MR-guided Adaptive Radiotherapy Research Articles

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

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. 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. 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. 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.

Feasibility and safety of contrast-enhanced magnetic resonance-guided adaptive radiotherapy for upper abdominal tumors: A preliminary exploration

This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p < 0.001) and T2w MR (4.41 vs. 7.42, p < 0.001).

Stereotactic ultrahypofractionated MR-guided radiotherapy for localized prostate cancer – Acute toxicity and patient-reported outcomes in the prospective, multicenter SMILE phase II trial

BackgroundDue to superior image quality and daily adaptive planning, MR-guided stereotactic body radiation therapy (MRgSBRT) has the potential to further widen the therapeutic window in radiotherapy of localized prostate cancer. This study reports on acute toxicity rates and patient-reported outcomes after MR-guided adaptive ultrahypofractionated radiotherapy for localized prostate cancer within the prospective, multicenter phase II SMILE trial. Materials and methodsA total of 69 patients with localized prostate cancer underwent MRgSBRT with daily online plan adaptation. Inclusion criteria comprised a tumor stage ≤ T3a, serum PSA value ≤ 20 ng/ml, ISUP Grade group ≤ 4. A dose of 37.5 Gy was prescribed to the PTV in five fractions on alternating days with an optional simultaneous boost of 40 Gy to the dominant intraprostatic lesion defined by multiparametric MRI. Acute genitourinary (GU-) and gastrointestinal (GI-) toxicity, as defined by CTCAE v. 5.0 and RTOG as well as patient-reported outcomes according to EORTC QLQ-C30 and -PR25 scores were analyzed at completion of radiotherapy, 6 and 12 weeks after radiotherapy and compared to baseline symptoms. ResultsThere were no toxicity-related treatment discontinuations. At the 12-week follow-up visit, no grade 3 + toxicities were reported according to CTCAE. Up until the 12-week visit, in total 16 patients (23 %) experienced a grade 2 GU or GI toxicity. Toxicity rates peaked at the end of radiation therapy and subsided within the 12-week follow-up period. At the 12-week follow-up visit, no residual grade 2 GU toxicities were reported and 1 patient (1 %) had residual grade 2 enteritic symptoms. With exception to a significant improvement in the emotional functioning score following MRgSBRT, no clinically meaningful changes in the global health status nor in relevant subscores were reported. ConclusionDaily online-adaptive MRgSBRT for localized prostate cancer resulted in an excellent overall toxicity profile without any major negative impact on quality of life.

Impact of intrafraction motion in pancreatic cancer treatments with MR-guided adaptive radiation therapy.

The total time of radiation treatment delivery for pancreatic cancer patients with daily online adaptive radiation therapy (ART) on an MR-Linac can range from 50 to 90min. During this period, the target and normal tissues undergo changes due to respiration and physiologic organ motion. We evaluated the dosimetric impact of the intrafraction physiological organ changes. Ten locally advanced pancreatic cancer patients were treated with 50 Gy in five fractions with intensity-modulated respiratory-gated radiation therapy on a 0.35-T MR-Linac. Patients received both pre- and post-treatment volumetric MRIs for each fraction. Gastrointestinal organs at risk (GI-OARs) were delineated on the pre-treatment MRI during the online ART process and retrospectively on the post-treatment MRI. The treated dose distribution for each adaptive plan was assessed on the post-treatment anatomy. Prescribed dose volume histogram metrics for the scheduled plan on the pre-treatment anatomy, the adapted plan on the pre-treatment anatomy, and the adapted plan on post-treatment anatomy were compared to the OAR-defined criteria for adaptation: the volume of the GI-OAR receiving greater than 33 Gy (V33Gy) should be ≤1 cubic centimeter. Across the 50 adapted plans for the 10 patients studied, 70% were adapted to meet the duodenum constraint, 74% for the stomach, 12% for the colon, and 48% for the small bowel. Owing to intrafraction organ motion, at the time of post-treatment imaging, the adaptive criteria were exceeded for the duodenum in 62% of fractions, the stomach in 36%, the colon in 10%, and the small bowel in 48%. Compared to the scheduled plan, the post-treatment plans showed a decrease in the V33Gy, demonstrating the benefit of plan adaptation for 66% of the fractions for the duodenum, 95% for the stomach, 100% for the colon, and 79% for the small bowel. Post-treatment images demonstrated that over the course of the adaptive plan generation and delivery, the GI-OARs moved from their isotoxic low-dose region and nearer to the dose-escalated high-dose region, exceeding dose-volume constraints. Intrafraction motion can have a significant dosimetric impact; therefore, measures to mitigate this motion are needed. Despite consistent intrafraction motion, plan adaptation still provides a dosimetric benefit.

