The Value of PET/CT in Particle Therapy Planning of Various Tumors with SSTR2 Receptor Expression: Comparative Interobserver Study

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.

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

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 (HD95 $_{95}$ ).

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 HD95 $_{95}$ , 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.

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation

Objective. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consuming and prone to large inter-observer variability. With the advent of deep learning (DL) models, automated contouring has become possible, speeding up procedures and assisting clinicians. However, these tools are currently used in the clinic mostly for contouring OARs, since these systems are not reliable yet for contouring GTVs. To improve the reliability of these systems, researchers have started exploring the topic of probabilistic neural networks. However, there is still limited knowledge of the practical implementation of such networks in real clinical settings.Approach. In this work, we developed a 3D probabilistic system that generates DL-based uncertainty maps for lung cancer CT segmentations. We employed the Monte Carlo (MC) dropout technique to generate probabilistic and uncertainty maps, while the model calibration was evaluated by using reliability diagrams. A clinical validation was conducted in collaboration with a radiation oncologist to qualitatively assess the value of the uncertainty estimates. We also proposed two novel metrics, namely mean uncertainty (MU) and relative uncertainty volume (RUV), as potential indicators for clinicians to assess the need for independent visual checks of the DL-based segmentation. Main results. Our study showed that uncertainty mapping effectively identified cases of under or over-contouring. Although the overconfidence of the model, a strong correlation was observed between the clinical opinion and MU metric. Moreover, both MU and RUV revealed high AUC values in discretising between low and high uncertainty cases.Significance. Our study is one of the first attempts to clinically validate uncertainty estimates in DL-based contouring. The two proposed metrics exhibited promising potential as indicators for clinicians to independently assess the quality of tumour delineation.

Deep learning for automated segmentation in radiotherapy: a narrative review

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.

Patient-Specific Surgical Correction of Adolescent Idiopathic Scoliosis: A Systematic Review

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.

Experimental Examination of Conventional, Semi-Automatic, and Automatic Volumetry Tools for Segmentation of Pulmonary Nodules in a Phantom Study

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.

Nodal Elective Volume Selection and Definition during Radiation Therapy for Early Stage (T1-T2 N0 M0) Perianal Squamous Cell Carcinoma: A Narrative Clinical Review and Critical Appraisal

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.

Integrating Artificial Intelligence Into Radiation Oncology: Can Humans Spot AI?

Introduction Artificial intelligence (AI) is transforming healthcare, particularly in radiation oncology. AI-based contouring tools like Limbus are designed to delineate Organs at Risk (OAR) and Target Volumes quickly. This study evaluates the accuracy and efficiency of AI contouring compared to human radiation oncologists and the ability of professionals to differentiate between AI-generated and human-generated contours. Methods At a recent AI conference in Abu Dhabi, a blind comparative analysis was performed to assess AI's performance in radiation oncology. Participants included four human radiation oncologists and the Limbus® AI software. They contoured specific regions from CT scans of a breast cancer patient. The audience, consisting of healthcare professionals and AI experts, was challenged to identify the AI-generated contours. The exercise was repeated twice to observe any learning effects. Time taken for contouring and audience identification accuracy were recorded. Results Initially, only 28% of the audience correctly identified the AI contours, which slightly increased to 31% in the second attempt. This indicated a difficulty in distinguishing between AI and human expertise. The AI completed contouring in up to 60 seconds, significantly faster than the human average of 8 minutes. Discussion The results indicate that AI can perform radiation contouring comparably to human oncologists but much faster. The challenge faced by professionals in identifying AI versus human contours highlights AI's advanced capabilities in medical tasks. Conclusion AI shows promise in enhancing radiation oncology workflow by reducing contouring time without quality compromise. Further research is needed to confirm AI contouring's clinical efficacy and its integration into routine practice.

Impact of inter-observer variability on first axillary level dosimetry in breast cancer radiotherapy: An AIRO multi-institutional study

This study quantified the incidental dose to the first axillary level (L1) in locoregional treatment plan for breast cancer. Eighteen radiotherapy centres contoured L1-L4 on three different patients (P1,2,3), created the L2-L4 planning target volume (single centre planning target volume, SC-PTV) and elaborated a locoregional treatment plan. The L2-L4 gold standard clinical target volume (CTV) along with the gold standard L1 contour (GS-L1) were created by an expert consensus. The SC-PTV was then replaced by the GS-PTV and the incidental dose to GS-L1 was measured. Dosimetric data were analysed with Kruskal-Wallis test. Plans were intensity modulated radiotherapy (IMRT)-based. P3 with 90° arm setup had statistically significant higher L1 dose across the board than P1 and P2, with the mean dose (Dmean) reaching clinical significance. Dmean of P1 and P2 was consistent with the literature (77.4% and 74.7%, respectively). The incidental dose depended mostly on L1 proportion included in the breast fields, underlining the importance of the setup, even in case of IMRT.

Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy

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.

Consistency in contouring of organs at risk by artificial intelligence vs oncologists in head and neck cancer patients

BACKGROUND

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy

Purpose/objectives

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 auto-segmentation 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.

Nordic anal cancer (NOAC) group consensus guidelines for risk-adapted delineation of the elective clinical target volume in anal cancer

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.

Parametric delineation uncertainties contouring (PDUC) modeling on CT scans of prostate cancer patients

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.

E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation

Purpose

Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a crowdsourced challenge engaging radiation oncologists across various expertise levels in segmentation. An obstacle to artificial intelligence (AI) development is the paucity of multiexpert datasets; consequently, we sought to characterize whether aggregate segmentations generated from multiple nonexperts could meet or exceed recognized expert agreement.

Approach

Participants who contoured ≥ 1 region of interest (ROI) for the breast, sarcoma, head and neck (H&N), gynecologic (GYN), or gastrointestinal (GI) cases were identified as a nonexpert or recognized expert. Cohort-specific ROIs were combined into single simultaneous truth and performance level estimation (STAPLE) consensus segmentations.STAPLE nonexpert ROIs were evaluated againstSTAPLE expert contours using Dice similarity coefficient (DSC). The expert interobserver DSC (IODSC expert ) was calculated as an acceptability threshold betweenSTAPLE nonexpert andSTAPLE expert . To determine the number of nonexperts required to match theIODSC expert for each ROI, a single consensus contour was generated using variable numbers of nonexperts and then compared to theIODSC expert .

Results

For all cases, the DSC values forSTAPLE nonexpert versusSTAPLE expert were higher than comparator expertIODSC expert for most ROIs. The minimum number of nonexpert segmentations needed for a consensus ROI to achieveIODSC expert acceptability criteria ranged between 2 and 4 for breast, 3 and 5 for sarcoma, 3 and 5 for H&N, 3 and 5 for GYN, and 3 for GI.

Conclusions

Multiple nonexpert-generated consensus ROIs met or exceeded expert-derived acceptability thresholds. Five nonexperts could potentially generate consensus segmentations for most ROIs with performance approximating experts, suggesting nonexpert segmentations as feasible cost-effective AI inputs.

FDA MAUDE database reported adverse events on noninvasive body contouring, cellulite treatment, and muscle stimulation from 2015 to 2021

OBJECTIVE

Noninvasive cosmetic procedures have continued to gain popularity, owing to their short, in-office treatments combined with little to no downtime. These procedures are also highly accessible, even offered at medical spas by nonphysician operators. The coronavirus disease 2019 (COVID-19) pandemic also saw heightened interest in all cosmetic procedures, presumably as social distancing and stay-at-home orders allotted time and space for postop recovery. As the market for these procedures expand, a thorough understanding of potential adverse events is critical for providers to better counsel their patients on risks and expectations when obtaining informed consent.

