Set Of Contours Research Articles

Cranial organs at risk delineation: heterogenous practices in radiotherapy planning

BackgroundSegmentation is a crucial step in treatment planning that directly impacts dose distribution and optimization. The aim of this study was to evaluate the inter-individual variability of common cranial organs at risk (OAR) delineation in neurooncology practice.MethodsAnonymized simulation contrast-enhanced CT and MR scans of one patient with a solitary brain metastasis was used for delineation and analysis. Expert professionals from 16 radiotherapy centers involved in brain structures delineation were asked to segment 9 OAR on their own treatment planning system. As reference, two experts in neurooncology, produced a unique consensual contour set according to guidelines. Overlap ratio, Kappa index (KI), volumetric ratio, Commonly Contoured Volume, Supplementary Contoured Volume were evaluated using Artiview™ v 2.8.2—according to occupation, seniority and level of expertise of all participants.ResultsFor the most frequently delineated and largest OAR, the mean KI are often good (0.8 for the parotid and the brainstem); however, for the smaller OAR, KI degrade (0.3 for the optic chiasm, 0.5% for the cochlea), with a significant discrimination (p < 0.01). The radiation oncologists, members of Association des Neuro-Oncologue d’Expression Française society performed better in all indicators compared to non-members (p < 0.01). Our exercise was effective in separating the different participating centers with 3 of the reported indicators (p < 0.01).ConclusionOur study illustrates the heterogeneity in normal structures contouring between professionals. We emphasize the need for cerebral OAR delineation harmonization—that is a major determinant of therapeutic ratio and clinical trials evaluation.

Non-parametric well-being comparisons

We study the problem of making interpersonal well-being comparisons when individuals have heterogeneous preferences. We present a robust, but incomplete, criterion for interpersonal well-being comparisons that states that one individual is better off than another if the intersection between the extended upper contour set of the better-off individual and the extended lower contour set of the worse-off individual is empty. We implement the criterion in the consumption-health space using an online survey of 2260 respondents in the United States to investigate how complete the resulting interpersonal well-being comparison is. To chart the contour sets of the respondents, we propose a new “adaptive bisectional dichotomous choice” (ABDC) procedure that is based on a limited number of dichotomous choices and some mild non-parametric assumptions about the preferences. We find that one fifth of all pairs of respondents can be ranked with the criterion.

Simultaneous Truth and Performance Level Estimation Method for Evaluation of Target Contouring in Radiosurgery.

The problem of lack of standardisation in target delineation and herewith the variability of target contours in Gamma Knife radiosurgery is as severe as in linac-based radiotherapy in general. The first aim of this study was to quantify the contouring variability for a group of five radiosurgery targets and estimate their true-volume based on multiple delineations using the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The second aim was to assess the robustness of the STAPLE method for the assessment of the true-volume, with respect to the number of contours available as input. A multicentre analysis of the variability in contouring of five cases was performed. Twelve contours were provided for each case by experienced planners for Gamma Knife. To assess the robustness of the STAPLE method with respect to the number of contours used as input, sets of contours were randomly selected in the analysis. A high similarity was observed between the STAPLE generated true-volume and the 50%-agreement volume when all 12 available contours were used as input (90-100%). Lower similarity was observed with smaller sets of contours (10-70%). If a high number of input contours is available, the STAPLE method provides a valuable tool in the estimation of the true volume of a target based on multiple contours as well as the sensitivity and specificity for each input contour relative to the true volume of that structure. The robustness of the STAPLE method for rendering the true target volume depends on the number of contours provided as input and their variability with respect to shape, size and position.

Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur

This paper proposes a basic taxonomy of image contours. Our goal is to classify smooth curves into five categories, namely, circles, ellipses, line segments, arcs of circles and arcs of ellipses. These geometrical structures have been chosen as they serve as input of many computer vision tasks. The proposed strategy is applied on a set of initial disjoint contours, which are grouped together to form the aforementioned structures. These, in turn, are validated using an a contrario approach that guarantees a reduced number of false detections. The use of a multiscale strategy permits the detection at different resolution levels, which makes the method robust to noise and blur.

