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ABSTRACT Background Aim Methods and Materials Results Conclusion The demand for aesthetic treatments targeting the middle and lower face is on the rise, especially because of changes in appearance associated with aging.This study aimed to assess the use of a hybrid filler for sculpting and contouring of the chin, jaw, and malar region.A retrospective analysis was performed on patients who underwent jaw and chin contouring and cheek augmentation using a hybrid filler (hyaluronic acid and dextranomer). The evaluation focused on the naturalness of appearance, enhancement in volume, and the durability of the results, employing a 5‐point scale. Both patient satisfaction and physician evaluations were measured using the Likert scale and the Global Aesthetic Improvement Scale (GAIS), respectively. Follow‐up with patients extended up to 6 months after treatment, during which any treatment‐related adverse events (AEs) were meticulously recorded and analyzed.Nineteen patients participated in the study, receiving an average injection volume of 2.4 ± 0.9 mL to attain the desired outcomes. The evaluation of natural appearance, volumizing effects, and durability at the analyzed time point consistently scored above 4. All 19 patients' aesthetic improvement was evaluated as “very much improved” and “much improved”, at the GAIS score. All patients report improvement in their appearance, with 89.5% rating it as “very much improved” or “much improved” on the Likert scale. Only expected AEs such as mild pain and lower swelling were registered.The hybrid filler proved effective and safe for facial contouring, with significant patient satisfaction and minimal adverse effects. [ABSTRACT FROM AUTHOR]

Background: This study aims to investigate the errors in structure volume and shape caused by the replication, expansion, and merging operations of the Monaco system and analyze their influence on dosimetry evaluation. Methods: A retrospective collection of 30 patients undergoing radiotherapy was utilized. Cylinders with radii of 5, 10, and 30 mm were delineated in computerized tomography (CT) images from 10 patients with thoracic and abdominal issues, and the Margins function in Monaco was used to expand the margins by 0, 3, 5, and 10 mm in 2D mode. In 10 patients with vertebral metastases, the Margins function was utilized to replicate and merge targets, and the Copy Structure function was employed to replicate targets. Cross-CT replication was performed for the targets of 10 patients with nasopharyngeal carcinoma. The deviation between the processed structure volume and the ideal value was compared. The difference in the maximum dose (Dmax) before and after lens replication was evaluated in 10 patients undergoing whole-brain radiotherapy. Results: Monaco's Margins function increased the volume of the processed structure during the copying procedure. The margin error was equivalent to expanding the structure by 0.3–0.4 mm, and a margin error of 0.3–0.4 mm was produced in each expansion instance. The volume deviation for a cylinder with a radius of 5 mm was 12.99%. The Merge function of Margins copied substructures and merged them. The Copy Structure function did not alter the structure during copying, but the volume was reduced by less than 1% after copying across CT. Dmax after lens replication was higher than that before replication, with a median difference of 31.3 cGy for the left lenses. Conclusion: Monaco's Margins function introduces errors in organ replication, expansion, and merging, resulting in incorrect dose assessment. Physicians should be mindful of the potential effects when utilizing them. [ABSTRACT FROM AUTHOR]

Single point incremental forming is a novel sheet metal forming process that crafts 3D shapes out of sheet metal using layerwise deformation of the metallic sheet with a simple tool, which is typically cylindrical with a hemispherical ball-end. In this work, a combination of intelligent clamping and toolpath strategies was used to manufacture tunnel-shaped parts using aluminium alloy, AA1050AH14. The toolpath strategies helped improve on the low forming limits for failure typically associated with the manufacture of such shapes. A new method for compensating the inaccuracies in the parts caused by springback and other plastic deformations associated with the process using predicted 2D sectional views was also tested. The predicted sectional views were generated using training sets from the scanned geometries consisting of large datasets of point clouds. The training sets helped generate multivariate regression equations which were then used to create the predicted sections. The predicted sections were interpolated to create compensated geometries which then enabled part manufacture with improvement in accuracy. The result from this new strategy was compared with improvements observed in 3D compensation followed by adaptive pocketing and contouring toolpath strategies. [ABSTRACT FROM AUTHOR]

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality. [ABSTRACT FROM AUTHOR]

Background/Aim: Intensity-modulated radiation therapy can deliver a highly conformal dose to a target while minimizing the dose to the organs at risk (OARs). Delineating the contours of OARs is time-consuming, and various automatic contouring software programs have been employed to reduce the delineation time. However, some software operations are manual, and further reduction in time is possible. This study aimed to automate running atlas-based auto-segmentation (ABAS) and software operations using a scripting function, thereby reducing work time. Materials and Methods: Dice coefficient and Hausdorff distance were used to determine geometric accuracy. The manual delineation, automatic delineation, and modification times were measured. While modifying the contours, the degree of subjective correction was rated on a four-point scale. Results: The model exhibited generally good geometric accuracy. However, some OARs, such as the chiasm, optic nerve, retina, lens, and brain require improvement. The average contour delineation time was reduced from 57 to 29 min (p<0.05). The subjective revision degree results indicated that all OARs required minor modifications; only the submandibular gland, thyroid, and esophagus were rated as modified from scratch. Conclusion: The ABAS model and scripted automation in head and neck cancer reduced the work time and software operations. The time can be further reduced by improving contour accuracy. [ABSTRACT FROM AUTHOR]

