Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer

Deshpande, Ruchi; DeMarco, John J.; Sayre, James W.; Liu, Brent J.

doi:10.1007/s11548-016-1403-6

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Knowledge about patient’s day-to-day anatomical variation is furthermore valuable when (automating) selecting similar patients from a database of patients’ CT scans for, e.g., automating treatment planning or dose reconstruction [ 30 – 34 ], because this provides a lower bound on the achievable precision of selection.…”

Section: Discussionmentioning

confidence: 99%

Abdominal organ position variation in children during image-guided radiotherapy

et al. 2018

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BackgroundInterfractional organ position variation might differ for abdominal organs and this could have consequences for defining safety margins. Therefore, the purpose of this study is to quantify interfractional position variations of abdominal organs in children in order to investigate possible correlations between abdominal organs and determine whether position variation is location-dependent.MethodsFor 20 children (2.2–17.8 years), we retrospectively analyzed 113 CBCTs acquired during the treatment course, which were registered to the reference CT to assess interfractional position variation of the liver, spleen, kidneys, and both diaphragm domes. Organ position variation was assessed in three orthogonal directions and relative to the bony anatomy. Diaphragm dome position variation was assessed in the cranial-caudal (CC) direction only. We investigated possible correlations between position variations of the organs (Spearman’s correlation test, ρ), and tested if organ position variations in the CC direction are related to the diaphragm dome position variations (linear regression analysis, R2) (both tests: significance level p < 0.05). Differences of variations of systematic (∑) and random errors (σ) between organs were tested (Bonferroni significance level p < 0.004).ResultsIn all directions, correlations between liver and spleen position variations, and between right and left kidney position variations were weak (ρ ≤ 0.43). In the CC direction, the position variations of the right and left diaphragm domes were significantly, and stronger, correlated with position variations of the liver (R2 = 0.55) and spleen (R2 = 0.63), respectively, compared to the right (R2 = 0.00) and left kidney (R2 = 0.25). Differences in ∑ and σ between all organs were small and insignificant.ConclusionsNo (strong) correlations between interfractional position variations of abdominal organs in children were observed. From present results, we concluded that diaphragm dome position variations could be more representative for superiorly located abdominal (liver, spleen) organ position variations than for inferiorly located (kidneys) organ position variations. Differences of systematic and random errors between abdominal organs were small, suggesting that for margin definitions, there was insufficient evidence of a dependence of organ position variation on anatomical location.Electronic supplementary materialThe online version of this article (10.1186/s13014-018-1108-9) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Abdominal organ position variation in children during image-guided radiotherapy

et al. 2018

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“…This group of methods aims to predict the entire DVH curve for a new patient and then frequently uses the predicted DVHs to guide the plan optimization process …”

Section: Resultsmentioning

confidence: 99%

“…The generation of atlases and matching of a query case to the best atlas were both based on two specially designed features, the PTV and SV concaveness angle and the percent distance (from SV) to the PTV Treatment parameters of the atlas case were transferred Deshpande et al 24 Direct Weighted sum of three difference values, the prescription dose differences, the OVH differences, and the difference of STS, which is a four-dimensional histogram encoding the radial distance, azimuth, and elevation of PTV in relation to the center of an OAR. The difference of histograms is calculated by the earth mover's distance…”

Section: Directmentioning

confidence: 99%

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

2019

Medical Physics

180

150

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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...

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“…A knowledge-driven decision support system was proposed by [10], which quantifies the spatial relationships between the tumor and surrounding critical structures through features that account for distance, volume, overlap, location, shape and orientation. These features are used to identify database cases that are anatomically similar to a new patient, and help clinicians to estimate an acceptable dose distribution.…”

Section: Modeling or Knowledge Based Approachesmentioning

confidence: 99%

Exploration of Overlap Volumes for Radiotherapy Plan Evaluation with the Aim of Healthy Tissue Sparing

Schlachter¹,

Peters²,

Camenisch³

et al. 2021

Preprint

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Development of a novel interactive visualization approach for the exploration of radiotherapy treatment plans with a focus on overlap volumes with the aim of healthy tissue sparing. Methods:We propose a visualization approach to include overlap volumes in the radiotherapy treatment plan evaluation process. Quantitative properties can be interactively explored to identify critical regions and used to steer the visualization for a detailed inspection of candidates. We evaluated our approach with a user study covering the individual visualizations and their interactions regarding helpfulness, comprehensibility, intuitiveness, decision-making and speed.Results: A user study with three domain experts was conducted using our software and evaluating five data sets each representing a different type of cancer and location by performing a set of tasks and filling out a questionnaire. The results show that the visualizations and interactions help to identify and evaluate overlap volumes according to their physical and dose properties. Furthermore, the task of finding dose hots spots can also benefit from our approach. Conclusions:The results indicate the potential to enhance the current treatment plan evaluation process in terms of healthy tissue sparing.

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Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer

Abstract: The radiation therapy treatment planning decision support system presented here can assist clinicians in determining good dose constraints and assessing dose distributions by using knowledge gained from retrospective treatment plans.

Cited by 10 publications

References 21 publications

Abdominal organ position variation in children during image-guided radiotherapy

Abdominal organ position variation in children during image-guided radiotherapy

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

Exploration of Overlap Volumes for Radiotherapy Plan Evaluation with the Aim of Healthy Tissue Sparing

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