Large Inter-observer Variation Research Articles

Anatomy-Guided Deep Learning Model for Accurate and Robust Gross Tumor Volume Segmentation in Lung Cancer Radiation Therapy

In lung cancer radiation therapy, clinicians must outline the gross tumor volume (GTV) precisely on the planning computed tomography (pCT) for accurate radiation dose delivery. However, due to the limited contrast between tumor and normal tissues in lung parenchyma, accurate delineation of tumor boundaries is difficult leading to large inter-observer variation. In this study, we develop an anatomy-guided lung GTV deep segmentation model using a training cohort of multi-center datasets. The quantitative segmentation performance is evaluated on an independent dataset, where the inter-observer delineation variation is also assessed. We collected and curated four publicly available lung datasets with GTV annotations (Lung-PET-CT-Dx, LIDC-IDRI, NSCLC-Radiogenomics and RIDER-CT) for deep learning model development. A total of 871 CT scans of patients, who were diagnosed with T1-T4 NSCLC, were available for training after data curation. The GTV annotations of primary tumor were examined and edited by two experienced radiation oncologists following the RTOG 1106 protocol. An anatomy-guided deep learning model was proposed, which consisted two deep networks. The first deep network used CT scan as input and segmented 4 anatomic organs (airway, heart, pulmonary artery and pulmonary vein), while the second deep network took both CT scan and these pre-segmented 4 organs as input and segmented the lung GTV. With the help of anatomic priors from 4 pre-segmented organs, the second deep network could more easily locate the GTV. We used nnUNet as the deep segmentation network. For evaluation, we used NSCLC-Radiomics as the testing dataset, which contains 20 CT scans each annotated by 5 radiation oncologists. The auto-segmented GTV were compared against each of the manual GTV reference. Inter-observer variation was also assessed using the 5 manual GTV references. The proposed anatomic-guided lung GTV segmentation model achieved a mean Dice score of 82.4% and 95% Hausdorff distance (HD95) of 6.9mm when averaged cross 20 patients and 5 GTV references (Table 1), which outperformed the basic deep GTV segmentation model by markedly reducing 19.4% HD95 error. The performance of proposed model was also comparable to the inter-observer variation (Dice score: 82.4% vs. 81.9%, HD95 6.9 vs. 6.4mm), indicating that our model had similar reproducibility as human observers. We developed and tested an anatomy-guided deep learning model for segmenting GTV in NSCLC patients. The model achieves high quantitative segmentation performance, which is comparable to the human observer variation. It can be potentially used in radiotherapy practice to improve GTV delineation consistency and reduce workloads of radiation oncologists.

Factors influencing aqueous flare after cataract surgery and its evaluation with laser flare photometry.

Despite the refinement of modern cataract surgery, postoperative inflammation still constitutes a substantial amount of visual morbidity worldwide. A surrogate for intraocular inflammation and blood-aqueous barrier breakdown can be objectively quantified by Laser flare photometry (LFP). This review outlines the utility of LFP in assessing the assessment of post-cataract surgery inflammation. It highlights the impact of preoperative pathological states such as uveitis and diabetes, intraoperative techniques, including efficient phacoemulsification and direct comparisons between postoperative anti-inflammatory regimes. There is a large interobserver variation in the subjective flare measurement after cataract surgery and the continued use of LFP amongst other objective, noninvasive measurements of intraocular inflammation, particularly in the further development of cataract surgery, is recommended.

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks.

As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic. By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity. However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians. Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN. A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians. All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction. A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS. Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation. The model with n}{}128times256n two fully connected layers gave the best accuracy of 87%. It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics.

Challenges in the target volume definition of lung cancer radiotherapy.

Radiotherapy, with or without systemic treatment has an important role in the management of lung cancer. In order to deliver the treatment accurately, the clinician must precisely outline the gross tumour volume (GTV), mostly on computed tomography (CT) images. However, due to the limited contrast between tumour and non-malignant changes in the lung tissue, it can be difficult to distinguish the tumour boundaries on CT images leading to large interobserver variation and differences in interpretation. Therefore the definition of the GTV has often been described as the weakest link in radiotherapy with its inaccuracy potentially leading to missing the tumour or unnecessarily irradiating normal tissue. In this article, we review the various techniques that can be used to reduce delineation uncertainties in lung cancer.