Widening the therapeutic window for central and ultra-central thoracic oligometastatic disease with stereotactic MR-guided adaptive radiation therapy (SMART)

Background/PurposeCentral/ultra-central thoracic tumors are challenging to treat with stereotactic radiotherapy due potential high-grade toxicity. Stereotactic MR-guided adaptive radiation therapy (SMART) may improve the therapeutic window through motion control with breath-hold gating and real-time MR-imaging as well as the option for daily online adaptive replanning to account for changes in target and/or organ-at-risk (OAR) location. Materials/Methods26 central (19 ultra-central) thoracic oligoprogressive/oligometastatic tumors treated with isotoxic (OAR constraints-driven) 5-fraction SMART (median 50 Gy, range 35–60) between 10/2019–10/2022 were reviewed. Central tumor was defined as tumor within or touching 2 cm around proximal tracheobronchial tree (PBT) or adjacent to mediastinal/pericardial pleura. Ultra-central was defined as tumor abutting the PBT, esophagus, or great vessel. Hard OAR constraints observed were ≤ 0.03 cc for PBT V40, great vessel V52.5, and esophagus V35. Local failure was defined as tumor progression/recurrence within the planning target volume. ResultsTumor abutted the PBT in 31 %, esophagus in 31 %, great vessel in 65 %, and heart in 42 % of cases. 96 % of fractions were treated with reoptimized plan, necessary to meet OAR constraints (80 %) and/or target coverage (20 %). Median follow-up was 19 months (27 months among surviving patients). Local control (LC) was 96 % at 1-year and 90 % at 2-years (total 2/26 local failure). 23 % had G2 acute toxicities (esophagitis, dysphagia, anorexia, nausea) and one (4 %) had G3 acute radiation dermatitis. There were no G4-5 acute toxicities. There was no symptomatic pneumonitis and no G2 + late toxicities. ConclusionIsotoxic 5-fraction SMART resulted in high rates of LC and minimal toxicity. This approach may widen the therapeutic window for high-risk oligoprogressive/oligometastatic thoracic tumors.

Dosimetric comparison of stereotactic MR-guided radiation therapy (SMART) and HDR brachytherapy boost in cervical cancer

The standard of care in locally advanced cervical cancer (LACC) is concomitant chemoradiotherapy followed by high-dose-rate brachytherapy (HDR-BT). Although previous studies compared HDR-BT with stereotactic body radiotherapy (SBRT), there is scarce data regarding the dosimetric outcomes of stereotactic MR-guided adaptive radiation therapy (SMART) boost in lieu of HDR-BT. In this single-institutional in-silico comparative study, LACC patients who were definitively treated with external beam radiotherapy followed by HDR-BT were selected. Target volumes and organs at risk (OARs) were delineated in MRI and HDR-planning CT. An HDR-BT and a SMART boost plan were generated with a prescribed dose of 28 Gy in four fractions for all patients. The HDR-BT and SMART boost plans were compared in regard to target coverage as well OARs doses. Mean EQD2 D90 to HR-CTV and IR-CTV for HDR-BT plans were 89.7 and 70.5 Gy, respectively. For SMART, the mean EQD2 D90 to HR-PTV, HR-CTV, and IR-CTV were 82.9, 95.4, and 70.2 Gy, respectively. The mean D2cc EQD2 of bladder, rectum, and sigmoid colon for HDR-BT plans were 86.4, 70.7, and 65.7 Gy, respectively. The mean D2cc EQD2 of bladder, rectum, and sigmoid colon for SMART plans were 81.4, 70.8, and 73.6 Gy, respectively. All dose constraints in terms of target coverage and OARs constraints were met for both HDR-BT and SMART plans. This dosimetric study demonstrates that SMART can be applied in cases where HDR-BT is not available or ineligible with acceptable target coverage and OAR sparing. However, prospective clinical studies are needed to validate these results.

Real-time motion-including dose estimation of simulated multi-leaf collimator-tracked magnetic resonance-guided radiotherapy.

Real-time dose estimation is a key-prerequisite to enable online intra-fraction treatment adaptation in magnetic resonance (MR)-guided radiotherapy (MRgRT). It is an essential component for the assessment of the dosimetric benefits and risks of online adaptive treatments, such as multi-leaf collimator (MLC)-tracking. We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions. A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation. On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148ms. Median gamma pass rate (2%/3mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12Gy for the investigated volume and dose criteria respectively. Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.

Novel Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients.

