Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery.

Background and purpose: Information in multiparametric Magnetic Resonance (mpMR) images is relatable to voxel-level tumor response to Radiation Treatment (RT). We have investigated a deep learning framework to predict (i) post-treatment mpMR images from pre-treatment mpMR images and the dose map ("forward models"), and, (ii) the RT dose map that will produce prescribed changes within the Gross Tumor Volume (GTV) on post-treatment mpMR images ("inverse model"), in Breast Cancer Metastases to the Brain (BCMB) treated with Stereotactic Radiosurgery (SRS). Materials and methods: Local outcomes, planning computed tomography (CT) images, dose maps, and pre-treatment and post-treatment Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced (T1w) and contrast-enhanced (T1wCE), T2-weighted (T2w) and Fluid-Attenuated Inversion Recovery (FLAIR) mpMR images were curated from 39 BCMB patients. mpMR images were co-registered to the planning CT and intensity-calibrated. A 2D pix2pix architecture was used to train 5 forward models (ADC, T2w, FLAIR, T1w, T1wCE) and 1 inverse model on 1940 slices from 18 BCMB patients, and tested on 437 slices from another 9 BCMB patients. Results: Root Mean Square Percent Error (RMSPE) within the GTV between predicted and ground-truth post-RT images for the 5 forward models, in 136 test slices containing GTV, were (mean ± SD) 0.12 ± 0.044 (ADC), 0.14 ± 0.066 (T2w), 0.08 ± 0.038 (T1w), 0.13 ± 0.058 (T1wCE), and 0.09 ± 0.056 (FLAIR). RMSPE within the GTV on the same 136 test slices, between the predicted and ground-truth dose maps, was 0.37 ± 0.20 for the inverse model. Conclusions: A deep learning-based approach for radiologic outcome-optimized dose planning in SRS of BCMB has been demonstrated.

Structure-specific rigid dose accumulation dosimetric analysis of ablative stereotactic MRI-guided adaptive radiation therapy in ultracentral lung lesions.

BACKGROUND: Definitive local therapy with stereotactic ablative radiation therapy (SABR) for ultracentral lung lesions is associated with a high risk of toxicity, including treatment related death. Stereotactic MR-guided adaptive radiation therapy (SMART) can overcome many of the challenges associated with SABR treatment of ultracentral lesions. METHODS: We retrospectively identified 14 consecutive patients who received SMART to ultracentral lung lesions from 10/2019 to 01/2021. Patients had a median distance from the proximal bronchial tree (PBT) of 0.38 cm. Tumors were most often lung primary (64.3%) and HILUS group A (85.7%). A structure-specific rigid registration approach was used for cumulative dose analysis. Kaplan-Meier log-rank analysis was used for clinical outcome data and the Wilcoxon Signed Rank test was used for dosimetric data. RESULTS: Here we show that SMART dosimetric improvements in favor of delivered plans over predicted non-adapted plans for PBT, with improvements in proximal bronchial tree DMax of 5.7 Gy (p = 0.002) and gross tumor 100% prescription coverage of 7.3% (p = 0.002). The mean estimated follow-up is 17.2 months and 2-year local control and local failure free survival rates are 92.9% and 85.7%, respectively. There are no grade ≥ 3 toxicities. CONCLUSIONS: SMART has dosimetric advantages and excellent clinical outcomes for ultracentral lung tumors. Daily plan adaptation reliably improves target coverage while simultaneously reducing doses to the proximal airways. These results further characterize the therapeutic window improvements for SMART. Structure-specific rigid dose accumulation dosimetric analysis provides insights that elucidate the dosimetric advantages of SMART more so than per fractional analysis alone.

Stereotactic MR-guided Adaptive Radiation Therapy (SMART) is a type of radiation therapy for cancer. With SMART, treatment can be adapted based on daily changes in the body seen via imaging. SMART can safely deliver radiation to lung tumors near the center of the body which are risky to treat, due to potential damage to nearby organs. We looked at 14 patients who received SMART to determine how much changing the radiation plan each day improved our ability to safely deliver high doses. We found that SMART not only improved our ability to cover the entirety of the tumor with the dose originally intended, but also reduced dose to nearby organs. Treatment resulted in excellent control of the tumor with few side effects. SMART shows promise for safer and more effective treatment for lung tumors in this part of the body.

