Knowledge‐based prediction of plan quality metrics in intracranial stereotactic radiosurgery

Shiraishi, S.; Tan, Jun; Olsen, Lindsey; Moore, Kevin L.

doi:10.1118/1.4906183

Cited by 96 publications

(111 citation statements)

References 21 publications

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“…A large and rather mature IMRT plan dataset was required to effectively guide VMAT planning. In most recent studies [24,25] a comprehensive knowledge-based method was used for predicting achievable dose-volume histograms (DVHs) to standardize and improve treatment planning. [25] Tol et al evaluated the method for head and neck cancer using a library of different patient plans to make a model that can predict achievable DVHs for defining optimization objectives for new patients.…”

Section: Discussionmentioning

confidence: 99%

Validation of Fully Automated VMAT Plan Generation for Library-Based Plan-of-the-Day Cervical Cancer Radiotherapy

et al. 2016

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PurposeTo develop and validate fully automated generation of VMAT plan-libraries for plan-of-the-day adaptive radiotherapy in locally-advanced cervical cancer.Material and MethodsOur framework for fully automated treatment plan generation (Erasmus-iCycle) was adapted to create dual-arc VMAT treatment plan libraries for cervical cancer patients. For each of 34 patients, automatically generated VMAT plans (autoVMAT) were compared to manually generated, clinically delivered 9-beam IMRT plans (CLINICAL), and to dual-arc VMAT plans generated manually by an expert planner (manVMAT). Furthermore, all plans were benchmarked against 20-beam equi-angular IMRT plans (autoIMRT). For all plans, a PTV coverage of 99.5% by at least 95% of the prescribed dose (46 Gy) had the highest planning priority, followed by minimization of V45Gy for small bowel (SB). Other OARs considered were bladder, rectum, and sigmoid.ResultsAll plans had a highly similar PTV coverage, within the clinical constraints (above). After plan normalizations for exactly equal median PTV doses in corresponding plans, all evaluated OAR parameters in autoVMAT plans were on average lower than in the CLINICAL plans with an average reduction in SB V45Gy of 34.6% (p<0.001). For 41/44 autoVMAT plans, SB V45Gy was lower than for manVMAT (p<0.001, average reduction 30.3%), while SB V15Gy increased by 2.3% (p = 0.011). AutoIMRT reduced SB V45Gy by another 2.7% compared to autoVMAT, while also resulting in a 9.0% reduction in SB V15Gy (p<0.001), but with a prolonged delivery time. Differences between manVMAT and autoVMAT in bladder, rectal and sigmoid doses were ≤ 1%. Improvements in SB dose delivery with autoVMAT instead of manVMAT were higher for empty bladder PTVs compared to full bladder PTVs, due to differences in concavity of the PTVs.ConclusionsQuality of automatically generated VMAT plans was superior to manually generated plans. Automatic VMAT plan generation for cervical cancer has been implemented in our clinical routine. Due to the achieved workload reduction, extension of plan libraries has become feasible.

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Section: Discussionmentioning

confidence: 99%

Validation of Fully Automated VMAT Plan Generation for Library-Based Plan-of-the-Day Cervical Cancer Radiotherapy

et al. 2016

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“…Several publications have reported the feasibility of using RapidPlan to improve OAR sparing (23–25) and Jim P. Tol et al (26) showed the potential of using RapidPlan DVH estimation model directly as a plan QC tool for head-and-neck cases. However, these studies built models directly on arbitrary training plans with no special selection criteria, which could influence the performance of the RapidPlan DVH estimation models (3,17). Moreover, none of these works have systematically investigated the influence of using different mix of optimization objectives and priorities for multiple OARs and PTVs.…”

Section: Discussionmentioning

confidence: 99%

“…Since the results that the model produces are roughly dependent on the mean quality of the training plans(18), a strategy to identify high quality plans for a refined training set for each OAR is critical(3,17). We decided to select the top 30 (~1/3 of the entire sample) plans with an apparent emphasis on PBM or bowel sparing to generate refined training sets for these models, since these are principal OARs for the protocol.…”

Section: Methodsmentioning

confidence: 99%

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Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials

Carmona

Sirák

et al. 2017

International Journal of Radiation Oncology*Biology*Physics

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102

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Purpose To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiotherapy clinical trial plan quality control (QC) system. Methods We analyzed 86 stage IB-IVA cervical cancer patients treated with intensity modulated radiotherapy (IMRT) at two institutions according to XXXXX multi-institutional protocol standards. The protocol utilized a planning target volume (PTV) and two primary organs-at-risk (OARs): pelvic bone marrow (PBM) and bowel. Secondary OARs were rectum and bladder. Initial UNFILTERED dose-volume histogram (DVH) estimation models were trained using all 86 plans. REFINED training sets and DVH-estimation models were constructed by identifying 30/86 plans emphasizing PBM-sparing (comparing protocol-specified V10 and V20 with UNFILTERED predictions), and another 30/86 plans emphasizing bowel sparing (comparing V40 and V45, 9 in common with PBM set). To obtain deliverable KBP plans, REFINED models must inform patient-specific optimization objectives/priorities (an auto-planning “routine”). Four candidate routines emphasizing different tradeoffs were composed and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine best meeting protocol objectives in the 51-patient training sample (KBPFINAL), protocol-specific DVH metrics and normal tissue complication probability (NTCP) were compared for original vs. KBPFINAL plans across the 35-patient validation set. Paired t-tests tested differences between planning sets. Results KBPFINAL plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean NTCP for GI toxicity was lower for KBPFINAL vs. validation-set plans (48.7% vs. 53.8%, p<.001). Similarly, the estimated mean white blood cell count (WBC) nadir was higher (2.77 vs 2.49, p<.001) with KBPFINAL plans, indicating lowered probability of hematologic toxicity. Conclusions This work demonstrates that a KBP system can be efficiently trained and refined for use in radiotherapy clinical trials with minimal effort. This patient-specific plan QC resulted in improvements on protocol-specific dosimetric endpoints.

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“…These steps can range from the estimation of field direction,1 weights of optimization objectives,2 and even dose distribution 3, 4. The majority of KBP work, however, has focused on estimating dose–volume histograms (DVHs)5, 6, 7, 8, 9 which are commonly used to evaluate plan quality and guide the inverse planning process.…”

Section: Introductionmentioning

confidence: 99%

Creation of knowledge‐based planning models intended for large scale distribution: Minimizing the effect of outlier plans

Aviles

Marcos²,

Sasaki

et al. 2018

J Applied Clin Med Phys

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Knowledge‐based planning (KBP) can be used to estimate dose–volume histograms (DVHs) of organs at risk (OAR) using models. The task of model creation, however, can result in estimates with differing accuracy; particularly when outlier plans are not properly addressed. This work used RapidPlan™ to create models for the prostate and head and neck intended for large‐scale distribution. Potential outlier plans were identified by means of regression analysis scatter plots, Cook's distance, coefficient of determination, and the chi‐squared test. Outlier plans were identified as falling into three categories: geometric, dosimetric, and over‐fitting outliers. The models were validated by comparing DVHs estimated by the model with those from a separate and independent set of clinical plans. The estimated DVHs were also used as optimization objectives during inverse planning. The analysis tools lead us to identify as many as 7 geometric, 8 dosimetric, and 20 over‐fitting outliers in the raw models. Geometric and over‐fitting outliers were removed while the dosimetric outliers were replaced after re‐planning. Model validation was done by comparing the DVHs at 50%, 85%, and 99% of the maximum dose for each OAR (denoted as V50, V85, and V99) and agreed within −2% to 4% for the three metrics for the final prostate model. In terms of the head and neck model, the estimated DVHs agreed from −2.0% to 5.1% at V50, 0.1% to 7.1% at V85, and 0.1% to 7.6% at V99. The process used to create these models improved the accuracy for the pharyngeal constrictor DVH estimation where one plan was originally over‐estimated by more than twice. In conclusion, our results demonstrate that KBP models should be carefully created since their accuracy could be negatively affected by outlier plans. Outlier plans can be addressed by removing them from the model and re‐planning.

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Knowledge‐based prediction of plan quality metrics in intracranial stereotactic radiosurgery

Cited by 96 publications

References 21 publications

Validation of Fully Automated VMAT Plan Generation for Library-Based Plan-of-the-Day Cervical Cancer Radiotherapy

Validation of Fully Automated VMAT Plan Generation for Library-Based Plan-of-the-Day Cervical Cancer Radiotherapy

Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials

Creation of knowledge‐based planning models intended for large scale distribution: Minimizing the effect of outlier plans

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