Abstract P4-12-23: Institut Curie guidelines on breast target volume delineation for patients treated in lateral position

Abstract Background and Purpose: Delineation of clinical target volumes (CTVs) is a weak link in radiation therapy (RT), and large inter-observer variation is seen in breast cancer patients. Several guidelines have been proposed, but most result in larger CTVs than based on conventional simulator-based RT. There is no consensual guideline for the delineation of the breast CTV in patients treated with alternative techniques as prone or lateral. The purpose of this study is to develop and describe the first delineation consensus-based guidelines defined by the radiation oncologists of the Institut Curie for the specific institutional technique of breast irradiation in lateral position. Materials and Methods: During the daily delineation in the Department of Radiation Oncology of the Institut Curie, the radiation oncologists specialized on breast cancer treatment sought consensus on delineation of CTV breast through dialogue based on cases. In all patients the breast was palpated and designed, initially in standing, then adjusted in lying position. One radiation oncologist delineated CTV on computed tomography (CT) scans of 5 to 20 patients. Totally 60 patients’ breasts were delineated after CT scan in treatment position. All patients were delineated in treatment position, skin markers were used to visualize the breast tissue. This delineation was followed by a discussion and an adaptation of the delineation between all doctors of the team. The consensus established between clinicians was discussed, corrected and improved. Results: The breast CTV is situated and delineated between pectoral muscle and 5 mm below the skin (dosimetric considerations), within the space outlined by skin markers, that showed the limits of the palpable breast tissue. In lateral position, two vessels were very useful: rami mammarii (from thoracica interna) and thoracica lateralis, both define the internal and external limits respectively. This is the first atlas proposed for delineation of the breast CTV for breast cancer treatment using alternative technique of breast irradiation (lateral). Future development is needed to develop the practical procedure of implementation and optimization of delineation using automatic software. Conclusion: The proposed guidelines are based on a consensus endorsed by a global radiation oncology group of breast cancer experts practicing this technique routinely. This atlas will be helpful for the volume definition in our daily practice of breast irradiation in lateral position and can open opportunities to develop atlases for alternative techniques as prone. Citation Format: Rebecca Msika, Magalie Robilliard, Alain Fourquet, Baptiste Porte, Youlia Kirova. Institut Curie guidelines on breast target volume delineation for patients treated in lateral position [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-12-23.

Impact of the starting point chromaticity on memory color matching accuracy.

In this study, the impact of starting point chromaticity and number of observers on memory color matching results was investigated. Matching data were obtained for 3 objects (neutral grey cube, yellow lemon and green apple) under a neutral white and a yellow background illumination. Memory color matchings were made for ten starting points of which eight chromaticities were symmetrically distributed along the hue circle and centered at the equal energy white (EEW) chromaticity of the neutral white background illumination; one starting point at the EEW chromaticity and one with the same chromaticity as the background. The matching track from starting point to the memory matched chromaticity was also recorded. It did not tend to cross over the central region towards the complementary hue, especially for experienced observers. The results also demonstrated a significant starting point bias, whereby the matched chromaticities were biased towards the chromaticity of the starting point. Starting point bias can be minimized by selecting three starting points symmetrically distributed around the expected memory color chromaticity. Furthermore, at least, ten observers are needed to achieve stable results for the grey cube and yellow lemon. For the green apple, the results are less conclusive and around 40 observers would be needed to obtain a stable average estimate for the chromaticity of the memory color. The large inter-observer variation may result from cultural differences or from natural variations in the "green" apple appearance. This study provides a well-founded guidance for future application of the memory color matching method.

Imaging as Part of a Quality Assurance Program: Predictors of Interobserver Variability for Pretreatment Image Registration for Lung SBRT