Novel Definitive Hypofractionated Accelerated Radiation Dose-painting (HARD) for Unresected Soft Tissue Sarcomas.

Purpose: Soft tissue sarcomas (STS) are historically radioresistant, with surgery being an integral component of their treatment. With their low α/ß, STS may be more responsive to hypofractionated radiation therapy (RT), which is often limited by long-term toxicity risk to surrounding normal tissue. An isotoxic approach using a hypofractionated accelerated radiation dose-painting (HARD) regimen allows for dosing based on clinical risk while sparing adjacent organs at risk. Methods and Materials: We retrospectively identified patients from 2019 to 2022 with unresected STS who received HARD with dose-painting to high, intermediate, and low-risk regions of 3.0 Gy, 2.5 Gy, and 2.0 to 2.3 Gy, respectively, in 20 to 22 fractions. Clinical endpoints included local control, locoregional control, progression free survival, overall survival, and toxicity outcomes. Results: Twenty-seven consecutive patients were identified and had a median age of 68 years and tumor size of 7.0 cm (range, 1.2-21.0 cm). Tumors were most often high-grade (70%), stage IV (70%), located in the extremities (59%), and locally recurrent (52%). With a median follow-up of 33.4 months, there was a 3-year locoregional control rate of 100%. The 3-year overall and progression-free survival were 44.9% and 23.3%, respectively. There were 5 (19%) acute and 2 (7%) late grade 3 toxicities, and there were no grade 4 or 5 toxicities at any point. Conclusions: The HARD regimen is a safe method of dose-escalating STS, with durable 3-year locoregional control. This approach is a promising alternative for unresected STS, though further follow-up is required to determine long-term control and toxicity.

Novel Virtual Reality App for Training Patients on MRI-guided Radiation Therapy.

Purpose: Patients receiving respiratory gated magnetic resonance imaging-guided radiation therapy (MRIgRT) for abdominal targets must hold their breath for ≥25 seconds at a time. Virtual reality (VR) has shown promise for improving patient education and experience for diagnostic MRI scan acquisition. We aimed to develop and pilot-test the first VR app to educate, train, and reduce anxiety and discomfort in patients preparing to receive MRIgRT. Methods and Materials: A multidisciplinary team iteratively developed a new VR app with patient input. The app begins with minigames to help orient patients to using the VR device and to train patients on breath-holding. Next, app users are introduced to the MRI linear accelerator vault and practice breath-holding during MRIgRT. In this quality improvement project, clinic personnel and MRIgRT-eligible patients with pancreatic cancer tested the VR app for feasibility, acceptability, and potential efficacy for training patients on using breath-holding during MRIgRT. Results: The new VR app experience was tested by 19 patients and 67 clinic personnel. The experience was completed on average in 18.6 minutes (SD = 5.4) by patients and in 14.9 (SD = 3.5) minutes by clinic personnel. Patients reported the app was "extremely helpful" (58%) or "very helpful" (32%) for learning breath-holding used in MRIgRT and "extremely helpful" (28%) or "very helpful (50%) for reducing anxiety. Patients and clinic personnel also provided qualitative feedback on improving future versions of the VR app. Conclusion: The VR app was feasible and acceptable for training patients on breath-holding for MRIgRT. Patients eligible for MRIgRT for pancreatic cancer and clinic personnel reported on future improvements to the app to enhance its usability and efficacy.

Habitat escalated adaptive therapy (HEAT): a phase 2 trial utilizing radiomic habitat-directed and genomic-adjusted radiation dose (GARD) optimization for high-grade soft tissue sarcoma.

