Open RT Structures: A Solution for TG-263 Accessibility.

PURPOSE: Consistency of nomenclature within radiation oncology is increasingly important as big data efforts and data sharing become more feasible. Automation of radiation oncology workflows depends on standardized contour nomenclature that enables toxicity and outcomes research, while also reducing medical errors and facilitating quality improvement activities. Recommendations for standardized nomenclature have been published in the American Association of Physicists in Medicine (AAPM) report from Task Group 263 (TG-263). Transitioning to TG-263 requires creation and management of structure template libraries and retraining of staff, which can be a considerable burden on clinical resources. Our aim is to develop a program that allows users to create TG-263-compliant structure templates in English, Spanish, or French to facilitate data sharing. METHODS AND MATERIALS: Fifty-three premade structure templates were arranged by treated organ based on an American Society for Radiation Oncology (ASTRO) consensus paper. Templates were further customized with common target structures, relevant organs at risk (OARs) (eg, spleen for anatomically relevant sites such as the gastroesophageal junction or stomach), subsite- specific templates (eg, partial breast, whole breast, intact prostate, postoperative prostate, etc) and brachytherapy templates. An informal consensus on OAR and target coloration was also achieved, although color selections are fully customizable within the program. RESULTS: The resulting program is usable on any Windows system and generates template files in practice-specific Digital Imaging and Communications In Medicine (DICOM) or XML formats, extracting standardized structure nomenclature from an online database maintained by members of the TG-263U1, which ensures continuous access to up-to-date templates. CONCLUSIONS: We have developed a tool to easily create and name DICOM radiation therapy (DICOM-RT) structures sets that are TG-263-compliant for all planning systems using the DICOM standard. The program and source code are publicly available via GitHub to encourage feedback from community users for improvement and guide further development.

Forecasting patient-specific dosimetric benefit from daily online adaptive radiotherapy for cervical cancer.

Objective. Adaptive Radiotherapy (ART) is an emerging technique for treating cancer patients which facilitates higher delivery accuracy and has the potential to reduce toxicity. However, ART is also resource-intensive, Requiring extra human and machine time compared to standard treatment methods. In this analysis, we sought to predict the subset of node-negative cervical cancer patients with the greatest benefit from ART, so resources might be properly allocated to the highest-yield patients.Approach. CT images, initial plan data, and on-treatment Cone-Beam CT (CBCT) images for 20 retrospective cervical cancer patients were used to simulate doses from daily non-adaptive and adaptive techniques. We evaluated the coefficient of determination (R2) between dose and volume metrics from initial treatment plans and the dosimetric benefits to theBowelV40Gy,BowelV45Gy,BladderDmean,andRectumDmeanfrom adaptive radiotherapy using reduced 3 mm or 5 mm CTV-to-PTV margins. The LASSO technique was used to identify the most predictive metrics forBowelV40Gy.The three highest performing metrics were used to build multivariate models with leave-one-out validation forBowelV40Gy.Main results. Patients with higher initial bowel doses were correlated with the largest decreases in BowelV40Gyfrom daily adaptation (linear best fit R2= 0.77 for a 3 mm PTV margin and R2= 0.8 for a 5 mm PTV margin). Other metrics had intermediate or no correlation. Selected covariates for the multivariate model were differences in the initialBowelV40GyandBladderDmeanusing standard versus reduced margins and the initial bladder volume. Leave-one-out validation had an R2of 0.66 between predicted and true adaptiveBowelV40Gybenefits for both margins.Significance. The resulting models could be used to prospectively triage cervical cancer patients on or off daily adaptation to optimally manage clinical resources. Additionally, this work presents a critical foundation for predicting benefits from daily adaptation that can be extended to other patient cohorts.

Automated treatment planning framework for brachytherapy of cervical cancer using 3D dose predictions.

