SU-G-TeP4-14: Quality Control of Treatment Planning Using Knowledge-Based Planning Across a System of Radiation Oncology Practices

2016 ◽  
Vol 43 (6Part28) ◽  
pp. 3688-3688
Author(s):  
K Masi ◽  
M Ditman ◽  
R Marsh ◽  
J Dai ◽  
M Huberts ◽  
...  
Keyword(s):  
Quality Control ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Knowledge Based ◽  
Knowledge Based Planning

Knowledge-based radiotherapy treatment planning

2015 ◽  
Author(s):  
◽  
Lindsey Appenzoller Olsen
Keyword(s):  
Quality Control ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Radiotherapy Planning ◽  
Patient Specific ◽  
Plan Quality ◽  
Radiotherapy Treatment Planning ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Radiotherapy Treatment

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Knowledge-based planning (KBP) has become a prominent area of research in radiation oncology in the last five years. The development of KBP aims to address the lack of systematic quality control and plan quality variability in radiotherapy treatment planning by providing achievable, patient-specific optimization objectives derived from a model trained with a cohort of previously treated, site-specific plans. This dissertation intended to develop, evaluate, and implement a knowledge-based planning system to reduce variability and improve radiotherapy treatment plan quality. The project aimed to 1) develop and validate an algorithm to train mathematical models that predict dose-volume histograms for organs at risk in radiotherapy planning, 2) implement the algorithm into a software application in order to transfer the technology into clinical practice, and 3) evaluate the impact of the software system (algorithm + application) on reducing variability and improving radiotherapy treatment plan quality through knowledge transfer. The presented work demonstrates that a KBP model is beneficial to radiotherapy planning. The developed models adequately describe what is dosimetrically achievable for patient specific anatomy and have proven useful in outlier detection for quality control of radiotherapy planning. The KBP paradigm has also demonstrated ability to improve treatment plan quality through benchmarking and transfer of knowledge between institutions.

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

2021 ◽  
pp. 134-142 ◽  
Author(s):  
Brent M. Covele ◽  
Kartikeya S. Puri ◽  
Karoline Kallis ◽  
James D. Murphy ◽  
Kevin L. Moore
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Estimation Methods ◽  
Web Based ◽  
Knowledge Based ◽  
Validation Set ◽  
Control Feedback ◽  
Dicom Rt

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.

scholarly journals Knowledge-based Planning using Pseudo-structures for Volumetric Modulated arc Therapy (VMAT) of Postoperative Uterine Cervical Cancer: A Multi-Institutional Study

2020 ◽  
Author(s):  
Tatsuya Kamima ◽  
Yoshihiro Ueda ◽  
Jun-ichi Fukunaga ◽  
Mikoto Tamura ◽  
Yumiko Shimizu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Treatment Planning ◽  
Planning Process ◽  
Volumetric Modulated Arc Therapy ◽  
Uterine Cervical Cancer ◽  
Planning System ◽  
Treatment Planning System ◽  
Knowledge Based ◽  
Arc Therapy ◽  
Knowledge Based Planning

Abstract Background: The aim of this study was to investigate the performance of the RapidPlan knowledge-based treatment planning system using models including registered pseudo-structures, and to determine how many structures are required for automatic optimization of volumetric modulated arc therapy (VMAT) for postoperative uterine cervical cancer. Methods: Pseudo-structures were retrospectively contoured for patients who had completed treatment at one of five institutions. For 22 patients, RPs were generated with a single optimization for models with two (RP_2), four (RP_4), or five (RP_5) registered structures, and the dosimetric parameters of these models were compared with a clinical plan with several optimizations. The total times for pseudo-structure creation and optimization were also measured.Results: Most dosimetric parameters showed no major differences between each RP. In particular, the rectum Dmax, V50Gy, and V40Gy with RP_2, RP_4, and RP_5 were not significantly different, and were lower than those of the clinical plan. In addition, the average proportions of plans achieving acceptable criteria for all dosimetric parameters were 98%, 99%, 98%, and 98% for the clinical plan, RP_2, RP_4, and RP_5, respectively. The average times for the creation and optimization of pseudo-structures were 105, 17, 21, and 29 minutes, for the clinical plan, RP_2, RP_4, and RP_5, respectively. Conclusions: The RapidPlan model with two registered pseudo-structures could generate clinically acceptable plans while saving time. This modeling approach using pseudo-structures could possibility be used for the VMAT planning process.

