian LINACs.

Convolution-based modified Clarkson integration (CMCI) for electron cutout factor calculation.

Electron therapy is widely used to treat shallow tumors because of its characteristic sharp dose fall-off beyond a certain range. A customized cutout is typically applied to block radiation to normal tissues. Determining the final monitor unit (MU) for electron treatment requires an output factor for the cutout, which is usually generated by measurement, especially for highly irregular cutouts. However, manual measurement requires a lengthy quality assurance process with possible errors. This work presents an accurate and efficient cutout output factor prediction model, convolution-based modified Clarkson integration (CMCI), to replace patient-specific output factor measurement. Like the Clarkson method, we decompose the field into basic sectors. Unlike the Clarkson integration method, we use annular sectors for output factor estimation. This decomposition method allows calculation via convolution. A 2D distribution of fluence is generated, and the output factor at any given point can be obtained. We applied our method to 10 irregularly shaped cutouts for breast patients for 6E, 9E, and 15E beams and compared the results with measurements and the electron Monte Carlo (eMC) calculation using the Eclipse planning system. While both the CMCI and eMC methods showed good agreement with chamber measurements and film measurements in relative distributions at the nominal source to surface distance (SSD) of 100 cm, eMC generated larger errors than the CMCI method at extended SSDs, with up to -9.28% deviations from the measurement for 6E beam. At extended SSD, the mean absolute errors of our method relative to measurements were 0.92 and 1.14, while the errors of eMC were 1.42 and 1.79 for SSD 105 cm and 110 cm, respectively. These results indicate that our method is more accurate than eMC, especially for low-energy beams, and can be used for MU calculation and as a QA tool for electron therapy.

Robotic radiosurgery system patient-specific QA for extracranial treatments using the planar ion chamber array and the cylindrical diode array.

Robotic radiosurgery system has been increasingly employed for extracranial treatments. This work is aimed to study the feasibility of a cylindrical diode array and a planar ion chamber array for patient-specific QA with this robotic radiosurgery system and compare their performance. Fiducial markers were implanted in both systems to enable image-based setup. An in-house program was developed to postprocess the movie file of the measurements and apply the beam-by-beam angular corrections for both systems. The impact of noncoplanar delivery was then assessed by evaluating the angles created by the incident beams with respect to the two detector arrangements and cross-comparing the planned dose distribution to the measured ones with/without the angular corrections. The sensitivity of detecting the translational (1-3 mm) and the rotational (1°-3°) delivery errors were also evaluated for both systems. Six extracranial patient plans (PTV 7-137 cm³) were measured with these two systems and compared with the calculated doses. The plan dose distributions were calculated with ray-tracing and the Monte Carlo (MC) method, respectively. With 0.8 by 0.8 mm² diodes, the output factors measured with the cylindrical diode array agree better with the commissioning data. The maximum angular correction for a given beam is 8.2% for the planar ion chamber array and 2.4% for the cylindrical diode array. The two systems demonstrate a comparable sensitivity of detecting the translational targeting errors, while the cylindrical diode array is more sensitive to the rotational targeting error. The MC method is necessary for dose calculations in the cylindrical diode array phantom because the ray-tracing algorithm fails to handle the high-Z diodes and the acrylic phantom. For all the patient plans, the cylindrical diode array/ planar ion chamber array demonstrate 100% / > 92% (3%/3 mm) and > 96% / ~ 80% (2%/2 mm) passing rates. The feasibility of using both systems for robotic radiosurgery system patient-specific QA has been demonstrated. For gamma evaluation, 2%/2 mm criteria for cylindrical diode array and 3%/3 mm criteria for planar ion chamber array are suggested. The customized angular correction is necessary as proven by the improved passing rate, especially with the planar ion chamber array system.

Intrafractional prostate motion during external beam radiotherapy monitored by a real-time target localization system.