Objective. To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs).Approach. A dose rate kerneld(r,θ,φ)was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (Dcalc). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error betweenDcalcand reference doseDref, computed using voxels withDref80%-120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans whenDref= clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0-3 needles. Then we demonstrated automated planning in 10 T&O usingDref= dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences (MAD=1N∑n=1Nabsxn-xn') over all voxels (xn= Dose,N= #voxels) and DTs (xn= DT,N= #dwell positions), mean differences (MD) in organD2ccand high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours.Main results. Validation plans agreed well with clinical plans (MADdose= 1.1%, MADDT= 4 s or 0.8% of total plan time,D2ccMD = -0.2% to 0.2% and D90 MD = -0.6%, DSC = 0.99). For automated plans, MADdose= 6.5% and MADDT= 10.3 s (2.1%). The slightly higher clinical metrics in automated plans (D2ccMD = -3.8% to 1.3% and D90 MD = -5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC = 0.91).Significance. Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience.

Examining the Effect of Direct Patient Care for Medical Physicists: A Randomized Prospective Phase III Trial.

PURPOSE: Our purpose was to investigate the effect of physicist-patient consults on patient anxiety and patient satisfaction with a randomized prospective phase III clinical trial. METHODS AND MATERIALS: Sixty-six patients were randomly assigned to the physics direct patient care (PDPC) arm or the control arm of the trial. Patients assigned to the PDPC arm received 2 physicist-patient consults to educate them on the technical aspects of their radiation therapy, while patients assigned to the control arm received the standard of care (ie, standard radiation therapy workflow without any additional physicist-patient consults). Questionnaires were administered to all patients at 4 time points (after enrollment, after the simulation, after the first treatment, and after the last treatment) to assess anxiety and satisfaction. RESULTS: The decrease in anxiety for the PDPC arm, compared with the control arm, was statistically significant at the first treatment (P = .027) time point. The increase in technical satisfaction for the PDPC arm, compared with the control arm, was statistically significant at the simulation (P = .005), first treatment (P < .001), and last treatment (P = .002) time points. The increase in overall satisfaction for the PDPC arm, compared with the control arm, was statistically significant at the first treatment (P = .014) and last treatment (P = .001) time points. CONCLUSIONS: Physicist-patient consults improved the patient experience by decreasing anxiety and increasing satisfaction. Future work is needed to modify current radiation oncology workflows and medical physics responsibilities to allow all patients to benefit from this advancement in patient care.

Safety of Nivolumab Added to Chemoradiation Therapy Platforms for Intermediate and High-Risk Locoregionally Advanced Head and Neck Squamous Cell Carcinoma: RTOG Foundation 3504.

PURPOSE: Programmed death-1 immune checkpoint blockade improves survival of patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), but the benefits of addition to (chemo)radiation for newly diagnosed patients with HNSCC remain unknown. METHODS AND MATERIALS: We evaluated the safety of nivolumab concomitant with 70 Gy intensity modulated radiation therapy and weekly cisplatin (arm 1), every 3-week cisplatin (arm 2), cetuximab (arm 3), or alone for platinum-ineligible patients (arm 4) in newly diagnosed intermediate- or high-risk locoregionally advanced HNSCC. Patients received nivolumab from 2 weeks prior to radiation therapy until 3 months post-radiation therapy. The primary endpoint was dose-limiting toxicity (DLT). If ≤2 of the first 8 evaluable patients experienced a DLT, an arm was considered safe. Secondary endpoints included toxicity and feasibility of adjuvant nivolumab to 1 year, defined as all 7 additional doses received by ≥4 of the first 8 evaluable patients across arms. RESULTS: Of 39 patients (10 in arms 1, 3, 4 and 9 in arm 2), 72% had T3-4 tumors, 85% had N2-3 nodal disease, and 67% had >10 pack-years of smoking. There were no DLTs in arms 1 and 2, 1 in arm 3 (mucositis), and 2 in arm 4 (lipase elevation and mucositis in 1 and fatigue in another). The most common grade ≥3 nivolumab-related adverse events were lipase increase, mucositis, diarrhea, lymphopenia, hyponatremia, leukopenia, fatigue, and serum amylase increase. Adjuvant nivolumab was feasible as defined in the protocol. CONCLUSIONS: Concomitant nivolumab with the 4 tested regimens was safe for patients with intermediate- and high-risk HNSCC, and subsequent adjuvant nivolumab was feasible as defined (NCT02764593).

Clinical commissioning of an adaptive radiotherapy platform: Results and recommendations.

