Pancreas Stereotactic Body Radiation Therapy Research Articles

Treatment planning for MR-guided SBRT of pancreatic tumors on a 1.5 T MR-Linac: A global consensus protocol

Background and purposeTreatment planning for MR-guided stereotactic body radiotherapy (SBRT) for pancreatic tumors can be challenging, leading to a wide variation of protocols and practices. This study aimed to harmonize treatment planning by developing a consensus planning protocol for MR-guided pancreas SBRT on a 1.5 T MR-Linac. Materials and methodsA consortium was founded of thirteen centers that treat pancreatic tumors on a 1.5 T MR-Linac. A phased planning exercise was conducted in which centers iteratively created treatment plans for two cases of pancreatic cancer. Each phase was followed by a meeting where the instructions for the next phase were determined. After three phases, a consensus protocol was reached. ResultsIn the benchmarking phase (phase I), substantial variation between the SBRT protocols became apparent (for example, the gross tumor volume (GTV) D99% ranged between 36.8 – 53.7 Gy for case 1, 22.6 – 35.5 Gy for case 2). The next phase involved planning according to the same basic dosimetric objectives, constraints, and planning margins (phase II), which led to a large degree of harmonization (GTV D99% range: 47.9–53.6 Gy for case 1, 33.9–36.6 Gy for case 2). In phase III, the final consensus protocol was formulated in a treatment planning system template and again used for treatment planning. This not only resulted in further dosimetric harmonization (GTV D99% range: 48.2–50.9 Gy for case 1, 33.5–36.0 Gy for case 2) but also in less variation of estimated treatment delivery times. ConclusionA global consensus protocol has been developed for treatment planning for MR-guided pancreatic SBRT on a 1.5 T MR-Linac. Aside from harmonizing the large variation in the current clinical practice, this protocol can provide a starting point for centers that are planning to treat pancreatic tumors on MR-Linac systems.

CBCT-DRRs superior to CT-DRRs for target-tracking applications for pancreatic SBRT

Objective. In current radiograph-based intra-fraction markerless target-tracking, digitally reconstructed radiographs (DRRs) from planning CTs (CT-DRRs) are often used to train deep learning models that extract information from the intra-fraction radiographs acquired during treatment. Traditional DRR algorithms were designed for patient alignment (i.e. bone matching) and may not replicate the radiographic image quality of intra-fraction radiographs at treatment. Hypothetically, generating DRRs from pre-treatment Cone-Beam CTs (CBCT-DRRs) with DRR algorithms incorporating physical modelling of on-board-imagers (OBIs) could improve the similarity between intra-fraction radiographs and DRRs by eliminating inter-fraction variation and reducing image-quality mismatches between radiographs and DRRs. In this study, we test the two hypotheses that intra-fraction radiographs are more similar to CBCT-DRRs than CT-DRRs, and that intra-fraction radiographs are more similar to DRRs from algorithms incorporating physical models of OBI components than DRRs from algorithms omitting these models. Approach. DRRs were generated from CBCT and CT image sets collected from 20 patients undergoing pancreas stereotactic body radiotherapy. CBCT-DRRs and CT-DRRs were generated replicating the treatment position of patients and the OBI geometry during intra-fraction radiograph acquisition. To investigate whether the modelling of physical OBI components influenced radiograph-DRR similarity, four DRR algorithms were applied for the generation of CBCT-DRRs and CT-DRRs, incorporating and omitting different combinations of OBI component models. The four DRR algorithms were: a traditional DRR algorithm, a DRR algorithm with source-spectrum modelling, a DRR algorithm with source-spectrum and detector modelling, and a DRR algorithm with source-spectrum, detector and patient material modelling. Similarity between radiographs and matched DRRs was quantified using Pearson’s correlation and Czekanowski’s index, calculated on a per-image basis. Distributions of correlations and indexes were compared to test each of the hypotheses. Distribution differences were determined to be statistically significant when Wilcoxon’s signed rank test and the Kolmogorov-Smirnov two sample test returned p ≤ 0.05 for both tests. Main results. Intra-fraction radiographs were more similar to CBCT-DRRs than CT-DRRs for both metrics across all algorithms, with all p ≤ 0.007. Source-spectrum modelling improved radiograph-DRR similarity for both metrics, with all p < 10−6. OBI detector modelling and patient material modelling did not influence radiograph-DRR similarity for either metric. Significance. Generating DRRs from pre-treatment CBCT-DRRs is feasible, and incorporating CBCT-DRRs into markerless target-tracking methods may promote improved target-tracking accuracies. Incorporating source-spectrum modelling into a treatment planning system’s DRR algorithms may reinforce the safe treatment of cancer patients by aiding in patient alignment.