Представление распределенных систем в виде совокупности независимых контуров

Представление распределенных систем в виде совокупности независимых контуров

On novel nondifferentiable exact solutions to local fractional Gardner's equation using an effective technique

One of the most interesting branches of fractional calculus is the local fractional calculus, which has been used successfully to describe many fractal problems in science and engineering. The main purpose of this contribution is to construct a novel efficient technique to retrieve exact fractional solutions to local fractional Gardner's equation defined on Cantor sets by an effective numerical methodology. In the framework of this technique, first a set of elementary functions are defined on the contour set. Taking these functions into account, the general structure of the exact solution for the equation is suggested as a specific combination of the defined basis functions. By determining the unknown coefficients in this expansion and by placing them in the original equation, specific forms of solutions to the fractional equation are determined. Several interesting numerical simulations of the achieved results are also presented in the article to give a better understanding of the dynamic behavior of these results. The results obtained in this research confirm that the method used is very simple and efficient in terms of application. Moreover, it is accurate and free of any errors in terms of calculation. Therefore, it can be employed to handle other partial differential equations including local fractional derivatives.

The effect of pulmonary arterial hypertension specific treatment on the left ventricle in patients with pulmonary arterial hypertension

Abstract Background Pulmonary arterial hypertension (PAH) is characterized by right ventricular (RV) pressure overload, leading to RV dilation, failure and death. In the course of the disease, the left ventricle (LV) is often impaired, due to interventricular interaction. Although the impact of PAH treatment on the RV has been well described, less is known on the LV. Purpose To examine effects of advanced PAH treatments on the volumes, function and strain of the left atrium and ventricle. Methods This is a retrospective study. All patients underwent CMR and right heart catheterization, both at diagnosis and at 12-months follow up. Ventricular volumes and LV filling rate were calculated from the stack of short axis cine images using Simpsons method while left atrial (LA) volumes from the 4-chamber cine images using the area-length method. Tissue tracking was used for the evaluation of myocardial deformation. The LV endocardial and epicardial borders were manually delineated in all analysed sections with the initial contour set at end-diastole. All analyses were performed offline using dedicated software. Results In total, 66 patients (mean age 56.3±17.9 years, 67%women, 77%idiopathic and heritable PAH, 23% connective tissue disease associated PAH) and 29 normal controls were included. The improvement in metrics of right and left heart size and function after the initiation of advanced PAH treatment, are presented in panel A. Of note, LV stroke volume was markedly increased (54.6±19.6ml at baseline vs 70.8±21.7ml at follow up, p&amp;lt;0.0001) to reach controls. LV filling was markedly increased in latter two-thirds of the diastolic phase (panel B), especially at atrial kick point (arrow). Change in LA max volume was associated with changes in diastolic filling (r=0.354, p=0.004), LV end-diastolic and end-systolic volumes and stroke volume. These correlations were more robust in patients that increased LV filling compared to those that failed to increase LV filling (panels C-E). No association between changes in LV circumferential strain and LV volume load was observed. A weak correlation of change of LV peak longitudinal strain with stroke volume (r=−0.345, p=0.006), LV end diastolic volume (r=−0.284, p=0.027), LV ejection fraction (r=−0.337, p=0.008) and LA maximum area (r=−0.447, p&amp;lt;0.0001) was observed. The changes of LV strain showed no correlation with the changes in patients' haemodynamics. Conclusion Improvement in stroke volume after the initiation of advanced PAH treatment is associated with an increase in LA size, LV end diastolic volume and normalisation of strain. This reflects the improved filling state of the left ventricle and the potential of the left atrium to monitor treatment effects. Changes after PAH treatment Funding Acknowledgement Type of funding source: None