Online adaptive radiotherapy platforms present a unique challenge for commissioning as guidance is lacking and specialized adaptive equipment, such as deformable phantoms, are rare. We designed a novel adaptive commissioning process consisting of end-to-end tests using standard clinical resources. These tests were designed to simulate anatomical changes regularly observed at patient treatments. The test results will inform users of the magnitude of uncertainty from on-treatment changes during the adaptive workflow and the limitations of their systems. We implemented these tests for the cone-beam computed tomography (CT)-based Varian Ethos online adaptive platform. Many adaptive platforms perform online dose calculation on a synthetic CT (synCT). To assess the impact of the synCT generation and online dose calculation on dosimetric accuracy, we conducted end-to-end tests using commonly available equipment: a CIRS IMRT Thorax phantom, PinPoint ionization chamber, Gafchromic film, and bolus. Four clinical scenarios were evaluated: weight gain and weight loss were simulated by adding and removing bolus, internal target shifts were simulated by editing the CTV during the adaptive workflow to displace it, and changes in gas were simulated by removing and reinserting rods in varying phantom locations. The effect of overriding gas pockets during planning was also assessed. All point dose measurements agreed within 2.7% of the calculated dose, with one exception: a scenario simulating gas present in the planning CT, not overridden during planning, and dissipating at treatment. Relative film measurements passed gamma analysis (3%/3 mm criteria) for all scenarios. Our process validated the Ethos dose calculation for online adapted treatment plans. Based on our results, we made several recommendations for our clinical adaptive workflow. This commissioning process used commonly available equipment and, therefore, can be applied in other clinics for their respective online adaptive platforms.

Knowledge-based three-dimensional dose prediction for tandem-and-ovoid brachytherapy.

PURPOSE: The purpose of this work was to develop a knowledge-based dose prediction system using a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid applicator. METHODS: A 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk (OAR), high-risk clinical target volume (HRCTV), and possible source location geometry. The model comprised 395 previously treated cases: training (273), validation (61), test (61). To assess voxel prediction accuracy, we evaluated dose differences in all cohorts across the dose range of 20-130% of prescription, mean (SD) and standard deviation (σ), as well as isodose dice similarity coefficients for clinical and/or predicted dose distributions. We examined discrete Dose-Volume Histogram (DVH) metrics utilized for brachytherapy plan quality assessment (HRCTV D90%; bladder, rectum, and sigmoid D2cc) with ΔDx=Dx,actual-Dx,predicted mean, standard deviation, and Pearson correlation coefficient further quantifying model performance. RESULTS: Ranges of voxel-wise dose difference accuracy (δD¯±σ) for 20-130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0% ± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ± 12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%] ([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9% ± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4% ± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric prediction in the training (test) set were HRCTV ΔD¯90±σΔD = -0.19 ± 0.55Gy (-0.09 ± 0.67 Gy), bladder ΔD¯2cc±σΔD = -0.06 ± 0.54Gy (-0.17 ± 0.67 Gy), rectum ΔD¯2cc±σΔD= -0.03 ± 0.36Gy (-0.04 ± 0.46 Gy), and sigmoid ΔD¯2cc±σΔD = -0.01 ± 0.34Gy (0.00 ± 0.44 Gy). CONCLUSIONS: A 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates that could be used for treatment plan quality control and data-driven plan guidance.

Evaluation of dose differences between intracavitary applicators for cervical brachytherapy using knowledge-based models.