The success and safety of modern radiotherapy relies on accurate contouring. Understanding the time taken to complete radiotherapy contours is critical to informing workforce planning and, in the context of a workforce shortfall, advocating for investment in technology and multi-professional skills mix. We aimed to quantify the time taken to delineate target volumes for radical radiotherapy. The Royal College of Radiologists circulated two electronic surveys via email to all clinical oncology consultants in the UK. The individual case survey requested anonymous data regarding the next five patients contoured for radical radiotherapy. The second survey collected data on respondents' usual practice in radiotherapy contouring. The median time to contour one radiotherapy case was 85 minutes (IQR = 50–131 minutes). Marked variability between and within tumour sites was evident: paediatric cancers took the most time (median = 210 minutes, IQR = 87.5 minutes), followed by head and neck and gynaecological cancers (median = 120 minutes, IQR = 71 and 72.5 minutes respectively). Breast cancer contouring required the least time (median = 43 minutes, IQR = 60 minutes). Radiotherapy technique, inclusion of nodes and 4D CT planning were associated with longer contouring times. A non-medical professional was involved in contouring in 65% of cases, but clinical oncology consultants were involved in target volume delineation in 90% of cases, and OARs in 74%. Peer review took place in 46% of cases with 56% of consultants reporting no time for peer review in their job plan. Contouring for radical radiotherapy is complex and time-consuming, and despite increasing involvement of non-medical professionals, clinical oncology consultants remain the primary practitioners. Peer review practice is variable and time is often a limiting factor. Many factors influence the time required for contouring, and departments should take these factors and the need for peer-review into account when developing job plans. • Contouring for radical radiotherapy requires considerable consultant time. • Time needed for contouring varies considerably, and is affected by patient, tumour and treatment related factors. • As more advanced treatment techniques become more commonplace, complexity and time requirements are certain to increase. • Despite involvement of non-medical professionals, clinical oncology consultants remain the primary contouring practitioners. • Time constraints are a major factor limiting peer review practice. [ABSTRACT FROM AUTHOR]

This study aims to establish a delineation guideline for the contouring of the hypoglossal nerve by dividing the nerve into different segments, and to test the possibility of a radiation dose reduction to the hypoglossal nerve in NPC patients receiving radiotherapy. Twenty NPC patients were selected arbitrarily. The hypoglossal nerves were delineated using anatomic landmarks and divided into the cisternal, intracanalicular, carotid, and transverse segments. The tumor coverage by radiation and dose-volume parameters of the nerve with and without various dose constraints to the hypoglossal nerve were compared. The hypoglossal nerve, which is invisible on CT images, can be delineated accurately with the assistance of several anatomic landmarks. Without a dose constraint to the hypoglossal nerve, the carotid space, intracanalicular, and transverse segments had high radiation dose-volumes. The dose-volume to the nerve, however, can be reduced when the nerve was defined and a dose constraint was given. The delineation of the hypoglossal nerve with its different segments is feasible. The carotid space, intracanalicular, and transverse segments received the highest dose, where the nerve damage was most likely located. The dose to the nerve can be reduced to less than 70 Gy using the intensity-modulated radiotherapy technique. [ABSTRACT FROM AUTHOR]

Purpose: Differences in the contours created during magnetic resonance imaging-guided online adaptive radiotherapy (MRgOART) affect dose distribution. This study evaluated the interobserver error in delineating the organs at risk (OARs) in patients with pancreatic cancer treated with MRgOART. Moreover, we explored the effectiveness of drugs that could suppress peristalsis in restraining intra-fractional motion by evaluating OAR visualization in multiple patients. Methods: This study enrolled three patients who underwent MRgOART for pancreatic cancer. The study cohort was classified into three conditions based on the MRI sequence and butylscopolamine administration (Buscopan): 1, T2 imaging without butylscopolamine administration; 2, T2 imaging with butylscopolamine administration; and 3, multi-contrast imaging with butylscopolamine administration. Four blinded observers visualized the OARs (stomach, duodenum, small intestine, and large intestine) on MR images acquired during the initial and final MRgOART sessions. The contour was delineated on a slice area of ±2 cm surrounding the planning target volume. The dice similarity coefficient (DSC) was used to evaluate the contour. Moreover, the OARs were visualized on both MR images acquired before and after the contour delineation process during MRgOART to evaluate whether peristalsis could be suppressed. The DSC was calculated for each OAR. Results: Interobserver errors in the OARs (stomach, duodenum, small intestine, large intestine) for the three conditions were 0.636, 0.418, 0.676, and 0.806; 0.725, 0.635, 0.762, and 0.821; and 0.841, 0.677, 0.762, and 0.807, respectively. The DSC was higher in all conditions with butylscopolamine administration compared with those without it, except for the stomach in condition 2, as observed in the last session of MR image. The DSCs for OARs (stomach, duodenum, small intestine, large intestine) extracted before and after contouring were 0.86, 0.78, 0.88, and 0.87; 0.97, 0.94, 0.90, and 0.94; and 0.94, 0.86, 0.89, and 0.91 for conditions 1, 2, and 3, respectively. Conclusion: Butylscopolamine effectively reduced interobserver error and intrafractional motion during the MRgOART treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Multiple tumor types feature frequent overexpression of somatostatin receptor type 2 (SSTR2). In addition to its well-established role in determining the primary diagnosis, a PET/CT can be helpful in the radiation treatment of these tumors. It has the potential to improve recognition of the tumor burden compared to CT or MRI alone. In a blinded comparative interobserver study, we anonymized 47 patients with various tumors with SSTR2 expression and instructed four radiation oncologists to independently contour the macroscopic tumor volume using MRI alone and subsequently with the addition of DOTA-conjugated PET/CT. This study showed that for meningioma and skull base paraganglioma (SBPGL), there was a better consensus and certainty between the observers, with the opposite trend for pituitary neuroendocrine tumors (PitNET). For PitNET and meningioma, the addition of PET/CT led to higher sensitivity between the observers compared to CT and MRI alone, suggesting the benefits of integrating DOTA-conjugated PET/CT for target definition. The overexpression of somatostatin receptor type 2 (SSTR2) is a property of various tumor types. Hybrid imaging utilizing [68Ga]1,4,7,10-tetraazacyclododecane-1,4,7,10-tetra-acetic acid (DOTA) may improve the differentiation between tumor and healthy tissue. We conducted an experimental study on 47 anonymized patient cases including 30 meningiomas, 12 PitNET and 5 SBPGL. Four independent observers were instructed to contour the macroscopic tumor volume on planning MRI and then reassess their volumes with the additional information from DOTA-PET/CT. The conformity between observers and reference volumes was assessed. In total, 46 cases (97.9%) were DOTA-avid and included in the final analysis. In eight cases, PET/CT additional tumor volume was identified that was not detected by MRI; these PET/CT findings were potentially critical for the treatment plan in four cases. For meningiomas, the interobserver and observer to reference volume conformity indices were higher with PET/CT. For PitNET, the volumes had higher conformity between observers with MRI. With regard to SBGDL, no significant trend towards conformity with the addition of PET/CT information was observed. DOTA PET/CT supports accurate tumor recognition in meningioma and PitNET and is recommended in SSTR2-expressing tumors planned for treatment with highly conformal radiation. [ABSTRACT FROM AUTHOR]