Assessment of interfraction dose variation in pancreas SBRT using daily simulation MR images

Pancreatic Cancer is associated with poor treatment outcomes compared to other cancers. High local control rates have been achieved by using hypofractionated stereotactic body radiotherapy (SBRT) to treat pancreatic cancer. Challenges in delivering SBRT include close proximity of several organs at risk (OARs) and target volume inter and intra fraction positional variations. Magnetic resonance image (MRI) guided radiotherapy has shown potential for online adaptive radiotherapy for pancreatic cancer, with superior soft tissue contrast compared to CT. The aim of this study was to investigate the variability of target and OAR volumes for different treatment approaches for pancreatic cancer, and to assess the suitability of utilizing a treatment-day MRI for treatment planning purposes. Ten healthy volunteers were scanned on a Siemens Skyra 3 T MRI scanner over two sessions (approximately 3 h apart), per day over 5 days to simulate an SBRT daily simulation scan for treatment planning. A pretreatment scan was also done to simulate patient setup and treatment. A 4D MRI scan was taken at each session for internal target volume (ITV) generation and assessment. For each volunteer a treatment plan was generated in the Raystation treatment planning system (TPS) following departmental protocols on the day one, first session dataset (D1S1), with bulk density overrides applied to enable dose calculation. This treatment plan was propagated through other imaging sessions, and the dose calculated. An additional treatment plan was generated on each first session of each day (S1) to simulate a daily replan process, with this plan propagated to the second session of the day. These accumulated mock treatment doses were assessed against the original treatment plan through DVH comparison of the PTV and OAR volumes. The generated ITV showed large variations when compared to both the first session ITV and daily ITV, with an average magnitude of 22.44% ± 13.28% and 25.83% ± 37.48% respectively. The PTV D95 was reduced by approximately 23.3% for both plan comparisons considered. Surrounding OARs had large variations in dose, with the small bowel V30 increasing by 128.87% when compared to the D1S1 plan, and 43.11% when compared to each daily S1 plan. Daily online adaptive radiotherapy is required for accurate dose delivery for pancreas cancer in the absence of additional motion management and tumour tracking techniques.

Scorecards: Quantifying Dosimetric Plan Quality in Pancreatic Ductal Adenocarcinoma Stereotactic Body Radiation Therapy

A scoring mechanism called the scorecard that objectively quantifies the dosimetric plan quality of pancreas stereotactic body radiation therapy treatment plans is introduced. A retrospective analysis of patients with pancreatic ductal adenocarcinoma receiving stereotactic body radiation therapy at our institution between November 2019 and November 2020 was performed. Ten patients were identified. All patients were treated to 36 Gy in 5 fractions, and organs at risk (OARs) were constrained based on Alliance A021501. The scorecard awarded points for OAR doses lower than those cited in Alliance A021501. A team of 3 treatment planners and 2 radiation oncologists, including a physician resident without plan optimization experience, discussed the relative importance of the goals of the treatment plan and added additional metrics for OARs and plan quality indexes to create a more rigorous scoring mechanism. The scorecard for this study consisted of 42 metrics, each with a unique piecewise linear scoring function which is summed to calculate the total score (maximum possible score of 365). The scorecard-guided plan, the planning and optimization for which were done exclusively by the physician resident with no prior plan optimization experience, was compared with the clinical plan, the planning and optimization for which were done by expert dosimetrists, using the Sign test. Scorecard-guided plans had, on average, higher total scores than those clinically delivered for each patient, averaging 280.1 for plans clinically delivered and 311.7 for plans made using the scorecard (P=.003). Additionally, for most metrics, the average score of each metric across all 10 patients was higher for scorecard-guided plans than for clinically delivered plans. The scorecard guided the planner toward higher coverage, conformality, and OAR sparing. A scorecard tool can help clarify the goals of a treatment plan and provide an objective method for comparing the results of different plans. Our study suggests that a completely novice treatment planner can use a scorecard to create treatment plans with enhanced coverage, conformality, and improved OAR sparing, which may have significant effects on both tumor control and toxicity. These tools, including the scorecard used in this study, have been made freely available.

Evaluation of deep learning based implanted fiducial markers tracking in pancreatic cancer patients

Real-time target position verification during pancreas stereotactic body radiation therapy (SBRT) is important for the detection of unplanned tumour motions. Fast and accurate fiducial marker segmentation is a Requirement of real-time marker-based verification. Deep learning (DL) segmentation techniques are ideal because they don’t require additional learning imaging or prior marker information (e.g., shape, orientation). In this study, we evaluated three DL frameworks for marker tracking applied to pancreatic cancer patient data. The DL frameworks evaluated were (1) a convolutional neural network (CNN) classifier with sliding window, (2) a pretrained you-only-look-once (YOLO) version-4 architecture, and (3) a hybrid CNN-YOLO. Intrafraction kV images collected during pancreas SBRT treatments were used as training data (44 fractions, 2017 frames). All patients had 1-4 implanted fiducial markers. Each model was evaluated on unseen kV images (42 fractions, 2517 frames). The ground truth was calculated from manual segmentation and triangulation of markers in orthogonal paired kV/MV images. The sensitivity, specificity, and area under the precision-recall curve (AUC) were calculated. In addition, the mean-absolute-error (MAE), root-mean-square-error (RMSE) and standard-error-of-mean (SEM) were calculated for the centroid of the markers predicted by the models, relative to the ground truth. The sensitivity and specificity of the CNN model were 99.41% and 99.69%, respectively. The AUC was 0.9998. The average precision of the YOLO model for different values of recall was 96.49%. The MAE of the three models in the left-right, superior-inferior, and anterior-posterior directions were under 0.88 ± 0.11 mm, and the RMSE were under 1.09 ± 0.12 mm. The detection times per frame on a GPU were 48.3, 22.9, and 17.1 milliseconds for the CNN, YOLO, and CNN-YOLO, respectively. The results demonstrate submillimeter accuracy of marker position predicted by DL models compared to the ground truth. The marker detection time was fast enough to meet the requirements for real-time application.