PURPOSE: Currently, there is a lack of patient-specific tools to guide brachytherapy planning and applicator choice for cervical cancer. The purpose of this study is to evaluate the accuracy of organ-at-risk (OAR) dose predictions using knowledge-based intracavitary models, and the use of these models and clinical data to determine the dosimetric differences of tandem-and-ring (T&R) and tandem-and-ovoids (T&O) applicators. MATERIALS AND METHODS: Knowledge-based models, which predict organ D2cc, were trained on 77/75 cases and validated on 32/38 for T&R/T&O applicators. Model performance was quantified using ΔD2cc=D2cc,actual-D2cc,predicted, with standard deviation (σ(ΔD2cc)) representing precision. Model-predicted applicator dose differences were determined by applying T&O models to T&R cases, and vice versa, and compared to clinically-achieved D2cc differences. Applicator differences were assessed using a Student's t-test (p < 0.05 significant). RESULTS: Validation T&O/T&R model precision was 0.65/0.55 Gy, 0.55/0.38 Gy, and 0.43/0.60 Gy for bladder, rectum and sigmoid, respectively, and similar to training. When applying T&O/T&R models to T&R/T&O cases, bladder, rectum and sigmoid D2cc values in EQD2 were on average 5.69/2.62 Gy, 7.31/6.15 Gy and 3.65/0.69 Gy lower for T&R, with similar HRCTV volume and coverage. Clinical data also showed lower T&R OAR doses, with mean EQD2 D2cc deviations of 0.61 Gy, 7.96 Gy (p < 0.01) and 5.86 Gy (p < 0.01) for bladder, rectum and sigmoid. CONCLUSIONS: Accurate knowledge-based dose prediction models were developed for two common intracavitary applicators. These models could be beneficial for standardizing and improving the quality of brachytherapy plans. Both models and clinical data suggest that significant OAR sparing can be achieved with T&R over T&O applicators, particularly for the rectum.

Computational ECG mapping and respiratory gating to optimize stereotactic ablative radiotherapy workflow for refractory ventricular tachycardia.

BACKGROUND: Stereotactic ablative radiotherapy (SAbR) is an emerging therapy for refractory ventricular tachycardia (VT). However, the current workflow is complicated, and the precision and safety in patients with significant cardiorespiratory motion and VT targets near the stomach may be suboptimal. OBJECTIVE: We hypothesized that automated 12-lead electrocardiogram (ECG) mapping and respiratory-gated therapy may improve the ease and precision of SAbR planning and facilitate safe radiation delivery in patients with refractory VT. METHODS: Consecutive patients with refractory VT were studied at 2 hospitals. VT exit sites were localized using a 3-D computational ECG algorithm noninvasively and compared to available prior invasive mapping. Radiotherapy (25 Gy) was delivered at end-expiration when cardiac respiratory motion was ≥0.6 cm or targets were ≤2 cm from the stomach. RESULTS: In 6 patients (ejection fraction 29% ± 13%), 4.2 ± 2.3 VT morphologies per patient were mapped. Overall, 7 out of 7 computational ECG mappings (100%) colocalized to the identical cardiac segment when prior invasive electrophysiology study was available. Respiratory gating was associated with smaller planning target volumes compared to nongated volumes (71 ± 7 vs 153 ± 35 cc, P < .01). In 2 patients with inferior wall VT targets close to the stomach (6 mm proximity) or significant respiratory motion (22 mm excursion), no GI complications were observed at 9- and 12-month follow-up. Implantable cardioverter-defibrillator shocks decreased from 23 ± 12 shocks/patient to 0.67 ± 1.0 (P < .001) post-SAbR at 6.0 ± 4.9 months follow-up. CONCLUSIONS: A workflow including computational ECG mapping and protocol-guided respiratory gating is feasible, is safe, and may improve the ease of SAbR planning. Studies to validate this workflow in larger populations are required.

Knowledge-based dose prediction models to inform gynecologic brachytherapy needle supplementation for locally advanced cervical cancer.

PURPOSE: The use of interstitial needles, combined with intracavitary applicators, enables customized dose distributions and is beneficial for complex cases, but increases procedure time. Overall, applicator selection is not standardized and depends on physician expertise and preference. The purpose of this study is to determine whether dose prediction models can guide needle supplementation decision-making for cervical cancer. MATERIALS AND METHODS: Intracavitary knowledge-based models for organ-at-risk (OAR) dose estimation were trained and validated for tandem-and-ring/ovoids (T&R/T&O) implants. Models were applied to hybrid cases with 1-3 implanted needles to predict OAR dose without needles. As a reference, 70/67 hybrid T&R/T&O cases were replanned without needles, following a standardized procedure guided by dose predictions. If a replanned dose exceeded the dose objective, the case was categorized as requiring needles. Receiver operating characteristic (ROC) curves of needle classification accuracy were generated. Optimal classification thresholds were determined from the Youden Index. RESULTS: Needle supplementation reduced dose to OARs. However, 67%/39% of replans for T&R/T&O met all dose constraints without needles. The ROC for T&R/T&O models had an area-under-curve of 0.89/0.86, proving high classification accuracy. The optimal threshold of 99%/101% of the dose limit for T&R/T&O resulted in classification sensitivity and specificity of 78%/86% and 85%/78%. CONCLUSIONS: Needle supplementation reduced OAR dose for most cases but was not always required to meet standard dose objectives, particularly for T&R cases. Our knowledge-based dose prediction model accurately identified cases that could have met constraints without needle supplementation, suggesting that such models may be beneficial for applicator selection.

Personalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer.

INTRODUCTION: Quality assurance (QA) of treatment plans in clinical trials improves protocol compliance and patient outcomes. Retrospective use of knowledge-based-planning (KBP) in clinical trials has demonstrated improved treatment plan quality and consistency. We report the results of prospective use of KBP for real-time QA of treatment plan quality in the TROG 15.03 FASTRACK II trial, which evaluates efficacy of stereotactic ablative body radiotherapy (SABR) for kidney cancer. METHODS: A KBP model was generated based on single institution data. For each patient in the KBP phase (open to the last 31 patients in the trial), the treating centre submitted treatment plans 7 days prior to treatment. A treatment plan was created by using the KBP model, which was compared with the submitted plan for each organ-at-risk (OAR) dose constraint. A report comparing each plan for each OAR constraint was provided to the submitting centre within 24 h of receiving the plan. The centre could then modify the plan based on the KBP report, or continue with the existing plan. RESULTS: Real-time feedback using KBP was provided in 24/31 cases. Consistent plan quality was in general achieved between KBP and the submitted plan. KBP review resulted in replan and improvement of OAR dosimetry in two patients. All centres indicated that the feedback was a useful QA check of their treatment plan. CONCLUSION: KBP for real-time treatment plan review was feasible for 24/31 cases, and demonstrated ability to improve treatment plan quality in two cases. Challenges include integration of KBP feedback into clinical timelines, interpretation of KBP results with respect to clinical trade-offs, and determination of appropriate plan quality improvement criteria.

Prospects for daily online adaptive radiotherapy via ethos for prostate cancer patients without nodal involvement using unedited CBCT auto-segmentation.

PURPOSE:  Implementing new online adaptive radiation therapy technologies is challenging because extra clinical resources are required particularly expert contour review. Here, we provide the first assessment of Varian's Ethos™ adaptive platform for prostate cancer using no manual edits after auto-segmentation to minimize this impact on clinical efficiency. METHODS: Twenty-five prostate patients previously treated at our clinic were re-planned using an Ethos™ emulator. Clinical target volumes (CTV) included intact prostate and proximal seminal vesicles. The following clinical margins were used: 3 mm posterior, 5 mm left/right/anterior, and 7 mm superior/inferior. Adapted plans were calculated for 10 fractions per patient using Ethos's auto-segmentation and auto-planning workflow without manual contouring edits. Doses and auto-segmented structures were exported to our clinical treatment planning system where contours were modified as needed for all 250 CTVs and organs-at-risk. Dose metrics from adapted plans were compared to unadapted plans to evaluate CTV and OAR dose changes. RESULTS: Overall 96% of fractions required auto-segmentation edits, although corrections were generally minor (<10% of the volume for 70% of CTVs, 88% of bladders, and 90% of rectums). However, for one patient the auto-segmented CTV failed to include the superior portion of prostate that extended into the bladder at all 10 fractions resulting in under-contouring of the CTV by 31.3% ± 6.7%. For the 24 patients with minor auto-segmentation corrections, adaptation improved CTV D98% by 2.9% ± 5.3%. For non-adapted fractions where bladder or rectum V90% exceeded clinical thresholds, adaptation reduced them by 13.1% ± 1.0% and 6.5% ± 7.3%, respectively. CONCLUSION:  For most patients, Ethos's online adaptive radiation therapy workflow improved CTV D98% and reduced normal tissue dose when structures would otherwise exceed clinical thresholds, even without time-consuming manual edits. However, for one in 25 patients, large contour edits were required and thus scrutiny of the daily auto-segmentation is necessary and not all patients will be good candidates for adaptation.