MATERIALS AND METHODS

We employed the Food and Drug Administration (FDA's) Manufacturer and User Facility Device Experience (MAUDE) database (http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfmaude/search.cfm), which compiles medical device reports (MDRs) for suspected injuries from device use or malfunction, submitted by manufactures and operators. We focused our query on three main categories: noninvasive body contouring, cellulite treatments, and muscle stimulation therapies that utilize electromagnetic energy. The query was performed in February 2022 using a comprehensive list of product names and manufacturers.

RESULTS

The initial search yielded 827 MDRs, which were individually reviewed for duplicate reports or insufficient data. Ultimately, 723 MDRs were analyzed (660 for noninvasive body contouring, 55 for cellulite treatment, and 8 for muscle stimulation). Paradoxical hyperplasia accounted for the majority of MDRs for noninvasive body contouring, while burns and scars were most common for muscle stimulation and cellulite treatments, respectively. Of the 7-year span we surveyed, 2021 accounted for 515 of the 723 total assessed MDRs (71.2%), the majority of which were from cryolipolysis procedures.

CONCLUSION

The MAUDE database remains an essential tool to monitor potential adverse events of medical devices, including those utilized for noninvasive, cosmetic procedures. Insight from the MAUDE database can be clinically translated when discussing treatment options with patients, helping to optimize patient safety and satisfaction.

Interobserver variability in gross tumor volume contouring in non-spine bone metastases

BACKGROUND AND AIM

The optimal imaging test for gross tumor volume (GTV) delineation in non-spine bone metastases has not been defined. The use of stereotactic body radiotherapy (SBRT) requires accurate target delineation. Magnetic resonance imaging (MRI) and/or ¹⁸fludesoxyglucose positron emission tomography (18FDG-PET) allow for better visualization of the extent of bone metastases and optimizes the accuracy of tumor delineation for stereotactic radiotherapy compared to computed tomography (CT) alone. We evaluated the interobserver agreement in GTV of non-spine bone metastases in a single center and compared MRI and/or 18FDG-PET and CT in GTV delineation.

METHODS

Anonymous CT and MRI and/or 18FDG-PET obtained from 10 non-spine bone metastases were analyzed by six radiation oncologists at our center. Images acquired by CT and MRI and/or 18FDG-PET were used to delineate 10 GTVs of non-spine bone metastases in the pelvis, extremities, and skull. The cases showed different characteristics: blastic and lytic metastases, and different primary cancers (lung, breast, prostate, rectum, urothelial, and biliary). In both CT and MRI and/or 18FDG-PET, the GTV volumes were compared. The index of agreement was evaluated according to Landis and Koch protocol.

RESULTS

The GTV volume as defined on MRI was in all cases larger or at least as large as the GTV volume on CT (P=0.25). The median GTV volume on MRI was 3.15 cc (0.027-70.64 cc) compared to 2.8 cc on CT (0.075-77.95 cc). Interobserver variance and standard deviation were lower in CT than MRI (576.3 vs. 722.2 and 24.0 vs. 26.9, respectively). The level of agreement was fair (kappa=0.36) between CT and MRI. The median GTV volume on 18FDG-PET in five patients was 5.8 cc (0.46-64.17 cc), compared to 4.1 cc on CT (0.99-54.2 cc) (P=0.236). Interobserver variance and standard deviation in CT, MRI, and 18FDG-PET were 576.3 versus 722.2 versus 730.5 and 24 versus 26.9 versus 27.0, respectively. The level of agreement was slight (kappa=0.08) between CT and 18FDG-PET.

CONCLUSIONS

Interobserver variance in non-spine bone metastases was equal when MRI and PET were compared to CT. CT was associated with the lowest variance and standard deviation. Compared to CT GTVs, the GTVs rendered from MRI images had fair agreement, while the GTVs rendered from 18FDG-PET had only slight agreement.

RELEVANCE FOR PATIENTS

The delimitation of the treatment volume in non-spine bone metastases with SBRT is important for the results determining its efficacy. It is therefore essential to know the variability and to manage it to achieve the highest quality of treatment.

Implementation of a Commercial Deep Learning-Based Auto Segmentation Software in Radiotherapy: Evaluation of Effectiveness and Impact on Workflow

Proper delineation of both target volumes and organs at risk is a crucial step in the radiation therapy workflow. This process is normally carried out manually by medical doctors, hence demanding timewise. To improve efficiency, auto-contouring methods have been proposed. We assessed a specific commercial software to investigate its impact on the radiotherapy workflow on four specific disease sites: head and neck, prostate, breast, and rectum. For the present study, we used a commercial deep learning-based auto-segmentation software, namely Limbus Contour (LC), Version 1.5.0 (Limbus AI Inc., Regina, SK, Canada). The software uses deep convolutional neural network models based on a U-net architecture, specific for each structure. Manual and automatic segmentation were compared on disease-specific organs at risk. Contouring time, geometrical performance (volume variation, Dice Similarity Coefficient-DSC, and center of mass shift), and dosimetric impact (DVH differences) were evaluated. With respect to time savings, the maximum advantage was seen in the setting of head and neck cancer with a 65%-time reduction. The average DSC was 0.72. The best agreement was found for lungs. Good results were highlighted for bladder, heart, and femoral heads. The most relevant dosimetric difference was in the rectal cancer case, where the mean volume covered by the 45 Gy isodose was 10.4 cm³ for manual contouring and 289.4 cm³ for automatic segmentation. Automatic contouring was able to significantly reduce the time required in the procedure, simplifying the workflow, and reducing interobserver variability. Its implementation was able to improve the radiation therapy workflow in our department.

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation

Functional magnetic resonance imaging (fMRI) of the human spinal cord (SC) is a unique non-invasive method for characterizing neurovascular responses to stimuli. Group-analysis of SC fMRI data involves co-registration of subject-level data to standard space, which requires manual masking of the cord and may result in bias of group-level SC fMRI results. To test this, we examined variability in SC masks drawn in fMRI data from 21 healthy participants from a completed study mapping responses to sensory stimuli of the C7 dermatome. Masks were drawn on temporal mean functional image by eight raters with varying levels of neuroimaging experience, and the rater from the original study acted as a reference. Spatial agreement between rater and reference masks was measured using the Dice Similarity Coefficient, and the influence of rater and dataset was examined using ANOVA. Each rater's masks were used to register functional data to the PAM50 template. Gray matter-white matter signal contrast of registered functional data was used to evaluate the spatial normalization accuracy across raters. Subject- and group-level analyses of activation during left- and right-sided sensory stimuli were performed for each rater's co-registered data. Agreement with the reference SC mask was associated with both rater (F(7, 140) = 32.12, P < 2 × 10-16, η2 = 0.29) and dataset (F(20, 140) = 20.58, P < 2 × 10-16, η2 = 0.53). Dataset variations may reflect image quality metrics: the ratio between the signal intensity of spinal cord voxels and surrounding cerebrospinal fluid was correlated with DSC results (p < 0.001). As predicted, variability in the manually-drawn masks influenced spatial normalization, and GM:WM contrast in the registered data showed significant effects of rater and dataset (rater: F(8, 160) = 23.57, P < 2 × 10-16, η2 = 0.24; dataset: F(20, 160) = 22.00, P < 2 × 10-16, η2 = 0.56). Registration differences propagated into subject-level activation maps which showed rater-dependent agreement with the reference. Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.

Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning

Objective: This study aimed to investigate the segmentation accuracy of organs at risk (OARs) when denoised computed tomography (CT) images are used as input data for a deep-learning-based auto-segmentation framework. Methods: We used non-contrast enhanced planning CT scans from 40 patients with breast cancer. The heart, lungs, esophagus, spinal cord, and liver were manually delineated by two experienced radiation oncologists in a double-blind manner. The denoised CT images were used as input data for the AccuContourTM segmentation software to increase the signal difference between structures of interest and unwanted noise in non-contrast CT. The accuracy of the segmentation was assessed using the Dice similarity coefficient (DSC), and the results were compared with those of conventional deep-learning-based auto-segmentation without denoising. Results: The average DSC outcomes were higher than 0.80 for all OARs except for the esophagus. AccuContourTM-based and denoising-based auto-segmentation demonstrated comparable performance for the lungs and spinal cord but showed limited performance for the esophagus. Denoising-based auto-segmentation for the liver was minimal but had statistically significantly better DSC than AccuContourTM-based auto-segmentation (p < 0.05). Conclusions: Denoising-based auto-segmentation demonstrated satisfactory performance in automatic liver segmentation from non-contrast enhanced CT scans. Further external validation studies with larger cohorts are needed to verify the usefulness of denoising-based auto-segmentation.

Quality assurance based on deep learning for pelvic OARs delineation in radiotherapy

BACKGROUND

Correct delineation of organs at risk (OARs) is an important step for radiotherapy and it is also a time-consuming process that depends on many factors.

OBJECTIVE

An automatic quality assurance (QA) method based on deep learning (DL) was proposed to improve efficiency for detecting contouring errors of OARs.

MATERIALS AND METHODS

A total of 180 planning CT scan sets at the pelvic site and the corresponding OARs contours from clinics were enrolled in this study. Among them, 140 cases were randomly chosen as the training datasets, 20 cases were used as the validation datasets, and the remaining 20 cases were used as the test datasets. DL-based models were trained through data curation for data cleaning based on the Dice similarity coefficient and the 95th percentile Hausdorff distance between the original contours and the predicted contours. All contouring errors could be classified into two types：minor modification required and major modification required. The pass criteria were established using Bias-Corrected and Accelerated bootstrap on 20 manually reviewed validation datasets. The performance of the QA method was evaluated with the metrics of sensitivity, specificity, the area under the receiving operator characteristic curve (AUC) and detection rate sensitivity on the 20 test datasets.

RESULTS

For all OARs, segmentation results after data curation were superior to those without. The sensitivity of the QA method was greater than 0.890 and the specificity was higher than 0.975. The AUCs were 0.948, 0.966, 0.965 and 0.932 for the bladder, right femoral head, left femoral head and rectum, respectively. Almost all major errors could be detected by the automatic QA method, and the lowest detection rate sensitivity of minor errors was 0.863 for the rectum.

CONCLUSIONS

QA of OARs is an important step for the correct implementation of radiotherapy. The DL-based QA method proposed in this study showed a high potential to automatically detect contouring errors with high precision. The method can be integrated into the existing radiotherapy procedures to improve the efficiency of delineating the OARs.

Evaluation of inter- and intra-observer variations in prostate gland delineation using CT-alone versus CT/TPUS

Background

This study aims to explore the role of four-dimensional (4D) transperineal ultrasound (TPUS) in the contouring of prostate gland with planning computed tomography (CT) images, in the absence of magnetic resonance imaging (MRI).

Materials and methods

Five radiation oncologists (ROs) performed two rounds of prostate gland contouring (single-blinded) on CT-alone and CT/TPUS datasets obtained from 10 patients who underwent TPUS-guided external beam radiotherapy. Parameters include prostate volume, DICE similarity coefficient (DSC) and centroid position. Wilcoxon signed-rank test assessed the significance of inter-modality differences, and the intraclass correlation coefficient (ICC ) reflected inter- and intra-observer reliability of parameters.

Results

Inter-modality analysis revealed high agreement (based on DSC and centroid position) of prostate gland contours between CT-alone and CT/TPUS. Statistical significant difference was observed in the superior-inferior direction of the prostate centroid position (p = 0.011). All modalities yielded excellent inter-observer reliability of delineated prostate volume with ICC > 0.9, mean DSC > 0.8 and centroid position: CT-alone (ICC = 1.000) and CT/TPUS (ICC = 0.999) left-right (L/R); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 0.998) anterior-posterior (A/P); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 1.000) superior-inferior (S/I). Similarly, all modalities yielded excellent intra-observer reliability of delineated prostate volume, ICC > 0.9 and mean DSC > 0.8. Lastly, intra-observer reliability was excellent on both imaging modalities for the prostate centroid position, ICC > 0.9.

Conclusion

TPUS does not add significantly to the amount of anatomical information provided by CT images. However, TPUS can supplement planning CT to achieve a higher positional accuracy in the S/I direction if access to CT/MRI fusion is limited.

Prior information guided auto-contouring of breast gland for deformable image registration in postoperative breast cancer radiotherapy

Background

Contouring of breast gland in planning CT is important to postoperative radiotherapy of patients after breast conserving surgery (BCS). However, the contouring task is difficult because of the poorer contrast of breast gland in planning CT. To improve its efficiency and accuracy, prior information was introduced in a 3D U-Net model to predict the contour of breast gland automatically.

Methods

The preoperative CT was first aligned to the planning CT via affine registration. The resulting transform was then applied to the contour of breast gland in preoperative CT, and the corresponding contour in planning CT was obtained. This transformed contour was a preliminary estimation of breast gland in planning CT and was used as prior information in a 3D U-Net model to obtain a more accurate contour. For evaluation, the dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess the deep learning (DL) model's prediction accuracy.

Results

The average DSC and HD of the prediction model were 0.775±0.065 and 44.979±20.565 for breast gland without the input of prior information, while the average values were 0.830±0.038 and 17.896±5.737 with the input of prior information (0.775 vs. 0.830, P=0.0014<0.05; 44.979 vs. 17.896, P=0.002<0.05).

Conclusions

The prediction accuracy was increased significantly with the introduction of prior information, which provided valuable geometrical distribution of target for model training. This method provides an effective way to identify low-contrast targets from surrounding tissues in CT and will be useful in other image modalities.

Longevity and subject-reported satisfaction after minimally invasive jawline contouring

BACKGROUND

Minimally invasive treatments as soft tissue filler injections can enhance the appearance of the jawline. This prospective, single-center study investigated aesthetic outcome, patient satisfaction, adverse events, and volume changes after jawline contouring using standardized reporting scales and objectifiable 3D surface analysis.

METHODS

A total of 30 patients (1 male and 29 females, mean age: 57.2 (±8.7) years) were investigated. Patients underwent jawline augmentation using a highly cross-linked hyaluronic acid-based soft tissue filler. Three-dimensional surface imaging was performed after 2 weeks, and 3, 6, 9, and 12 months. Furthermore, the aesthetic results and the occurrence of complications were investigated after two weeks, and 3, 6, 9, and 12 months.

RESULTS

The surface-volume coefficient (SVC) had an average of 1.10 ± 0.2 after 14 days, 0.95 ± 0.1 after 3 months, 0.83 ± 0.1 after 6 months, 0.74 ± 0.1 after 9 months, and 0.63 ± 0.1 after 12 months. A significant correlation was revealed between time of measurement and measured SVC with r_p  = -0.761, p < 0.001. Multivariate analysis revealed a significant difference between the measured SVC and the different time points of measurement with p < 0.001. The data revealed strong aesthetic improvement with results most often reported as "very much improved" according to the 5-point GAIS after 3, 6, and 9 months, both by the investigator and by the patients. A 12-month follow-up analysis showed "much improved" results in a majority of cases.

CONCLUSION

The result of this investigation showed that jawline enhancement using minimally invasive soft tissue filler injections produces durable, safe results that are generally rated as very satisfying from a patient's and investigator's perspective over a time period of 12 months.