MR-guided adaptive radiotherapy (MRgART) improves target coverage and organ-at-risk (OAR) sparing in pancreatic cancer radiation therapy (RT). Inter-fractional changes in patients undergoing RT require time intensive re-delineation of gross tumor volume (GTV) and OARs prior to adaptive optimization. Accurate automatic segmentation has the potential to significantly improve efficiency of the adaptive workflow. We hypothesized that state-of-the-art deep learning (DL) segmentation models could adequately segment GTV and OARs in both planning and daily fractional MR scans. The study included 21 patients with pancreatic cancer treated with MRgART (10 Gy x 5 fractions). The planning MR as well as all daily MR images and registrations were collected (6 image sets per patient and a total of 126 image sets). The planning MR and fraction 1-4 image sets were used as the training set (N = 105), while the test set (N = 21) comprised images for fraction 5, to simulate the last step of incremental learning from planning to final fraction. Evaluated contours included the GTV, Small Bowel, Large Bowel, Duodenum, Left and Right Kidney, Liver, Spinal Cord, and Stomach. To mimic clinical conditions, contour accuracy was evaluated within the ring structure surrounding the PTV, inside of which daily adaptive re-contouring is applied (2 cm expansion in the cradio-caudal direction, 3 cm expansion otherwise). We evaluated three DL model architectures: SegResNet, SegResNet 2D, and SwinUNETR to autosegment GTV and OARs. The segmentation models were trained on the training set using 5-fold cross-validation (CV) and quantitatively analyzed by comparing against clinically used contours with DICE scores. Qualitative analysis was performed by a radiation oncologist using a scoring scale: 1 = perfect, 2 = minor discrepancy, 3 = moderate discrepancy, and 4 = rejected. Overall, the DL segmentations were in acceptable agreement with clinical contours. The best performing model was the SwinUNETR model with overall training DICE = 0.88±0.06, test DICE = 0.78±0.11, and qualitative score of 1.6±0.8. The agreement between the DL model and clinical segmentation for the GTV was 0.79±0.08, with a qualitative score of 2.2±0.9. The highest and lowest OAR DICE scores were for the Left Kidney (DICE = 0.93) and Small Bowel (DICE = 0.68), respectively. The highest qualitative OAR scores were for the Kidney, Liver, and Spinal Cord (score = 1.0) and the lowest qualitative score was for the Duodenum (score = 2.3) CONCLUSION: We report here the most comprehensive work on DL segmentation for pancreatic cancer MRgART, including quantitative and clinically-pertinent qualitative evaluations of 126 image sets and 3 DL architectures. Our data show good quantitative agreement between DL and clinical contours, and acceptable clinician evaluations for the majority of GTVs and OARs. The current work has great potential to significantly reduce a major bottleneck in the MRgART workflow for pancreatic cancer patients.

Integrating a Tool to Automatically Determine Necessity of Online Adaptive Replanning

As online adaptive replanning (OLAR) is labor-intensive and time-consuming, it's desirable to determine when OLAR is necessary before OLAR is initiated. We have previously reported a novel method to automatically determine the necessity of OLAR using machine leaning algorithms based on the structural similarity maps (SSIM) and wavelet texture maps (WMT) extracted from the daily MRI during MR-guided adaptive radiation therapy (MRgART). This study aims to integrate this method into a commercial software platform that has been used during our routine MRgART. The method of automatically determining the necessity of OLAR based on daily MRI was implemented and integrated into the software platform through a specifically developed workflow. The obtained workflow was tested using 25 daily MRI sets acquired from 5 patients with pancreatic cancer in the following procedure: 1) rigidly registering the daily and reference MRIs, 2) identifying the region enclosed by the 50-100% iso-dose surfaces on the daily MRI by transferring the iso-dose surfaces from the reference to the daily MRIs, 3) launching our in-house codes to calculate significant changes in textures extracted from SSIM and WMT maps, 4) inputting the feature values into the pre-trained classifier models for SSIM and WMT, and 5) outputting results considering the WMT based prediction as the primary indicator and the SSIM-based as the secondary (validation) indicator on whether OLAR is needed for the daily MRI. The execution of the developed workflow was fast and can be used to streamline the process. It provides the ability to scroll through the images for better decision making while providing quantitative prediction within 30-38 seconds. Eighty percent of the daily MRIs required OLAR. The SSIM map displayed was able to successfully captured the areas of similarity between the reference and daily MRIs and the WMT prediction agreed with the prediction class. The integration of the prediction method for automatically determining the necessity of OLAR based on two independent machine learning classifiers into a commercially available software is feasible and can be used to streamline the process of MRgART. With larger verification studies, this workflow-based tool may be developed into a generalized tool that assist in OLAR using different platforms.

Patient-Specific Deep Learning Auto-Segmentation for MR-Guided Adaptive Radiotherapy of Prostate Cancer