Wide-Scale Clinical Implementation of Knowledge-Based Planning: An Investigation of Workforce Efficiency, Need for Post-automation Refinement, and Data-Driven Model Maintenance.

PURPOSE: Our purpose was to investigate the effect of automated knowledge-based planning (KBP) on real-world clinical workflow efficiency, assess whether manual refinement of KBP plans improves plan quality across multiple disease sites, and develop a data-driven method to periodically improve KBP automated planning routines. METHODS AND MATERIALS: Using clinical knowledge-based automated planning routines for prostate, prostatic fossa, head and neck, and hypofractionated lung disease sites in a commercial KBP solution, workflow efficiency was compared in terms of planning time in a pre-KBP (n = 145 plans) and post-KBP (n = 503) patient cohort. Post-KBP, planning was initialized with KBP (KBP-only) and subsequently manually refined (KBP +human). Differences in planning time were tested for significance using a 2-tailed Mann-Whitney U test (P < .05, null hypothesis: planning time unchanged). Post-refinement plan quality was assessed using site-specific dosimetric parameters of the original KBP-only plan versus KBP +human; 2-tailed paired t test quantified statistical significance (Bonferroni-corrected P < .05, null hypothesis: no dosimetric difference after refinement). If KBP +human significantly improved plans across the cohort, optimization objectives were changed to create an updated KBP routine (KBP'). Patients were replanned with KBP' and plan quality was compared with KBP +human as described previously. RESULTS: KBP significantly reduced planning time in all disease sites: prostate (median: 7.6 hrs â†’ 2.1 hrs; P < .001), prostatic fossa (11.1 hrs â†’ 3.7 hrs; P = .001), lung (9.9 hrs â†’ 2.0 hrs; P < .001), and head and neck (12.9 hrs â†’ 3.5 hrs; P <.001). In prostate, prostatic fossa, and lung disease sites, organ-at-risk dose changes in KBP +human versus KBP-only were minimal (<1% prescription dose). In head and neck, KBP +human did achieve clinically relevant dose reductions in some parameters. The head and neck routine was updated (KBP'HN) to incorporate dose improvements from manual refinement. The only significant dosimetric differences to KBP +human after replanning with KBP'HN were in favor of the new routine. CONCLUSIONS: KBP increased clinical efficiency by significantly reducing planning time. On average, human refinement offered minimal dose improvements over KBP-only plans. In the single disease site where KBP +human was superior to KBP-only, differences were eliminated by adjusting optimization parameters in a revised KBP routine.

OpenKBP: The open-access knowledge-based planning grand challenge and dataset.

PURPOSE: To advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research. METHODS: We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three-dimensional (3D) dose distributions, and (b) dose-volume histogram (DVH) score, which evaluates a set DVH metrics. We used these scores to quantify the quality of the models based on their out-of-sample predictions. To develop and test their models, participants were given the data of 340 patients who were treated for head-and-neck cancer with radiation therapy. The data were partitioned into training ( n = 200 ), validation ( n = 40 ), and testing ( n = 100 ) datasets. All participants performed training and validation with the corresponding datasets during the first (validation) phase of the Challenge. In the second (testing) phase, the participants used their model on the testing data to quantify the out-of-sample performance, which was hidden from participants and used to determine the final competition ranking. Participants also responded to a survey to summarize their models. RESULTS: The Challenge attracted 195 participants from 28 countries, and 73 of those participants formed 44 teams in the validation phase, which received a total of 1750 submissions. The testing phase garnered submissions from 28 of those teams, which represents 28 unique prediction methods. On average, over the course of the validation phase, participants improved the dose and DVH scores of their models by a factor of 2.7 and 5.7, respectively. In the testing phase one model achieved the best dose score (2.429) and DVH score (1.478), which were both significantly better than the dose score (2.564) and the DVH score (1.529) that was achieved by the runner-up models. Lastly, many of the top performing teams reported that they used generalizable techniques (e.g., ensembles) to achieve higher performance than their competition. CONCLUSION: OpenKBP is the first competition for knowledge-based planning research. The Challenge helped launch the first platform that enables researchers to compare KBP prediction methods fairly and consistently using a large open-source dataset and standardized metrics. OpenKBP has also democratized KBP research by making it accessible to everyone, which should help accelerate the progress of KBP research. The OpenKBP datasets are available publicly to help benchmark future KBP research.