An Independent and Automatic Quality Assurance Tool for Auto-Segmentation

Deep-learning based auto-segmentation has been shown to be efficient and accurate or consistent for radiotherapy planning. However, in current practice, manual review of auto-segmented contours before their clinical use is necessary and can be labor intensive and time consuming. This study aims to develop an independent and automatic contour quality assurance (QA) tool that can quickly identify inaccurate or inconsistent contour slices from auto-segmented contours, eliminating the need of manual QA process. The automatic contour QA tool starts from building a model, which includes: (1) pre-processing images, (2) extracting quantitative geometric and multi-scale texture features from the test contours and the feature changes between 4-mm inner/outer shells and a core region of the test contours on a slice-by-slice basis, (3) selecting a subset of organ-specific discriminative features that maximizes the model’s predictive performance using a recursive feature elimination method, and (4) determining the best performing model among 12 different machine learning-based supervised classification models. The process was tested using an MRI head and neck dataset. Contours of the parotid glands and submandibular glands on T2-weighted non-contrast MRIs from 31 patients were utilized, where 21 were used for training and 10 for testing. During model training, manually-delineated contours for each training case were utilized as accurate samples. The inaccurate samples were collected from a set of deformable image registration (DIR) propagated contours using a labeling criterion of mean distance to agreement ≥ 2 mm or Hausdorff distance ≥ 8mm as compared to ground truth contours. For the 10 MRIs in the test set, two sets of contours using a deep-learning based auto-segmentation software and DIR propagation were used to evaluate the model performance. All the contour slices were labeled using the same labeling criterion and manually checked to avoid mislabeling. The classification accuracy was used to measure a model’s performance. For each contour slice, a total of 437 features were extracted. At the training stage, 58, 41, 72, and 56 features were automatically selected for left and right parotid gland and submandibular glands contours, respectively. The best model had an accuracy of 89% for the four organs. The time required to evaluate one patient dataset is less than 30 seconds using a workstation equipped with a i7-6700 CPU. The developed automatic contour QA tool can quickly identify accurate and inaccurate contour slices with high accuracy from auto-segmented contours in head and neck. This tool is being integrated into a deep learning-based auto-segmentation package, to eventually develop a fully automatic and robust auto-segmentation pipeline for radiotherapy planning and delivery.

Evaluation Of A Deep Learning-Based Auto-Segmentation Method For Quality Assurance Of Both Male And Female Pelvic Organ-At-Risk Contours In NCTN Clinical Trials

We previously reported a cascaded atrous convolution and spatial pyramid pooling module-based auto-segmentation method for organ-at-risk (OAR) segmentation and its quality assurance (QA) for radiotherapy clinical trial cases submitted from various disease sites. In this study, we enhanced the technique and evaluated the enhanced method for QA of OAR contouring using data submitted from both a genitourinary (GU) trial (NRG-RTOG 0938) and a gynecology (GYN) trial (NRG GY006). Three convolutional neural network (CNN)-based models were trained for rectum, bladder, left femur, and right femur: One GU model used data of 192 patients from RTOG 0938, one GYN model used data of 111 patients from GY006 and the third model used mixed data from both trials. Another 20 patient data sets from each trial, a total of 40 patient data sets, were used for model validation. Using these models, two sets of OAR contours were automatically generated on each test patient: One set used the GU and GYN models on 20 male and 20 female patients, respectively and another set used the mixed model for all 40 test patients. The performance of the models was evaluated using dice indices calculated using MiM software. For GU patients, contours generated using GU model vs. mixed model, the Dice Similarity Coefficient values were 0.94±0.03 vs. 0.94±0.03 for the bladder, 0.80±0.05 vs. 0.81±0.06 for the rectum, 0.87±0.04 vs. 0.86±0.05 for the left femur, and 0.88±0.03 vs. 0.87±0.05 for the right femur, respectively. For GYN patients’ contours generated using GYN model vs. mixed model, corresponding values were 0.80±0.15 vs. 0.85±0.18 for the bladder, 0.70±0.11 vs. 0.65±0.11 for the rectum, 0.45±0.21 vs. 0.45±0.18 for the left femur, and 0.46±0.20 vs. 0.44±0.19 for the right femur, respectively. The GYN data had fewer cases and relatively low image quality due to image artifacts caused by the inserted applicator. There was no guideline provided for GY006 femur contours in the protocol, which resulted in large variations in submitted contours. These factors have affected the auto-segmentation accuracy of the GYN models and test results using the GYN data. The CNN model-based method generates OAR segmentation with reasonable accuracy and has the potential to be used for contour QA for future patients enrolled on both GU and GYN trials. The models built using mixed male and female data also demonstrate acceptable accuracy for pelvic OARs for both male and female patients.