Identifying predictors of on-table adaptation for pancreas stereotactic body radiotherapy (SBRT)

PurposeTo identify any clinical or dosimetric parameters that predict which individuals may benefit from on-table adaptation during pancreas stereotactic body radiotherapy (SBRT) with MRI-guided radiotherapy. Methods and materialsThis was a retrospective study of patients undergoing MRI-guided SBRTfrom 2016 to 2022.Pre-treatment clinical variables and dosimetric parameters on the patient’s simulation scan were recorded for each SBRT course, and their ability to predict for on-table adaptation was analyzed using ordinal logistic regression. The outcome measure was number of fractions adapted. ResultsSixty-three SBRT courses consisting of 315 fractions were analyzed. Median prescription dose was 40 Gy in five fractions (range, 33–50 Gy); 52% and 48% of courses were prescribed ≤40 Gy and >40 Gy, respectively. The median minimum dose delivered to 95% (D95) of the gross tumor volume (GTV) and planning target volume (PTV) was 40.1 Gy and 37.0 Gy, respectively. Median number of fractions adapted per course was three, with 58% (183 out of 315) total fractions adapted. On univariable analysis, the prescription dose (>40 Gy vs ≤40 Gy), GTV volume, stomach V20 and V25, duodenum V20 and dose maximum, large bowel V33 and V35, GTV dose minimum, PTV dose minimum, and gradient index were significant determinants foradaptation (all p < 0.05). On multivariable analysis, only the prescription dose was significant (adjusted odds ratio 19.7, p = 0.005), but did not remain significant after multiple test correction (p = 0.08). ConclusionsThe likelihood of needing on-table adaptation could not be reliably predicted a priori using pre-treatment clinical characteristics, dosimetry to nearby organs at risk, or other dosimetric parameters based on the patient’s anatomy at the time of simulation, suggesting the critical importance of day-to-day variations in anatomy and increasing access to adaptive technology for pancreas SBRT. A higher (ablative) prescription dose was associated with increased use of adaptation.

Identifying predictors of on-table adaptation for pancreas stereotactic body radiotherapy (SBRT).

702 Background: There is growing interest in the use of stereotactic body radiotherapy (SBRT) to treat pancreatic cancer. The purpose of this study was to identify any clinical or dosimetric parameters that predict which individuals may benefit from on-table adaptation during pancreas SBRT with MRI-guided radiotherapy. Methods: This was a single center retrospective study of patients undergoing MRI-guided SBRT to the pancreas from 2016 to 2022. An online daily adaptive workflow was in place for all fractions according to pre-specified criteria. Pre-treatment clinical variables and dosimetric parameters on the patient’s simulation scan were recorded for each SBRT course, and their ability to predict for on-table adaptation was analyzed using ordinal logistic regression. The outcome measure was number of fractions adapted. Results: Seventy-three SBRT courses consisting of 365 fractions were included in this study. Median prescription dose was 40 Gy in five fractions (range, 33-50 Gy); 56% and 44% of courses were prescribed ≤ 40 Gy and &gt; 40 Gy, respectively. Median gross and planning target volume (GTV and PTV) coverage were 94.7% and 84.3%, respectively. Median number of fractions adapted per course was three, with overall 58% (211 out of 365) total fractions adapted. On univariable analysis, the prescription dose ( &gt; 40 Gy vs ≤ 40 Gy), GTV size, stomach V20 and V25, duodenum V20 and Dmax, large bowel V33 and V35, heterogeneity index, and gradient index were significant determinants of the use of on-table adaptation (all p &lt; 0.05). On multivariable analysis, only prescription dose remained significant (adjusted odds ratio 7.66, p = 0.003). Conclusions: Prescription dose in MRI-guided pancreas SBRT is the only reliable predictor of on-table adaptation. Providers should strongly consider utilization of adaptive technology when delivering ablative doses to the pancreas.[Table: see text]

Machine learning for tracking planned versus delivered dose in pancreas SBRT.