A practical method to quantify knowledge-based DVH prediction accuracy and uncertainty with reference cohorts.

The adoption of knowledge-based dose-volume histogram (DVH) prediction models for assessing organ-at-risk (OAR) sparing in radiotherapy necessitates quantification of prediction accuracy and uncertainty. Moreover, DVH prediction error bands should be readily interpretable as confidence intervals in which to find a percentage of clinically acceptable DVHs. In the event such DVH error bands are not available, we present an independent error quantification methodology using a local reference cohort of high-quality treatment plans, and apply it to two DVH prediction models, ORBIT-RT and RapidPlan, trained on the same set of 90 volumetric modulated arc therapy (VMAT) plans. Organ-at-risk DVH predictions from each model were then generated for a separate set of 45 prostate VMAT plans. Dose-volume histogram predictions were then compared to their analogous clinical DVHs to define prediction errors V c l i n , i - V p r e d , i (ith plan), from which prediction bias µ, prediction error variation σ, and root-mean-square error R M S E pred ≡ 1 N ∑ i V c l i n , i - V p r e d , i 2 ≅ σ 2 + µ 2 could be calculated for the cohort. The empirical R M S E pred was then contrasted to the model-provided DVH error estimates. For all prostate OARs, above 50% Rx dose, ORBIT-RT µ and σ were comparable to or less than those of RapidPlan. Above 80% Rx dose, µ < 1% and σ < 3-4% for both models. As a result, above 50% Rx dose, ORBIT-RT R M S E pred was below that of RapidPlan, indicating slightly improved accuracy in this cohort. Because µ ≈ 0, R M S E pred is readily interpretable as a canonical standard deviation σ, whose error band is expected to correctly predict 68% of normally distributed clinical DVHs. By contrast, RapidPlan's provided error band, although described in literature as a standard deviation range, was slightly less predictive than R M S E pred (55-70% success), while the provided ORBIT-RT error band was confirmed to resemble an interquartile range (40-65% success) as described. Clinicians can apply this methodology using their own institutions' reference cohorts to (a) independently assess a knowledge-based model's predictive accuracy of local treatment plans, and (b) interpret from any error band whether further OAR dose sparing is likely attainable.

ORBIT-RT: A Real-Time, Open Platform for Knowledge-Based Quality Control of Radiotherapy Treatment Planning.

PURPOSE: Access to knowledge-based treatment plan quality control has been hindered by the complexity of developing models and integration with different treatment planning systems (TPS). Online Real-time Benchmarking Information Technology for RadioTherapy (ORBIT-RT) provides a free, web-based platform for knowledge-based dose estimation that can be used by clinicians worldwide to benchmark the quality of their radiotherapy plans. MATERIALS AND METHODS: The ORBIT-RT platform was developed to satisfy four primary design criteria: web-based access, TPS independence, Health Insurance Portability and Accountability Act compliance, and autonomous operation. ORBIT-RT uses a cloud-based server to automatically anonymize a user's Digital Imaging and Communications in Medicine for RadioTherapy (DICOM-RT) file before upload and processing of the case. From there, ORBIT-RT uses established knowledge-based dose-volume histogram (DVH) estimation methods to autonomously create DVH estimations for the uploaded DICOM-RT. ORBIT-RT performance was evaluated with an independent validation set of 45 volumetric modulated arc therapy prostate plans with two key metrics: (i) accuracy of the DVH estimations, as quantified by their error, DVHclinical - DVHprediction and (ii) time to process and display the DVH estimations on the ORBIT-RT platform. RESULTS: ORBIT-RT organ DVH predictions show < 1% bias and 3% error uncertainty at doses > 80% of prescription for the prostate validation set. The ORBIT-RT extensions require 3.0 seconds per organ to analyze. The DICOM upload, data transfer, and DVH output display extend the entire system workflow to 2.5-3 minutes. CONCLUSION: ORBIT-RT demonstrated fast and fully autonomous knowledge-based feedback on a web-based platform that takes only anonymized DICOM-RT as input. The ORBIT-RT system can be used for real-time quality control feedback that provides users with objective comparisons for final plan DVHs.