Toward a Machine Learning Based Framework for Improved Automatic Segmentation of Head and Neck: A Single Institution Implementation

Manual delineation of organs at risk (OAR) is a time-consuming step in the radiation therapy treatment planning workflow. Automatic contouring is used in some clinics to streamline this process. Deformable atlas algorithms are presently the most prevalent method of automatic contouring, but the results often require significant revision. We aimed to show that, for head and neck cancer patients, deep learning methods can train a convolutional neural network to generate more accurate contours when compared to manually drawn expert contours than atlas methods, reducing the manual correction required. A total of 143 cases of head and neck CT scans and manually drawn expert contours from a single institution’s database were utilized in this study: 128 cases to train a CNN based on a modified U-Net architecture, and 15 cases for validation. Data augmentation and dropout layers were used to prevent overfitting. The model trained over 200 epochs for the following OARs: brainstem, cochlea, and the submandibular and parotid glands. We then used the trained model to generate contour data for these OARs on the 15 test cases. All generated contours were written to DICOM format. A commercial atlas-based program, which has been used clinically at the same institution, was also used to generate contour sets for the test cases. Both the atlas-based contours and deep learning-based contours (DLBC) were measured against the manually drawn expert contours as the “ground truth”. The accuracy performance was validated using three metrics: Dice Similarity Coefficient (DSC), which measures area overlap, Mean Surface Distance (MSD), and 95% Hausdorff Distance (HD), defined as the separation distance from ground truth that encompasses 95% of contour points in the automatic contour. For both the parotid and submandibular glands, the DLBC were more closely aligned with the ground truth contours than the atlas contours for all evaluation metrics (p-value < 0.005). For the parotid gland, the mean DLBC improvement over atlas for DSC, MSD, and HD was 17% ± 1.9%, 44% ± 13%, and 37% ± 15%, respectively. For the submandibular, the mean improvement was 58% ± 15%, 54% ± 19%, and 45% ± 15%, respectively. For the brainstem, the DLBC scored comparably to the atlas contours on all three metrics with no significant improvement. For the cochlea, the DSC scores for both methods were comparable, while the MSD and HD scores of the DLBC were superior to the atlas contours (p-value < 0.015), with mean improvement of 38% ± 13% and 33% ± 10% respectively. The measurably more accurate contours generated for the parotid gland, submandibular gland, and cochlea by the neural network would require less correction by a reviewer than those generated by the atlas method used in the clinic. The contours generated by the neural network for the brainstem were comparable. Expanding the neural network to define every OAR could offer a superior method of automatic contour generation for use in the clinic.

A New Geometric Metric in the Shape and Size Space of Curves in R n

Shape analysis of curves in Rn is an active research topic in computer vision. While shape itself is important in many applications, there is also a need to study shape in conjunction with other features, such as scale and orientation. The combination of these features, shape, orientation and scale (size), gives different geometrical spaces. In this work, we define a new metric in the shape and size space, S2, which allows us to decompose S2 into a product space consisting of two components: S4×R, where S4 is the shape space. This new metric will be associated with a distance function, which will clearly distinguish the contribution that the difference in shape and the difference in size of the elements considered makes to the distance in S2, unlike the previous proposals. The performance of this metric is checked on a simulated data set, where our proposal performs better than other alternatives and shows its advantages, such as its invariance to changes of scale. Finally, we propose a procedure to detect outlier contours in S2 considering the square-root velocity function (SRVF) representation. For the first time, this problem has been addressed with nearest-neighbor techniques. Our proposal is applied to a novel data set of foot contours. Foot outliers can help shoe designers improve their designs.