MR guided conventional prostate radiotherapy requires agreement of bladder and rectum position, size, and shape with reference plan anatomy. In many instances, treatments may be delayed to allow optimal filling of these organs at risk (OARs). To account for such changes, we propose a patient specific deep learning based auto-segmentation (PS-DLAS) solution to implement pseudo (adapt-to-shape) ATS planning as a replacement for (adapt-to-position) ATP for conventionally fractionated treatments, and to generate auto-segmented contours for ATS SBRT treatments. A commercially available DLAS tool that allows to customize/re-train DLAS models to an individual patient was implemented and automated in 3 iterative steps: 1) training of initial PS-DLAS model based on the first daily MRI with manual contours for the patient, 2) using the trained model to auto-segment the next daily MRI (acquired at next fraction), and 3) re-training/updating the model with newly available daily MRI sets and the verified contours. Steps 1) and 3) were performed offline while the step 2 was online. The solution was tested using daily MRI sets collected for 6 prostate cancer patients treated with MR guided adaptive radiotherapy (MRgART) either in 5-fraction SBRT or conventionally fractionated with adapt-to-shape (ATS) or adapt-to-position (ATP) workflows on a 1.5T MR-Linac. The quality of the auto-segmented OAR contours, including bladder, penile bulb, prostate, rectum, and seminal vesicles, obtained in step 2) were assessed, where the acceptable contour slices (no editing required) were identified. Additionally, ATP was simulated using a pseudo ATS workflow on patient 6, allowing the PS-DLAS to be used on daily MRI sets and the obtained contours to be used in the daily plan optimization. The time saving and the plan quality of using the PS-DLAS were compared to those from the current standard clinical workflow. The times for the offline model training in steps 1) or 3) were < 50 minutes and the times of applying the updated PS-DLAS to auto-segment a daily MRI set in step 2) were within 1 minute using a hardware of Intel(R) Xeon(R) Gold 5222 CPU. Among the auto-segmented contour slices, 87%, 92%, 88%, 86% and 84% were found to be acceptable for bladder, penile bulb, prostate, rectum, and seminal vesicles, respectively. The time savings of using the PS-DLAS were about 7 minutes, compared to the manual contouring in the current ATS workflow. Manual editing was almost always required in 1-3 most superior slices for each organ. For the patient tested with the pseudo ATS, time saving was 10 minutes with comparable plan quality to the standard ATP workflow. In this study, we demonstrated use of a PS-DLAS approach to reduce contouring time in MR-guided ATS prostate radiotherapy. In addition, these results suggest a PS-DLAS approach combined with pseudo-ATS may reduce overall treatment times in conventionally fractionated MRgRT by eliminating time required for matching of OAR filling.

Effectiveness of Bladder Filling Control during Online MR-Guided Adaptive Radiotherapy for Rectum Cancer

MR-guided adaptive radiotherapy (MRgART) treatment sessions at MR-Linac are time-consuming and changes in bladder filling during the session can impact the treatment dosimetry. In this work, we present the procedure implemented in the clinical workflow to stabilize bladder filling during the MR based adaptive radiotherapy sessions and evaluate its effectiveness and the resulting dosimetric impact on the adaptive plan. Twenty-five rectum cancer patients treated at 1.5T MR-Linac with a short course radiotherapy (25 Gy in 5 fractions of 5 Gy each) were included in this retrospective study. Patients were treated with the adapt-to-shape workflow consisting of a plan adaptation based on the MRI acquired in each session and optimized on the corresponding MR-based synthetic CT. Considering the significant interval time between the acquisition of the first daily MRI used for plan adaptation, and the beam delivery, a bladder catheter was used to stabilize the bladder filling; the procedure consists of emptying the bladder and refilling it with a well-known amount of physiological solution before each MRI acquisition. Two MRIs were acquired at each session: the first was used for plan adaptation and the second was acquired while approving the adapted plan, to be rigidly registered with the first to ensure the appropriateness of the isodoses on the ongoing delivery treatment. A total of 125 sessions and 250 MRI images and bladder contours were analyzed; for each fraction, the time interval between the first and second MRI and the corresponding bladder volumes were recorded; the consistency of bladder volumes and shapes along each online session was assessed with the dice similarity index (DSC) and Hausdorff distance (HD); the impact on plan dosimetry was evaluated by comparing target and bladder DVH cut off points of the plan on the two different MRI datasets. The time interval between the first and second MRI, averaged over the 125 sessions is 39.0 min, range (18.6-75.8) min. The changes in bladder volumes, DSC index, HD, and the differences between the bladder and target DVH cut-off points are shown in the table below. The DSC and HD are comparable to inter-observer variability in manual contour segmentation, with an average DSC of 0.91 and average HD of 2.13 mm; the average differences in bladder and target dosimetry remain under 0.63% and 0.10%, respectively. The use of a procedure in the clinical workflow of MRgART to stabilize the bladder filling throughout the online session may be helpful to guarantee the accuracy of the ongoing delivered treatment.