561 Background: Cone-beam CT (CBCT) used for daily image guidance in pancreatic stereotactic body radiotherapy (SBRT) is fraught with imaging artifacts leading to poor visualization of target and avoidance structures. Machine learning methods can be used to generate synthetic CT (sCT) images from CBCT to eliminate imaging artifacts. We sought to compare planned versus delivered radiation dose to luminal GI organs at risk (OARs) during pancreas SBRT using sCT. Methods: Previously treated patients from a single institution were eligible if treated with 5 fractions of pancreas SBRT using daily CBCT and inspiration breath hold. CBCT-based sCT was generated for each treatment fraction using our previously reported cycleGAN technique. Physicians manually contoured GI OARs (stomach, duodenum, and bowel [remaining small bowel and large bowel]) on sCTs. Rigid online registration from treatment was used to re-calculate the delivered dose on each sCT. Fractional delivered dose to OARs was compared to planned dose and dose constraints with descriptive statistics. Max dose to 1cc or 20cc of an OAR was defined as D1cc and D20cc respectively. Results: 7 patients and 35 cumulative SBRT CBCT datasets were included. The median SBRT cumulative dose was 8 Gy x 5 fractions (40 Gy total). Each GI OAR was constrained per fraction to D1cc &lt; 7 Gy (cumulative D1cc &lt; 35 Gy) and D20cc &lt; 4 Gy (cumulative D20cc &lt; 20 Gy) during SBRT planning. Table shows the estimated OAR delivered doses on each sCT compared to planned doses and dose constraints. Each patient had at least 1 fraction that exceeded a planned GI OAR dose; 2 patients had delivered dose to 1cc of duodenum exceeding constraint for all 5 fractions. After a median follow-up of 9 months (range 5-34), there were 2 CTCAE Grade 1 and no Grade 2+ GI toxicity events possibly or likely related to SBRT. Conclusions: Physician contouring and dose calculation using CBCT-based sCT was feasible. Estimated delivered doses of pancreas SBRT generally exceed planned doses. Further study of sCT in pancreas SBRT is warranted for improved localization, dose calculation, and adaptive radiotherapy.[Table: see text]

Transfer learning for fluence map prediction in adrenal stereotactic body radiation therapy

Objective: To design a deep transfer learning framework for modeling fluence map predictions for stereotactic body radiation therapy (SBRT) of adrenal cancer and similar sites that usually have a small number of cases. Approach: We developed a transfer learning framework for adrenal SBRT planning that leverages knowledge in a pancreas SBRT planning model. Treatment plans from the two sites had different dose prescriptions and beam settings but both prioritized gastrointestinal sparing. A base framework was first trained with 100 pancreas cases. This framework consists of two convolutional neural networks (CNN), which predict individual beam doses (BD-CNN) and fluence maps (FM-CNN) sequentially for 9-beam intensity-modulated radiation therapy (IMRT) plans. Forty-five adrenal plans were split into training/validation/test sets with the ratio of 20/10/15. The base BD-CNN was re-trained with transfer learning using 5/10/15/20 adrenal training cases to produce multiple candidate adrenal BD-CNN models. The base FM-CNN was directly used for adrenal cases. The deep learning (DL) plans were evaluated by several clinically relevant dosimetric endpoints, producing a percentage score relative to the clinical plans. Main results: Transfer learning significantly reduced the number of training cases and training time needed to train such a DL framework. The adrenal transfer learning model trained with 5/10/15/20 cases achieved validation plan scores of 85.4/91.2/90.7/89.4, suggesting that model performance saturated with 10 training cases. Meanwhile, a model using all 20 adrenal training cases without transfer learning only scored 80.5. For the final test set, the 5/10/15/20-case models achieved scores of 73.5/75.3/78.9/83.3. Significance: It is feasible to use deep transfer learning to train an IMRT fluence prediction framework. This technique could adapt to different dose prescriptions and beam configurations. This framework potentially enables DL modeling for clinical sites that have a limited dataset, either due to few cases or due to rapid technology evolution.

Assessing the robustness of artificial intelligence powered planning tools in radiotherapy clinical settings-a phantom simulation approach.