BACKGROUND: Soft tissue sarcomas (STS), have significant inter- and intra-tumoral heterogeneity, with poor response to standard neoadjuvant radiotherapy (RT). Achieving a favorable pathologic response (FPR ≥ 95%) from RT is associated with improved patient outcome. Genomic adjusted radiation dose (GARD), a radiation-specific metric that quantifies the expected RT treatment effect as a function of tumor dose and genomics, proposed that STS is significantly underdosed. STS have significant radiomic heterogeneity, where radiomic habitats can delineate regions of intra-tumoral hypoxia and radioresistance. We designed a novel clinical trial, Habitat Escalated Adaptive Therapy (HEAT), utilizing radiomic habitats to identify areas of radioresistance within the tumor and targeting them with GARD-optimized doses, to improve FPR in high-grade STS. METHODS: Phase 2 non-randomized single-arm clinical trial includes non-metastatic, resectable high-grade STS patients. Pre-treatment multiparametric MRIs (mpMRI) delineate three distinct intra-tumoral habitats based on apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) sequences. GARD estimates that simultaneous integrated boost (SIB) doses of 70 and 60 Gy in 25 fractions to the highest and intermediate radioresistant habitats, while the remaining volume receives standard 50 Gy, would lead to a > 3 fold FPR increase to 24%. Pre-treatment CT guided biopsies of each habitat along with clip placement will be performed for pathologic evaluation, future genomic studies, and response assessment. An mpMRI taken between weeks two and three of treatment will be used for biological plan adaptation to account for tumor response, in addition to an mpMRI after the completion of radiotherapy in addition to pathologic response, toxicity, radiomic response, disease control, and survival will be evaluated as secondary endpoints. Furthermore, liquid biopsy will be performed with mpMRI for future ancillary studies. DISCUSSION: This is the first clinical trial to test a novel genomic-based RT dose optimization (GARD) and to utilize radiomic habitats to identify and target radioresistance regions, as a strategy to improve the outcome of RT-treated STS patients. Its success could usher in a new phase in radiation oncology, integrating genomic and radiomic insights into clinical practice and trial designs, and may reveal new radiomic and genomic biomarkers, refining personalized treatment strategies for STS. TRIAL REGISTRATION: NCT05301283. TRIAL STATUS: The trial started recruitment on March 17, 2022.

Novel Postoperative Hypofractionated Accelerated Radiation Dose-Painting Approach for Soft Tissue Sarcoma.

Purpose: Hypofractionated radiation therapy (RT) offers benefits in the treatment of soft tissue sarcomas (STS), including exploitation of the lower α/ß, patient convenience, and cost. This study evaluates the acute toxicity of a hypofractionated accelerated RT dose-painting (HARD) approach for postoperative treatment of STS. Methods and Materials: This is a retrospective review of 53 consecutive patients with STS who underwent resection followed by postoperative RT. Standard postoperative RT dosing for R0/R1/gross disease with sequential boost (50 Gy + 14/16/20 Gy in 32-35 fractions) were replaced with dose-painting, which adapts dose based on risk of disease burden, to 50.4 and 63, 64.4, 70 Gy in 28 fractions, respectively. The first 10 patients were replanned with a sequential boost RT approach and dosimetric indices were compared. Time-to-event outcomes, including local control, regional control, distant control, and overall survival, were estimated with Kaplan-Meier analysis. Results: Median follow-up was 25.2 months. Most patients had high-grade (59%) STS of the extremity (63%) who underwent resection with either R1 (40%) or close (36%) margins. Four patients experienced grade 3 acute dermatitis which resolved by the 3-month follow-up visit. The 2-year local control, regional control, distant control, and overall survival were 100%, 92%, 68%, and 86%, respectively. Compared with the sequential boost plan, HARD had a significantly lower field size (total V50 Gy; P = .002), bone V50 (P = .031), and maximum skin dose (P = .008). Overall treatment time was decreased by 4 to 7 fractions, which translated to a decrease in estimated average treatment cost of $3056 (range, $2651-$4335; P < .001). Conclusions: In addition to benefits in cost, convenience, and improved biologic effect in STS, HARD regimen offers a safe treatment approach with dosimetric advantages compared with conventional sequential boost, which may translate to improved long-term toxicity.

Technical feasibility of novel immunostimulatory low-dose radiation for polymetastatic disease with CBCT-based online adaptive and conventional approaches.