Purpose/Objective (s): Due to manual OAR contouring challenges, various automatic contouring solutions have been introduced. Historically, common clinical auto-segmentation algorithms used were atlas-based, which required maintaining a library of self-made contours. Searching the collection was computationally intensive and could take several minutes to complete. Deep learning approaches have shown significant benefits compared to atlas-based methods in improving segmentation accuracy and efficiency in auto-segmentation algorithms. This work represents the first multi-institutional study to describe and evaluate an AI algorithm for the auto-segmentation of organs at risk (OARs) based on a deep image-to-image network (DI2IN). Materials/Methods: The AI-Rad Companion Organs RT (AIRC) algorithm(Siemens Healthineers, Erlangen, Germany) uses a two-step approach for segmentation. In the first step, the target organ region in the optimal input image is extracted using a trained deep reinforcement learning network (DRL), which is then used as input to create the contours in the second step based on DI2IN. The study was initially designed as a prospective single-center evaluation. The automated contours generated by AIRC were evaluated by three experienced board-certified radiation oncologists using a four-point scale where 4 is clinically usable and 1 requires re-contouring. After seeing favorable results in a single-center pilot study, we decided to expand the study to six additional institutions, encompassing eight additional evaluators for a total of 11 physician evaluators across seven institutions. Results: One hundred and fifty-six patients and 1366 contours were prospectively evaluated. The five most commonly contoured organs were the lung (136 contours, average rating = 4.0), spinal cord (106 contours, average rating = 3.1), eye globe (80 contours, average rating = 3.9), lens (77 contours, average rating = 3.9), and optic nerve (75 contours, average rating = 4.0). The average rating per evaluator per contour was 3.6. On average, 124 contours were evaluated by each evaluator. 65% of the contours were rated as 4, and 31% were rated as 3. Only 4% of contours were rated as 1 or 2. Thirty-three organs were evaluated in the study, with 19 structures having a 3.5 or above average rating (ribs, abdominopelvic cavity, skeleton, larynx, lung, aorta, brachial plexus, lens, eye globe, glottis, heart, parotid glands, bladder, kidneys, supraglottic larynx, submandibular glands, esophagus, optic nerve, oral cavity) and the remaining organs having a rating of 3.0 or greater (female breast, proximal femur, seminal vesicles, rectum, sternum, brainstem, prostate, brain, lips, mandible, liver, optic chiasm, spinal cord, spleen). No organ had an average rating below 3. Conclusion: AIRC performed well with greater than 95% of contours accepted by treating physicians with no or minor edits. It supported a fully automated workflow with the potential for time savings and increased standardization with the use of AI-powered algorithms for high-quality OAR contouring. [ABSTRACT FROM AUTHOR]

Our proposed method uses a three-dimensional (3D) measurement approach that focuses mainly on the lower jaw from basal, lateral, and frontal views applied to the volumetric skull model derived from a computed tomography (CT) of the head. Likewise, we discuss the geometrical features and clinical considerations involved in the 3D biomodeling of the surgical osteotomy. The workflow that allowed this virtual planning to be developed was composed of medical imaging processing software, data extraction software from images, and statistical software that allows the creation and generation of curve-fitting (nonlinear regression) graphs from data. Thirty-two (32) anatomical points were positioned, sixteen (16) measurements were taken, and two-dimensional (2D) sketches in three views (frontal, lateral, and inferior) were generated to overlap in a 3D environment, which informed the cutting of the desired bone segments. Implementing a nonlinear regression curve-fitting on the contours of the original jaws allowed optimal planning of the osteotomy. Desired cutting shapes were extrapolated for the front view by third-order equations, while for the side and bottom views, log-normal distribution curves and second-order polynomial curves were used, respectively. The reduction in the mandibular volume was between 6.55 and 10.27 %, with two of the most important measurements related to vertical reduction in the lateral views and the difference to determine gonion reduction.