Artificial intelligence (AI) based radiotherapy treatment planning tools have gained interest in automating the treatment planning process. It is essential to understand their overall robustness in various clinical scenarios. This is an existing gap between many AI based tools and their actual clinical deployment. This study works to fill the gap for AI based treatment planning by investigating a clinical robustness assessment (CRA) tool for the AI based planning methods using a phantom simulation approach. A cylindrical phantom was created in the treatment planning system (TPS) with the axial dimension of 30 cm by 18 cm. Key structures involved in pancreas stereotactic body radiation therapy (SBRT) including PTV25, PTV33, C-Loop, stomach, bowel and liver were created within the phantom. Several simulation scenarios were created to mimic multiple scenarios of anatomical changes, including displacement, expansion, rotation and combination of three. The goal of treatment planning was to deliver 25 Gy to PTV25 and 33 Gy to PTV33 in 5 fractions in simultaneous integral boost (SIB) manner while limiting luminal organ-at-risk (OAR) max dose to be under 29 Gy. A previously developed deep learning based AI treatment planning tool for pancreas SBRT was identified as the validation object. For each scenario, the anatomy information was fed into the AI tool and the final fluence map associated to the plan was generated, which was subsequently sent to TPS for leaf sequencing and dose calculation. The final auto plan's quality was analyzed against the treatment planning constraint. The final plans' quality was further analyzed to evaluate potential correlation with anatomical changes using the Manhattan plot. A total of 32 scenarios were simulated in this study. For all scenarios, the mean PTV25 V25Gy of the AI based auto plans was 96.7% while mean PTV33 V33Gy was 82.2%. Large variation (16.3%) in PTV33 V33Gy was observed due to anatomical variations, a.k.a. proximity of luminal structure to PTV33. Mean max dose was 28.55, 27.68 and 24.63 Gy for C-Loop, bowel and stomach, respectively. Using D0.03cc as max dose surrogate, the value was 28.03, 27.12 and 23.84 Gy for C-Loop, bowel and stomach, respectively. Max dose constraint of 29 Gy was achieved for 81.3% cases for C-Loop and stomach, and 78.1% for bowel. Using D0.03cc as max dose surrogate, the passing rate was 90.6% for C-Loop, and 81.3% for bowel and stomach. Manhattan plot revealed high correlation between the OAR over dose and the minimal distance between the PTV33 and OAR. The results showed promising robustness of the pancreas SBRT AI tool, providing important evidence of its readiness for clinical implementation. The established workflow could guide the process of assuring clinical readiness of future AI based treatment planning tools.

An AI-Enabled Virtual Hands-On Teaching Tool for Treatment Planning: A Pancreas SBRT Pilot Study

<h3>Purpose/Objective(s)</h3> To develop a tutoring program to help physician and physics residents to learn pancreas stereotactic body radiation therapy (SBRT) treatment planning via carefully collected cases and a series of specially designed knowledge models as teaching aid. Such programs are especially important during the Covid pandemic when most traditional hands-on medical teaching had to be moved online and asynchronous. <h3>Materials/Methods</h3> The pilot tutoring program was composed of 5 teaching cases (1 benchmark case and 4 teaching cases), an interactive knowledge module (IKM) and a performance grading system. The tutoring program started with the benchmark case completed by each trainee independently to benchmark the baseline skill level. This same case was re-tested to evaluate the performance and clinical planning readiness after the trainee completes the tutoring program. 4 teaching cases were included: simple (1), intermediate (2) and complex (1). All 5 cases have simultaneous integrated boost prescription of 25Gy to the elective volume and 33Gy to the gross tumor volume. The IKM included a dose-volume prediction knowledge model for pancreas SBRT and is integrated via a graphic user interface in the treatment planning system. The trainee can seek reference and guidance from the IKM during learning – mimicking the hands-on tutoring of human expert. The grading system summarize the plan quality by weighing key dosimetric endpoints and their relative importance. Grade point average (GPA) was introduced to qualitatively appraise the plan quality into A, B and C (within 3%, 3-10% and > 10% difference of clinical plan score, corresponding to 4, 3, 2 point respectively). 5 trainees with minimum planning experience completed the teaching course. <h3>Results</h3> Trainees achieved an average of 65.1% of total points (3.6 GPA) with 84 minutes planning time for the benchmark case pre-teaching, and improved to an average of 75.7% (4.2 GPA) using 48 minutes post-teaching. The clinical plan scored 72.7% of total points. All trainees improved their teaching plans' scores after taking the virtual tutoring program. Post-teaching, all trainees received the GPA of A (clinical quality level) on the benchmark case planning. The total teaching time for each trainee ranged between 5 and 7 hours. <h3>Conclusion</h3> The tutoring program with knowledge support modules provides encouraging learning outcomes in pancreas SBRT planning for inexperienced planners. This AI-enabled virtual teaching tool could provide valuable addition to the traditional human resource heavy in-person teaching of IMRT and SBRT treatment planning.

Implementation of a Stereotactic Body Radiotherapy Program for Unresectable Pancreatic Cancer in an Integrated Community Academic Radiation Oncology Satellite Network

