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.

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.

A Knowledge-Based Imaging Informatics Approach for Managing Proton Beam Therapy of Cancer Patients

2007 ◽  
Vol 6 (4_suppl) ◽  
pp. 77-84 ◽  
Author(s):  
Brent J. Liu
Keyword(s):  
Decision Making ◽  
Radiation Therapy ◽  
Decision Support ◽  
Treatment Planning ◽  
Imaging Informatics ◽  
Patient Treatment ◽  
Decision Support Tools ◽  
Knowledge Based ◽  
Support Tools ◽  
Dicom Rt

The need for a unified patient-oriented information system to handle complex proton therapy (PT) imaging and informatics data during the course of patient treatment is becoming steadily apparent due to the ever increasing demands for better diagnostic treatment planning and more accurate information. Currently, this information is scattered throughout each of the different treatment and information systems in the oncology department. Furthermore, the lack of organization with standardized methods makes it difficult and time-consuming to navigate through the maze of data, resulting in challenges during patient treatment planning. We present a methodology to develop this electronic patient record (ePR) system based on DICOM standards and perform knowledge-based medical imaging informatics research on specific clinical scenarios where patients are treated with PT. Treatment planning is similar in workflow to traditional radiation therapy (RT) methods such as intensity-modulated radiation therapy (IMRT), which utilizes a priori knowledge to drive the treatment plan in an inverse manner. In March 2006, two new RT objects were drafted in a DICOM-RT Supplement 102 specifically for ion therapy, which includes PT. The standardization of DICOM-RT-ION objects and the development of a knowledge base as well as decision-support tools that can be add-on features to the ePR DICOM-RT system were researched. This methodology can be used to extend to PT and the development of future clinical decision-making scenarios during the course of the patient's treatment that utilize “inverse treatment planning.” We present the initial steps of this imaging and informatics methodology for PT and lay the foundation for development of future decision-support tools tailored to cancer patients treated with PT. By integrating decision-support knowledge and tools designed to assist in the decision-making process, a new and improved “ knowledge-enhanced treatment planning” approach can be realized.

scholarly journals A web-based, real-time quality control and progress monitoring tool for multicenter environmental health surveys

2019 ◽  
Vol 4 (3) ◽  
pp. 81
Author(s):  
Tiantian Li ◽  
Yi Zhang ◽  
Jianlong Fang ◽  
Peng Du ◽  
Jiaonan Wang ◽  
...  
Keyword(s):  
Quality Control ◽  
Environmental Health ◽  
Real Time ◽  
Health Surveys ◽  
Progress Monitoring ◽  
Monitoring Tool ◽  
Web Based

scholarly journals Personalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Nicholas Hardcastle ◽  
Olivia Cook ◽  
Xenia Ray ◽  
Alisha Moore ◽  
Kevin L. Moore ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real Time ◽  
Kidney Cancer ◽  
Treatment Plan ◽  
Plan Quality ◽  
Knowledge Based ◽  
Trade Offs ◽  
Stereotactic Ablative Body Radiotherapy ◽  
Knowledge Based Planning ◽  
Treatment Plans

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.

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

Inter-planner variation in treatment-plan quality of plans created with a knowledge-based treatment planning system

2019 ◽  
Vol 67 ◽  
pp. 132-140 ◽  
Author(s):  
Kazuki Kubo ◽  
Hajime Monzen ◽  
Kentaro Ishii ◽  
Mikoto Tamura ◽  
Yuta Nakasaka ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Treatment Plan ◽  
Planning System ◽  
Treatment Planning System ◽  
Plan Quality ◽  
Knowledge Based

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.

scholarly journals A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy

2020 ◽  
Author(s):  
Penggang Bai ◽  
Xing Weng ◽  
Kerun Quan ◽  
Jihong Chen ◽  
Yitao Dai ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Locally Advanced ◽  
Intensity Modulated Radiation Therapy ◽  
Patient Specific ◽  
Intensity Modulated ◽  
Knowledge Based ◽  
Planning Technique

Abstract Background To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy. Methods 140 NPC patients treated with definitive radiation therapy with the step-and-shoot IMRT techniques were retrospectively selected and consisted of a knowledge library (115 patients) and a test library (25 patients). For each patient in the knowledge library, their overlap volume histogram (OVH), target volume histogram (TVH) and dose objectives were extracted from the patient’s manual treatment plan, and these were used to train a 3 layer neural network (NN) model. The OVH and TVH from the test library were input into the trained model to derive patients’ dose objectives which were subsequently used to generate automated plans (APs) by an in-house developed Perl and HotScripts planning scripts with a single iteration optimization. The corresponding manual plans (MPs) of patients in the test library were manually generated by an experienced medical physicist according to clinical protocols. Plan quality was ranked and dosimetric parameters were compared between the APs and MPs. Results Qualitatively, the APs and MPs had the same rank for the majority of the patients (19 of 25). PTV achieved each given criteria in the majority of the patients (greater than 80%) between both the APs and MPs. For each OAR, the number achieving its criterion in the APs was close to that in the MPs. The APs would improve the treatment delivery efficiency by reducing total plan MUs by ~5% (685.24±58.89 vs. 721.36±63.36, P =0.004). AP also significantly improved planning efficiency by reducing the planning duration to ~17% of the MP (9.73±1.80 min vs. 57.10±6.35 min, P <0.001). Conclusion A robust and effective knowledge-based IMRT treatment planning technique for locally advanced NPC is developed. Patient specific TDV can be predicted by a trained NN model based on the individual’s OVH and clinical TVH goals. The automated planning scripts can use these TDV to generate APs with largely shortened planning time with comparable or improved dosimetric qualities compared to our clinic’s manual plans.

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