Purpose:To evaluate the magnitude of interobserver variability in pretreatment image registration for lung stereotactic body radiation therapy patients in aggregate and within 3 clinical subgroups and to determine methods to identify patients expected to demonstrate larger variability.Methods and Materials:Retrospective image registration was performed for the first and last treatment fraction for 10 lung stereotactic body radiation therapy patients by 16 individual observers (5 physicians, 6 physicists, and 5 therapists). Registration translation values were compared within and between subgroups overall and between the first and the last fractions. Four metrics were evaluated as possible predictors for large interobserver variability.Results:The mean 3-dimensional displacement vector for all patients over all comparisons was 2.4 ± 1.8 mm. Three patients had mean 3-dimensional vector differences >3 mm. This cohort of patients showed a significant interfraction difference in variance (P value = .01), increasing from first fraction to last. A significant difference in interobserver variability was observed between physicians and physicists (P value < .01) and therapists and physicists (P value < .01) but not between physicians and therapists (P value = .07). Three of the 4 quantities evaluated as potential predictive metrics showed statistical correlation with increased interobserver variation, including target excursion and local target/lung contrast.Conclusion:Variability in pretreatment image guidance represents an important treatment consideration, particularly for stereotactic body radiation therapy, which employs small margins and a small number of treatment fractions. As a result of the data presented here, we have initiated weekly “registration rounds” to familiarize all staff physicians with the target and normal anatomy for each stereotactic body radiation therapy patient and minimize interobserver variations in image registration prior to treatment. The metrics shown here are capable of identifying patients for which large interobserver variations would be anticipated. These metrics may be used in the future to develop thresholds for additional interventions to mitigate registration variations.

The impact of training and professional collaboration on the interobserver variation of lung cancer delineations: a multi-institutional study

Background: To assess the impact of training and interprofessional collaboration on the interobserver variation in the delineation of the lung gross tumor volume (GTVp) and lymph node (GTVln).Material and methods: Eight target volume delineations courses were organized between 2008 and 2013. Specialists and trainees in radiation oncology were asked to delineate the GTVp and GTVln on four representative CT images of a patient diagnosed with lung cancer individually prior each course (baseline), together as group (interprofessional collaboration) and post-training. The mean delineated volume and local standard deviation (local SD) between the contours for each course group were calculated and compared with the expert delineations.Results: A total 410 delineations were evaluated. The average local SD was lowest for the interprofessional collaboration (GTVp = 0.194 cm, GTVln = 0.371 cm) followed by the post-training (GTVp = 0.244 cm, GTVln = 0.607 cm) and baseline delineations (GTVp = 0.274 cm, GTVln: 0.718 cm). The mean delineated volume was smallest for the interprofessional (GTVp = 4.93 cm3, GTVln = 4.34 cm3) followed by the post-training (GTVp = 5.68 cm3, GTVln = 5.47 cm3) and baseline delineations (GTVp = 6.65 cm3, GTVln = 6.93 cm3). All delineations were larger than the expert for both GTVp and GTVln (p < .001).Conclusion: Our findings indicate that image interpretational differences can lead to large interobserver variation particularly when delineating the GTVln. Interprofessional collaboration was found to have the greatest impact on reducing interobserver variation in the delineation of the GTVln. This highlights the need to develop a clinical workflow so as to ensure that difficult cases are reviewed routinely by a second radiation oncologist or radiologist so as to minimize the risk of geographical tumor miss and unnecessary irradiation to normal tissue.

Predicting Increased Inter-Observer Variation in Pre-Treatment Image Guidance for Lung SBRT Patients

To use CT simulation data to identify lung SBRT patients that may be more difficult to register during pre-treatment image guidance. Retrospective image registration was performed for the first and last treatment fraction for 10 lung SBRT patients by 16 individual observers. In the parameter descriptions below, GTV0 is defined as the GTV as contoured using the 0% inhalation phase of a 4DCT reconstruction while surrounding normal lung is defined as the lung volume within the PTV less GTV0. Four metrics were evaluated as possible predictors of higher inter-observer variability, and were named as follows: (1) “target excursion” defined as ITV volume-GTV0 volume, (2) “local target contrast” defined as the area of overlap of the normalized HU distributions for target and surrounding normal lung, (3) “mean HU difference’ defined as local lung mean HU – GTV0 mean HU, and (4) “target density variability” defined as GTV HU standard deviation. Regression analysis was performed to assess significance of the correlation. Three of the 4 quantities evaluated as potential predictive metrics showed statistical correlation with increased inter-observer variation. For “target excursion”, a correlation between ITV-GTV0 volume and mean inter-observer 3D vector difference was observed for both first fraction (r2 = 0.51) and last fraction (r2 = 0.39). For “local target contrast”, one significant outlier was identified having mean local lung HU beyond two standard deviations from the mean of all patients. Upon removal of this outlier, “mean HU difference” (r2 = 0.56 – first fraction, and r2 = 0.56 – last fraction) and “local target contrast” (r2 = 0.48 – first fraction, and r2 = 0.40 – last fraction) were correlated with mean 3D vector difference. Three of 10 patients had inter-observer variability that was considered clinically significant, defined here as having a 95% confidence width greater than 5 mm. All of these patients had higher “target excursion” and/or “local target contrast” scores compared to the remaining patients. Three metrics were shown here to potentially identify patients for which large inter-observer variations would be anticipated and may be used in the future to develop thresholds for additional interventions to mitigate these registration variations.

Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression.

Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models leave room for interpretation leading to a large inter-observer and intra-observer variation. In this work, we explore a novel method for automated bone age assessment to assist physicians with their estimation. It consists of a powerful combination of deep learning and Gaussian process regression. Using this combination, sensitivity of the deep learning model to rotations and flips of the input images can be exploited to increase overall predictive performance compared to only using the deep learning network. We validate our approach retrospectively on a set of 12611 radiographs of patients between 0 and 19 years of age.

Judgment of White Appearance for Surface Colors under Individual and Simultaneous Observations

ABSTRACT The weakness of the CIE whiteness formula, the most widely used formula to characterize the whiteness of surface colors, has been well documented. It cannot characterize the whiteness under the illuminants other than CIE standard D65 and has a small whiteness boundary. A psychophysical experiment was carried out to investigate the whiteness boundary toward the blue-tint direction under 3000 and 6500 K illuminants with high violet radiations to excite the fluorescent whitening agents (FWAs) and to cover a wide range of whiteness appearance. Sixty samples (that is, 30 paper and 30 fabric samples) were individually and simultaneously evaluated for whether the samples appeared white or not by the same group of human observers. The judgments made by the observers were significantly different between the two evaluations. When the samples were simultaneously evaluated, the samples needed to have higher whiteness values to be judged as white and there were large interobserver variations, which was speculated to be due to the variation in chromatic adaptation among the observers. Such a variation was likely caused by the wide range of whiteness appearance included in this experiment and each observer was adapted to the color of the whitest sample selected by him or her during the simultaneous evaluations.

LF-SegNet: A Fully Convolutional Encoder–Decoder Network for Segmenting Lung Fields from Chest Radiographs

Segmentation of lung fields is an important pre-requisite step in chest radiographic computer-aided diagnosis systems as it precisely defines the region-of-interest on which different operations are applied. However, it is immensely challenging due to extreme variations in shape and size of lungs. Manual segmentation is also prone to large inter-observer and intra-observer variations. Thus, an automated method for lung field segmentation with sufficiently high accuracy is unsparingly required. This paper presents a deep learning-based fully convolutional encoder-decoder network for segmenting lung fields from chest radiographs. The major contribution of this work is in the unique design of the encoder-decoder network that makes it especially suitable for lung field segmentation. The proposed network is trained, tested and evaluated on publicly available standard datasets. The result of evaluation indicates that the performance of the proposed method, i.e. accuracy of 98.73% and overlap of 95.10%, is better than state-of-the-art methods.

ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer

Background and purposeDelineation of clinical target volumes (CTVs) remains a weak link in radiation therapy (RT), and large inter-observer variation is seen. Guidelines for target and organs at risk delineation for prostate cancer in the primary setting are scarce. The aim was to develop a delineation guideline obtained by consensus between a broad European group of radiation oncologists. Material and methodsAn ESTRO contouring consensus panel consisting of leading radiation oncologists and one radiologist with known subspecialty expertise in prostate cancer was asked to delineate the prostate, seminal vesicles and rectum on co-registered CT and MRI scans. After evaluation of the different contours, literature review and multiple informal discussions by electronic mail a CTV definition was defined and a guide for contouring the CTV of the prostate and the rectum was developed. ResultsThe panel achieved consensus CTV contouring definitions to be used as guideline for primary RT of localized prostate cancer. ConclusionThe ESTRO consensus on CT/MRI based CTV delineation for primary RT of localized prostate cancer, endorsed by a broad base of the radiation oncology community, is presented to improve consistency and reliability.

