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

Covele, Brent; Puri, Kartikeya; Kallis, Karoline; Murphy, James D.; Moore, Kevin L.

doi:10.1200/cci.20.00093

Cited by 4 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For ORBIT-RT, this was hypothesized; at 40-65% predictive success, ORBIT-RT's error band more closely resembles an IQR, as described in Ref. [11].…”

Section: Resultsmentioning

confidence: 97%

See 1 more Smart Citation

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

Covele

Carroll

Moore

2021

J Applied Clin Med Phys

Self Cite

View full text Add to dashboard Cite

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-atrisk 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 clin,i À V pred,i (ith plan), from which prediction bias μ, prediction error variation σ, and root-mean-square error RMSE pred ≡ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 N ∑ i V clin,i À V pred,i À Á 2 r ≅ ffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi σ 2 þ μ 2 p could be calculated for the cohort. The empirical RMSE 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 RMSE pred was below that of RapidPlan, indicating slightly improved accuracy in this cohort. Because μ ≈ 0, RMSE 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 RMSE 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.

show abstract

“…For ORBIT-RT, this was hypothesized; at 40-65% predictive success, ORBIT-RT's error band more closely resembles an IQR, as described in Ref. [11].…”

Section: Resultsmentioning

confidence: 97%

“…Then we apply this method to compare the prediction success rate of our empirically derived error bands to the model-provided OAR DVH error bands. We examine two knowledge-based models: ORBIT-RT, 11 a free, web-based DVH prediction platform (www.orbit-rt.com), and commercially available RapidPlan.…”

Section: Introductionmentioning

confidence: 99%

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

Covele

Carroll

Moore

2021

J Applied Clin Med Phys

Self Cite

View full text Add to dashboard Cite

show abstract

“…To ensure the exact same daily CTVs and OARs were used, these plans were reoptimized using a clinically validated RapidPlan TM model for cervical cancer [26][27][28][29] in the Eclipse TPS. This step was performed in Eclipse rather than repeating the adaptive process using the Ethos emulator because the emulator would have required re-contouring the targets and OARs at each fraction and thus led to small variations in volumes.…”

Section: Adp MM 3mentioning

confidence: 99%

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

Ghimire

Moore

Branco

et al. 2023

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

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 correlation (R2) between dose and volume metrics from initial treatment plans and the dosimetric benefits to the Bowel V40Gy, Bowel V45Gy, Bladder Dmean, and Rectum Dmean from adaptive radiotherapy using reduced 3mm or 5mm CTV-to-PTV margins. The LASSO technique was used to identify the most predictive metrics for Bowel V40Gy. The three highest performing metrics were used to build multivariate models with leave-one-out validation for Bowel V40Gy. Main Results: Patients with higher initial bowel doses were correlated with the largest decreases in Bowel V40Gy from daily adaptation (linear best fit R2=0.77 for a 3mm PTV margin and R2=0.8 for a 5mm PTV margin). Other metrics had intermediate or no correlation. Selected covariates for the multivariate model were differences in the initial Bowel V40Gy and Bladder Dmean using standard versus reduced margins and the initial bladder volume. Leave-one-out validation had an R2 of 0.66 between predicted and true adaptive Bowel V40Gy benefits 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.

show abstract

“…A differential DVH for each OAR shell is extracted from each treatment plan in the training set, and averaged to produce dose kernels as a function of radial distance. For model predictions, OAR contours are discretized in the same manner, and the differential DVH of the considered OAR is calculated from the sum of differential DVH kernels, weighted by the volume of each OAR sub-volume (8,13,20). DVHs for new patients can be predicted using HRCTV and OAR contours alone.…”

Section: Knowledge-based Dose-prediction Modelsmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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

Cited by 4 publications

References 11 publications

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

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

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

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

Contact Info

Product

Resources

About