Remote Contouring and Virtual Review during the COVID-19 Pandemic (RECOVR-COVID19): Results of a Quality Improvement Initiative for Virtual Resident Training in Radiation Oncology

The need to minimize in-person interactions during the COVID-19 pandemic has led to fewer clinical learning opportunities for trainees. With ongoing utilization of virtual platforms for resident education, efforts to maximize their value are essential. Herein we describe a resident-led quality improvement initiative to optimize remote contouring and virtual contour review. From April to June 2020, radiation oncology (RO) residents at our institution were assigned modified duties. We implemented a program to source and assign cases to residents for remote contouring and to promote and optimize virtual contour review. Resident-perceived educational value was prospectively collected and analyzed. All nine RO residents at our institution (PGY1–5) participated, and 97 cases were contoured during the evaluation period. Introduction of the Remote Contouring and Virtual Review (RECOVR) program coincided with a significant increase in mean cases contoured per week, from 5.5 to 17.3 (p = 0.015), and an increased proportion of cases receiving virtual review, from 14.8% to 58.6% (p < 0.001). Residents reported that the value of immediate feedback during virtual review was similar to that of in-person review (4.6 ± 0.1 vs. 4.5 ± 0.2, p = 0.803) and significantly higher than feedback received post hoc (e.g., email; 3.6 ± 0.2, p < 0.001). The implementation of a remote process for contour review led to significant increases in contouring, and virtual contour review was rated as highly as in-person interactions. Our findings provide a data-driven rationale and framework for integrating remote contouring and virtual review into competency-based medical education.

Deep learning for segmentation in radiation therapy planning: a review

Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual segmentation is a tedious and time-consuming task for clinicians, and inter-observer variability can affect the outcomes of radiation therapy. The recent hype over deep neural networks has added many powerful auto-segmentation methods as variations of convolutional neural networks (CNN). This paper presents a descriptive review of the literature on deep learning techniques for segmentation in radiation therapy planning. The most common CNN architecture across the four clinical sub sites considered was U-net, with the majority of deep learning segmentation articles focussed on head and neck normal tissue structures. The most common data sets were CT images from an inhouse source, along with some public data sets. N-fold cross-validation was commonly employed; however, not all work separated training, test and validation data sets. This area of research is expanding rapidly. To facilitate comparisons of proposed methods and benchmarking, consistent use of appropriate metrics and independent validation should be carefully considered.

Evaluation of T2-Weighted MRI for Visualization and Sparing of Urethra with MR-Guided Radiation Therapy (MRgRT) On-Board MRI

Simple Summary

Stereotactic body radiation therapy (SBRT) has become a standard of care option for prostate cancer patients, utilizing large fractionated dose to shorten treatment times. However, genitourinary (GU) toxicity associated with urethral injury remains prevalent due to non-trivial urethra delineation and sparing at treatment planning and treatment delivery. The aim of our study was to evaluate two optimized urethral MRI sequences (3D HASTE and 3D TSE) with a 0.35T MR-guided radiotherapy (MRgRT) system for urethral visibility and delineation. Among 11 prostate cancer patients, a radiation oncologist qualitatively scored MRgRT 3D HASTE as having the best urethra visibility, superior to CT, clinical MRgRT 3D bSSFP, MRgRT 3D TSE, and similar to diagnostic 3T (2D/3D) T2-weighetd MRI. Moreover, urethra contours from different imaging and clinical workflows demonstrated significant urethra localization variability. Optimized 3D MRgRT HASTE can provide urethral visualization and delineation within an MRgRT workflow for urethral sparing, avoiding cross-modality/system registration errors.

Abstract

Purpose: To evaluate urethral contours from two optimized urethral MRI sequences with an MR-guided radiotherapy system (MRgRT). Methods: 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. Results: 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. Conclusion: 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.

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

BACKGROUND

Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain.

OBJECTIVE

Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice.

METHODS

The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions.

RESULTS

We demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training.

CONCLUSIONS

Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Interactive contouring through contextual deep learning

PURPOSE

To investigate a deep learning approach that enables three-dimensional (3D) segmentation of an arbitrary structure of interest given a user provided two-dimensional (2D) contour for context. Such an approach could decrease delineation times and improve contouring consistency, particularly for anatomical structures for which no automatic segmentation tools exist.

METHODS

A series of deep learning segmentation models using a Recurrent Residual U-Net with attention gates was trained with a successively expanding training set. Contextual information was provided to the models, using a previously contoured slice as an input, in addition to the slice to be contoured. In total, 6 models were developed, and 19 different anatomical structures were used for training and testing. Each of the models was evaluated for all 19 structures, even if they were excluded from the training set, in order to assess the model's ability to segment unseen structures of interest. Each model's performance was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance, and relative added path length (APL).

RESULTS

The segmentation performance for seen and unseen structures improved when the training set was expanded by addition of structures previously excluded from the training set. A model trained exclusively on heart structures achieved a DSC of 0.33, HD of 44 mm, and relative APL of 0.85 when segmenting the spleen, whereas a model trained on a diverse set of structures, but still excluding the spleen, achieved a DSC of 0.80, HD of 13 mm, and relative APL of 0.35. Iterative prediction performed better compared to direct prediction when considering unseen structures.

CONCLUSIONS

Training a contextual deep learning model on a diverse set of structures increases the segmentation performance for the structures in the training set, but importantly enables the model to generalize and make predictions even for unseen structures that were not represented in the training set. This shows that user-provided context can be incorporated into deep learning contouring to facilitate semi-automatic segmentation of CT images for any given structure. Such an approach can enable faster de-novo contouring in clinical practice.

An anomaly detector as a clinical decision support system for parotid gland delineations

Purpose. Auto-contouring (AC) is rapidly becoming standard practice for OAR contouring. However, in clinical practice, clinicians still need to manually check and correct contours. Anomaly detection systems (ADS) can aid the clinical decision process by suggesting which structures require corrections or not, greatly enhancing the value of AC. The purpose of this work is to develop and evaluate a decision support system for detecting anomalies in the case of parotid gland delineations.

METHODS

Head and neck parotid gland delineations (1037 right, 1038 left), were retrieved from the Netherlands Cancer Institute (NKI) database. Morphological and image-based features were extracted from each patient's CT and structure set. An isolation forest model was initially trained on 70% of the data, of which 10% had synthetically generated anomalies and validated on the remaining 30% of clinical data. The ADS was tested on an independent set of 250 patients (Normal: 174, Anomalies: 76) and on a clinical autocontouring software.

RESULTS

Applied to the validation set, the ADS system resulted in area under the curve (AUC) values of 0.93 and 0.94 for the parotid left and right respectively. Image features appeared more important than morphological, but using all features resulted marginally in the best model. Applied to the test set the ADS system reached an accuracy level of 0.83 and 0.81 for the parotid left and right respectively. The ADS was particularly sensitive to uniform expansions/contractions, misplacements, extra/missing slices and anisotropic over-contouring.

CONCLUSION

Anomaly detection can serve as a powerful contour quality assurance tool, especially for cases of organ misplacement and over-contouring.

A Novel Technique for Fixation of Non-union Unicondylar Hoffa's Fracture: A Case Report

Hoffa fractures are rare and difficult fractures to manage. Hoffa fracture involves a coronal plane fracture of posterior femoral condyle. Non-union in Hoffa fracture is further difficult to manage. The surgical management for such nonunion includes open reduction with recon/LCP plate or screw fixation with bone grafting. The problem with plates is the difficulty in contouring the plates according to the shape of posterior femoral condyles. We describe a new technique with 2 L shaped neutralisation plates placed in a circular fashion. This technique provides a more rigid construct and gives better holding strength of screws in Hoffa fragment. This enhances union and mobilisation can be started early.

A systematic approach to contouring and polishing anterior resin composite restorations: A checklist manifesto

PURPOSE

This article presents a systematic, step-by-step checklist approach to be used for contouring and polishing anterior resin composite restorations to achieve maximum esthetics efficiently.