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

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Christine H. Feng ◽  
Mariel Cornell ◽  
Kevin L. Moore ◽  
Roshan Karunamuni ◽  
Tyler M. Seibert
Keyword(s):  
Artificial Intelligence ◽  
Treatment Planning ◽  
Whole Brain Radiotherapy ◽  
Planning System ◽  
Whole Brain ◽  
Knowledge Based ◽  
Brain Radiotherapy ◽  
Manual Intervention ◽  
Dose Volume ◽  
Knowledge Based Planning

Abstract 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 oesophageal cancers using a model trained with plans from a different treatment planning system

2019 ◽  
Vol 59 (3) ◽  
pp. 274-283 ◽  
Author(s):  
Yoshihiro Ueda ◽  
Masayoshi Miyazaki ◽  
Iori Sumida ◽  
Shingo Ohira ◽  
Mikoto Tamura ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Planning System ◽  
Treatment Planning System ◽  
Knowledge Based ◽  
Knowledge Based Planning

scholarly journals Automated planning, knowledge-based planning and other novel developments in radiotherapy treatment planning - how do they work and perform at the ESTRO 36th conference, Vienna, Austria.

2020 ◽  
Vol 17 (1) ◽  
pp. 64-40
Author(s):  
Agnieszka Skrobała
Keyword(s):  
Treatment Planning ◽  
Automated Planning ◽  
Annual Conference ◽  
Radiotherapy Treatment Planning ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Research Problems ◽  
Radiotherapy Treatment ◽  
Planning Knowledge

The aim of this study was to review and presentation system of automated planning, knowledge-based planning and other novel developments in radiotherapy treatment planning, with the answer on the question; how do these system work and perform.  The review was based on selected reports and research problems presented during ESTRO 36th annual conference held in Vienna, Austria.

scholarly journals A hybrid automated treatment planning solution for esophageal cancer

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Chifang Ling ◽  
Xu Han ◽  
Peng Zhai ◽  
Hao Xu ◽  
Jiayan Chen ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Estimation Model ◽  
Knowledge Based ◽  
Plan Optimization ◽  
Dose Volume ◽  
Knowledge Based Planning ◽  
Planning Methods ◽  
Planning Strategies

Abstract Objective This study aims to investigate a hybrid automated treatment planning (HAP) solution that combines knowledge-based planning (KBP) and script-based planning for esophageal cancer. Methods In order to fully investigate the advantages of HAP, three planning strategies were implemented in the present study: HAP, KBP, and full manual planning. Each method was applied to 20 patients. For HAP and KBP, the objective functions for plan optimization were generated from a dose–volume histogram (DVH) estimation model, which was based on 70 esophageal patients. Script-based automated planning was used for HAP, while the regular IMRT inverse planning method was used for KBP. For full manual planning, clinical standards were applied to create the plans. Paired t-tests were performed to compare the differences in dose-volume indices among the three planning methods. Results Among the three planning strategies, HAP exhibited the best performance in all dose-volume indices, except for PTV dose homogeneity and lung V5. PTV conformity and spinal cord sparing were significantly improved in HAP (P < 0.001). Compared to KBP, HAP improved all indices, except for lung V5. Furthermore, the OAR sparing and target coverage between HAP and full manual planning were similar. Moreover, HAP had the shortest average planning time (57 min), when compared to KBP (63 min) and full manual planning (118 min). Conclusion HAP is an effective planning strategy for obtaining a high quality treatment plan for esophageal cancer.

Sign in / Sign up

Export Citation Format

Share Document