PURPOSE: A workflow/planning strategy delivering low-dose radiation therapy (LDRT) (1 Gy) to all polymetastatic diseases using conventional planning/delivery (Raystation/Halcyon = "conventional") and the AI-based Ethos online adaptive RT (oART) platform is developed/evaluated. METHODS: Using retrospective data for ten polymetastatic non-small cell lung cancer patients (5-52 lesions each) with PET/CTs, gross tumor volumes (GTVs) were delineated using PET standardized-uptake-value (SUV) thresholding. A 1 cm uniform expansion of GTVs to account for setup/contour uncertainty and organ motion-generated planning target volumes (PTVs). Dose optimization/calculation used the diagnostic CT from PET/CT. Dosimetric objectives were: Dmin,0.03cc ≥ 95% (acceptable variation (Δ) ≥ 90%), V100% ≥ 95% (Δ ≥ 90%), and D0.03cc ≤ 120% (Δ ≤ 125%). Additionally, online adaptation was simulated. When available, subsequent diagnostic CT was used to represent on-treatment CBCT. Otherwise, the CT from PET/CT used for initial planning was deformed to simulate clinically representative changes. RESULTS: All initial plans generated, both for Raystation and Ethos, achieved clinical goals within acceptable variation. For all patients, Dmin,0.03cc ≥ 95%, V100% ≥ 95%, and D0.03cc ≤ 120% goals were achieved for 84.8%/99.5%, 97.7%/98.7%, 97.4%/92.3%, in conventional/Ethos plans, respectively. The ratio of 50% isodose volume to PTV volume (R50%), maximum dose at 2 cm from PTV (D2cm), and the ratio of the 100% isodose volume to PTV volume (conformity index) in Raystation/Ethos plans were 7.9/5.9; 102.3%/88.44%; and 0.99/1.01, respectively. In Ethos, online adapted plans maintained PTV coverage whereas scheduled plans often resulted in geographic misses due to changes in tumor size, patient position, and body habitus. The average total duration of the oART workflow was 26:15 (min:sec) ranging from 6:43 to 57:30. The duration of each oART workflow step as a function of a number of targets showed a low correlation coefficient for influencer generation and editing (R2 = 0.04 and 0.02, respectively) and high correlation coefficient for target generation, target editing and plan generation (R2 = 0.68, 0.63 and 0.69, respectively). CONCLUSIONS: This study demonstrates feasibility of conventional planning/treatment with Raystation/Halcyon and highlights efficiency gains when utilizing semi-automated planning/online-adaptive treatment with Ethos for immunostimulatory LDRT conformally delivered to all sites of polymetastatic disease.

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.

Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.

Quality of organ at risk (OAR) autosegmentation is often judged by concordance metrics against the human-generated gold standard. However, the ultimate goal is the ability to use unedited autosegmented OARs in treatment planning, while maintaining the plan quality. We tested this approach with head and neck (HN) OARs generated by a prototype deep-learning (DL) model on patients previously treated for oropharyngeal and laryngeal cancer. Forty patients were selected, with all structures delineated by an experienced physician. For each patient, a set of 13 OARs were generated by the DL model. Each patient was re-planned based on original targets and unedited DL-produced OARs. The new dose distributions were then applied back to the manually delineated structures. The target coverage was evaluated with inhomogeneity index (II) and the relative volume of regret. For the OARs, Dice similarity coefficient (DSC) of areas under the DVH curves, individual DVH objectives, and composite continuous plan quality metric (PQM) were compared. The nearly identical primary target coverage for the original and re-generated plans was achieved, with the same II and relative volume of regret values. The average DSC of the areas under the corresponding pairs of DVH curves was 0.97 ± 0.06. The number of critical DVH points which met the clinical objectives with the dose optimized on autosegmented structures but failed when evaluated on the manual ones was 5 of 896 (0.6%). The average OAR PQM score with the re-planned dose distributions was essentially the same when evaluated either on the autosegmented or manual OARs. Thus, rigorous HN treatment planning is possible with OARs segmented by a prototype DL algorithm with minimal, if any, manual editing.

Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator.

Background and purpose: Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated. Materials and methods: Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints. Results: Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL. Conclusions: The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.

Trimodal Therapy Using an MR-guided Radiation Therapy Partial Bladder Tumor Boost in Muscle Invasive Bladder Cancer.