This paper investigates the clinical significance of real-time monitoring of intrafractional prostate motion during external beam radiotherapy using a commercial 4D localization system. Intrafractional prostate motion was tracked during 8,660 treatment fractions for 236 patients. The following statistics were analyzed: 1) the percentage of fractions in which the prostate shifted 2-7 mm for a certain duration; 2) the proportion of the entire tracking time during which the prostate shifted 2-7mm; and 3) the proportion of each minute in which the shift exceeded 2-7 mm. The ten patients exhibiting maximum intrafractional-motion patterns were analyzed separately. Our results showed that the percentage of fractions in which the prostate shifted by > 2, 3, 5, and 7 mm off the baseline in any direction for > 30 s was 56.8%, 27.2%, 4.6%, and 0.7% for intact prostate and 68.7%, 35.6%, 10.1%, and 1.8% for postprostatectomy patients, respectively. For the ten patients, these percentages were 91.3%, 72.4%, 36.3%, and 6%, respectively. The percentage of tracking time during which the prostate shifted > 2, 3, 5, and 7 mm was 27.8%, 10.7%, 1.6%, and 0.3%, respectively, and it was 56.2%, 33.7%, 11.2%, and 2.1%, respectively, for the ten patients. The percentage of tracking time for a > 3 mm posterior motion was four to five times higher than that in other directions. For treatments completed in 5 min (VMAT) and 10 min (IMRT), the proportion for the prostate to shift by > 3mm was 4% and 12%, respectively. Although intrafractional prostate motion was generally small, caution should be taken for patients who exhibit frequent large intrafractional motion. For those patients, adjustment of patient positioning may be necessary or a larger treatment margin may be used. After the initial alignment, the likelihood of prostate motion increases with time. Therefore, it is favorable to use advanced techniques (e.g., VMAT) that require less delivery time in order to reduce the treatment uncertainty resulting from intrafractional prostate motion.

Optimizing Online Adaptation Timing in the Treatment of Locally Advanced Cervical Cancer.

PURPOSE: Online adaptive radiation therapy (ART) has emerged as a new treatment modality for cervical cancer. Daily online adapting improves target coverage and organ-at-risk (OAR) sparing compared with traditional image guided radiation therapy (IGRT); however, the required resources may not be feasible in a busy clinical setting. Less frequent adapting may still benefit cervical cancer patients due to large volume changes of the uterocervix of the treatment course. In this study, the dosimetry from different online adapt-on-demand schedules was compared. MATERIALS AND METHODS: A retrospective cohort of 10 patients with cervical cancer treated with 260 fractions of definitive daily online ART was included. Plans with different adaptation schedules were simulated with adaptations weekly, every other week, once during treatment, and no adaptations (IGRT). These plans were applied to the synthetic computed tomography (CT) images and contours generated during the patient's delivered daily adaptive workflow. The dosimetry of the weekly replan, every-other-week replan, once replan, and IGRT plans were compared using a paired t test. RESULTS: Compared with traditional IGRT plans, weekly and every-other-week ART plans had similar clinical target volume (CTV) coverage, but statistically significant improved sparing of OARs. Weekly and every-other-week ART had reduced bowel bag V40 by 1.57% and 1.41%, bladder V40 by 3.82% and 1.64%, rectum V40 by 8.49% and 7.50%, and bone marrow Dmean by 0.81% and 0.61%, respectively. Plans with a single adaptation had statistically significantly worse target coverage, and moderate improvements in OAR sparing. Of the 18-dose metrics evaluated, improvements were seen in 15 for weekly ART, 14 for every-other-week ART, and 10 for single ART plans compared with IGRT. When every-other-week ART was compared with weekly ART, both plans had similar CTV coverage and OAR sparing with only small improvements in bone marrow dosimetry with weekly ART. CONCLUSIONS: This retrospective work compares different adapt-on-demand treatment schedules using data collected from patients treated with daily online adaptive radiation therapy. Results suggest weekly or every-other-week online ART is beneficial for reduced OAR dose compared with IGRT by exploiting the gradual changes in the uterocervix target volume.

Master Breast Radiation Planning: Simple Guide for Radiation Oncology Residents.

This article focuses on various aspects of breast radiation treatment planning, from simulation to field design. It covers the most common techniques including tangents, mono isocentric, dual isocentric, electron-photon match, and VMAT. This can serve as a guide for radiation oncology residents and medical students to advance their understanding of key aspects of breast radiation treatment and planning processes.

In Silico Analysis of Adjuvant Head and Neck Online Adaptive Radiation Therapy.

