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.

OBJECTIVE: The occurrence of genitourinary (GU) toxicity is a common adverse event observed after external beam radiation therapy (EBRT) for prostate cancer (PCa). Recent findings suggest that the dose delivered to specific urinary organs at risk (OARs) such as the ureters, bladder trigone, and urethra is involved in the development of GU toxicity.
METHODS: A multidisciplinary task force including 3 radiation oncologists, a uroradiologist, and a urologist was created in 2022. First, OARs potentially involved in GU toxicity were identified and discussed. A literature review was performed, addressing several questions relative to urinary OARs: anatomic and radiological definition, radiation-induced injury, and dose-volume parameters. Second, results were presented and discussed with a panel of radiation oncologists and members of the \"Francophone Group of Urological Radiation Therapy.\" Thereafter, the \"Francophone Group of Urological Radiation Therapy\" experts were asked to answer a dedicated questionnaire, including 35 questions on the controversial issues related to the delineation of urinary OARs.
RESULTS: The following structures were identified as critical for PCa EBRT: ureters, bladder, bladder neck, bladder trigone, urethra (intraprostatic, membranous, and spongious), striated sphincter, and postenucleation or posttransurethral resection of the prostate cavity. A consensus was obtained for 32 out of 35 items.
CONCLUSIONS: This consensus highlights contemporary urinary structures in both the upper and lower urinary tract to be considered for EBRT treatment planning of PCa. The current recommendations also propose a standardized definition of urinary OARs for both daily practice and future clinical trials.

The use of injectables can effectively treat the areas of greatest facial esthetic concern in males. Due to significant differences in the facial anatomy of men compared to women, treatment strategy, dosage, and technique differs. This article will review the pharmacology, preparation, pertinent anatomy, technique, risks, and adverse events associated with injectable agents emphasizing unique differences in male anatomy and esthetics.

OBJECTIVE: 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.
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.
CONCLUSIONS: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Butylscopolamine effectively reduced interobserver error and intra-fractional motion during the MRgOART treatment.

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.

OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.

OBJECTIVE: Contouring accuracy is critical in modern radiotherapy. Several tools are available to assist clinicians in this task. This study aims to evaluate the performance of the smoothing tool in the ARIA system to obtain more consistent volumes.
METHODS: Eleven different geometric shapes were delineated in ARIA v15.6 (Sphere, Cube, Square Prism, Six-Pointed Star Prism, Arrow Prism, And Cylinder and the respective volumes at 45° of axis deviation (_45)) in 1, 3, 5, 7, and 10 cm side or diameter each. Post-processing drawing tools to smooth those first-generated volumes were applied in different options (2D-ALL vs 3D) and grades (1, 3, 5, 10, 15, and 20). These volumetric transformations were analyzed by comparing different parameters: volume changes, center of mass, and DICE similarity coefficient index. Then we studied how smoothing affected two different volumes in a head and neck cancer patient: a single rounded node and the volume delineating cervical nodal areas.
RESULTS: No changes in data were found between 2D-ALL or 3D smoothing. Minimum deviations were found (range from 0 to 0.45 cm) in the center of mass. Volumes and the DICE index decreased as the degree of smoothing increased. Some discrepancies were found, especially in figures with cleft and spikes that behave differently. In the clinical case, smoothing should be applied only once throughout the target delineation process, preferably in the largest volume (PTV) to minimize errors.
CONCLUSIONS: Smoothing is a good tool to reduce artifacts due to the manual delineation of radiotherapy volumes. The resulting volumes must be always carefully reviewed.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.