Purpose: Bladder preservation with trimodal therapy (TMT; maximal tumor resection followed by chemoradiation) is an effective paradigm for select patients with muscle invasive bladder cancer. We report our institutional experience of a TMT protocol using nonadaptive magnetic resonance imaging-guided radiation therapy (MRgRT) for partial bladder boost (PBB). Methods and Materials: A retrospective analysis was performed on consecutive patients with nonmetastatic muscle invasive bladder cancer who were treated with TMT using MRgRT between 2019 and 2022. Patients underwent intensity modulated RT-based nonadaptive MRgRT PBB contoured on True fast imaging with steady state precession (FISP) images (full bladder) followed sequentially by computed tomography-based RT to the whole empty bladder and pelvic lymph nodes with concurrent chemotherapy. MRgRT treatment time, table shifts, and dosimetric parameters of target coverage and normal tissue exposure were described. Prospectively assessed acute and late genitourinary and gastrointestinal (GI) toxicity were reported. Two-year local control was assessed with Kaplan-Meier methods. Results: Seventeen patients were identified for analysis. PBB planning target volume margins were ≤8 mm in 94% (n = 16) of cases. Dosimetric target coverage parameters were favorable and all normal tissue dose constraints were met. For MRgRT PBB fractions, median table shifts were 0.4 cm (range, 0-3.15), 0.45 cm (0-2.65), and 0.75 cm (0-4.8) in the X, Y, and Z planes, respectively. Median treatment time for MRgRT PBB fractions was 9 minutes (range, 6.9-17.4). We identified 32 out of 100 total MRgRT fractions that may have benefitted from online adaptation based on changes in organ position relative to planning target volume, predominantly because of small bowel (13/32, 41%) or rectum (8/32, 25%). Two patients discontinued RT prematurely. The incidence of highest-grade acute genitourinary toxicity was 1 to 2 (69%) and 3 (6%), whereas the incidence of acute GI toxicity was 1 to 2 (81%) and 3 (6%). There were no late grade 3 events; 17.6% had late grade 2 cystitis and none had late GI toxicity. With median follow-up of 18.2 months (95% CI, 12.4-22.5), the local control rate was 92%, and no patient has required salvage cystectomy. Conclusions: Nonadaptive MRgRT PBB is feasible with favorable dosimetry and low resource utilization. Larger studies are needed to evaluate for potential benefits in toxicity and local control associated with this approach in comparison to standard treatment techniques.

Treatment of Central Nervous System Tumors on Combination MR-Linear Accelerators: Review of Current Practice and Future Directions.

Magnetic resonance imaging (MRI) provides excellent visualization of central nervous system (CNS) tumors due to its superior soft tissue contrast. Magnetic resonance-guided radiotherapy (MRgRT) has historically been limited to use in the initial treatment planning stage due to cost and feasibility. MRI-guided linear accelerators (MRLs) allow clinicians to visualize tumors and organs at risk (OARs) directly before and during treatment, a process known as online MRgRT. This novel system permits adaptive treatment planning based on anatomical changes to ensure accurate dose delivery to the tumor while minimizing unnecessary toxicity to healthy tissue. These advancements are critical to treatment adaptation in the brain and spinal cord, where both preliminary MRI and daily CT guidance have typically had limited benefit. In this narrative review, we investigate the application of online MRgRT in the treatment of various CNS malignancies and any relevant ongoing clinical trials. Imaging of glioblastoma patients has shown significant changes in the gross tumor volume over a standard course of chemoradiotherapy. The use of adaptive online MRgRT in these patients demonstrated reduced target volumes with cavity shrinkage and a resulting reduction in radiation dose to uninvolved tissue. Dosimetric feasibility studies have shown MRL-guided stereotactic radiotherapy (SRT) for intracranial and spine tumors to have potential dosimetric advantages and reduced morbidity compared with conventional linear accelerators. Similarly, dosimetric feasibility studies have shown promise in hippocampal avoidance whole brain radiotherapy (HA-WBRT). Next, we explore the potential of MRL-based multiparametric MRI (mpMRI) and genomically informed radiotherapy to treat CNS disease with cutting-edge precision. Lastly, we explore the challenges of treating CNS malignancies and special limitations MRL systems face.

Feasibility and Toxicity of Full-Body Volumetric Modulated Arc Therapy Technique for High-Dose Total Body Irradiation.