NRG Oncology/RTOG Consensus Guidelines for Delineation of Clinical Target Volume for Intensity Modulated Pelvic Radiation Therapy in Postoperative Treatment of Endometrial and Cervical Cancer: An Update

Accurate target definition is critical for the appropriate application of radiation therapy. In 2008, the Radiation Therapy Oncology Group (RTOG) published an international collaborative atlas to define the clinical target volume (CTV) for intensity modulated pelvic radiation therapy in the postoperative treatment of endometrial and cervical cancer. The current project is an updated consensus of CTV definitions, with removal of all references to bony landmarks and inclusion of the para-aortic and inferior obturator nodal regions. An international consensus guideline working group discussed modifications of the current atlas and areas of controversy. A document was prepared to assist in contouring definitions. A sample case abdominopelvic computed tomographic image was made available, on which experts contoured targets. Targets were analyzed for consistency of delineation using an expectation-maximization algorithm for simultaneous truth and performance level estimation with kappa statistics as a measure of agreement between observers. Sixteen participants provided 13 sets of contours. Participants were asked to provide separate contours of the following areas: vaginal cuff, obturator, internal iliac, external iliac, presacral, common iliac, and para-aortic regions. There was substantial agreement for the common iliac region (sensitivity 0.71, specificity 0.981, kappa 0.64), moderate agreement in the external iliac, para-aortic, internal iliac and vaginal cuff regions (sensitivity 0.66, 0.74, 0.62, 0.59; specificity 0.989, 0.966, 0.986, 0.976; kappa 0.60, 0.58, 0.52, 0.47, respectively), and fair agreement in the presacral and obturator regions (sensitivity 0.55, 0.35; specificity 0.986, 0.988; kappa 0.36, 0.21, respectively). A 95% agreement contour was smoothed and a final contour atlas was produced according to consensus. Agreement among the participants was most consistent in the common iliac region and least in the presacral and obturator nodal regions. The consensus volumes formed the basis of the updated NRG/RTOG Oncology postoperative atlas. Continued patterns of recurrence research are encouraged to refine these volumes.

The Perception of Macro-rhythm in Jewish English Intonation

This article investigates intonation’s place in what Sarah Bunin Benor calls the American Jewish English repertoire, a collection of features that speakers can use to index Jewish identity. Results from a perceptual experiment show variation in which intonational contours listeners associate with Jewishness. Jewish listeners, particularly those with connections with Yiddish speakers, pick out a phonetically distinct rise-fall as indicating Jewishness; however, non-Jewish listeners hear a different set of contours—a less phonetically distinct rise-fall and a rise—as sounding Jewish. The author proposes that there is a unifying feature being perceived as “Jewish”: specifically, more macro-rhythmic contours (with regular alternations of high and low pitch) are heard as more Jewish. For Jewish speakers, only the contour with the greatest degree of macro-rhythm (the rise-fall with higher peaks) is heard as Jewish; for non-Jewish speakers, a lower degree of macro-rhythm suffices. Intonation thus behaves much like other parts of the sound system in that the social meaning of a particular linguistic feature is highly dependent on an individual’s linguistic and social history.

Near Zero Index Perfect Metasurface Absorber using Inverted Conformal Mapping

The geometry of the characteristic element forming the artificial structure of an electromagnetic metamaterial defines the way the metamaterial will interact with electromagnetic waves, and accordingly, how it will transmit, reflect, and absorb electromagnetic energy. Metamaterials have been discovered that can manipulate electromagnetic waves to create perfect absorption of incident electromagnetic energy using relatively simple elemental geometries. But the phenomenon is confined to very narrow frequency bandwidths owing to the mono-resonance characteristics of simple cellular structures. Complex cellular geometries based on the combination of many different fundamental building blocks may be able to constructively couple many more resonances and broaden the perfect absorption bandwidth. We describe here a metasurface based upon geometric inversion of a set of conformal mapping contours. The resulting geometry forms a nearly continuous series of perfect absorption resonances within an ultrathin (λ/165) metasurface to develop broadband absorption in a frequency range of interest for downhole chemical spectroscopy. The metasurface is derived from a geometric inversion of the Rhodonea, or more commonly called four-leaf roses, conformal mapping contours and was found to exhibit a near zero index metamaterial (NZIM) behavior. An uncooled microbolometer design is described that uses the metasurface geometry on a single VO2 thermometric substrate leading to an infrared detector with predicted maximum absorption of 99.94% at 4.3 μm and an absorption bandwidth of 170% FWHM on 15.8 μm center wavelength, coincident with important chemical spectra of downhole hydrocarbons. The infrared detector design has a predicted maximum detectivity D* = 1.5 × 109 cmsqrt{text{Hz}}/W and noise equivalent difference temperature NEDT of 70 mK at a frame rate of 60 Hz. These levels of detector performance conventionally would be achievable only with cryogenically cooled technologies and could represent a significant step in the effort towards deploying an in situ infrared chemical spectroscopy sensor into downhole logging applications.