Standardization of Stereotactic MR-Guided Adaptive Radiation Therapy Planning for Prostate Cancer

To assess the impact of a standardized planning class solution (PCS) in stereotactic MR-guided adaptive radiation therapy (SMART) for prostate cancer on efficiency and robustness for daily adaptive radiation therapy treatments. Data collection of 20 previous SMART prostate plans: number of objective structures, number of beam segments, treatment delivery time (TDT) and the plan adaption robustness (PAR). The PAR was scored from 1 to 3 for the robustness of the plan to re-adapt and achieve similar dose metric scores to the baseline plan on all fractions. These scores were, 1: a single re-optimization required, 2: cost-function adjustments required and 3: medical physics advice required. Five medical dosimetrists provided preferences for objective structures and the optimization thresholds used. A PCS was created based on this data and implemented into SMART prostate plans between October 2022 and December 2022. A consecutive sample of 20 patients (PCSpost) were then retrospectively compared with the previous 20 patients prior to the PCS (PCSpre). The PCS showed minimal difference in the TDT and segment number mean ± (standard deviation), 9.5 ± (0.7) mins and 77.8 ± (6.9) segments PCSpost respectively compared to 9.5 ± (0.8) mins and 80.8 ± (5.9) segments PCSpre. However, the PCS resulted in a considerable reduction in PAR, with 11 patients requiring only a single re-optimization for each fraction to achieve dose metric targets compared to 7 PCSpre patients. Also, the median PAR score was 1 in PCSpost compared to a score of 2 with PCSpre. The PCS had good compliance with the criteria met in 18/20 patient baseline plans. Feedback surveys showed positive results for efficiency saving from multiple disciplines; in therapeutic radiographers from reduced PAR, medical physicists increased automation in plan checking and consistent planning guidelines for medical dosimetrists. The PCS is in clinical use, results have shown improved treatment adaption efficiency and robustness. Fewer re-optimizations per treatment were required onset to achieve similar dose metrics than before its implementation.

Neoadjuvant Stereotactic MR-Guided Ablative Radiation Therapy (SMART) and Surgical Outcomes in Patients with Pancreatic Cancer

The benefit of neoadjuvant radiation therapy for patients undergoing surgery for pancreatic ductal adenocarcinoma (PDAC) remains unclear. Stereotactic MR-guided adaptive radiation therapy (SMART) treatment to ablative doses is a newer technique that is well tolerated and has increased local control in unresectable pancreatic cancer. For resectable pancreatic cancer, neoadjuvant SMART has the potential to decrease local recurrence risk and positive margin rates. However, there is concern for perioperative risks associated with ablative dose treatments. We report the efficacy and safety of surgical resection in patients who have received neoadjuvant SMART at our institution. We conducted a retrospective analysis of all consecutive patients diagnosed with PDAC who had noted vascular involvement of the celiac axis, superior mesenteric, and/or portal vessels between January 2016 and December 2022 at a single, high-volume, academic institution. Perioperative events were defined according to the Clavien-Dindo classification. The Kaplan Meier method was applied to estimate disease free survival (DFS) and overall survival (OS). Seventeen patients with PDAC and vessel involvement at time of diagnosis who received SMART were included. Median follow-up time was 14.3 months; all patients underwent surgery, at a median time after radiation of 28 days (range: 15 - 90). Median length of postoperative stay was 7 days (range: 3 - 15). Five patients (29%) underwent vascular resection. Fifteen patients (88%) achieved R0 resection, with two R1 resections noted at the SMA and pancreatic neck respectively. Seven patients (41%) had adverse events attributable to surgery, with the majority being defined as abscess or infection (n = 5; 29%). One (6%) Clavien-Dindo grade III or higher toxicity was observed - a cortical cerebrovascular event following surgery. No major bleeding events requiring surgical intervention were noted. At time of event censorship, there were no observable locoregional failures. The median DFS and OS were not reached; however, 1-year DFS and OS were 62% and 87%, respectively. Neoadjuvant SMART appears to be safe, with low rates of surgical complications and promising outcomes. Further identification of patients for this approach requires additional investigation.

Volumes Changes in Adrenal Metastases during Delivery of 5-Fraction Stereotactic Magnetic Resonance-Guided Adaptive Radiotherapy