Introduction: High-dose total body irradiation (TBI) is often part of myeloablative conditioning in acute leukemia. Modern volumetric modulated arc therapy (VMAT)-based plans employ arcs to the inferior-most portion of the body that can be simulated in a head-first position and use 2D planning for the inferior body which can result in heterogeneous doses. Here, we describe our institution's unique protocol for delivering high-dose TBI entirely with VMAT and retrospectively compare dosimetric outcomes with helical tomotherapy (HT) plans. Additionally, we describe our method of oropharyngeal mucosal sparing that was implemented after fatal mucositis occurred in two patients. Methods: Thirty-one patients were simulated and treated in head-first (HFS) and feet-first (FFS) orientations. Patients were treated with VMAT (n = 26) or HT (n = 5). In VMAT plans, to synchronize doses between the orientations, images were deformably registered and the HFS dose was transferred to the FFS plan and used as a background dose when optimizing plans. Six to eight isocenters with two arcs per isocenter were generated. HT was delivered with an established technique. Patients were treated to 13.2 Gy over eight twice daily fractions. Dosimetric outcomes and toxicities were retrospectively compared. Results: Prescription dose and organ at risk (OAR) constraints were met for all patients. Lower lung doses were achieved with VMAT relative to HT plans (7.4 vs 7.7 Gy, P = .009). Statistically significant improvement in mucositis was not achieved after adopting a mucosal-sparing technique, however lower doses to the oropharyngeal mucosal were achieved (6.9 vs 14.1 Gy, P = .009), and no further mucositis-related deaths occurred. Conclusions: This full-body VMAT method of TBI achieves dose goals, eliminates risk of heterogenous doses within the femur, and demonstrates that selective OAR sparing with the purpose of reducing TBI-related morbidity and mortality is possible at any institution with a VMAT-capable linear accelerator.

Magnetic Resonance-Guided Stereotactic Body Radiation Therapy/Hypofractionated Radiation therapy for Metastatic and Primary Central and Ultracentral Lung Lesions.

Introduction: The recent results from the Nordic-HILUS study indicate stereotactic body radiation therapy (SBRT) is associated with high-grade toxicity for ultracentral (UC) tumors. We hypothesized that magnetic resonance-guided SBRT (MRgSBRT) or hypofractionated radiation therapy (MRgHRT) enables the safe delivery of high-dose radiation to central and UC lung lesions. Methods: Patients with UC or central lesions were treated with MRgSBRT/MRgHRT with real-time gating or adaptation. Central lesions were defined as per the Radiation Therapy Oncology Group and UC as per the HILUS study definitions: (1) group A or tumors less than 1 cm from the trachea and/or mainstem bronchi; or (2) group B or tumors less than 1 cm from the lobar bronchi. The Kaplan-Meier estimate and log-rank test were used to estimate survival. Associations between toxicities and other patient factors were tested using the Mann-Whitney U test and Fisher's exact test. Results: A total of 47 patients were included with a median follow-up of 22.9 months (95% confidence interval: 16.4-29.4). Most (53%) had metastatic disease. All patients had central lesions and 55.3% (n = 26) had UC group A. The median distance from the proximal bronchial tree was 6.0 mm (range: 0.0-19.0 mm). The median biologically equivalent dose (α/ß = 10) was 105 Gy (range: 75-151.2). The most common radiation schedule was 60 Gy in eight fractions (40.4%). Most (55%) had previous systemic therapy, 32% had immunotherapy and 23.4% had previous thoracic radiation therapy. There were 16 patients who underwent daily adaptation. The 1-year overall survival was 82% (median = not reached), local control 87% (median = not reached), and progression-free survival 54% (median = 15.1 mo, 95% confidence interval: 5.1-25.1). Acute toxicity included grade 1 (26%) and grade 2 (21%) with only two patients experiencing grade 3 (4.3%) in the long term. No grade 4 or 5 toxicities were seen. Conclusions: Previous studies noted high rates of toxicity after SBRT to central and UC lung lesions, with reports of grade 5 toxicities. In our cohort, the use of MRgSBRT/MRgHRT with high biologically effective doses was well tolerated, with two grade 3 toxicities and no grade 4/5.

Stereotactic Magnetic Resonance-Guided Adaptive and Non-Adaptive Radiotherapy on Combination MR-Linear Accelerators: Current Practice and Future Directions.