Delineation of whole heart and substructures in thoracic radiation therapy: National guidelines and contouring atlas by the Danish Multidisciplinary Cancer Groups

Background and purposeThis study presents Danish consensus guidelines for delineation of the heart and cardiac substructures across relevant Danish Multidisciplinary Cancer Groups. Material and methodsConsensus guidelines for the heart and cardiac substructures were reached among 15 observers representing the radiotherapy (RT) committees of four Danish Multidisciplinary Cancer Groups. The guidelines were validated on CT scans of 12 patients, each with five independent contour sets. The Sørensen-Dice similarity coefficient (DSC), the distance between the centers of the arteries and the mean surface distance were used to evaluate the inter-observer variation. ResultsNational guidelines for contouring the heart and cardiac substructures were achieved. The median DSC was 0.78–0.96 for the heart and the four cardiac chambers. For the four substructures of the left ventricle, the median DSC was 0.35–0.57. The coronary arteries were contoured in ten segments, with the best agreement for the left anterior descending coronary artery segments, with a median distance between the arteries ranging from 2.4–4.4 mm. The median variation was 3.7–12.8 mm for the right coronary artery segments and 3.7–6.2 mm for the left circumflex coronary artery segments, with the most pronounced inter-observer variation in the distal segment for all three coronary arteries. ConclusionNational guidelines for contouring the heart and cardiac substructures were developed across relevant Danish Multidisciplinary Cancer Groups, where RT dose to the heart is of concern. The inter-observer contour overlap was best for the heart and chambers and decreased for smaller structures.

Consumer equilibrium, random choice and hemi-Bayesian revision rule

We axiomatically characterise a class of updating rules in the contexts of: (a) consumer’s equilibrium; and (b) random choice by a decision maker. The updating rule captures the effect of absence of a product on the expenditure-share function in (a) and the effect of removal of an alternative on the choice probabilities of the remaining alternatives in (b). The class of updating rules, called hemi-Bayesian random choice rule can be described as follows: the expenditure-share or the probability-weight of the alternative that is removed is distributed proportionately to the alternatives belonging to its lower contour set according to some linear order. We show that this class of rules is the same as random consideration set rule as in Manzini and Mariotti (2014).

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.

Optimization of Non-axisymmetric Endwall Contours for the Rotor of a Low Speed, 112-Stage Research Turbine With Unshrouded Blades—Optimization and Experimental Validation

Abstract This paper presents the predicted, as well as final experimental results for the design of an automatically optimized non-axisymmetric endwall and as such, attempts to close the loop between design and practice, providing additional information to other groups involved in the design of endwall contours. The contours designed in this investigation were manufactured using the direct laser sintering rapid prototyping method and installed and tested in the low-speed, 112-stage turbine at the CSIR’s test turbine facility (TTF) in Pretoria, South Africa. Steady-state 5-hole pressure probe traverses were used to characterize the performance and flow profiles upstream, immediately downstream and in a quasi-“mixed-out” sense downstream of the rotor. In addition to the datum (annular) case, both the computed as well as experimental results were compared to the corresponding results generated for a “generically” contoured rotor which was originally designed for a linear cascade test case, but one which used the same blade profile to the current case. The results show that in general both sets of contours performed well, although the added emphasis on flow correction for the contours produced in this investigation resulted in slightly worse performance in terms of loss at the rotor exit (X3) but greatly improved performance in terms of the efficiency and flow angles at the “mixed-out” (X4) measurement plane.