Stereotactic MR-guided adaptive radiotherapy (SMART) improves adrenal target coverage and reduces organ at risk doses. Changes in adrenal tumor volumes were observed during contouring for daily-adaptive planning. We hypothesized that such changes in GTV may correlate with long-term outcomes. An Ethics approved institutional database was accessed for details of 5-fraction adrenal SABR treatments on a 0.35T MR-Linac between 2016-2023. Volumetric MR-scans were acquired at baseline, and prior to each of 5 fractions. Manually contoured GTVs were accessed. Volume changes between simulation and first fraction (ΔPF1), as well as between first and subsequent fractions (ΔF1F2 to ΔF1F5) were computed. Daily relative adrenal volume change (DRVC) and the absolute volume change (DAVC) before treatment, were calculated by dividing ΔPF1 by the number of days between simulation and F1. Paired Wilcoxon tests were used to compare volumes at different timepoints. Univariate linear regression (LinR) models were created to study per-treatment volume changes. A logistic regression (LogR) analysis explored associations between baseline characteristics and volumetric changes with radiological complete response (CR) at the date of last news. Adrenal SMART in 5 fractions of either 8 or 10Gy was delivered to 70 patients. The median interval between simulation and F1 was 13 days (range, 2-35), and 13 days between F1-F5 (range, 9-31). Solitary metastasis comprised 44%; metastases were mainly from non-small cell lung (64%) or renal (9%) primaries. Concurrent systemic therapy (± 3 months) was delivered in 41% of patients. Mean baseline GTV at simulation and F1 were respectively 38.5 and 41.4cc (p<0.001). Mean ΔPF1 was +9.1% (-10.2% to +50.1%) or +2.9cc (-4.4 to +27.4cc). Mean DRVC was +0.8% per day (-1.0% to +8.4%), and did not correlate with histology, GTV or concurrent systemic treatment. Mean DAVC was +0.26cc per day (-0.31 to +2.4cc), and was associated with GTV size (p = 0.04). In total, 47% of adrenal GTVs decreased in volume at F5 compared to F1. A ≥10% increase in GTV was observed in 39% of patients during SMART. GTV variations of ≥20% occurred in 36% patients at some point during SMART. At a median follow-up of 20.3 months, radiological CR was seen in 23% of 64 evaluable patients. Radiological CR was significantly associated with baseline GTV (p = 0.03) and ΔF1F5 (p = 0.03), but not DRVC (p = 0.6) or ΔF1F3 (p = 0.3). Local relapses were seen in 6% of patients. Changes in adrenal volumes during SMART were common, with variations ≥20% occurring in 36% patients. The probability of a radiological CR correlated with both baseline GTV size and median relative volume decline between fractions 1 and 5. These changes support use of daily SABR plan adaptation.

Dosimetric Implications of Weekly On-Line MR-Guided Adaptive Radiotherapy (RT) for Glioblastomas (GBM) Growing during RT

The UNITED trial (NCT04726397) involves once-weekly on-line adaptive RT for patients with GBM on a 1.5T MRI-Linac (MRL). For tumors that continue to enlarge during the course of RT, we hypothesize that the adaptive strategy improves the dosimetric coverage of the target relative to a non-adaptive strategy. As per the trial protocol, T1+contrast (T1c) and FLAIR MRI sequences were acquired once per week during RT to re-define and adapt the gross tumor volume (GTV) as the contrast enhanced volume, the clinical target volume (CTV) as a 5mm expansion around the GTV plus adjacent FLAIR hyperintense regions considered at-risk by the radiation oncologist, and a 3 mm PTV around the CTV. Nine UNITED patients with tumors that grew throughout RT (GTV and/or CTV) were identified. 5/9 patients were treated with 60 Gy in 30 fractions (6 weekly adaptions) and 4/9 with 40 Gy in 15 fractions (3 weekly adaptions). For the final week's GTV and CTV (GTVfinal and CTVfinal), the dosimetric outcomes of the delivered and adaptive summative plans (Dsum) were compared to dose of the baseline plan (Dbaseline) generated on Fraction 1 of treatment, the latter being indicative of the theoretical situation where no further adaption was taken. We measured the dose to 99% of the GTVfinal and CTVfinal: Dsum99 and Dbaseline99. For each adaptive fraction, the plan was optimized to achieve an objective of D99 greater than 95% of the prescription (D99>95%) for both the GTV and CTV. The relative increase in GTVfinal and CTVfinal from baseline was on average 119% (range: 98% to 144.7%) and 128% (range: 109% to 176%), respectively. The proportion of CTVfinal that was outside the baseline plan's PTV was on average 11.5% (range: 0% to 32%). The GTVfinal did not extend beyond the baseline PTV for any of the 9 cases. GTVfinal Dsum99 was >95% for all cases while CTVfinal Dsum99 was < 95% in 2 of 9 cases (74%, and 87%). By contrast, the baseline plan, if given for all fractions with no further adaptation, yielded a D99 for CTVfinal of <95% in 5 of 9 cases (28%, 52%, 75%, 84%, and 87%). In general, coverage of the CTVfinal decreased with increasing levels of CTVfinal outside of the baseline PTV. For all 5 cases where CTVfinal D99<95% on the baseline plan, more than 10% of CTVfinal was outside of the baseline PTV. Small margin, weekly adaptive RT on an MRL for GBM maintains coverage of the GTV in the presence of tumor growth while minimizing the degree of normal brain tissue irradiated. Dosimetric impact on non-GTV/CTV brain is outside the scope of the present study. Preliminary results indicate that a once weekly adaptive approach for small margin MR-guided RT improves tumor coverage for progressive tumors compared to a static (baseline) plan without adaptation.

A transformer-based hierarchical registration framework for multimodality deformable image registration.