Stereotactic body radiotherapy (SBRT) is an effective radiation therapy technique that has allowed for shorter treatment courses, as compared to conventionally dosed radiation therapy. As its name implies, SBRT relies on daily image guidance to ensure that each fraction targets a tumor, instead of healthy tissue. Magnetic resonance imaging (MRI) offers improved soft-tissue visualization, allowing for better tumor and normal tissue delineation. MR-guided RT (MRgRT) has traditionally been defined by the use of offline MRI to aid in defining the RT volumes during the initial planning stages in order to ensure accurate tumor targeting while sparing critical normal tissues. However, the ViewRay MRIdian and Elekta Unity have improved upon and revolutionized the MRgRT by creating a combined MRI and linear accelerator (MRL), allowing MRgRT to incorporate online MRI in RT. MRL-based MR-guided SBRT (MRgSBRT) represents a novel solution to deliver higher doses to larger volumes of gross disease, regardless of the proximity of at-risk organs due to the (1) superior soft-tissue visualization for patient positioning, (2) real-time continuous intrafraction assessment of internal structures, and (3) daily online adaptive replanning. Stereotactic MR-guided adaptive radiation therapy (SMART) has enabled the safe delivery of ablative doses to tumors adjacent to radiosensitive tissues throughout the body. Although it is still a relatively new RT technique, SMART has demonstrated significant opportunities to improve disease control and reduce toxicity. In this review, we included the current clinical applications and the active prospective trials related to SMART. We highlighted the most impactful clinical studies at various tumor sites. In addition, we explored how MRL-based multiparametric MRI could potentially synergize with SMART to significantly change the current treatment paradigm and to improve personalized cancer care.

Evaluation of an MRI linac magnetic isocenter walkout with gantry rotation in the presence of angle-specific corrections.

Objective. To reduce the magnetic isocenter position variation with gantry rotation on an 0.35 T MRI-guided linac to a practically negligible level.Approach. Central fRequency (CF) offset, eddy current calibration, cross-term calibration, gradient delay, and gradient offsets are tuned for each MR linac installation at every 30° of gantry rotation and stored in a look-up table (LUT). During treatment, the CF is tuned only once in the beginning at an arbitrary gantry angle. After that, imaging paramters are offset based on the stored LUT values for any given gantry angle.Main results. For the same hardware configuration, the implementation of the gantry-angle-specific parameter corrections reduced the total isocenters range of travel in the transverse plane from 1.1 to 0.3 mm and from 0.8 to 0.2 mm in horizontal and vertical directions, respectively. With the longitudinal shift always being negligible (≤0.2 mm), the radius of the sphere encompassing the isocenter locations was reduced from 0.6 to 0.2 mm. Geometric distortion improved as well; in particular, the gantry-angle-averaged maximum longitudinal distortion within a 35 cm diameter sphere was reduced from 1.4 to 0.8 mm. Since the CF is tuned only once during treatment, imaging may resume promptly after the gantry reaches the next target position.Significance. The MRI-guided linear accelerator was conceived primarily as an instrument for precision image-guided therapy. Thus, it is important to keep the treatment and imaging isocentres as close as possible while minimizing the geometric distortion. The described solution reduces the walkout of the imaging isocenter to a fraction of 1 mm, while keeping geometric distortion in a substantial volume below 1 mm. The approach is robust and does not increase the overall procedure time.

Adaptive hypofractionted and stereotactic body radiotherapy for lung tumors with real-time MRI guidance.

The treatment of central and ultracentral lung tumors with radiotherapy remains an ongoing clinical challenge. The risk of Grade 5 toxicity with ablative radiotherapy doses to these high-risk regions is significant as shown in recent prospective studies. Magnetic resonance (MR) image-guided adaptive radiotherapy (MRgART) is a new technology and may allow the delivery of ablative radiotherapy to these high-risk regions safely. MRgART is able to achieve this by utilizing small treatment margins, real-time gating/tracking and on-table plan adaptation to maintain dose to the tumor but limit dose to critical structures. The process of MRgART is complex and has nuances and challenges for the treatment of lung tumors. We outline the critical steps needed for appropriate delivery of MRgART for lung tumors safely and effectively.

Dose-Limiting Pulmonary Toxicity in a Phase 1/2 Study of Radiation and Chemotherapy with Ipilimumab Followed by Nivolumab for Patients With Stage 3 Unresectable Non-Small Cell Lung Cancer.