<h3>Purpose/Objective(s)</h3> With increasing interest in stereotactic body radiotherapy (SBRT) for unresectable pancreatic cancer, quality improvement (QI) initiatives to develop integrated clinical workflows are crucial to ensure quality assurance (QA) when introducing this high risk technique into radiation practices. <h3>Materials/Methods</h3> In 2017, we used the Plan, Do, Study, Act (PDSA) QI methodology to implement a new pancreas SBRT program in an integrated community radiation oncology satellite. A unified integrated information technology infrastructure was used to virtually integrate the planned workflow into the community satellite network (P – Plan/D – Do). This workflow included multiple prospective quality assurance (QA) measures including multidisciplinary evaluation, prospective radiation target delineation scrutiny, radiation plan evaluation, and real-time monitoring of patient outcomes. Institutional review board approval was obtained to retrospectively study and report outcomes of patients treated in this program (S – Study). <h3>Results</h3> There were 12 consecutive patients identified who were treated in this program from 2017-2020 with a median follow-up of 27 months. The median survival was 13 months, median local failure free survival was 12 months and median progression free survival was 6 months from SBRT. There were no acute or late Common Terminology Criteria for Adverse Effects (CTCAE) version 5 toxicities ≥ Grade 3. <h3>Conclusion</h3> We report the successful implementation of a community pancreas SBRT program involving multiple prospective QA measures, providing the groundwork to safely expand community access to pancreas SBRT in our community satellite network (A – Act).

Impact of Tumor Dosimetry on Clinical Outcomes after Stereotactic Body Radiation Therapy for Pancreatic Adenocarcinoma

We hypothesized that escalated biological effective dose (BED) via stereotactic body radiation therapy (SBRT) would improve outcomes for patients with borderline resectable (BRPC) and locally advanced pancreatic cancer (LAPC).A retrospective review was performed on consecutive patients who received pancreas SBRT from June 2009 to June 2020, were non-metastatic at the time of SBRT, received neoadjuvant chemotherapy, and had available gross tumor volume (GTV) dosimetry. Variables of interest included: age, gender, tumor location (body/tail vs head/neck), chemotherapy type (5-fluorouracil-based vs gemcitabine-based), AJCC V8 stage, pre- and post-radiotherapy (RT) CA19-9, GTV D95, and GTV V55-40 Gy. GTV volume variables were analyzed as continuous (cont) and as binary (+) functions. All patients with BRPC at diagnosis were analyzed by a logistic regression model for variables that predicted a higher likelihood of undergoing surgical resection (Model A). A second model of all patients (BRPC and LAPC) unable to undergo surgery was constructed by Cox regression analysis investigating variables that predicted for improved overall survival (OS), progression free survival (PFS), time to loco-regional failure (TTLRF), and time to distant failure (TTDF; Model B). All clinical time points were calculated from the end of SBRT and data analysis was performed in SAS with backward elimination variable selection.A total of 365 patients had a median follow up from SBRT of 12.1 months and 251 patients (69%) were diagnosed with BRPC. Model A composed of 200 BRPC patients (56% resected) with 3 clinical variables: chemotherapy (P = .39), AJCC stage (OR 0.54, P = 0.01), post-RT CA19-9 (OR 0.27, P = < 0.001); and 3 dosimetric variables: GTV D95 (OR 3.81, P = 0.03), GTV V40+ (OR 2.52, P = 0.07), and GTV V40cont (OR 0.98, P = 0.03). Model B included 168 patients (51% BRPC) unable to undergo surgery. In this group, 5-fluorouracil-based chemotherapy was associated with improved OS (HR 0.51, P = < 0.001), PFS (HR 0.48, P = < 0.001), TTLRF (HR 0.47, P = 0.004), and TTDF (HR 0.54, P = 0.006). GTV V40+ was significantly associated with improved PFS (HR 0.59, P = 0.005) and TTLRF (HR 0.58, P = 0.036), but not with OS (HR 1.06, P = 0.78) or TTDF (HR 0.68, P = 0.113). Pre-RT CA19-9 was associated with worse OS (HR 1.43, P = 0.010) and TTLRF (HR 1.59, P = 0.022). The proportion of patients receiving 5-flurouracil-based chemo and achieving GTV V40+ was evenly distributed across the cohort (χ2 = 0.55, P = 0.45).In the largest series of pancreatic cancer SBRT to date, dose-escalated tumor coverage specifically to 40 Gy (BED10 = 72 Gy) increases the likelihood of surgical resection in patients with upfront BRPC and is associated with improved PFS that is driven by improvement in loco-regional disease control for unresected patients. These data may help to inform the evolving landscape on the use of neoadjuvant and definitive radiotherapy in pancreatic cancer.

Dosimetric Uncertainties Resulting From Interfractional Anatomic Variations for Patients Receiving Pancreas Stereotactic Body Radiation Therapy and Cone Beam Computed Tomography Image Guidance