CLINICAL CONSIDERATIONS

This checklist is intended to be used to take the guesswork out and streamline the process to predictably, practically, and repeatedly contour and polish anterior resin composite restorations. The practitioner's knowledge of basic dental anatomy combined with this step-by-step checklist facilitates identifying and modifying the final restoration to an anatomically correct form, thus satisfying the most esthetically demanding patients. This approach is demonstrated with case presentation of direct resin veneers in a young female, which resulted in an improved smile that satisfied her esthetic desires.

CONCLUSIONS

The use of standardized protocols facilitates and expedites daily procedures in dentistry. Specifically, this checklist protocol, which is geared towards contouring and polishing anterior direct resin composite restorations.

CLINICAL SIGNIFICANCE

The clinical technique presented in this article shows the advantages of using a step-by-step checklist approach to predictably and efficiently obtain ideal esthetics when performing anterior resin composite restorations.

Comparison of Manual and Semi-Automatic [18F]PSMA-1007 PET Based Contouring Techniques for Intraprostatic Tumor Delineation in Patients With Primary Prostate Cancer and Validation With Histopathology as Standard of Reference

PURPOSE

Accurate contouring of intraprostatic gross tumor volume (GTV) is pivotal for successful delivery of focal therapies and for biopsy guidance in patients with primary prostate cancer (PCa). Contouring of GTVs, using 18-Fluor labeled tracer prostate specific membrane antigen positron emission tomography ([18F]PSMA-1007/PET) has not been examined yet.

PATIENTS AND METHODS

Ten Patients with primary PCa who underwent [18F]PSMA-1007 PET followed by radical prostatectomy were prospectively enrolled. Coregistered histopathological gross tumor volume (GTV-Histo) was used as standard of reference. PSMA-PET images were contoured on two ways: (1) manual contouring with PET scaling SUVmin-max: 0-10 was performed by three teams with different levels of experience. Team 1 repeated contouring at a different time point, resulting in n = 4 manual contours. (2) Semi-automatic contouring approaches using SUVmax thresholds of 20-50% were performed. Interobserver agreement was assessed for manual contouring by calculating the Dice Similarity Coefficient (DSC) and for all approaches sensitivity, specificity were calculated by dividing the prostate in each CT slice into four equal quadrants under consideration of histopathology as standard of reference.

RESULTS

Manual contouring yielded an excellent interobserver agreement with a median DSC of 0.90 (range 0.87-0.94). Volumes derived from scaling SUVmin-max 0-10 showed no statistically significant difference from GTV-Histo and high sensitivities (median 87%, range 84-90%) and specificities (median 96%, range 96-100%). GTVs using semi-automatic segmentation applying a threshold of 20-40% of SUVmax showed no significant difference in absolute volumes to GTV-Histo, GTV-SUV50% was significantly smaller. Best performing semi-automatic contour (GTV-SUV20%) achieved high sensitivity (median 93%) and specificity (median 96%). There was no statistically significant difference to SUVmin-max 0-10.

CONCLUSION

Manual contouring with PET scaling SUVmin-max 0-10 and semi-automatic contouring applying a threshold of 20% of SUVmax achieved high sensitivities and very high specificities and are recommended for [18F]PSMA-1007 PET based focal therapy approaches. Providing high specificities, semi-automatic approaches applying thresholds of 30-40% of SUVmax are recommend for biopsy guidance.

Evaluation of natural growth rate and recommended age for shaving procedure by volumetric analysis of craniofacial fibrous dysplasia

BACKGROUND

We evaluated the preoperative natural growth pattern of craniofacial fibrous dysplasia and postoperative volume changes in patients undergoing shaving procedures.

METHODS

Thirty-three patients who underwent serial computed tomography (CT) preoperatively and/or postoperatively were identified. The natural tumor growth rate was assessed using preoperative CT scans. The postoperative tumor regrowth rates and relevant variables were analyzed.

RESULTS

The preoperative tumor growth rates were significantly lower in patients aged ≥ 16 years than in those aged < 16 years (P < .001). The postoperative tumor regrowth rates were significantly greater when a shaving operation was performed at age < 16 years than at age ≥ 16 years (P = .04). In patients with clinical recurrence, the postoperative remnant tumor volume was inversely correlated with the tumor regrowth rate.

CONCLUSIONS

The tumor growth rate of craniofacial fibrous dysplasia significantly decreased after age 16. This should be considered when conducting functional and aesthetic assessments in planning for the shaving of craniofacial fibrous dysplasia.

The emerging role of radiation therapists in the contouring of organs at risk in radiotherapy: analysis of inter-observer variability with radiation oncologists for the chest and upper abdomen

Aims

To compare the contouring of organs at risk (OAR) between a clinical specialist radiation therapist (CSRT) and radiation oncologists (ROs) with different levels of expertise (senior–SRO, junior–JRO, fellow–FRO).

Methods

On ten planning computed tomography (CT) image sets of patients undergoing breast radiotherapy (RT), the observers independently contoured the contralateral breast, heart, left anterior descending artery (LAD), oesophagus, kidney, liver, spinal cord, stomach and trachea. The CSRT was instructed by the JRO e SRO. The inter-observer variability of contoured volumes was measured using the Dice similarity coefficient (DSC) (threshold of ≥ 0.7 for good concordance) and the centre of mass distance (CMD). The analysis of variance (ANOVA) was performed and a p-value < 0.01 was considered statistically significant.

Results

Good overlaps (DSC > 0.7) were obtained for all OARs, except for LAD (DSC = 0.34 ± 0.17, mean ± standard deviation) and oesophagus (DSC = 0.66 ± 0.06, mean ± SD). The mean CMD < 1 cm was achieved for all the OARs, but spinal cord (CMD = 1.22 cm). By pairing the observers, mean DSC > 0.7 and mean CMD < 1 cm were achieved in all cases. The best overlaps were seen for the pairs JRO-CSRT(DSC = 0.82; CMD = 0.49 cm) and SRO-JRO (DSC = 0.80; CMD = 0.51 cm).

Conclusions

Overall, good concordance was found for all the observers. Despite the short training in contouring, CSRT obtained good concordance with his tutor (JRO). Great variability was seen in contouring the LAD, due to its difficult visualization and identification of CT scans without contrast.

Implementing user-defined atlas-based auto-segmentation for a large multi-centre organisation: the Australian Experience

INTRODUCTION

Contouring has become an increasingly important aspect of radiation therapy due to inverse planning, and yet is extremely time-consuming. To improve contouring efficiency and reduce potential inter-observer variation, the atlas-based auto-segmentation (ABAS) function in Velocity was introduced to ICON cancer centres (ICC) throughout Australia as a solution for automatic contouring.

METHODS

This paper described the implementation process of the ABAS function and the construction of user-defined atlas sets and compared the contouring efficiency before and after the introduction of ABAS.

RESULTS

The results indicate that the main limitation to the ABAS performance was Velocity's sub-optimal atlas selection method. Three user-defined atlas sets were constructed. Results suggested that the introduction of the ABAS saved at least 5 minutes of manual contouring time (P < 0.05), although further verification was required due to limitations in the data collection method. The pilot rollout adopting a 'champion' approach was successful and provided an opportunity to improve the user-defined atlases prior to the national implementation.

CONCLUSION

The implementation of user-defined ABAS for head and neck (H&N) and female thorax patients at ICCs was successful, which achieved at least 5 minutes of efficiency gain.

Improved dosimetric accuracy with semi-automatic contour propagation of organs-at-risk in glioblastoma patients undergoing chemoradiation

Background

We study the changes in organs‐at‐risk (OARs) morphology as contoured on serial MRIs during chemoradiation therapy (CRT) of glioblastoma (GBM). The dosimetric implication of assuming non‐deformable OAR changes and the accuracy and feasibility of semi‐automatic OAR contour propagation are investigated.