Background: Accurate and consistent contouring of organs‐at‐risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR‐only RT, and MR‐guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated. Purpose: To analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the same patients. Methods: In the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board‐certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95‐percentile Hausdorff distance (HD 95$_{95}$). Results: The mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1 mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3 mm in terms of DC and HD 95$_{95}$, respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5 mm, which was considered as valid for the analysis of intermodality variability. Conclusions: The contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR‐only and MR‐guided RT. [ABSTRACT FROM AUTHOR]

Accurate target delineation (ie, contouring) is essential for radiation treatment planning and radiotherapy efficacy. As a result, improving the quality of target delineation is an important goal in the education of radiation oncology residents. The purpose of this study was to track the concordance of radiation oncology residents' contours with those of faculty physicians over the course of 1 year to assess for patterns. Residents in postgraduate year (PGY) levels 2 to 4 were asked to contour target volumes that were then compared to the finalized, faculty physician–approved contours. Concordance between resident and faculty physician contours was determined by calculating the Jaccard concordance index (JCI), ranging from 0, meaning no agreement, to 1, meaning complete agreement. Multivariate mixed-effect models were used to assess the association of JCI to the fixed effect of PGY level and its interactions with cancer type and other baseline characteristics. Post hoc means of JCI were compared between PGY levels after accounting for multiple comparisons using Tukey's method. In total, 958 structures from 314 patients collected during the 2020-2021 academic year were studied. The mean JCI was 0.77, 0.75, and 0.61 for the PGY-4, PGY-3, and PGY-2 levels, respectively. The JCI score for PGY-2 was found to be lower than those for PGY-3 and PGY-4, respectively (all P <.001). No statistically significant difference of JCI score was found between the PGY-3 and PGY-4 levels. The average JCI score was lowest (0.51) for primary head and/or neck cancers, and it was highest (0.80) for gynecologic cancers. Tracking and comparing the concordance of resident contours with faculty physician contours is an intriguing method of assessing resident performance in contouring and target delineation and could potentially serve as a quantitative metric, which is lacking currently, in radiation oncology resident evaluation. However, additional study is necessary before this technique can be incorporated into residency assessments. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the main directions of the modern study of landscape structure is the timely updating of the structural and dynamic features of geosystems, taking into account the degree of anthropogenic load. This article examines the historical prerequisites for the development of ideas about the physical and geographical division of Orenburg oblast. A geoinformation analysis of remote sensing data has been carried out using neural network algorithms based on self-organizing Kohonen maps in order to compare the structure of natural boundaries with the actual structure of natural–anthropogenic complexes. For this purpose, we have calculated quantitative indicators (namely, the area of the physical-geographical region, the number of classes (types of tracts), the number of landscape contours, the average number of contours in a class, the average area of one contour, the density of contours in the physical-geographical region, the coefficient of complexity, the maximum possible complexity of a landscape, the absolute organization of a landscape (a measure of imbalance), the relative organization of a landscape, and the coefficient of landscape fragmentation) and indices of differentiation of the landscape structure (coefficients of entropic complexity and Shannon diversity and Ivashutina–Nikolaev, Odum, Gleason–Margalef, and Simpson indices of heterogeneity). Moreover, schematic maps of the region's territory have been compiled, reflecting their spatial distribution over landscape areas. Based on the results of the study, tendencies of changes in the landscape structure of Orenburg oblast have been determined. They include changes in the degree of contouring of geosystems, dynamics of the severity of interlandscape boundaries, anthropogenic dispersion of geosystems, and the degree of dominance of individual elements of the landscape. Differences in the tendencies of changes in the landscape structure of forest-steppe, petromorphic, and hydromorphic geosystems, in comparison with the arid steppe landscapes prevailing in the region, have been identified depending on the degree of agrogenic and technogenic transformation. [ABSTRACT FROM AUTHOR]

The segmentation of organs and structures is a critical component of radiation therapy planning, with manual segmentation being a laborious and time-consuming task. Interobserver variability can also impact the outcomes of radiation therapy. Deep neural networks have recently gained attention for their ability to automate segmentation tasks, with convolutional neural networks (CNNs) being a popular approach. This article provides a descriptive review of the literature on deep learning (DL) techniques for segmentation in radiation therapy planning. This review focuses on five clinical sub-sites and finds that U-net is the most commonly used CNN architecture. The studies using DL for image segmentation were included in brain, head and neck, lung, abdominal, and pelvic cancers. The majority of DL segmentation articles in radiation therapy planning have concentrated on normal tissue structures. N -fold cross-validation was commonly employed, without external validation. This research area is expanding quickly, and standardization of metrics and independent validation are critical to benchmarking and comparing proposed methods. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the precision of semi-automatic, conventional and automatic volumetry tools for pulmonary nodules in chest CT with phantom N1 LUNGMAN. The phantom is a life-size anatomical chest model with pulmonary nodules representing solid and subsolid metastases. Gross tumor volumes (GTVis) were contoured using various approaches: manually (0); as a means of semi-automated, conventional contouring with (I) adaptive-brush function; (II) flood-fill function; and (III) image-thresholding function. Furthermore, a deep-learning algorithm for automatic contouring was applied (IV). An intermodality comparison of the above-mentioned strategies for contouring GTVis was performed. For the mean GTVref (standard deviation (SD)), the interquartile range (IQR)) was 0.68 mL (0.33; 0.34–1.1). GTV segmentation was distributed as follows: (I) 0.61 mL (0.27; 0.36–0.92); (II) 0.41 mL (0.28; 0.23–0.63); (III) 0.65 mL (0.35; 0.32–0.90); and (IV) 0.61 mL (0.29; 0.33–0.95). GTVref was found to be significantly correlated with GTVis (I) p < 0.001, r = 0.989 (III) p = 0.001, r = 0.916, and (IV) p < 0.001, r = 0.986, but not with (II) p = 0.091, r = 0.595. The Sørensen–Dice indices for the semi-automatic tools were 0.74 (I), 0.57 (II) and 0.71 (III). For the semi-automatic, conventional segmentation tools evaluated, the adaptive-brush function (I) performed closest to the reference standard (0). The automatic deep learning tool (IV) showed high performance for auto-segmentation and was close to the reference standard. For high precision radiation therapy, visual control, and, where necessary, manual correction, are mandatory for all evaluated tools. [ABSTRACT FROM AUTHOR]