PurposeTo estimate the effects of interfractional anatomic changes on dose to organs at risk (OARs) and tumors, as measured with cone beam computed tomography (CBCT) image guidance for pancreatic stereotactic body radiation therapy. Methods and MaterialsWe evaluated 11 patients with pancreatic cancer whom were treated with stereotactic body radiation therapy (33-40 Gy in 5 fractions) using daily CT-on-rails (CTOR) image guidance immediately before treatment with breath-hold motion management. CBCT alignment was simulated in the treatment planning software by aligning the original planning CT to each fractional CTOR image set via fiducial markers. CTOR data sets were used to calculate fractional doses after alignment by applying the rigid shift of the planning CT and CTOR image sets to the planning treatment isocenter and recalculating the fractional dose. Accumulated dose to the gross tumor volume (GTV), tumor vessel interface, duodenum, small bowel, and stomach were calculated by summing the 5 fractional absolute dose-volume histograms into a single dose-volume histogram for comparison with the original planned dose. ResultsFour patients had a GTV D100% of at least 1.5 Gy less than the fractional planned value in several fractions; 4 patients had fractional underestimation of duodenum dose by 1.0 Gy per fraction. The D1.0 cm3 <35 Gy constraint was violated for at least 1 OAR in 3 patients, with either the duodenum (n = 2) or small bowel (n = 1) D1.0 cm3 being higher on the accumulated dose distribution (P = .01). D100% was significantly lower according to accumulated dose GTV (P = .01) and tumor vessel interface (P = .02), with 4 and 2 patients having accumulated D100% ≥4 Gy lower than the planned value for the GTV and tumor vessel interface, respectively. ConclusionsFor some patients, CBCT image guidance based on fiducial alignment may cause large dosimetric uncertainties for OARs and target structures, according to accumulated dose.

The first real-time intrafraction target position monitoring in pancreas SBRT on an Elekta linear accelerator.

To perform implanted fiducial based real-time target position monitoring in pancreas stereotactic body radiotherapy (SBRT) using the x-ray imaging system available in a Elekta linear accelerator. An in-house system was developed and clinically utilised for real-time target position monitoring of pancreas SBRT delivery. The developed system was used for the target position monitoring of a pancreas cancer patient treated in free breathing treatment within the study entitled 'Mfolfirinox And STEreotactic Radiotherapy for Patients with Locally Advanced paNcreas cancer (MASTERPLAN): a feasibility study' (ACTRN 12617001642370) consisting of five treatment fractions. The clinical efficacy of the system was studied by performing a retrospective cumulative dose assessment of delivered dose using observed position deviations. The developed system identified two events of baseline shifts in target position that exceeded the accepted tolerance level of ± 3 mm from reference planned position. The retrospective dose assessment study showed that if the position deviations were not detected and corrected for, the maximum dose to duodenum would have increased from 34.6 to 38.8 Gy. The first real-time position monitoring in pancreas SBRT on an Elekta linear accelerator was successfully performed. The developed system was shown to improve the safety and accuracy of SBRT delivery.

Deep Learning–Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost

PurposeTreatment planning for pancreas stereotactic body radiation therapy (SBRT) is a challenging task, especially with simultaneous integrated boost treatment approaches. We propose a deep learning (DL) framework to accurately predict fluence maps from patient anatomy and directly generate intensity modulated radiation therapy plans. Methods and MaterialsThe framework employs 2 convolutional neural networks (CNNs) to sequentially generate beam dose prediction and fluence map prediction, creating a deliverable 9-beam intensity modulated radiation therapy plan. Within the beam dose prediction CNN, axial slices of combined structure contour masks are used to predict 3-dimensional (3D) beam doses for each beam. Each 3D beam dose is projected along its beam’s-eye-view to form a 2D beam dose map, which is subsequently used by the fluence map prediction CNN to predict its fluence map. Finally, the 9 predicted fluence maps are imported into the treatment planning system to finalize the plan by leaf sequencing and dose calculation. One hundred patients receiving pancreas SBRT were retrospectively collected for this study. Benchmark plans with unified simultaneous integrated boost prescription (25/33 Gy) were manually optimized for each case. The data set was split into 80/20 cases for training and testing. We evaluated the proposed DL framework by assessing both the fluence maps and the final predicted plans. Further, clinical acceptability of the plans was evaluated by a physician specializing in gastrointestinal cancer. ResultsThe DL-based planning was, on average, completed in under 2 minutes. In testing, the predicted plans achieved similar dose distribution compared with the benchmark plans (-1.5% deviation for planning target volume 33 V33Gy), with slightly higher planning target volume maximum (+1.03 Gy) and organ at risk maximum (+0.95 Gy) doses. After renormalization, the physician rated 19 cases clinically acceptable and 1 case requiring minor improvement. ConclusionsThe DL framework can effectively plan pancreas SBRT cases within 2 minutes. The predicted plans are clinically deliverable, with plan quality approaching that of manual planning.