Purpose: Recently developed online adaptive radiation therapy (OnART) systems enable frequent treatment plan adaptation, but data supporting a dosimetric benefit in postoperative head and neck radiation therapy (RT) are sparse. We performed an in silico dosimetric study to assess the potential benefits of a single versus weekly OnART in the treatment of patients with head and neck squamous cell carcinoma in the adjuvant setting. Methods and Materials: Twelve patients receiving conventionally fractionated RT over 6 weeks and 12 patients receiving hypofractionated RT over 3 weeks on a clinical trial were analyzed. The OnART emulator was used to virtually adapt either once midtreatment or weekly based on the patient's routinely performed cone beam computed tomography. The planning target volume (PTV) coverage, dose heterogeneity, and cumulative dose to the organs at risk for these 2 adaptive approaches were compared with the nonadapted plan. Results: In total, 13, 8, and 3 patients had oral cavity, oropharynx, and larynx primaries, respectively. In the conventionally fractionated RT cohort, weekly OnART led to a significant improvement in PTV V100% coverage (6.2%), hot spot (-1.2 Gy), and maximum cord dose (-3.1 Gy), whereas the mean ipsilateral parotid dose increased modestly (1.8 Gy) versus the nonadapted plan. When adapting once midtreatment, PTV coverage improved with a smaller magnitude (0.2%-2.5%), whereas dose increased to the ipsilateral parotid (1.0-1.1 Gy) and mandible (0.2-0.7 Gy). For the hypofractionated RT cohort, similar benefit was observed with weekly OnART, including significant improvement in PTV coverage, hot spot, and maximum cord dose, whereas no consistent dosimetric advantage was seen when adapting once midtreatment. Conclusions: For head and neck squamous cell carcinoma adjuvant RT, there was a limited benefit of single OnART, but weekly adaptations meaningfully improved the dosimetric criteria, predominantly PTV coverage and dose heterogeneity. A prospective study is ongoing to determine the clinical benefit of OnART in this setting.

Simulation and pre-planning omitted radiotherapy (SPORT): a feasibility study for prostate cancer.

This study explored the feasibility of on-couch intensity modulated radiotherapy (IMRT) planning for prostate cancer (PCa) on a cone-beam CT (CBCT)-based online adaptive RT platform without an individualized pre-treatment plan and contours. Ten patients with PCa previously treated with image-guided IMRT (60 Gy/20 fractions) were selected. In contrast to the routine online adaptive RT workflow, a novel approach was employed in which the same preplan that was optimized on one reference patient was adapted to generate individual on-couch/initial plans for the other nine test patients using Ethos emulator. Simulation CTs of the test patients were used as simulated online CBCT (sCBCT) for emulation. Quality assessments were conducted on synthetic CTs (sCT). Dosimetric comparisons were performed between on-couch plans, on-couch plans recomputed on the sCBCT and individually optimized plans for test patients. The median value of mean absolute difference between sCT and sCBCT was 74.7 HU (range 69.5-91.5 HU). The average CTV/PTV coverage by prescription dose was 100.0%/94.7%, and normal tissue constraints were met for the nine test patients in on-couch plans on sCT. Recalculating on-couch plans on the sCBCT showed about 0.7% reduction of PTV coverage and a 0.6% increasing of hotspot, and the dose difference of the OARs was negligible (<0.5 Gy). Hence, initial IMRT plans for new patients can be generated by adapting a reference patient's preplan with online contours, which had similar qualities to the conventional approach of individually optimized plan on the simulation CT. Further study is needed to identify selection criteria for patient anatomy most amenable to this workflow.

Stereotactic ablative radiotherapy for T1-2 glottic larynx cancer: mature results from the phase II LT-SABR trial.