Delineation of the primary tumour Clinical Target Volumes (CTV-P) in laryngeal, hypopharyngeal, oropharyngeal and oral cavity squamous cell carcinoma: AIRO, CACA, DAHANCA, EORTC, GEORCC, GORTEC, HKNPCSG, HNCIG, IAG-KHT, LPRHHT, NCIC CTG, NCRI, NRG Oncology, PHNS, SBRT, SOMERA, SRO, SSHNO, TROG consensus guidelines

PurposeFew studies have reported large inter-observer variations in target volume selection and delineation in patients treated with radiotherapy for head and neck squamous cell carcinoma. Consensus guidelines have been published for the neck nodes (see Grégoire et al., 2003, 2014), but such recommendations are lacking for primary tumour delineation. For the latter, two main schools of thoughts are prevailing, one based on geometric expansion of the Gross Tumour Volume (GTV) as promoted by DAHANCA, and the other one based on anatomical expansion of the GTV using compartmentalization of head and neck anatomy. MethodFor each anatomic location within the larynx, hypopharynx, oropharynx and oral cavity, and for each T-stage, the DAHANCA proposal has been comprehensively reviewed and edited to include anatomic knowledge into the geometric Clinical Target Volume (CTV) delineation concept. A first proposal was put forward by the leading authors of this publication (VG and CG) and discussed with opinion leaders in head and neck radiation oncology from Europe, Asia, Australia/New Zealand, North America and South America to reach a worldwide consensus. ResultsThis consensus proposes two CTVs for the primary tumour, the so called CTV-P1 and CVT-P2, corresponding to a high and lower tumour burden, and which should be associated with a high and a lower dose prescription, respectively. ConclusionImplementation of these guidelines in the daily practice of radiation oncology should contribute to reduce treatment variations from clinicians to clinicians, facilitate the conduct of multi-institutional clinical trials, and contribute to improved care of patients with head and neck carcinoma.

Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials.

In recent years, new radiotherapy techniques have emerged that aim to improve treatment outcome and reduce toxicity. The standard method of evaluating such techniques is to conduct large scale multicenter clinical trials, often across continents. A major challenge for such trials is quality assurance to ensure consistency of treatment across all participating centers. Analyses from previous studies have shown that poor compliance and protocol violation have a significant adverse effect on treatment outcomes. The results of the clinical trials may, therefore, be confounded by poor quality radiotherapy. Target volume delineation (TVD) is one of the most critical steps in the radiotherapy process. Many studies have shown large inter-observer variations in contouring, both within and outside of clinical trials. High precision techniques, such as intensity-modulated radiotherapy, image-guided brachytherapy, and stereotactic radiotherapy have steep dose gradients, and errors in contouring may lead to inadequate dose to the tumor and consequently, reduce the chance of cure. Similarly, variation in organ at risk delineation will make it difficult to evaluate dose response for toxicity. This article reviews the literature on TVD variability and its impact on dosimetry and clinical outcomes. The implications for quality assurance in clinical trials are discussed.

Automated and observer based light field indicator edge evaluation in diagnostic X-ray equipment

Abstract Introduction To analyze edge detection and optical contrast calculation of light field-indicators used in X-ray via automated- and observer-based methods, and comparison with current standard approaches, which do not give exact definition for light field edge determination. Methods Automated light sensor array was used to measure the penumbra zone of the edge in the standard X-ray equipment, while trained and naive human observers were asked to mark the light field edge according to their own determination. Different interpretations of the contrast were then calculated and compared. Results In contrast to automated measurements of edge definition and detection, measurements by human observers showed large inter-observer variation independent of their training with X-ray equipment. Different contrast calculations considering the different edge definitions gave very different contrast values. Conclusion As the main conclusion, we propose a more exact edge definition of the X-ray light field, corresponding well to the average human observer’s edge determination. The new edge definition method with automated systems would reduce human variability in edge determination. Such errors could potentially affect the approval of X-ray equipment, and also increase the radiation dose. The automated measurement based on human observers’ edge definition and the corresponding contrast calculation may lead to a more precise light field calibration, which enables reduced irradiation doses on radiology patients.