Deformable image registration (DIR) between daily and reference images is fundamentally important for adaptive radiotherapy. In the last decade, deep learning-based image registration methods have been developed with faster computation time and improved robustness compared to traditional methods. However, the registration performance is often degraded in extra-cranial sites with large volume containing multiple anatomic regions, such as Computed Tomography (CT)/Magnetic Resonance (MR) images used in head and neck (HN) radiotherapy. In this study, we developed a hierarchical deformable image registration (DIR) framework, Patch-based Registration Network (Patch-RegNet), to improve the accuracy and speed of CT-MR and MR-MR registration for head-and-neck MR-Linac treatments. Patch-RegNet includes three steps: a whole volume global registration, a patch-based local registration, and a patch-based deformable registration. Following a whole-volume rigid registration, the input images were divided into overlapping patches. Then a patch-based rigid registration was applied to achieve accurate local alignment for subsequent DIR. We developed a ViT-Morph model, a combination of a convolutional neural network (CNN) and the Vision Transformer (ViT), for the patch-based DIR. A modality independent neighborhood descriptor was adopted in our model as the similarity metric to account for both inter-modality and intra-modality registration. The CT-MR and MR-MR DIR models were trained with 242 CT-MR and 213 MR-MR image pairs from 36 patients, respectively, and both tested with 24 image pairs (CT-MR and MR-MR) from 6 other patients. The registration performance was evaluated with 7 manually contoured organs (brainstem, spinal cord, mandible, left/right parotids, left/right submandibular glands) by comparing with the traditional registration methods in Monaco treatment planning system and the popular deep learning-based DIR framework, Voxelmorph. Evaluation results show that our method outperformed VoxelMorph by 6 % for CT-MR registration, and 4 % for MR-MR registration based on DSC measurements. Our hierarchical registration framework has been demonstrated achieving significantly improved DIR accuracy of both CT-MR and MR-MR registration for head-and-neck MR-guided adaptive radiotherapy.

Initial clinical experience building a dual CT- and MR-guided adaptive radiotherapy program.

Our institution was the first in the world to clinically implement MR-guided adaptive radiotherapy (MRgART) in 2014. In 2021, we installed a CT-guided adaptive radiotherapy (CTgART) unit, becoming one of the first clinics in the world to build a dual-modality ART clinic. Herein we review factors that lead to the development of a high-volume dual-modality ART program and treatment census over an initial, one-year period. The clinical adaptive service at our institution is enabled with both MRgART (MRIdian, ViewRay, Inc, Mountain View, CA) and CTgART (ETHOS, Varian Medical Systems, Palo Alto, CA) platforms. We analyzed patient and treatment information including disease sites treated, radiation dose and fractionation, and treatment times for patients on these two platforms. Additionally, we reviewed our institutional workflow for creating, verifying, and implementing a new adaptive workflow on either platform. From October 2021 to September 2022, 256 patients were treated with adaptive intent at our institution, 186 with MRgART and 70 with CTgART. The majority (106/186) of patients treated with MRgART had pancreatic cancer, and the most common sites treated with CTgART were pelvis (23/70) and abdomen (20/70). 93.0% of treatments on the MRgART platform were stereotactic body radiotherapy (SBRT), whereas only 72.9% of treatments on the CTgART platform were SBRT. Abdominal gated cases were allotted a longer time on the CTgART platform compared to the MRgART platform, whereas pelvic cases were allotted a shorter time on the CTgART platform when compared to the MRgART platform. Our adaptive implementation technique has led to six open clinical trials using MRgART and seven using CTgART. We demonstrate the successful development of a dual platform ART program in our clinic. Ongoing efforts are needed to continue the development and integration of ART across platforms and disease sites to maximize access and evidence for this technique worldwide.

Online adaptive radiotherapy: Assessment of planning technique and its impact on longitudinal plan quality robustness in pancreatic cancer

Background and PurposePlanning on a static dataset that reflects the simulation day anatomy is routine for SBRT. We hypothesize the quality of on-table adaptive plans is similar to the baseline plan when delivering stereotactic MR-guided adaptive radiotherapy (SMART) for pancreatic cancer (PCa). Materials and MethodsSixty-seven inoperable PCa patients were prescribed 50 Gy/5-fraction SMART. Baseline planning included: 3–5 mm gastrointestinal (GI) PRV, 50 Gy optimization target (PTVopt) based on GI PRV, conformality rings, and contracted GTV to guide the hotspot. For each adaptation, GI anatomy was re-contoured, followed by re-optimization. Plan quality was evaluated for target coverage (TC = PTVopt V100%/volume), PTV D90% and D80%, homogeneity index (HI = PTVopt D2%/D98%), prescription isodose/target volume (PITV), low-dose conformity (D2cm = maximum dose at 2 cm from PTVopt/Rx dose), and gradient index (R50%=50% Rx isodose volume/PTVopt volume).A novel global planning metric, termed the Pancreas Adaptive Radiotherapy Score (PARTS), was developed and implemented based on GI OAR sparing, PTV/GTV coverage, and conformality. Adaptive robustness (baseline to fraction 1) and stability (difference between two fractions with highest GI PRV variation) were quantified. ResultsOAR constraints were met on all baseline (n = 67) and adaptive (n = 318) plans. Coverage for baseline/adaptive plans was mean ± SD at 44.9 ± 5.8 Gy/44.3 ± 5.5 Gy (PTV D80%), 50.1 ± 4.2 Gy/49.1 ± 4.7 Gy (PTVopt D80%), and 80%±18%/74%±18% (TC), respectively. Mean homogeneity and conformality for baseline/adaptive plans were 0.87 ± 0.25/0.81 ± 0.30 (PITV), 3.81 ± 1.87/3.87 ± 2.0 (R50%), 1.53 ± 0.23/1.55 ± 0.23 (HI), and 58%±7%/59%±7% (D2cm), respectively. PARTS was found to be a sensitive metric due to its additive influence of geometry changes on PARTS’ sub-metrics. There were no statistical differences (p > 0.05) for stability, except for PARTS (p = 0.04, median difference −0.6%). Statistical differences for robustness when significant were small for most metrics (<2.0% median). Median adaptive re-optimizations were 2. ConclusionWe describe a 5-fraction ablative SMART planning approach for PCa that is robust and stable during on-table adaption, due to gradients controlled by a GI PRV technique and the use of rings. These findings are noteworthy given that daily interfraction anatomic GI OAR differences are routine, thus necessitating on-table adaptation. This work supports feasibility towards utilizing a patient-independent, template on-table adaptive approach.

