Clinically Oriented Contour Evaluation Using Dosimetric Indices Generated From Automated Knowledge-Based Planning

Lim, Tze Yee; Gillespie, Erin F.; Murphy, James D.; Moore, Kevin L.

doi:10.1016/j.ijrobp.2018.11.048

Cited by 24 publications

(30 citation statements)

References 25 publications

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“…Therefore, when the anterior direction translation occurs in this area, the minimum dose (D98%) of the target in this area is almost unchanged, thus resulting in a weaker correlation coe cient. This is consistent with the study by Lim et al [10], which found that the correlation between geometric indices and dosimetric indices was affected by the goals of the treatment plan. Feng et al [24] considered that the contour changes of oropharyngeal carcinoma OAR had little effect on the dose, but Nelms et al [1] reported that this had a great effect on the dose.…”

Section: Discussionsupporting

confidence: 93%

“…Many studies have shown that although it is important to quantify the degree of variation or uncertainty of the contouring, it is more important to determine the dose difference and clinical impact [10,11,14,16,17,22,23]. In earlier work, van Rooij et al [17] studied the accuracy of automatic delineation of organs at risk in the head and neck region based on deep learning techniques while using geometric indices and dosimetric indices, and they analyzed the correlation between the geometric index SDC (mean value of the DSC) and dose difference.…”

Section: Discussionmentioning

confidence: 99%

“…Hausdorff distances (HD95)) and volumetric geometric indices (e.g., DSC and Jaccard) [9]. Although these indices are easy to calculate, they do not consider the clinical effect and may lack clinical relevance [10,11]. In addition, there is considerable variability between them, and different auto-segmentation studies use different geometric indices to evaluate the contouring results; further, different indices have different properties [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, there is considerable variability between them, and different auto-segmentation studies use different geometric indices to evaluate the contouring results; further, different indices have different properties [12][13][14]. Under the assumption of a reference contour, the method for clinically assessing the accuracy of radiotherapy (RT) contours is to determine and predict the deviation of its dosimetric indices based on the dose distribution of the radiation treatment plan [10,[15][16][17]. The relationships between geometric indices and dosimetric indices are yet to be further studied.…”

Section: Introductionmentioning

confidence: 99%

“…The relationships between geometric indices and dosimetric indices are yet to be further studied. At present, the research on the evaluation of geometric indices and dose parameters of auto-segmentation results is carried out under the condition of unknown contouring error [10,11,16]. The purpose of our study is to explore the evaluation accuracy of the geometric indices for evaluating the contours under the known errors.…”

Section: Introductionmentioning

confidence: 99%

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Clinically oriented contour evaluation using geometric and dosimetric indices based on simple geometric transformations

Xian

Xiao

et al. 2020

Preprint

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Abstract Background: In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring; however, the ability of geometric indices to identify the error of contouring results is limited, and they do not consider any clinical background. Based on the reference contouring, we systematically introduced the known geometric errors to study the relationship between geometric indices and dosimetric indices and evaluated the clinical feasibility of assessing the accuracy of contouring based on geometric indices alone.Materials and Methods: A C-shaped target, organ at risk (Core), and intensity-modulated radiotherapy (IMRT) plan outlined in the American Association of Physicists in Medicine (AAPM) TG-119 report (The report of Task Group 119 of the AAPM) were used as references. Translation, scaling, rotation (except for the Core), and sine function transformation were performed to simulate the test contours. The corresponding dosimetric indices were obtained from the original dose distribution of the radiotherapy plan, and correlations (R²) between geometric and dosimetric indices were quantified through linear regression. The Wilcoxon signed rank test was used to compare the differences between the geometric indices of three different directions of translation transformations.Results: The correlations between the geometric and dosimetric indices were inconsistent for the contouring of the target and Core after the geometric transformation. Except for the sine function transformation (R²: 0.04–0.023, P > 0.05), the other geometric transformations of the planning target volume (PTV) had correlations with the dosimetric indices D98% and Dmean (R²: 0.689–0.988), 80% of which were strongly correlated. The correlation results for the other geometric transformations in the Core were similar to those in the PTV except for the posterior direction transformation. The results of Wilcoxon signed rank test showed that only the P-values of volumetric geometric indices of PTV were less than 0.05.Conclusions: The dosimetric indices are heavily influenced by the contour differences, thus highlighting their importance in the evaluation process. Clinically, an assessment of the contour accuracy of the region of interest is not feasible based on geometric indices alone, and should be combined with dosimetric indices.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Clinically oriented contour evaluation using geometric and dosimetric indices based on simple geometric transformations

Xian

Xiao

et al. 2020

Preprint

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Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

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Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modalityboth computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OARthe spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image databaseseveral image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodologycurrent methods have shifted from atlas-based to deep learning autosegmentation, which is expected to become even more sophisticated; ground truthdelineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metricsthe Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performancethe best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

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Artificial Intelligence-Guided Prediction of Dental Doses Before Planning of Radiation Therapy for Oropharyngeal Cancer: Technical Development and Initial Feasibility of Implementation

Chan

Hohenstein

Carpenter³

et al. 2022

Advances in Radiation Oncology

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Clinically Oriented Contour Evaluation Using Dosimetric Indices Generated From Automated Knowledge-Based Planning

Cited by 24 publications

References 25 publications

Clinically oriented contour evaluation using geometric and dosimetric indices based on simple geometric transformations

Clinically oriented contour evaluation using geometric and dosimetric indices based on simple geometric transformations

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Artificial Intelligence-Guided Prediction of Dental Doses Before Planning of Radiation Therapy for Oropharyngeal Cancer: Technical Development and Initial Feasibility of Implementation

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