A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites)

Nwankwo, Obioma; Sihono, Dwi Seno Kuncoro; Schneider, Frank; Wenz, Frederik

doi:10.1088/0031-9155/59/18/5575

Cited by 25 publications

(41 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some are commercially available. The models aim to predict either achievable dose metrics of interest, entire DVHs, or doses to individual voxels …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, achieved OAR doses can be highly institution-, planner-and patient dependent. Proposed solutions are knowledge-based (KB) treatment plan QA [1][2][3][4][5][6][7] or KB (semi-)automated treatment planning. [8][9][10][11][12][13] With KB treatment plan QA, treatment plans of prior patients are used to predict the achievable doses for a new patient.…”

Section: Introductionmentioning

confidence: 99%

“…The models aim to predict either achievable dose metrics of interest, 1,5,6,[14][15][16] entire DVHs, 4,7,12,17 or doses to individual voxels. 3,10,18,19 Prediction inaccuracy of KB models should be minimized to avoid generation of suboptimal treatment plans. However, it is not straightforward to determine the accuracy of KB models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Knowledge‐based dose prediction models for head and neck cancer are strongly affected by interorgan dependency and dataset inconsistency

2018

View full text Add to dashboard Cite

Purpose The goal of this study was to generate a large treatment plan database for head and neck (H&N) cancer patients that can be considered as the gold standard to train and validate models for knowledge‐based (KB) treatment planning and QA. With this dataset, the intrinsic prediction performance, the effect of interorgan dependency, and the impact of dataset inconsistency was investigated for an existing treatment planning QA model. Methods The CT scans of 108 previously treated oropharyngeal patients were used to establish the plan database. For each patient, 15 Pareto optimal treatment plans with different planning priorities for the parotid glands were generated with fully automatic multicriterial treatment planning (1620 plans in total). For each of the 15 sets of plans in the database, a KB model was trained with 54 patients and validated on the other 54 by comparing the predictions with the achieved doses. The dose prediction accuracy (predicted—achieved) of the KB models was assessed and compared among the different models to characterize the intrinsic performance and effect of interorgan dependency. In addition, the effect of dataset inconsistency with respect to planning prioritizations was investigated by mixing plans with different prioritizations, for the training, the validation dataset, and for both combined. Results In the case of a high planning priority, the mean ± SD of the prediction error for the mean dose of the parotid glands was only 0.2 ± 2.2 Gy, but this increased to 1.0 ± 5.0 Gy in the case that the parotid glands had a low planning priority. Dataset inconsistency (in planning priority) led to a large increase in prediction error for the parotid glands (mean ± SD) from 0.2 ± 2.2 Gy to 2.8 ± 3.3 Gy, −3.2 ± 5.0 Gy or −0.6 ± 5.4 Gy, depending on the way the datasets were mixed. Conclusions The generated plan database can be used to validate and characterize KB prediction models for H&N cancer and will be made available upon request. The investigated KB model performed well in case the parotid glands had a high planning priority (little dependence on lower priority OARs), but poorly for organs for which the dose strongly depends on other higher priority OARs. To improve the performance of KB prediction models for H&N cancer, interorgan dependency should be modeled and accounted for. Dataset inconsistency has a large negative impact on the prediction errors of KB models and should be avoided as much as possible.

show abstract

“…Some are commercially available. The models aim to predict either achievable dose metrics of interest, entire DVHs, or doses to individual voxels …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge‐based dose prediction models for head and neck cancer are strongly affected by interorgan dependency and dataset inconsistency

2018

View full text Add to dashboard Cite

show abstract

“…Voxel-level doses (13 articles) This group of methods predicts dose at each voxel in 3D space. [56][57][58][59][60][61][62][63][64][65][66][67][68] The predicted dose map is used to guide plan optimization or generate final plans directly (e.g., using dose mimicking algorithm 68 ).…”

Section: Dose-volume Histogram (Dvh) (36 Articles)mentioning

confidence: 99%

Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

2019

Medical Physics

170

150

View full text Add to dashboard Cite

Purpose Intensity‐Modulated Radiation Therapy ( IMRT ), including its variations (including IMRT , Volumetric Arc Therapy ( VMAT ), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning ( KBP ) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results. Methods In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here. Results The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improve...

show abstract

“…Thereafter, the model predicts the dose that similar organs will receive in RT plans on the basis of the anatomies of the organs for newly submitted cases. These models can also be used to predict the feasibility of planning objectives, in addition to objective assessment of the quality of RT plans (16)(17)(18). Our predictive models cover multiple disease sites, including head and neck, brain, lung, and prostate.…”

Section: Iroc Philadelphia Rt and Imagingmentioning

confidence: 99%

The role of Imaging and Radiation Oncology Core for precision medicine era of clinical trial

Xiao

Rosen

2017

Transl. Lung Cancer Res.

View full text Add to dashboard Cite

Imaging and Radiation Oncology Core (IROC) services have been established for the quality assurance (QA) of imaging and radiotherapy (RT) for NCI's Clinical Trial Network (NCTN) for any trials that contain imaging or RT. The randomized clinical trial is the gold standard for evidence-based medicine.QA ensures data quality, preventing noise from inferior treatments obscuring clinical trial outcome. QA is also found to be cost-effective. IROC has made great progress in multi-institution standardization and is expected to lead QA standardization, QA science in imaging and RT and to advance quality data analysis with big data in the future. The QA in the era of precision medicine is of paramount importance, when individualized decision making may depend on the quality and accuracy of RT and imaging.

show abstract

A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites)

Abstract: We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.

Cited by 25 publications

References 17 publications

Knowledge‐based dose prediction models for head and neck cancer are strongly affected by interorgan dependency and dataset inconsistency

Knowledge‐based dose prediction models for head and neck cancer are strongly affected by interorgan dependency and dataset inconsistency

Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

The role of Imaging and Radiation Oncology Core for precision medicine era of clinical trial

Contact Info

Product

Resources

About