PURPOSE: Traditional radiotherapy for early-stage larynx cancer irradiates the whole larynx over 5.5-6 weeks. Phase I data suggest that stereotactic ablative radiotherapy (SAbR) is a viable strategy to reduce the irradiated volume and compress treatment time. This phase II study evaluated the efficacy of gLoTtic larynx SAbR (LT-SABR) in 5 or 16 fractions. METHODS: Eligibility required stage 0-II squamous cell carcinoma of the glottic larynx. The arytenoid cartilage could not be involved beyond the vocal process, and patients smoking more than one pack-per-day were excluded. The treatment volume consisted of the gross tumor volume, with a 3 mm margin (5 mm craniocaudal) to create the planning target volume (PTV). Patients without active smoking and PTV volume < 10 cc receive 4250 cGy in 5 fractions, twice per week; other patients received 58.08 Gy in 16 daily fractions. The primary endpoint was 2-year incidence of local failure. RESULTS: Twenty-five patients were accrued to this study, with 21 and 4 treated with 5 and 16 fractions, respectively. The stage distribution was in situ (n=1, 4%), T1a/b (n=16/5, 64%/20%), and T2 (n=3, 12%). The median age was 72 years, with a prior smoking history in 16 (64%) and active smoking in one (4%). At a median follow-up for surviving patients of 3.7 years (IQR 3.1-4.4 years), there have been two in-field recurrences (one in each dose cohort). The cumulative incidences of local failure were 4% (90% CI 0.8% - 20%) and 8% (90% CI 3%-24%) at 1 and 2 years, respectively. There have been no acute or late grade 3+ toxicities in disease-free patients. The median baseline, 6, 12 and 24 month Voice Handicap Index scores were 57 (IQR, 32-69), 28.5 (8-48), 4 (0-12), 7.5 (0-12), and 5 (0-24). CONCLUSIONS: Highly conformal stereotactic radiotherapy appears safe and efficacious for early-stage glottic larynx cancer, with encouraging patient-reported. These results need to be interpreted with caution given the small sample size and large non-inferiority margin. Additional follow-up and ultimately comparative studies are necessary to validate this paradigm.

Adapt-On-Demand: A Novel Strategy for Personalized Adaptive Radiation Therapy for Locally Advanced Lung Cancer.

PURPOSE: Real-time adaptation of thoracic radiation plans is compelling because offline adaptive experiences show that tumor volumes and lung anatomy can change during therapy. We present and analyze a novel adaptive-on-demand (AOD) workflow combining online adaptive radiation therapy (o-ART) on the ETHOS system with image guided radiation therapy delivery on a Halcyon unit for conventional fractionated radiation therapy of locally advanced lung cancer (LALC). METHODS AND MATERIALS: We analyzed 26 patients with LALC treated with the AOD workflow, adapting weekly. We timed segments of the workflow to evaluate efficiency in a real-world clinic. Target coverage and organ at risk (OAR) doses were compared between adaptive plans (ADP) and nonadaptive scheduled plans (SCH). Planning robustness was evaluated by the frequency of preplanning goals achieved in ADP plans, stratified by tumor volume change. RESULTS: The AOD workflow was achievable within 30 minutes for most radiation fractions. Over the course of therapy, we observed an average 26.6% ± 23.3% reduction in internal target volume (ITV). Despite these changes, with o-ART, ITV and planning target volume (PTV) coverage (V100%) was 99.2% and 93.9% for all members of the cohort, respectively. This represented a 2.9% and 6.8% improvement over nonadaptive plans (P < .05), respectively. For tumors that grew >10%, V100% was 93.1% for o-ART and 76.4% for nonadaptive plans, representing a median 17.2% improvement in the PTV coverage (P < .05). In these plans, critical OAR constraints were met 94.1% of the time, whereas in nonadaptive plans, this figure was 81.9%. This represented reductions of 1.32 Gy, 1.34 Gy, or 1.75 Gy in the heart, esophagus, and lung, respectively. The effect was larger when tumors had shrunk more than 10%. Regardless of tumor volume alterations, the PTV/ITV coverage was achieved for all adaptive plans. Exceptional cases, where dose constraints were not met, were due to large initial tumor volumes or tumor growth. CONCLUSIONS: The AOD workflow is efficient and robust in responding to anatomic changes in LALC patients, providing dosimetric advantages over standard therapy. Weekly adaptation was adequate to keep pace with changes. This approach is a feasible alternative to conventional offline replanning workflows for managing anatomy changes in LALC radiation therapy.

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.

ART2Dose: A comprehensive dose verification platform for online adaptive radiotherapy.

BACKGROUND: Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone-beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image-guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation-based checks and independent verification of volumetric dose distribution, as measurement-based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time-sensitive nature of online ART procedures. PURPOSE: The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic. METHODS: To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre-clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use. RESULTS: With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre-treatment checks by 3-4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively. CONCLUSION: Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor-intensive work.