The restoration of sagittal alignment is fundamental to the surgical correction of adolescent idiopathic scoliosis (AIS). Despite established techniques, some patients present with inadequate postoperative thoracic kyphosis (TK), which may increase the risk of proximal junctional kyphosis (PJK) and imbalance. There is a lack of knowledge concerning the effectiveness of patient-specific rods (PSR) with measured sagittal curves in achieving a TK similar to that planned in AIS surgery, the factors influencing this congruence, and the incidence of PJK after PSR use. This is a systematic review of all types of studies reporting on the PSR surgical correction of AIS, including research articles, proceedings, and gray literature between 2013 and December 2023. From the 28,459 titles identified in the literature search, 81 were assessed for full-text reading, and 7 studies were selected. These included six cohort studies and a comparative study versus standard rods, six monocentric and one multicentric, three prospective and four retrospective studies, all with a scientific evidence level of 4 or 3. They reported a combined total of 355 AIS patients treated with PSR. The minimum follow-up was between 4 and 24 months. These studies all reported a good match between predicted and achieved TK, with the main difference ranging from 0 to 5 degrees, p > 0.05, despite the variability in surgical techniques and the rods' properties. There was no proximal junctional kyphosis, whereas the current rate from the literature is between 15 and 46% with standard rods. There are no specific complications related to PSR. The exact role of the type of implants is still unknown. The preliminary results are, therefore, encouraging and support the use of PSR in AIS surgery. [ABSTRACT FROM AUTHOR]

Abstract Purpose The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs. Methods This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results of each study are selected as effect sizes for meta-analysis. Furthermore, this study conducted a subgroup analysis of OARs characterized by image modality and image type. Results 149 articles were retrieved, and 22 studies were included in the meta-analysis after excluding duplicate literature, primary screening, and re-screening. The combined effect sizes of DSC for brainstem, spinal cord, mandible, left eye, right eye, left optic nerve, right optic nerve, optic chiasm, left parotid, right parotid, left submandibular, and right submandibular are 0.87, 0.83, 0.92, 0.90, 0.90, 0.71, 0.74, 0.62, 0.85, 0.85, 0.82, and 0.82, respectively. For subgroup analysis, the combined effect sizes for segmentation of the brainstem, mandible, left optic nerve, and left parotid gland using CT and MRI images are 0.86/0.92, 0.92/0.90, 0.71/0.73, and 0.84/0.87, respectively. Pooled effect sizes using 2D and 3D images of the brainstem, mandible, left optic nerve, and left parotid gland for contouring are 0.88/0.87, 0.92/0.92, 0.75/0.71 and 0.87/0.85. Conclusions The use of automated contouring technology based on DL algorithms is an essential tool for contouring head and neck OARs, achieving high accuracy, reducing the workload of clinical radiation oncologists, and providing individualized, standardized, and refined treatment plans for implementing "precision radiotherapy". Improving DL performance requires the construction of high-quality data sets and enhancing algorithm optimization and innovation.

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was max and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.

Background and Purpose: Auto-contouring of complex anatomy in computed tomography (CT) scans is a highly anticipated solution to many problems in radiotherapy. In this study, artificial intelligence (AI)-based auto-contouring models were clinically validated for lymph node levels and structures of swallowing and chewing in the head and neck. Materials and Methods: CT scans of 145 head and neck radiotherapy patients were retrospectively curated. One cohort (n = 47) was used to analyze seven lymph node levels and the other (n = 98) used to analyze 17 swallowing and chewing structures. Separate nnUnet models were trained and validated using the separate cohorts. For the lymph node levels, preference and clinical acceptability of AI vs human contours were scored. For the swallowing and chewing structures, clinical acceptability was scored. Quantitative analyses of the test sets were performed for AI vs human contours for all structures using overlap and distance metrics. Results: Median Dice Similarity Coefficient ranged from 0.77 to 0.89 for lymph node levels and 0.86 to 0.96 for chewing and swallowing structures. The AI contours were superior to or equally preferred to the manual contours at rates ranging from 75% to 91%; there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all AI-generated lymph node level contours, 92% were rated as usable with stylistic to no edits. Of the 340 contours in the chewing and swallowing cohort, 4% required minor edits. Conclusions: An accurate approach was developed to auto-contour lymph node levels and chewing and swallowing structures on CT images for patients with intact nodal anatomy. Only a small portion of test set auto-contours required minor edits.

Simple Summary: Early-stage (T1–T2 N0 M0) true perianal tumors are very uncommon, and the scientific literature is scant. Based on common features with anal canal carcinomas (aCCs), perianal skin cancers and aCCs are included in the same tumor classification and treated similarly. In fact, contouring radiation therapy guidelines do not differentiate between the two subsites. However, anal canal tumors and perianal skin cancers have different lymphatic drainage patterns. Modulation of radiotherapy treatment volumes can be considered for the latter. We performed a literature review to analyze the sites at higher risk of microscopic spread in patients with early-stage perianal cancer to tailor the selection of radiation therapy elective volumes. Distinction between anal canal and perianal squamous cell carcinomas (pSCCs) is essential, as these two subgroups have different anatomical, histological, and lymphatic drainage features. Early-stage true perianal tumors are very uncommon and have been rarely included in clinical trials. Perianal skin cancers and aCCs are included in the same tumor classification, even though they have different lymphatic drainage features. Furthermore, pSCCs are treated similarly to carcinomas originating from the anal canal. Radiation therapy (RT) is an essential treatment for anal canal tumors. Guidelines do not differentiate between treatment volumes for perianal tumors and anal cancers. So far, in pSCC, no study has considered modulating treatment volume selection according to the stage of the disease. We conducted a narrative literature review to describe the sites at higher risk for microscopic disease in patients with early-stage perianal cancers (T1–T2 N0 M0) to propose a well-thought selection of RT elective volumes. [ABSTRACT FROM AUTHOR]