Automated contouring and planning pipeline for hippocampal-avoidant whole-brain radiotherapy.

BACKGROUND: Whole-brain radiotherapy (WBRT) remains an important treatment for over 200,000 cancer patients in the United States annually. Hippocampal-avoidant WBRT (HA-WBRT) reduces neurocognitive toxicity compared to standard WBRT, but HA-WBRT contouring and planning are more complex and time-consuming than standard WBRT. We designed and evaluated a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable HA-WBRT radiotherapy plans. METHODS: We retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient's T1 post-contrast brain MRI was processed using NeuroQuant, an FDA-approved software that provides segmentations of brain structures in less than 8 min. Automated hippocampal segmentations were reviewed for accuracy, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives (Brown et al. in J Clin Oncol, 2020). RESULTS: Of the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 min 59 s (range 6:53-13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 65.5% of plans on NRG CC001 met PTV goals per protocol, with 26.1% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.5% and 48.3% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001. 8.4% of plans from NRG CC001 were not scored or unable to be evaluated. CONCLUSIONS: An automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

Knowledge-Based Planning for Intact Cervical Cancer.

Cervical cancer radiotherapy is often complicated by significant variability in the quality and consistency of treatment plans. Knowledge-based planning (KBP), which utilizes prior patient data to correlated achievable optimal dosimetry with patient-specific anatomy, has demonstrated promise as a quality control tool for controlling this variability, with consequences for patient outcomes, as well as for the reliability of data from multi-institutional clinical trials. In this article we highlight the application of KBP-based quality control to cervical cancer radiotherapy. We discuss the potential impact of KBP on multi-institutional clinical trials to standardize cervical cancer treatment planning across diverse clinics, and discuss challenges and progress in the implementation of KBP for brachytherapy treatment planning. Additionally, we briefly discuss secondary applications of KBP for cervical cancer. The emerging picture from these studies indicates several exciting opportunities for increasing the utilization of KBP in day-to-day cervical cancer radiotherapy.

A Multi-atlas Approach for Active Bone Marrow Sparing Radiation Therapy: Implementation in the NRG-GY006 Trial.

PURPOSE: Sparing active bone marrow (ABM) can reduce acute hematologic toxicity in patients undergoing chemoradiotherapy for cervical cancer, but ABM segmentation based on positron emission tomography/computed tomography (PET/CT) is costly. We sought to develop an atlas-based ABM segmentation method for implementation in a prospective clinical trial. METHODS AND MATERIALS: A multiatlas was built on a training set of 144 patients and validated in 32 patients from the NRG-GY006 clinical trial. ABM for individual patients was defined as the subvolume of pelvic bone greater than the individual mean standardized uptake value on registered 18F-fluorodeoxyglucose PET/CT images. Atlas-based and custom ABM segmentations were compared using the Dice similarity coefficient and mean distance to agreement and used to generate ABM-sparing intensity modulated radiation therapy plans. Dose-volume metrics and normal tissue complication probabilities of the two approaches were compared using linear regression. RESULTS: Atlas-based ABM volumes (mean [standard deviation], 548.4 [88.3] cm3) were slightly larger than custom ABM volumes (535.1 [93.2] cm3), with a Dice similarity coefficient of 0.73. Total pelvic bone marrow V20 and Dmean were systematically higher and custom ABM V10 was systematically lower with custom-based plans (slope: 1.021 [95% confidence interval (CI), 1.005-1.037], 1.014 [95% CI, 1.006-1.022], and 0.98 [95% CI, 0.97-0.99], respectively). We found no significant differences between atlas-based and custom-based plans in bowel, rectum, bladder, femoral heads, or target dose-volume metrics. CONCLUSIONS: Atlas-based ABM segmentation can reduce pelvic bone marrow dose while achieving comparable target and other normal tissue dosimetry. This approach may allow ABM sparing in settings where PET/CT is unavailable.