Methods

Fourteen GBM patients who were treated with adjuvant CRT for GBM prospectively underwent MRIs on fractions 0 (i.e., planning), 10, 20, and 1 month post last fraction of CRT. Three sets of OAR contours — (a) manual, (b) rigidly registered (static), and (c) semi‐automatically propagated — were compared using Dice similarity coefficient (DSC) and Hausdorff distance (HD). Dosimetric impact was determined by comparing the minimum dose to the 0.03 cc receiving the highest dose (D0.03 cc) on a clinically approved reference, non‐adapted radiation therapy plan.

Results

The DSC between the manual contours and the static contours decreased significantly over time (fraction 10: [mean ± 1 SD] 0.78 ± 0.17, post 1 month: 0.76 ± 0.17, P = 0.02) while the HD (P = 0.74) and the difference in D0.03cc did not change significantly (P = 0.51). Using the manual contours as reference, compared to static contours, propagated contours have a significantly higher DSC (propagated: [mean ± 1 SD] 0.81 ± 0.15, static: 0.77 ± 0.17, P < 0.001), lower HD (propagated: 3.77 ± 1.8 mm, static: 3.96 ± 1.6 mm, P = 0.002), and a significantly lower absolute difference in D0.03cc (propagated: 101 ± 159 cGy, static: 136 ± 243 cGy, P = 0.019).

Conclusions

Nonrigid changes in OARs over time lead to different maximum doses than planned. By using semi‐automatic OAR contour propagation, OARs are more accurately delineated on subsequent fractions, with corresponding improved accuracy of the reported dose to the OARs.

Provision of Organ at Risk Contouring Guidance in UK Radiotherapy Clinical Trials

AIMS

Accurate delineation of organs at risk (OAR) is vital to the radiotherapy planning process. Inaccuracies in OAR delineation arising from imprecise anatomical definitions may affect plan optimisation and risk inappropriate dose delivery to normal tissues. The aim of this study was to review the provision of OAR contouring guidance in National Institute of Health Research Clinical Research Network (NIHR CRN) portfolio clinical trials.

MATERIALS AND METHODS

The National Radiotherapy Quality Trials Assurance (RTTQA) Group carried out a two-round Delphi assessment to determine which OAR descriptions provided optimal guidance.

RESULTS

Eighty-four clinical trials involving radiotherapy quality assurance were identified as either in recruitment or in setup within the NIHR CRN portfolio. Fifty-nine trials mandated OAR contouring. In total there were 412 OAR; 171 were uniquely named; 159 OAR had more than one name associated with a single structure, with the greatest nomenclature variation seen for the femoral head ± neck, the parotid gland, and bowel. The two-round Delphi assessment determined 42 OAR descriptions as providing optimal contouring guidance.

CONCLUSIONS

This study identified the need for OAR nomenclature and contouring guidance consistency across clinical trials. In response to this study and in conjunction with the Global Quality Assurance of Radiation Therapy Clinical Trials Harmonisation Group, the RTTQA Group is in collaboration with international partners to provide consensus recommendations for OAR delineation in clinical trials.

Clinical Evaluation of Commercial Atlas-Based Auto-Segmentation in the Head and Neck Region

Background: While atlas segmentation (AS) has proven to be a time-saving and promising method for radiation therapy contouring, optimal methods for its use have not been well-established. Therefore, we investigated the relationship between the size of the atlas patient population and the atlas segmentation auto contouring (AC) performance.

Methods: A total of 110 patients' head planning CT images were selected. The mandible and thyroid were selected for this study. The mandibles and thyroids of the patient population were carefully segmented by two skilled clinicians. Of the 110 patients, 100 random patients were registered to 5 different atlas libraries as atlas patients, in groups of 20 to 100, with increments of 20. AS was conducted for each of the remaining 10 patients, either by simultaneous atlas segmentation (SAS) or independent atlas segmentation (IAS). The AS duration of each target patient was recorded. To validate the accuracy of the generated contours, auto contours were compared to manually generated contours (MC) using a volume-overlap-dependent metric, Dice Similarity Coefficient (DSC), and a distance-dependent metric, Hausdorff Distance (HD).

Results: In both organs, as the population increased from n = 20 to n = 60, the results showed better convergence. Generally, independent cases produced better performance than simultaneous cases. For the mandible, the best performance was achieved by n = 60 [DSC = 0.92 (0.01) and HD = 6.73 (1.31) mm] and the worst by n = 100 [DSC = 0.90 (0.03) and HD = 10.10 (6.52) mm] atlas libraries. Similar results were achieved with the thyroid; the best performance was achieved by n = 60 [DSC = 0.79 (0.06) and HD = 10.17 (2.89) mm] and the worst by n = 100 [DSC = 0.72 (0.13) and HD = 12.88 (3.94) mm] atlas libraries. Both IAS and SAS showed similar results. Manual contouring of the mandible and thyroid required an average of 1,044 (±170.15) seconds, while AS required an average of 46.4 (±2.8) seconds.

Conclusions: The performance of AS AC generally increased as the population of the atlas library increased. However, the performance does not drastically vary in the larger atlas libraries in contrast to the logic that bigger atlas library should lead to better results. In fact, the results do not vary significantly toward the larger atlas library. It is necessary for the institutions to independently research the optimal number of subjects.

Multi-observer contouring of male pelvic anatomy: Highly variable agreement across conventional and emerging structures of interest

INTRODUCTION

This study quantified inter-observer contouring variations for multiple male pelvic structures, many of which are of emerging relevance for prostate cancer radiotherapy progression and toxicity response studies.

METHODS

Five prostate cancer patient datasets (CT and T2-weighted MR) were distributed to 13 observers for contouring. CT structures contoured included the clinical target volume (CTV), seminal vesicles, rectum, colon, bowel bag, bladder and peri-rectal space (PRS). MR contours included CTV, trigone, membranous urethra, penile bulb, neurovascular bundle and multiple pelvic floor muscles. Contouring variations were assessed using the intraclass correlation coefficient (ICC), Dice similarity coefficient (DSC), and multiple additional metrics.

RESULTS

Clinical target volume (CT and MR), bladder, rectum and PRS contours showed excellent inter-observer agreement (median ICC = 0.97; 0.99; 1.00; 0.95; 0.90, DSC = 0.83 ± 0.05; 0.88 ± 0.05; 0.93 ± 0.03; 0.81 ± 0.07; 0.80 ± 0.06, respectively). Seminal vesicle contours were more variable (ICC = 0.75, DSC = 0.73 ± 0.14), while colon and bowel bag contoured volumes were consistent (ICC = 0.97; 0.97), but displayed poor overlap (DSC = 0.58 ± 0.22; 0.67 ± 0.21). Smaller MR structures showed significant inter-observer variations, with poor overlap for trigone, membranous urethra, penile bulb, and left and right neurovascular bundles (DSC = 0.44 ± 0.22; 0.41 ± 0.21; 0.66 ± 0.21; 0.16 ± 0.17; 0.15 ± 0.15). Pelvic floor muscles recorded moderate to strong inter-observer agreement (ICC = 0.50-0.97), although large outlier variations were observed.

CONCLUSIONS

Inter-observer contouring variation was significant for multiple pelvic structures contoured on MR.

Safety and efficacy of a novel high-intensity focused electromagnetic technology device for noninvasive abdominal body shaping

BACKGROUND

Thermal fat reduction technologies are leading the market for nonsurgical abdominal contouring. However, they are ideal principally for patients with fat bulges.

OBJECTIVES

Our study investigates the effects of a novel nonthermal technology affecting the abdominal musculature and subcutaneous adipose tissue.

MATERIALS AND METHODS

A total of 22 patients (avg. BMI 23.8 kg m^-2 ) underwent 4 treatments on abdomen with high-intensity focused electromagnetic (HIFEM) field device. Treatments took 30 minutes and were spaced apart by 2-3 days. Photographs, weight, and waist measurements were taken at the baseline, after the last treatment, and at month 3 follow-up. Patient satisfaction was noted. Photographs were evaluated by blinded evaluators.