Implementation of a stereotactic body radiotherapy program for unresectable pancreatic cancer in an integrated community academic radiation oncology satellite network

BackgroundWith increasing interest in stereotactic body radiotherapy (SBRT) for unresectable pancreatic cancer, quality improvement (QI) initiatives to develop integrated clinical workflows are crucial to ensure quality assurance (QA) when introducing this challenging technique into radiation practices.Materials/Methods: In 2017, we used the Plan, Do, Study, Act (PDSA) QI methodology to implement a new pancreas SBRT program in an integrated community radiation oncology satellite. A unified integrated information technology infrastructure was used to virtually integrate the planned workflow into the community radiation oncology satellite network (P – Plan/D – Do). This workflow included multiple prospective quality assurance (QA) measures including multidisciplinary evaluation, prospective scrutiny of radiation target delineation, prospective radiation plan evaluation, and monitoring of patient outcomes. Institutional review board approval was obtained to retrospectively study and report outcomes of patients treated in this program (S – Study).Results:There were 12 consecutive patients identified who were treated in this program from 2017 to 2020 with a median follow-up of 27 months. The median survival was 13 months, median local failure free survival was 12 months and median progression free survival was 6 months from SBRT. There were no acute or late Common Terminology Criteria for Adverse Effects (CTCAE) version 5 toxicities ≥ Grade 3.Conclusion:We report the successful implementation of a community pancreas SBRT program involving multiple prospective QA measures, providing the groundwork to safely expand access to pancreas SBRT in our community satellite network (A – Act).

Type of fiducial marker and clinical outcomes in stereotactic body radiation therapy for pancreatic cancer.

414 Background: Stereotactic body radiation therapy (SBRT) for pancreatic cancers has been shown to improve local control, and is an important option for treatment, especially for unresectable disease. Verifying the tumor location prior to delivery of SBRT is challenging, so fiducial markers are used to track tumor location. There is currently no standard of which fiducials to use in treatment. This study would like to compare outcomes of patients treated with SBRT using different fiducial markers. Methods: Records of patients diagnosed with primary pancreas cancer who were treated with chemotherapy and SBRT were reviewed from 2006-2019. Patients were excluded if they were treated with Cyberknife, were metastatic at presentation, recurrence /persistent disease after Whipple/radiation therapy, were secondary metastatic disease (from another primary), and if they were resected after SBRT. Patients were categorized according to the fiducial used for tumor tracking during SBRT treatment: gold seeds, intrabiliary stent, or both. Cumulative incidence of local recurrence (CIR) was analyzed with death as competing event, and time to over-all survival was estimated using Kaplan-Meier curves. Results: A total of 129 patients with available fiducial information were included in this study, of which 64 (49.6%) were treated with SBRT using gold seeds, 23 (17.8%) using intrabiliary stent, and 42 (32.6%) using both the seeds and stent. There were no difference between groups in terms of baseline characteristics such as age (p = 0.169), sex (p = 0.293), and stage grouping (p = 0.293). Median follow-up time was 15 months (range: 0.3-37.3 months). The 6- and 12-month CIR were 1.5% (95%CI, 0.1%-7.4%) and 11% (95%CI, 4.8%-20.2%) for patients treated with seeds, 4.3% (95%CI, 0.2%-18.6%) and 30.4% (95%CI, 13.1%-49.8%) for patients treated with stent, and 4.8% (95%CI, 0.8%-14.6%) and 19.5% (95%CI, 9.0%-32.9%) for patients treated with both (p = 0.007). Median time to overall survival was 15.3 months (95%CI, 13-17.8 months) for patients treated with seeds, 21.3 months (95%CI, 14.7-29.6 months) for patients treated with stent, and 15.7 months (95%CI, 11.5-19.7 months) for patients treated with both (p = 0.307). Univariate analysis for predictors of local failure did not show significance for age (p = 0.812), or advanced stage (p = 0.483), but was significant for the presence of seeds (p = 0.006). Conclusions: The type of fiducial marker used for tracking during pancreas SBRT treatment was associated with local failure but no difference in overall survival. Further analysis is warranted to see which clinical factors contribute to this difference.

An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy

Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners' experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set. All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process. We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies.

Novel MR-Guided Radiotherapy Elective Rotation for Radiation Oncology Trainees.

MR-guided adaptive radiation therapy (RT) is emerging as an integral treatment modality for certain applications and is poised to become an exciting opportunity for greater treatment precision and personalization. However, this is still a relatively nascent technology and only a few institutions and programs have access to this technology for clinical use and trainee education. To increase the diversity of elective offerings and improve the understanding of an MR-guided radiotherapy program, we initiated a unique MR-guided radiotherapy elective rotation for radiation oncology residents. During a representative four-week rotation, 21 simulations were completed by the resident on service. A plurality of simulations were for pancreas stereotactic body radiation therapy (SBRT; 48%) and a majority (71%) of simulations were for adaptive treatments. Additionally, 74 adaptive fractions were completed during this month, of which a significant majority (74%) were for pancreas SBRT. Of the non-adaptive fractions, the majority were for prostate SBRT and intensity-modulated radiation therapy (IMRT). Although many programs may offer training in some aspects of MR-guided radiotherapy as trainees rotate through certain disease sites, we hope this may serve as a blueprint to encourage programs with this technology to fully embrace training in essential competencies related to MR-guided radiotherapy. MR-guided radiotherapy has unique challenges that trainees need to understand to deliver treatment safely: geometric uncertainty, MRI to RT isocenter, and uncertainties with voxel size/tracking.