Though dosimetry has a multitude of treatment modalities, software, and workflows to aid in the treatment planning process, treatment planners are still responsible for several tedious and monotonous tasks that could decrease their planning efficiency. The purpose of this study was to determine if scripting could improve treatment planning efficiency for multiple brain lesion stereotactic radiosurgery (SRS) volumetric arc therapy cases by reducing planning time commitment. A script was developed for multiple brain lesion SRS cases using Eclipse scripting application programming interface with the intention of improving treatment planning efficiency by creating optimization structures and importing prescription and suggested OS dose metrics to the optimizer. Nine treatment planners were each provided with 3 different multiple brain lesion, single-isocenter SRS cases. Each planner created 2 plans for each case. One of these 2 plans used the SRS script, and the other did not. There were 54 treatment plans developed, totaling 27 plan comparisons. Each of the 54 treatment plans were considered clinically acceptable based on the participating institution's plan quality guidelines. Statistical analyses of planning time commitment with and without the SRS script were performed using RStudio. The mean and median planning times with and without the SRS script were compared using a paired T-test and Wilcoxon Signed Rank test, respectively, and effect size was evaluated using Cohen's classification. Using the SRS script resulted in statistically significant reduction in total contouring time (11.3 vs 2.8 minutes, p < 0.001), optimizer preparation time (7.7 vs 2.1 minutes, p < 0.001), and overall planning time (105.1 vs 77.9 minutes, p < 0.001). This study concluded that scripts developed using Eclipse scripting application programming interface offer an opportunity to improve treatment planning efficiency by reducing the planning time commitment for treatment planners. [ABSTRACT FROM AUTHOR]

Purpose: The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs. Methods: This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results of each study are selected as effect sizes for meta-analysis. Furthermore, this study conducted a subgroup analysis of OARs characterized by image modality and image type. Results: 149 articles were retrieved, and 22 studies were included in the meta-analysis after excluding duplicate literature, primary screening, and re-screening. The combined effect sizes of DSC for brainstem, spinal cord, mandible, left eye, right eye, left optic nerve, right optic nerve, optic chiasm, left parotid, right parotid, left submandibular, and right submandibular are 0.87, 0.83, 0.92, 0.90, 0.90, 0.71, 0.74, 0.62, 0.85, 0.85, 0.82, and 0.82, respectively. For subgroup analysis, the combined effect sizes for segmentation of the brainstem, mandible, left optic nerve, and left parotid gland using CT and MRI images are 0.86/0.92, 0.92/0.90, 0.71/0.73, and 0.84/0.87, respectively. Pooled effect sizes using 2D and 3D images of the brainstem, mandible, left optic nerve, and left parotid gland for contouring are 0.88/0.87, 0.92/0.92, 0.75/0.71 and 0.87/0.85. Conclusions: The use of automated contouring technology based on DL algorithms is an essential tool for contouring head and neck OARs, achieving high accuracy, reducing the workload of clinical radiation oncologists, and providing individualized, standardized, and refined treatment plans for implementing "precision radiotherapy". Improving DL performance requires the construction of high-quality data sets and enhancing algorithm optimization and innovation. [ABSTRACT FROM AUTHOR]

In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours. The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia. The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 − 0.90] and 0.68 [0.51 − 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 − 1.1] mm and 1.9 mm [1.5 − 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in Δ NTCP. The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the Δ NTCP calculations could be discerned. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the clinical feasibility of the Siemens Healthineers AI‐Rad Companion Organs RT VA30A (Organs‐RT) auto‐contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N). Methods: Computed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, and 10 H&N) were collected. Four sets of OARs were generated on each scan, one set by Organs‐RT and the others by three experienced users independently. A physician (expert) then evaluated each contour by assigning a score from the following scale: 1‐Must Redo, 2‐Major Edits, 3‐Minor Edits, 4‐Clinically usable. Using the highest‐scored OAR from the human users as a reference, the contours generated by Organs‐RT were evaluated via Dice Similarity Coefficient (DSC), Hausdorff Distance (HDD), Mean Distance to Agreement (mDTA), Volume comparison, and visual inspection. Additionally, each human user recorded the time to delineate each structure set and time‐saving efficiency was measured. Results: The average DSC obtained for the pelvic OARs ranged between (0.81 ± 0.06)Rectum and (0.94 ± 0.03)Bladder. (0.75 ± 0.09)Esophagus to (0.96±0.02)Rt.Lung${({0.96 \pm 0.02})}_{{\mathrm{Rt}}.{\mathrm{\ Lung}}}$ for the thoracic OARs and (0.66 ± 0.07)Lips to (0.83 ± 0.04)Brainstem for the H&N. The average HDD in cm for the pelvis cohort ranged between (0.95 ± 0.35)Bladder to (3.62 ± 2.50)Rectum, (0.42 ± 0.06)SpinalCord to (2.09 ± 2.00)Esophagus for the thoracic set and (0.53±0.22)Cerv_SpinalCord${({0.53 \pm 0.22})}_{{\mathrm{Cerv}}\_{\mathrm{SpinalCord}}}$ to (1.50 ± 0.50)Mandible for the H&N region. The time‐saving efficiency was 67% for H&N, 83% for pelvis, and 84% for thorax. 72.5%, 82%, and 50% of the pelvis, thorax, and H&N OARs were scored as clinically usable by the expert, respectively. Conclusions: The highest agreement registered between OARs generated by Organs‐RT and their respective references was for the bladder, heart, lungs, and femoral heads, with an overall DSC≥0.92. The poorest agreement was for the rectum, esophagus, and lips, with an overall DSC⩽0.81. Nonetheless, Organs‐RT serves as a reliable auto‐contouring tool by minimizing overall contouring time and increasing time‐saving efficiency in radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