PO-0710: Large interobserver variation of delineated target volumes of pancreatic cancer in the Netherlands

PO-0710: Large interobserver variation of delineated target volumes of pancreatic cancer in the Netherlands

Right ventricular strain by MR quantitatively identifies regional dysfunction in patients with arrhythmogenic right ventricular cardiomyopathy

Analysis of regional wall motion of the right ventricle (RV) is primarily qualitative with large interobserver variation in clinical practice. Thus, the purpose of this study was to use feature tracking to analyze regional wall motion abnormalities in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). We enrolled 110 subjects (39 overt ARVC [mutation+/phenotype+] (35.5%), 40 preclinical ARVC [mutation+/phenotype-] (36.3%), and 31 control subjects (28.2%)). Cine steady state free precession cardiac MR was performed with temporal resolution ≤40 ms in the horizontal long axis (HLA), axial, and short axis directions. Regional strain was analyzed using feature tracking software and reproducibility was assessed by means of intraclass correlation coefficient. Dunnett's test was used in univariate analysis for comparisons to control subjects; cumulative odds logistic regression was used for minimally and fully adjusted multivariate models. Strain was significantly impaired in overt ARVC compared with control subjects both globally (P < 0.01) and regionally (all segments of HLA view, P < 0.01). In the HLA view, regional reproducibility was excellent within (intraclass correlation coefficient [ICC] = 0.81) and moderate between (ICC = 0.62) observers. Using a threshold of -31% subtricuspid strain in the HLA view, the sensitivity and specificity for overt ARVC were 75.0% and 78.2%, respectively. In multivariable analysis involving all three groups, subtricuspid strain less than -31% (beta = 1.38; P = 0.014) and RV end diastolic volume index (beta = 0.06; P = 0.001) were significant predictors of disease presence. RV strain can be reproducibly assessed with MR feature tracking, and regional strain is abnormal in overt ARVC compared with control subjects.

Ultrasound Volume Projection Imaging for Assessment of Scoliosis.

The standing radiograph is used as a gold standard to diagnose spinal deformity including scoliosis, a medical condition defined as lateral spine curvature > 10°. However, the health concern of X-ray and large inter-observer variation of measurements on X-ray images have significantly restricted its application, particularly for scoliosis screening and close follow-up for adolescent patients. In this study, a radiation-free freehand 3-D ultrasound system was developed for scoliosis assessment using a volume projection imaging method. Based on the obtained coronal view images, two measurement methods were proposed using transverse process and spinous profile as landmarks, respectively. As a reliability study, 36 subjects (age: 30.1 ±14.5; male: 12; female: 24) with different degrees of scoliosis were scanned using the system to test the inter- and intra-observer repeatability. The intra- and inter-observer tests indicated that the new assessment methods were repeatable, with ICC larger than 0.92. Small intra- and inter-observer variations of measuring spine curvature were observed for the two measurement methods (intra-: 1.4 ±1.0° and 1.4 ±1.1°; inter-: 2.2 ±1.6° and 2.5 ±1.6°). The results also showed that the spinal curvature obtained by the new method had good linear correlations with X-ray Cobb's method (R2 = 0.8, p < 0.001, 29 subjects). These results suggested that the ultrasound volume projection imaging method can be a promising approach for the assessment of scoliosis, and further research should be followed up to demonstrate its potential clinical applications for mass screening and curve progression and treatment outcome monitoring of scoliosis patients.

ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer

Background and purposeDelineation of clinical target volumes (CTVs) is a weak link in radiation therapy (RT), and large inter-observer variation is seen in breast cancer patients. Several guidelines have been proposed, but most result in larger CTVs than based on conventional simulator-based RT. The aim was to develop a delineation guideline obtained by consensus between a broad European group of radiation oncologists. Material and methodsDuring ESTRO teaching courses on breast cancer, teachers sought consensus on delineation of CTV through dialogue based on cases. One teacher delineated CTV on CT scans of 2 patients, followed by discussion and adaptation of the delineation. The consensus established between teachers was sent to other teams working in the same field, both locally and on a national level, for their input. This was followed by developing a broad consensus based on discussions. ResultsBorders of the CTV encompassing a 5mm margin around the large veins, running through the regional lymph node levels were agreed, and for the breast/thoracic wall other vessels were pointed out to guide delineation, with comments on margins for patients with advanced breast cancer. ConclusionThe ESTRO consensus on CTV for elective RT of breast cancer, endorsed by a broad base of the radiation oncology community, is presented to improve consistency.