Evaluation and prediction of pelvic dose in postoperative IMRT for cervical cancer

Objective To evaluate and predict the pelvic dose by analyzing two pelvic contour definitions and identify the influencing factors of the pelvic dose in postoperative IMRT for cervical cancer, aiming to provide reference for postoperative pelvis-sparing IMRT for cervical cancer. Methods Sixty cervical cancer patients receiving postoperative IMRT with unrestricted pelvic dose were selected. Two sets of pelvic contours (pelvic anatomy and pelvic Mell) were delineated as per the anatomical and Mell methods. The dose relationship between two methods was analyzed after redesigning the treatment plan by limiting dose of pelvic anatomy. The correlation analysis was performed by Pearson’s correlation method. The factors affecting the pelvic anatomy dose were identified by Logistic multivariate regression analysis and a dose prediction model was subsequently established. Results The volumes of pelvic anatomy and pelvic Mell were 925.82 cm3 and 1141.20 cm3(P=0.000). There was a significant correlation between them (r>0.622, P=0.000). The dose of pelvic anatomy was significantly higher than that of pelvic Mell. The relationship of V10, V20 and V30 between them was y=-8+ 1.01x, y=-13+ 1.05x and y=-4+ 0.9x, respectively. The dose limits of pelvic Mell recommended by literatures(V10<90%, V20<75%, V30<60%) were translated into V10<97%, V20<83% and V30<70%, respectively. The pelvic anatomy dose was significantly reduced after dose limiting. The V10, V20, V30 and Dmean were significantly decreased by 3.64%, 12.69%, 12.02% and 6.93%(P=0.000, 0.000, 0.000), respectively. Multivariate analysis showed that the overlapping volume of pelvic anatomy within PTV was an independent influencing factor of pelvic anatomy dose (P<0.05). Patients with a relative overlapping volume of less than 18% could easily meet the dose limiting requirement. Conclusions Both two pelvic contour definitions can be applied in postoperative pelvis-sparing IMRT for cervical cancer. Use of pelvic dose limiting can significantly reduce the IMRT dose. The overlapping volume of the pelvis within PTV is an independent influencing factor of pelvic dose. Patients whose overlapping volume within the PTV relative to pelvis is less than 18% can easily meet the dose limiting requirement. Key words: Cervical cancer/intensity-modulated radiotherapy; Pelvis; Dosimetry

Dose planning variations related to delineation variations in MRI-guided brachytherapy for locally advanced cervical cancer

PurposeTo examine the variability in prescribed dose due to contouring variations in intracavitary image-guided adaptive brachytherapy for cervical cancer. To identify correlations between dosimetric outcomes and delineation uncertainty metrics. Methods and MaterialsA data set from an EMBRACE sub-study on contouring uncertainties was used, consisting of magnetic resonance images of six patients with cervical cancer delineated by 10 experienced observers (target volumes and organs at risk). Two gold standard contours were generated, an expert consensus and the simultaneous truth and performance level estimation. Plans were individually optimised to all of the contour sets (12 in total). Plans were applied to the gold standard contour sets, and dose volume histogram parameters including D90, D98 and D2cm3 were determined. The variability between plans was assessed. Dose volume histogram parameters and delineation uncertainty metrics were correlated using the Spearman's non-parametric rank correlation. ResultsThere is a dosimetric variability between observers, patients and the gold standard contour used for analysis. Approximately 3 Gy D90 EQD210 variability (SD) was observed for the CTVHR and 1.2-3.6 Gy D2cm3 EQD23 for the organs at risk. The maximum geometric dimensions of the delineations are most commonly correlated with dosimetry changes. Although the correlations are similar across gold standards, the direction of these correlations differs, indicating that the dosimetric outcomes are dependent on the contour that the plan is optimised to. ConclusionThis study highlights the dosimetric differences interobserver uncertainty in contouring can have for cervical cancer brachytherapy. The importance of carefully choosing a gold standard from which to benchmark is reiterated.