Use of a constrained hierarchical optimization dataset enhances knowledge‐based planning as a quality assurance tool for prostate bed irradiation

Lin, Yen Hwa; Hong, Linda; Hunt, Margie A.; Berry, Sean L.

doi:10.1002/mp.13163

Cited by 5 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another comparison between RP models based on different initial plans has been recently published. In their work, Lin et al [36] compared a model generated from clinical trial-and-error based plans on prostate, with a model generated for the same patients, whose plans were optimized according to a constrained hierarchical optimization procedure, a very complex and time consuming process able to produce Pareto optimal plans. They concluded that the RP model populated with this second group of plans improved significantly the model quality, in terms of R 2 and DVH estimation bound width; the final clinical plan quality, however, was not significantly improved using the RP generated by this second group of plans, proving that the model quality does not necessarily translate into clinical plan quality.…”

Section: Discussionmentioning

confidence: 99%

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

et al. 2019

View full text Add to dashboard Cite

PurposeTo determine if the performance of a knowledge based RapidPlan (RP) planning model could be improved with an iterative learning process, i.e. if plans generated by an RP model could be used as new input to re-train the model and achieve better performance.MethodsClinical VMAT plans from 83 patients presenting with head and neck cancer were selected to train an RP model, CL-1. With this model, new plans on the same patients were generated, and subsequently used as input to train a novel model, CL-2. Both models were validated on a cohort of 20 patients and dosimetric results compared. Another set of 83 plans was realised on the same patients with different planning criteria, by using a simple template with no attempt to manually improve the plan quality. Those plans were employed to train another model, TP-1. The differences between the plans generated by CL-1 and TP-1 for the validation cohort of patients were compared with respect to the differences between the original plans used to build the two models.ResultsThe CL-2 model presented an improvement relative to CL-1, with higher R2 values and better regression plots. The mean doses to parallel organs decreased with CL-2, while D1% to serial organs increased (but not significantly). The different models CL-1 and TP-1 were able to yield plans according to each original strategy.ConclusionA refined RP model allowed the generation of plans with improved quality, mostly for parallel organs at risk and, possibly, also the intrinsic model quality.

show abstract

Section: Discussionmentioning

confidence: 99%

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Lin et al. 22 demonstrated that building KBP models with constrained hierarchical optimization plan data could result in a high-quality KBP model to be used to quickly adapt KBP to changes in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…Once the model is built, however, KBP can quickly produce new plans by leveraging the predicted achievable plan for the new patient. Lin et al 22 demonstrated that building KBP models with constrained hierarchical optimization plan data could result in a high-quality KBP model to be used to quickly adapt KBP to changes in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Clinical Experience of Automated SBRT Paraspinal and Other Metastatic Tumor Planning With Constrained Hierarchical Optimization

Hong

Zhou

Yang

et al. 2020

Advances in Radiation Oncology

Self Cite

View full text Add to dashboard Cite

Purpose We report on the clinical performance of a fully automated approach to treatment planning based on a Pareto optimal, constrained hierarchical optimization algorithm, named Expedited Constrained Hierarchical Optimization (ECHO). Methods and materials From April 2017 to October 2018, ECHO produced 640 treated plans for 523 patients who underwent stereotactic body radiation therapy (RT) for paraspinal and other metastatic tumors. A total of 182 plans were for 24 Gy in a single fraction, 387 plans were for 27 Gy in 3 fractions, and the remainder were for other prescriptions or fractionations. Of the plans, 84.5% were for paraspinal tumors, with 69, 302, and 170 in the cervical, thoracic, and lumbosacral spine, respectively. For each case, after contouring, a template plan using 9 intensity modulated RT fields based on disease site and tumor location was sent to ECHO through an application program interface plug-in from the treatment planning system. ECHO returned a plan that satisfied all critical structure hard constraints with optimal target volume coverage and the lowest achievable normal tissue doses. Upon ECHO completion, the planner received an e-mail indicating the plan was ready for review. The plan was accepted if all clinical criteria were met. Otherwise, a limited number of parameters could be adjusted for another ECHO run. Results The median planning target volume size was 84.3 cm 3 (range, 6.9-633.2). The median time to produce 1 ECHO plan was 63.5 minutes (range, 11-340 minutes) and was largely dependent on the field sizes. Of the cases, 79.7% required 1 run to produce a clinically accepted plan, 13.3% required 1 additional run with minimal parameter adjustments, and 7.0% required ≥2 additional runs with significant parameter modifications. All plans met or bettered the institutional clinical criteria. Conclusions We successfully implemented automated stereotactic body RT paraspinal and other metastatic tumors planning. ECHO produced high-quality plans, improved planning efficiency and robustness, and enabled expedited treatment planning at our clinic.

show abstract

“…Such parameters can be, e.g., beam settings, DVHs for target volumes and OARs, or full 3-D dose distributions. DVH prediction has been widely studied in recent years for most disease sites, e.g., head and neck [214], [226], [258], [272], [274], [281], prostate [210], [238], [251], [255], [264], [266]- [268], [270], [276], [281], upper GI [259], [265], [273], [280], lower GI [229], [254], [269], [277], [278], and breast [259], [262], [275]. A commercial software for DVH prediction is Varian RapidPlan.…”

Section: A Classical Auto-planning Strategiesmentioning

confidence: 99%

Artificial Intelligence in Radiation Therapy

Zhang

Morris

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

View full text Add to dashboard Cite

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy, including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

show abstract

Use of a constrained hierarchical optimization dataset enhances knowledge‐based planning as a quality assurance tool for prostate bed irradiation

Cited by 5 publications

References 24 publications

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

Clinical Experience of Automated SBRT Paraspinal and Other Metastatic Tumor Planning With Constrained Hierarchical Optimization

Artificial Intelligence in Radiation Therapy

Contact Info

Product

Resources

About