Stereotactic MR-guided adaptive radiotherapy (SMART) for primary rectal cancer: evaluation of early toxicity and pathological response.

The purpose of this study is to measure the effects of stereotactic MR-guided adaptive radiotherapy (SMART) for rectal cancer patients in terms of early toxicity and pathological response. For this prospective pilot study, patients diagnosed with locally advanced rectal cancer (LARC) with positive lymph node clinical staging underwent SMART on rectal lesion and mesorectum using hybrid MR-Linac (MRIdian ViewRay). Dose prescription at 80% isodose for the rectal lesion and mesorectum was 40 Gy (8 Gy/fr) and 25 Gy (5 Gy/fr), respectively, delivered on 5 days (3 fr/week). Response assessment by MRI was performed 3 weeks after SMART, then patients fit for surgery underwent total mesorectal excision. Primary endpoint was evaluation of adverse effect of radiotherapy. Secondary endpoint was pathological complete response rate. Early toxicity was graded according to the Common Terminology Criteria for Adverse Events (CTCAE v5.0). From October 2020 to January 2022, twenty patients underwent rectal SMART. No grade 3-5 toxicity was recorded. Twelve patients were eligible for total mesorectal excision (TME). Mean interval between the completion of SMART and surgery was 4 weeks. Pathological downstaging occurred in all patients; rate of pathological complete response (pCR) was 17%. pCR occurred with a prolonged time to surgery (> 7 weeks). To our knowledge, this is the first study to use stereotactic radiotherapy for primary rectal cancer. SMART for rectal cancer is well tolerated and effective in terms of tumor regression, especially if followed by delayed surgery.

Effectiveness of bladder filling control during online MR-guided adaptive radiotherapy for rectal cancer

BackgroundMagnetic resonance-guided adaptive radiotherapy (MRgART) treatment sessions at MR-Linac are time-consuming and changes in organs at risk volumes can impact the treatment dosimetry. This study aims to evaluate the feasibility to control bladder filling during the rectum MRgART online session and its effectiveness on plan dosimetry.MethodsA total of 109 online adaptive sessions of 24 rectum cancer patients treated at Unity 1.5 T MR-Linac with a short course radiotherapy (25 Gy, 5 Gy × 5) for whom the adaptive plan was optimized and recalculated online based on the daily magnetic resonance imaging (MRI) were analysed. Patients were fitted with a bladder catheter to control bladder filling; the bladder is emptied and then partially filled with a known amount of saline at the beginning and end of the online session. A first MRI ({mathrm{MRI}}_{mathrm{i}}) acquired at the beginning of the session was used for plan adaptation and the second ({mathrm{MRI}}_{mathrm{f}}) was acquired while approving the adapted plan and rigidly registered with the first to ensure the appropriateness of the isodoses on the ongoing delivery treatment. For each fraction, the time interval between the two MRIs and potential bladder changes were assessed with independent metrics, and the impact on the plan dosimetry was evaluated by comparing target and organs at risk dose volume histogram cut-off points of the plan adapted on {mathrm{MRI}}_{mathrm{i}} and recalculated on {mathrm{MRI}}_{mathrm{f}}.ResultsMedian bladder volume variations, DSC, and HD of 8.17%, 0.922, and 2.92 mm were registered within a median time of 38 min between {mathrm{MRI}}_{mathrm{i}} and {mathrm{MRI}}_{mathrm{f}}; dosimetric differences < 0.65% were registered for target coverage, and < 0.5% for bladder, small bowel and femoral heads constraints, with a p value > 0.05.ConclusionThe use of a bladder filling control procedure can help ensure the dosimetric accuracy of the online adapted treatment delivered.