PURPOSE: We hypothesized that concurrent ipilimumab with chemoradiationtherapy (chemoRT) followed by maintenance nivolumab would be safe for patients with unresectable stage III non-small cell lung cancer (NSCLC). We aimed to assess the safety (phase 1) and the 12-month progression-free survival (PFS) (phase 2) in a multi-institution prospective trial. METHODS AND MATERIALS: Eligible patients had unresectable stage III NSCLC. The treatment included platinum doublet chemotherapy with concurrent thoracic radiation therapy to 60 Gy in 30 fractions and ipilimumab (1 mg/kg) delivered during weeks 1 and 4. After chemoRT, maintenance nivolumab (480 mg) was given every 4 weeks for up to 12 cycles. Adverse events (AEs) were assessed according to the Common Terminology Criteria for Adverse Events, version 5.0. Survival analyses were performed with Kaplan Meier (KM) methods and log-rank tests. RESULTS: The trial was discontinued early after enrolling 19 patients without proceeding to the phase 2 component because of unacceptable toxicity. Sixteen patients (84%) had grade ≥3 (G3+) possible treatment-related toxicity, most commonly pulmonary AEs (n = 8, 42%). Fourteen patients (74%) discontinued study therapy early because of AEs (n = 12, 63%) or patient choice (n = 2, 11%). Eleven patients (58%) experienced G2+ pulmonary toxicity with median time to onset 4.1 months (95% CI 2.6-not reached [NR]), and 12-month freedom from G2+ pulmonary toxicity 37% (95% CI, 16-59). Five patients had G5 AEs, including 3 with G5 pulmonary AEs (1 respiratory failure with pneumonitis and pulmonary embolism, 1 pneumonia/chronic obstructive pulmonary disease exacerbation, 1 pulmonary fibrosis). Despite toxicities, the median PFS was 19.2 months (95% CI 6.1-NR) and the median overall survival was NR (95% CI 6.1-NR) with median follow-up of 30.1 months by the reverse KM method. CONCLUSIONS: Concurrent ipilimumab with chemoRT for unresectable stage III NSCLC is associated with pulmonary toxicity that may limit opportunities for improved outcomes. Future studies aiming to incorporate ipilimumab or other anti-CTLA4 therapies into management of unresectable stage III NSCLC should consider careful measures to minimize toxicity risk.

A Practical Workflow for Magnetic Resonance-Guided Stereotactic Body Radiation Therapy to the Pancreas.

The increased adoption of stereotactic body radiation therapy has allowed for delivery of higher doses, potentially associated with better outcomes but at the risk of higher toxicity. The intimate association of radiosensitive organs at risk (eg, stomach, duodenum, bowel) has historically limited the delivery of ablative doses to the pancreas. The advent of magnetic resonance-guided radiation therapy with improved soft-tissue contrast allows for gated delivery without an internal target volume and online adaptive replanning to maximize the therapeutic ratio. Patient selection requires additional resources, including increased patient on-table time, physician time, and physics support. Within our center's workflow, integrating an educational video at consultation as well as optimizing biofeedback mechanisms have significantly improved the experience for our patients.

Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy.

PURPOSE: To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm. METHODS: A total of 864 HN cancer cases were available to train and evaluate a prototype algorithm. The algorithm is based on a fully convolutional network with combined U-Net and V-net. A Dice loss plus Cross-Entropy Loss function with Adam optimizer was used in training. For 75 validation cases, OAR sets were generated with three DL-based models (A: the prototype model trained with gold data, B: a commercial software trained with the same data, and C: the same software trained with data from another institution). The auto-segmented structures were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), voxel-penalty metric (VPM) and DSC of area under dose-volume histograms. A subjective qualitative evaluation was performed on 20 random cases. RESULTS: Overall trend was for the prototype algorithm to be the closest to the gold data by all five metrics. The average DSC/VPM/HD for algorithms A, B, and C were 0.81/84.1/1.6 mm, 0.74/62.8/3.2 mm, and 0.66/46.8/3.3 mm, respectively. 93% of model A structures were evaluated to be clinically useful. CONCLUSION: The superior performance of the prototype was validated, even when trained with the same data. In addition to the challenges of perfecting the algorithms, the auto-segmentation results can differ when the same algorithm is trained at different institutions.