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

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.

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Knowledge-based radiotherapy treatment planning

10.32469/10355/47060 ◽

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.

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Analysis of EORTC-1219-DAHANCA-29 trial plans demonstrates the potential of knowledge-based planning to provide patient-specific treatment plan quality assurance

Radiotherapy and Oncology ◽

10.1016/j.radonc.2018.10.005 ◽

2019 ◽

Vol 130 ◽

pp. 75-81 ◽

Cited By ~ 4

Author(s):

Jim P. Tol ◽

Max Dahele ◽

Vincent Gregoire ◽

Jens Overgaard ◽

Ben J. Slotman ◽

...

Keyword(s):

Quality Assurance ◽

Treatment Plan ◽

Specific Treatment ◽

Patient Specific ◽

Plan Quality ◽

Knowledge Based ◽

Knowledge Based Planning

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Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-0030 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Stefan Gerlach ◽

Christoph Fürweger ◽

Theresa Hofmann ◽

Alexander Schlaefer

Keyword(s):

Deep Learning ◽

Treatment Planning ◽

Treatment Plan ◽

Plan Quality ◽

New Approach ◽

Beam Generation ◽

Radiological Features ◽

Improve Treatment ◽

Robotic Radiosurgery ◽

Treatment Plans

AbstractAlthough robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Furthermore, different clinical goals have to be considered during planning and generally different sets of beams correspond to different clinical goals. Typically, candidate beams sampled from a randomized heuristic form the basis for treatment planning. We propose a new approach to generate candidate beams based on deep learning using radiological features as well as the desired constraints. We demonstrate that candidate beams generated for specific clinical goals can improve treatment plan quality. Furthermore, we compare two approaches to include information about constraints in the prediction. Our results show that CNN generated beams can improve treatment plan quality for different clinical goals, increasing coverage from 91.2 to 96.8% for 3,000 candidate beams on average. When including the clinical goal in the training, coverage is improved by 1.1% points.

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Knowledge-Based Planning for Identifying High-Risk Stereotactic Ablative Radiation Therapy Treatment Plans for Lung Tumors Larger Than 5 cm

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2018.08.013 ◽

2019 ◽

Vol 103 (1) ◽

pp. 259-267 ◽

Cited By ~ 3

Author(s):

Saar van't Hof ◽

Alexander R. Delaney ◽

Hilâl Tekatli ◽

Jos Twisk ◽

Ben J. Slotman ◽

...

Keyword(s):

Radiation Therapy ◽

High Risk ◽

Lung Tumors ◽

Knowledge Based ◽

Knowledge Based Planning ◽

Treatment Plans ◽

Therapy Treatment ◽

Stereotactic Ablative Radiation Therapy

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Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.551763 ◽

2021 ◽

Vol 10 ◽

Author(s):

Jiang Hu ◽

Boji Liu ◽

Weihao Xie ◽

Jinhan Zhu ◽

Xiaoli Yu ◽

...

Keyword(s):

Radiation Therapy ◽

Nasopharyngeal Carcinoma ◽

Prediction Model ◽

Intensity Modulated Radiation Therapy ◽

Optic Chiasm ◽

Conformity Index ◽

Plan Quality ◽

Target Dose ◽

Knowledge Based ◽

Knowledge Based Planning

Background and purposeTo validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.Methods and materialsA knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated.ResultsWith comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable.ConclusionThe automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.

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

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2017.06.255 ◽

2017 ◽

Vol 99 (2) ◽

pp. S107-S108

Author(s):

K.C. Younge ◽

R. Marsh ◽

D. Owen ◽

H. Geng ◽

Y. Xiao ◽

...

Keyword(s):

Clinical Trials ◽

Knowledge Based ◽

Knowledge Based Planning

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Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy

British Journal of Radiology ◽

10.1259/bjr.20190535 ◽

2020 ◽

Vol 93 (1106) ◽

pp. 20190535

Author(s):

Nilesh S Tambe ◽

Isabel M Pires ◽

Craig Moore ◽

Christopher Cawthorne ◽

Andrew W Beavis

Keyword(s):

Lung Volume ◽

Clinical Implementation ◽

Lung Dose ◽

Treatment Delivery ◽

Lung Cancer Patients ◽

Knowledge Based ◽

Knowledge Based Planning ◽

Dose Metrics ◽

Treatment Plans ◽

Stage Lung Cancer

Objectives: Radiotherapy plan quality may vary considerably depending on planner’s experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. The achieved treatment plans may still be suboptimal if there is further scope to reduce organs-at-risk doses without compromising target coverage and deliverability. This study aims to develop a knowledge-based planning (KBP) model to reduce variability of volumetric modulated arc therapy (VMAT) lung plans by predicting minimum achievable lung volume-dose metrics. Methods: Dosimetric and geometric data collected from 40 retrospective plans were used to develop KBP models aiming to predict the minimum achievable lung dose metrics via calculating the ratio of the residual lung volume to the total lung volume. Model accuracy was verified by replanning 40 plans. Plan complexity metrics were calculated using locally developed script and their effect on treatment delivery was assessed via measurement. Results: The use of KBP resulted in significant reduction in plan variability in all three studied dosimetric parameters V5, V20 and mean lung dose by 4.9% (p = 0.007, 10.8 to 5.9%), 1.3% (p = 0.038, 4.0 to 2.7%) and 0.9 Gy (p = 0.012, 2.5 to 1.6Gy), respectively. It also increased lung sparing without compromising the overall plan quality. The accuracy of the model was proven as clinically acceptable. Plan complexity increased compared to original plans; however, the implication on delivery errors was clinically insignificant as demonstrated by plan verification measurements. Conclusion: Our in-house model for VMAT lung plans led to a significant reduction in plan variability with concurrent decrease in lung dose. Our study also demonstrated that treatment delivery verifications are important prior to clinical implementation of KBP models. Advances in knowledge: In-house KBP models can predict minimum achievable lung dose-volume constraints for advance-stage lung cancer patients treated with VMAT. The study demonstrates that plan complexity could increase and should be assessed prior to clinical implementation.

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