To evaluate urethral contours from two optimized urethral MRI sequences with an MR-guided radiotherapy system (MRgRT). Eleven prostate cancer patients were scanned on a MRgRT system using optimized urethral 3D HASTE and 3D TSE. A resident radiation oncologist contoured the prostatic urethra on the patients' planning CT, diagnostic 3T T2w MRI, and both urethral MRIs. An attending radiation oncologist reviewed/edited the resident's contours and additionally contoured the prostatic urethra on the clinical planning MRgRT MRI (bSSFP). For each image, the resident radiation oncologist, attending radiation oncologist, and a senior medical physicist qualitatively scored the prostatic urethra visibility. Using MRgRT 3D HASTE-based contouring workflow as baseline, prostatic urethra contours drawn on CT, diagnostic MRI, clinical bSSFP and 3D TSE were evaluated relative to the contour on 3D HASTE using 95th percentile Hausdorff distance (HD95), mean-distance-to-agreement (MDA), and DICE coefficient. Additionally, prostatic urethra contrast-to-noise-ratios (CNR) were calculated for all images. For two out of three observers, the urethra visibility score for 3D HASTE was significantly higher than CT, and clinical bSSFP, but was not significantly different from diagnostic MRI. The mean HD95/MDA/DICE values were 11.35 ± 3.55 mm/5.77 ± 2.69 mm/0.07 ± 0.08 for CT, 7.62 ± 2.75 mm/3.83 ± 1.47 mm/0.12 ± 0.10 for CT + diagnostic MRI, 5.49 ± 2.32 mm/2.18 ± 1.19 mm/0.35 ± 0.19 for 3D TSE, and 6.34 ± 2.89 mm/2.65 ± 1.31 mm/0.21 ± 0.12 for clinical bSSFP. The CNR for 3D HASTE was significantly higher than CT, diagnostic MRI, and clinical bSSFP, but was not significantly different from 3D TSE. The urethra's visibility scores showed optimized urethral MRgRT 3D HASTE was superior to the other tested methodologies. The prostatic urethra contours demonstrated significant variability from different imaging and workflows. Urethra contouring uncertainty introduced by cross-modality registration and sub-optimal imaging contrast may lead to significant treatment degradation when urethral sparing is implemented to minimize genitourinary toxicity.

Background and purpose: Children receiving radiotherapy for head-and-neck tumours often experience severe dentofacial side effects. Despite this, recommendations for contouring and dose constraints to dentofacial structures are lacking in clinical practice. We report on a survey aiming to understand current practice in contouring and dose assessment to dentofacial structures. Methods: A digital survey was distributed to European Society for Paediatric Oncology members of the Radiation Oncology Working Group, and member-affiliated centres in Europe, Australia, and New Zealand. The questions focused on clinical practice and aimed to establish areas for future development. Results: Results from 52 paediatric radiotherapy centres across 27 countries are reported. Only 29/52 centres routinely delineated some dentofacial structures, with the most common being the mandible (25 centres), temporo-mandibular joint (22), dentition (13), orbit (10) and maxillary bone (eight). For most bones contoured, an ‘As Low As Reasonably Achievable’ dose objective was implemented. Only four centres reported age-adapted dose constraints.The largest barrier to clinical implementation of dose constraints was firstly, the lack of contouring guidance (49/52, 94%) and secondly, that delineation is time-consuming (33/52, 63%). Most respondents who routinely contour dentofacial structures (25/27, 90%) agreed a contouring atlas would aid delineation. Conclusion: Routine delineation of dentofacial structures is infrequent in paediatric radiotherapy. Based on survey findings, we aim to 1) define a consensus-contouring atlas for dentofacial structures, 2) develop auto-contouring solutions for dentofacial structures to aid clinical implementation, and 3) carry out treatment planning studies to investigate the importance of delineation of these structures for planning optimisation.

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Purpose/objective(s): Auto-segmentation with artificial intelligence (AI) offers an opportunity to reduce inter- and intra-observer variability in contouring, to improve the quality of contours, as well as to reduce the time taken to conduct this manual task. In this work we benchmark the AI autosegmentation contours produced by five commercial vendors against a common dataset. Methods and materials: The organ at risk (OAR) contours generated by five commercial AI auto-segmentation solutions (Mirada (Mir), MVision (MV), Radformation (Rad), RayStation (Ray) and TheraPanacea (Ther)) were compared to manually-drawn expert contours from 20 breast, 20 head and neck, 20 lung and 20 prostate patients. Comparisons were made using geometric similarity metrics including volumetric and surface Dice similarity coefficient (vDSC and sDSC), Hausdorff distance (HD) and Added Path Length (APL). To assess the time saved, the time taken to manually draw the expert contours, as well as the time to correct the AI contours, were recorded. Results: There are differences in the number of CT contours offered by each AI auto-segmentation solution at the time of the study (Mir 99; MV 143; Rad 83; Ray 67; Ther 86), with all offering contours of some lymph node levels as well as OARs. Averaged across all structures, the median vDSCs were good for all systems and compared favorably with existing literature: Mir 0.82; MV 0.88; Rad 0.86; Ray 0.87; Ther 0.88. All systems offer substantial time savings, ranging between: breast 14-20 mins; head and neck 74-93 mins; lung 20-26 mins; prostate 35-42 mins. The time saved, averaged across all structures, was similar for all systems: Mir 39.8 mins; MV 43.6 mins; Rad 36.6 min; Ray 43.2 mins; Ther 45.2 mins. Conclusions: All five commercial AI auto-segmentation solutions evaluated in this work offer high quality contours in significantly reduced time compared to manual contouring, and could be used to render the radiotherapy workflow more efficient and standardized. [ABSTRACT FROM AUTHOR]

