Improving Quality and Consistency in Clinical Trials via Knowledge-Based Planning, NRG Oncology RTOG 0631

Abstract 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.

Download Full-text

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

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2016.10.005 ◽

2017 ◽

Vol 97 (1) ◽

pp. 164-172 ◽

Cited By ~ 41

Author(s):

Nan Li ◽

Ruben Carmona ◽

Igor Sirak ◽

Linda Kasaova ◽

David Followill ◽

...

Keyword(s):

Quality Control ◽

Clinical Trials ◽

Radiation Therapy ◽

Control System ◽

Quality Control System ◽

Highly Efficient ◽

Knowledge Based ◽

Knowledge Based Planning

Download Full-text

Knowledge-Based Planning

10.21236/ada279697 ◽

1993 ◽

Author(s):

Drew McDermott ◽

Gregory Hager

Keyword(s):

Knowledge Based ◽

Knowledge Based Planning

Download Full-text

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

Medical Physics ◽

10.1002/mp.14845 ◽

2021 ◽

Cited By ~ 1

Author(s):

Aaron Babier ◽

Binghao Zhang ◽

Rafid Mahmood ◽

Kevin L. Moore ◽

Thomas G. Purdie ◽

...

Keyword(s):

Open Access ◽

Grand Challenge ◽

Knowledge Based ◽

Knowledge Based Planning

Download Full-text

Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

Radiation Oncology ◽

10.1186/s13014-021-01783-9 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Mingli Wang ◽

Huikuan Gu ◽

Jiang Hu ◽

Jian Liang ◽

Sisi Xu ◽

...

Keyword(s):

Cervical Cancer ◽

Prediction Model ◽

Volumetric Modulated Arc Therapy ◽

Model Evolution ◽

Prediction Ability ◽

Knowledge Based ◽

Proposed Model ◽

Arc Therapy ◽

Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

Download Full-text

A knowledge-based planning assessment approach for timely delivery of construction projects

10.1063/5.0067582 ◽

2021 ◽

Author(s):

Mahmoud Ershadi ◽

Marcus Jefferies ◽

Peter Davis ◽

Mohammad Mojtahedi

Keyword(s):

Construction Projects ◽

Knowledge Based ◽

Assessment Approach ◽

Knowledge Based Planning ◽

Timely Delivery

Download Full-text

Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study

Cancers ◽

10.3390/cancers10110420 ◽

2018 ◽

Vol 10 (11) ◽

pp. 420 ◽

Cited By ~ 4

Author(s):

Alexander Delaney ◽

Lei Dong ◽

Anthony Mascia ◽

Wei Zou ◽

Yongbin Zhang ◽

...

Keyword(s):

Target Volume ◽

Single Center ◽

Intensity Modulated Proton Therapy ◽

Intensity Modulated ◽

Knowledge Based ◽

Model Library ◽

Beam Angle Optimization ◽

Knowledge Based Planning ◽

Automated Knowledge ◽

Comparable Quality

Background: Radiotherapy treatment planning is increasingly automated and knowledge-based planning has been shown to match and sometimes improve upon manual clinical plans, with increased consistency and efficiency. In this study, we benchmarked a novel prototype knowledge-based intensity-modulated proton therapy (IMPT) planning solution, against three international proton centers. Methods: A model library was constructed, comprising 50 head and neck cancer (HNC) manual IMPT plans from a single center. Three external-centers each provided seven manual benchmark IMPT plans. A knowledge-based plan (KBP) using a standard beam arrangement for each patient was compared with the benchmark plan on the basis of planning target volume (PTV) coverage and homogeneity and mean organ-at-risk (OAR) dose. Results: PTV coverage and homogeneity of KBPs and benchmark plans were comparable. KBP mean OAR dose was lower in 32/54, 45/48 and 38/53 OARs from center-A, -B and -C, with 23/32, 38/45 and 23/38 being >2 Gy improvements, respectively. In isolated cases the standard beam arrangement or an OAR not being included in the model or being contoured differently, led to higher individual KBP OAR doses. Generating a KBP typically required <10 min. Conclusions: A knowledge-based IMPT planning solution using a single-center model could efficiently generate plans of comparable quality to manual HNC IMPT plans from centers with differing planning aims. Occasional higher KBP OAR doses highlight the need for beam angle optimization and manual review of KBPs. The solution furthermore demonstrated the potential for robust optimization.

Download Full-text