RESULTS

The study protocol was completed by 19 patients. At month 3, the average waist size reduction was 4.37 ± 2.63 cm (P < 0.01). The evaluators identified the before image from the 3-month image 89.47% of the time. About 91% of patients reported their abdominal appearance improved, and 92% stated they are satisfied with treatment results at month 3. No adverse events occurred.

CONCLUSION

Observed waist size reduction and aesthetic improvement appear to be a combination of fat reduction and increased muscle definition of abdominal wall. In lower BMI patients, the increased abdominal muscle definition was largely responsible for the improvement. This novel energy device provides an additional tool for body contouring with primary application for lower and medium BMI patients.

Implementation of temporal lobe contouring protocol in head and neck cancer radiotherapy planning: A quality improvement project

Temporal lobe necrosis as result of radiation for nasopharyngeal cancer (NPC) occurs up to 28% of NPC patients. The only effective mitigation is by strict adherence to temporal lobe dose tolerances during radiotherapy planning, which in turn hinges on accurate temporal lobe delineation. We aim to improve the accuracy and to standardize temporal lobe contouring for patients receiving head and neck radiotherapy for NPC in a tertiary teaching hospital in Singapore.The baseline data were obtained from 10 patients in the diagnostic phase and the effect of interventions were measured in 37 patients who underwent head and neck radiotherapy over a 6-month period.We conducted the project based on the Clinical Practice Improvement Program methodology. The baseline pooled mean percentage variation in temporal lobe contouring was 39.9% (0.8%-60.2%). There was a low level of temporal lobe contouring concordance and this provided the impetus for implementation of strategies to improve the accuracy and reproducibility of temporal lobe contouring. The interventions included supervision and training of radiation therapists and residents in temporal lobe contouring, and standardization of temporal lobe contouring with a protocol and contouring atlas.Thirty-seven patients were treated during the study period from June to November 2014. Following implementation of the first set of interventions, the pooled mean percentage variation in temporal lobe contouring decreased but was not sustained. The implementation of the second set of interventions resulted in a decrease from 39.9% (January to September 2014) to 17.3% (October to November 2014) where P = .004 using t test. Weekly variation was seen throughout the study period but the decrease was sustained after standardizing and providing a contouring atlas for temporal lobe contouring.Temporal lobe contouring can be standardized through effective implementation of a temporal lobe contouring protocol and atlas.

Dosimetric Implications of Computerised Tomography-Only versus Magnetic Resonance-Fusion Contouring in Stereotactic Body Radiotherapy for Prostate Cancer

Background: Magnetic resonance (MR)-fusion contouring is the standard of care in prostate stereotactic body radiotherapy (SBRT) for target volume localisation. However, the planning computerised tomography (CT) scan continues to be used for dose calculation and treatment planning and verification. Discrepancies between the planning MR and CT scans may negate the benefits of MR-fusion contouring and it adds a significant resource burden. We aimed to determine whether CT-only contouring resulted in a dosimetric detriment compared with MR-fusion contouring in prostate SBRT planning. Methods: We retrospectively compared target volumes and SBRT plans for 20 patients treated clinically with MR-fusion contouring (standard of care) with those produced by re-contouring using CT data only. Dose was 36.25 Gy in 5 fractions. CT-only contouring was done on two occasions blind to MR data and reviewed by a separate observer. Primary outcome was the difference in rectal volume receiving 36 Gy or above. Results: Absolute target volumes were similar: 63.5 cc (SD ± 27.9) versus 63.2 (SD ± 26.5), Dice coefficient 0.86 (SD ± 0.04). Mean difference in apex superior-inferior position was 1.1 (SD ± 3.5; CI: −0.4–2.6). Small dosimetric differences in favour of CT-only contours were seen, with the mean rectal V36 Gy 0.3 cc (95% CI: 0.1–0.5) lower for CT-only contouring. Conclusions: Prostate SBRT can be successfully planned without MR-fusion contouring. Consideration can be given to omitting MR-fusion from the prostate SBRT workflow, provided reference to diagnostic MR imaging is available. Development of MR-only work flow is a key research priority to gain access to the anatomical fidelity of MR imaging.

Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: A multi-institutional study

PURPOSE

To investigate the performance of various algorithms for deformable image registration (DIR) to propagate regions of interest (ROIs) using multiple commercial platforms.

METHODS AND MATERIALS

Thirteen institutions participated in the study with six commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH, USA), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA, USA), Mirada XD (Mirada Medical Ltd, Oxford, UK), and ABAS (Elekta AB, Stockholm, Sweden). The DIR algorithms were tested on synthetic images generated with the ImSimQA package (Oncology Systems Limited, Shrewsbury, UK) by applying two specific Deformation Vector Fields (DVF) to real patient data-sets. Head-and-neck (HN), thorax, and pelvis sites were included. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Statistical inference on validation results was carried out in order to identify the prognostic factors of DIR performances.

RESULTS

DVF intensity, anatomic site and participating center were significant prognostic factors of DIR performances. Sub-voxel accuracy was obtained in the HN by all algorithms. Large errors, with MDC ranging up to 6 mm, were observed in low-contrast regions that underwent significant deformation, such as in the pelvis, or large DVF with strong contrast, such as the clinical tumor volume (CTV) in the lung. Under these conditions, the hybrid DIR algorithms performed significantly better than the free-form intensity based algorithms and resulted robust against intercenter variability.

CONCLUSIONS

The performances of the systems proved to be site specific, depending on the DVF type and the platforms and the procedures used at the various centers. The pelvis was the most challenging site for most of the algorithms, which failed to achieve sub-voxel accuracy. Improved reproducibility was observed among the centers using the same hybrid registration algorithm.

Interobserver reliability of computed tomographic contouring of canine tonsils in radiation therapy treatment planning

In radiation therapy (RT) treatment planning for canine head and neck cancer, the tonsils may be included as part of the treated volume. Delineation of tonsils on computed tomography (CT) scans is difficult. Error or uncertainty in the volume and location of contoured structures may result in treatment failure. The purpose of this prospective, observer agreement study was to assess the interobserver agreement of tonsillar contouring by two groups of trained observers. Thirty dogs undergoing pre- and post-contrast CT studies of the head were included. After the pre- and postcontrast CT scans, the tonsils were identified via direct visualization, barium paste was applied bilaterally to the visible tonsils, and a third CT scan was acquired. Data from each of the three CT scans were registered in an RT treatment planning system. Two groups of observers (one veterinary radiologist and one veterinary radiation oncologist in each group) contoured bilateral tonsils by consensus, obtaining three sets of contours. Tonsil volume and location data were obtained from both groups. The contour volumes and locations were compared between groups using mixed (fixed and random effect) linear models. There was no significant difference between each group's contours in terms of three-dimensional coordinates. However there was a significant difference between each group's contours in terms of the tonsillar volume (P < 0.0001). Pre- and postcontrast CT can be used to identify the location of canine tonsils with reasonable agreement between trained observers. Discrepancy in tonsillar volume between groups of trained observers may affect RT treatment outcome.

Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status

Radiomic features characterize tumor imaging phenotype. Nonsmall cell lung cancer (NSCLC) tumors are known for their complexity in shape and wide range in density. We explored the effects of variable tumor contouring on the prediction of epidermal growth factor receptor (EGFR) mutation status by radiomics in NSCLC patients treated with a targeted therapy (Gefitinib). Forty-six early stage NSCLC patients (EGFR mutant:wildtype = 20:26) were included. Three experienced radiologists independently delineated the tumors using a semiautomated segmentation software on a noncontrast-enhanced baseline and three-week post-therapy CT scan images that were reconstructed using 1.25-mm slice thickness and lung kernel. Eighty-nine radiomic features were computed on both scans and their changes (radiomic delta-features) were calculated. The highest area under the curves (AUCs) were 0.87, 0.85, and 0.80 for the three radiologists and the number of significant features ([Formula: see text]) was 3, 5, and 0, respectively. The AUCs of a single feature significantly varied among radiologists (e.g., 0.88, 0.75, and 0.73 for run-length primitive length uniformity). We conclude that a three-week change in tumor imaging phenotype allows identifying the EGFR mutational status of NSCLC. However, interobserver variability in tumor contouring translates into a significant variability in radiomic metrics accuracy.

Contouring and dose calculation in head and neck cancer radiotherapy after reduction of metal artifacts in CT images

BACKGROUND

Delineation accuracy of the gross tumor volume (GTV) in radiotherapy planning for head and neck (H&N) cancer is affected by computed tomography (CT) artifacts from metal implants which obscure identification of tumor as well as organs at risk (OAR). This study investigates the impact of metal artifact reduction (MAR) in H&N patients in terms of delineation consistency and dose calculation precision in radiation treatment planning.

MATERIAL AND METHODS

Tumor and OAR delineations were evaluated in planning CT scans of eleven oropharynx patients with streaking artifacts in the tumor region preceding curative radiotherapy (RT). The GTV-tumor (GTV-T), GTV-node and parotid glands were contoured by four independent observers on standard CT images and MAR images. Dose calculation was evaluated on thirty H&N patients with dental implants near the treated volume. For each patient, the dose derived from the clinical treatment plan using the standard image set was compared with the recalculated dose on the MAR image dataset.

RESULTS

Reduction of metal artifacts resulted in larger volumes of all delineated structures compared to standard reconstruction. The GTV-T and the parotids were on average 22% (p < 0.06) and 7% larger (p = 0.005), respectively, in the MAR image plan compared to the standard image plan. Dice index showed reduced inter-observer variations after reduction of metal artifacts for all structures. The average surface distance between contours of different observers improved using the MAR images for GTV and parotids (p = 0.04 and p = 0.01). The median volume receiving a dose difference larger than ±3% was 2.3 cm³ (range 0-32 cm³).

CONCLUSIONS

Delineation of structures in the head and neck were affected by metal artifacts and volumes were generally larger and more consistent after reduction of metal artifacts, however, only small changes were observed in the dose calculations.

MRI Reduces Variation of Contouring for Boost Clinical Target Volume in Breast Cancer Patients Without Surgical Clips in the Tumour Bed

Background

Omitting the placement of clips inside tumour bed during breast cancer surgery poses a challenge for delineation of lumpectomy cavity clinical target volume (CTV_LC). We aimed to quantify inter-observer variation and accuracy for CT- and MRI-based segmentation of CTV_LC in patients without clips.

Patients and methods

CT- and MRI-simulator images of 12 breast cancer patients, treated by breast conserving surgery and radiotherapy, were included in this study. Five radiation oncologists recorded the cavity visualization score (CVS) and delineated CTV_LC on both modalities. Expert-consensus (EC) contours were delineated by a senior radiation oncologist, respecting opinions of all observers. Inter-observer volumetric variation and generalized conformity index (CI_gen) were calculated. Deviations from EC contour were quantified by the accuracy index (AI) and inter-delineation distances (IDD).

Results

Mean CVS was 3.88 +/− 0.99 and 3.05 +/− 1.07 for MRI and CT, respectively (p = 0.001). Mean volumes of CTV_LC were similar: 154 +/− 26 cm³ on CT and 152 +/− 19 cm³ on MRI. Mean CI_gen and AI were superior for MRI when compared with CT (CI_gen: 0.74 +/− 0.07 vs. 0.67 +/− 0.12, p = 0.007; AI: 0.81 +/− 0.04 vs. 0.76 +/− 0.07; p = 0.004). CI_gen and AI increased with increasing CVS. Mean IDD was 3 mm +/− 1.5 mm and 3.6 mm +/− 2.3 mm for MRI and CT, respectively (p = 0.017).

Conclusions

When compared with CT, MRI improved visualization of post-lumpectomy changes, reduced interobserver variation and improved the accuracy of CTV_LC contouring in patients without clips in the tumour bed. Further studies with bigger sample sizes are needed to confirm our findings.

ATX-101 for reduction of submental fat: A phase III randomized controlled trial

BACKGROUND

ATX-101, an injectable form of deoxycholic acid, causes adipocytolysis when injected subcutaneously into fat.

OBJECTIVE

We sought to evaluate the efficacy and safety of ATX-101.

METHODS

In this phase III trial (REFINE-2), adults dissatisfied with their moderate or severe submental fat (SMF) were randomized to ATX-101 or placebo. Coprimary end points, evaluated at 12 weeks after last treatment, were composite improvements of 1 or more grades and 2 or more grades in SMF observed on both the validated Clinician- and Patient-Reported SMF Rating Scales. Other end points included magnetic resonance imaging-based assessment of submental volume, assessment of psychological impact of SMF, and additional patient-reported outcomes.

RESULTS

Among those treated with ATX-101 or placebo (n = 258/treatment group), 66.5% versus 22.2%, respectively, achieved a composite improvement of 1 or more grades (Mantel-Haenszel risk ratio 2.98; 95% confidence interval 2.31-3.85) and 18.6% versus 3.0% achieved a composite improvement of 2 or more grades in SMF (Mantel-Haenszel risk ratio 6.27; 95% confidence interval 2.91-13.52; P < .001 for both). Those treated with ATX-101 were more likely to achieve submental volume reduction confirmed by magnetic resonance imaging, greater reduction in psychological impact of SMF, and satisfaction with treatment (P < .001 for all). Overall, 85.7% of adverse events in the ATX-101 group and 76.9% in the placebo group were localized to the injection site.

LIMITATIONS

Follow-up was limited to 44 weeks.

CONCLUSION

ATX-101 is an alternative treatment for SMF reduction.

A Prostate Fossa Contouring Instructional Module: Implementation and Evaluation

PURPOSE/OBJECTIVE

Radiation oncology trainees frequently learn to contour through clinical experience and lectures. A hands-on contouring module was developed to teach delineation of the postoperative prostate clinical target volume (CTV) and improve contouring accuracy.

METHODS

Medical students independently contoured a prostate fossa CTV before and after receiving educational materials and live instruction detailing the RTOG approach to contouring this CTV. Metrics for volume overlap and surface distance (Dice similarity coefficient, Hausdorff distance (HD), and mean distance) determined discordance between student and consensus contours. An evaluation assessed perception of session efficacy (1 = "not at all" to 5 = "extremely"; reported as median[interquartile range]). Non-parametric statistical tests were used.

RESULTS

Twenty-four students at two institutions completed the module, and 21 completed the evaluation (88% response). The content was rated as "quite" important (4[3.5-5]). The module improved comfort contouring a prostate fossa (pre 1[1-2] vs. post 4[3-4], p<.01), ability to find references (pre 2[1-3] vs. post 4[3.5-4], p<0.01), knowledge of CT prostate/pelvis anatomy (pre 2[1.5-3] vs. post 3[3-4], p<.01), and ability to use contouring software tools (pre 2[2-3.5] vs. post 3[3-4], p=.01). After intervention, mean DSC increased (0.29 to 0.68, p<0.01) and HD and mean distance both decreased, respectively (42.8 to 30.0, p<.01; 11.5 to 1.9, p<.01).

CONCLUSIONS

A hands-on module to teach CTV delineation to medical students was developed and implemented. Student and expert contours exhibited near "excellent agreement" (as defined in the literature) after intervention. Additional modules to teach target delineation to all educational levels can be developed using this model.