Background: To date, anal cancer patients are treated with radiotherapy to similar volumes despite a marked difference in risk profile based on tumor location and stage. A more individualized approach to delineation of the elective clinical target volume (CTVe) could potentially provide better oncological outcomes as well as improved quality of life. The aim of the present work was to establish Nordic Anal Cancer (NOAC) group guidelines for delineation of the CTVe in anal cancer. Methods: First, 12 radiation oncologists reviewed the literature in one of the following four areas: (1) previous delineation guidelines; (2) patterns of recurrence; (3) anatomical studies; (4) common iliac and para-aortic recurrences and delineation guidelines. Second, areas of controversy were identified and discussed with the aim of reaching consensus. Results: We present consensus-based recommendations for CTVe delineation in anal cancer regarding (a) which regions to include, and (b) how the regions should be delineated. Some of our recommendations deviate from current international guidelines. For instance, the posterolateral part of the inguinal region is excluded, decreasing the volume of irradiated normal tissue. For the external iliac region and the cranial border of the CTVe, we agreed on specifying two different recommendations, both considered acceptable. One of these recommendations is novel and risk-adapted; the external iliac region is omitted for low-risk patients, and several different cranial borders are used depending on the individual level of risk. Conclusion: We present NOAC consensus guidelines for delineation of the CTVe in anal cancer, including a risk-adapted strategy. [ABSTRACT FROM AUTHOR]

With the improvement of current online communication schemes, it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate. Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission. Both of the techniques mentioned above aid in the confidentiality of feature content. The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red, Green, and Blue. The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet. The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers. The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit (LSB) substitution technique to build the secret Content embedded image known as the stego image, which is subsequently transmitted across the internet medium to the server end. The experimental results are computed using the inputs from "Open-Access Medical Image Repositories (aylward.org)" and demonstrate the scheme's impudence as the Content concealing procedure progresses. [ABSTRACT FROM AUTHOR]

Purpose: Variability in contouring contributes to large variations in radiation therapy planning and treatment outcomes. The development and testing of tools to automatically detect contouring errors require a source of contours that includes well‐understood and realistic errors. The purpose of this work was to develop a simulation algorithm that intentionally injects errors of varying magnitudes into clinically accepted contours and produces realistic contours with different levels of variability. Methods: We used a dataset of CT scans from 14 prostate cancer patients with clinician‐drawn contours of the regions of interest (ROI) of the prostate, bladder, and rectum. Using our newly developed Parametric Delineation Uncertainties Contouring (PDUC) model, we automatically generated alternative, realistic contours. The PDUC model consists of the contrast‐based DU generator and a 3D smoothing layer. The DU generator transforms contours (deformation, contraction, and/or expansion) as a function of image contrast. The generated contours undergo 3D smoothing to obtain a realistic look. After model building, the first batch of auto‐generated contours was reviewed. Editing feedback from the reviews was then used in a filtering model for the auto‐selection of clinically acceptable (minor‐editing) DU contours. Results: Overall, C values of 5 and 50 consistently produced high proportions of minor‐editing contours across all ROI compared to the other C values (0.936 ±$ \pm \;$0.111 and 0.552 ±$ \pm \;$0.228, respectively). The model performed best on the bladder, which had the highest proportion of minor‐editing contours (0.606) of the three ROI. In addition, the classification AUC for the filtering model across all three ROI is 0.724 ±$ \pm \;$0.109. Discussion: The proposed methodology and subsequent results are promising and could have a great impact on treatment planning by generating mathematically simulated alternative structures that are clinically relevant and realistic enough (i.e., similar to clinician‐drawn contours) to be used in quality control of radiation therapy. [ABSTRACT FROM AUTHOR]

Background/Aim: The optimal imaging test for delineation of the gross tumor volume (GTV) in hepatocellular carcinoma has not been defined. The hypothesis is that magnetic resonance imaging (MRI) allows for better visualization of the extent of tumor and will optimize the accuracy of tumor delineation for liver stereotactic radiotherapy compared with computed tomography (CT) only. We evaluated the interobserver agreement in GTV of hepatocellular carcinoma in a multicenter panel and compared MRI and CT in GTV delineation. Materials and Methods: After the institutional review boards approved the study, we analyzed anonymous CT and MRI obtained from five patients with hepatocellular carcinoma. Eight radiation oncologists at our center used CT and MRI to delineate five GTVs of liver tumors. In both CT and MRI, the GTV volumes were compared. Results: The median GTV volume on MRI was 2.4 cm³ (range=0.59-15.6 cm³) compared to 3.5 cm3 (range=0.52-24.9 cm³) on CT (p=0.36). The GTV volume as defined on MRI was larger or at least as large as the GTV volume on CT in two cases. Variance and standard deviation between observers in CT and MRI were minor (6 vs. 7.87 cm³, and 2.5 vs. 2.8 cm³ respectively). Conclusion: In cases with welldefined tumors, CT is easier and reproducible. In cases with no defined tumor in CT, other tools are needed and MRI can be complementary. The interobserver variability in target delineation of hepatocellular carcinoma in this study is noteworthy. [ABSTRACT FROM AUTHOR]