Eating patterns, physical activity, and attempts to change weight among adolescents

OBJECTIVE

To determine eating patterns and demographic and dietary factors associated with adolescents' attempts to change weight.

METHODS

Data from students participating in the Massachusetts Youth Risk Behavior Survey (YRBS) were analyzed. Race, age, school grade, academic achievement, gender, body image, eating patterns, level of exercise, participation in team sports, cigarette use, diet pill use, and vomiting were examined. The weighted sample included 3055 students: 49% female and 78% white. Mean age was 16 years (+/- 1.2 years). Associations were measured using Spearman's r, Chi-square, Mantel-Haenszel test for trend, and logistic regression analyses.

RESULTS

A total of 61.5% of females and 21.5% of males reported trying to lose weight; 6.8% of females and 36.3% of males were trying to gain weight. There was a strong correlation (r = 0.62, p < 0.0001) between attempting to gain weight and self-perception of underweight for both genders. Females reported having changed their intake of several foods if attempting to change weight; males changed their intake of dessert foods. Hard exercise, stretching, and toning were associated with trying to lose weight among females and with trying to gain weight among males.

CONCLUSIONS

High-calorie food consumption and exercise were associated with attempts at weight change. Unhealthy eating to change weight highlights the need for effective nutrition and weight counseling among adolescents.

Lipolysis and ultrastructural changes of intracellular lipid vesicles after cooling of bovine and porcine GV-oocytes

The aim of our investigation was to compare the ultrastructure of lipid droplets, and the effect of cooling on intracellular lipid vesicles of bovine and porcine GV oocytes. The lipid droplets in bovine GV oocytes have a homogeneous structure. The utilization of lipids takes place directly from these vesicles without formation of interim lipid compounds. In contrast, there are two kinds of lipid droplets in porcine GV oocytes: 'dark', homogeneous vesicles next to 'grey' vesicles with electron-lucent streaks. Vesicles of each specific group are connected to each other. After a 12-h culture, the formation of the cisternal smooth endoplasmic reticulum layer was always associated with 'grey' lipid vesicles. This is evidence that during oogenesis lipolysis takes place only in 'grey' vesicles. It is supposed that cytoplasmic lipolysis has two stages: 'dark' vesicles change into a 'grey' form followed by a utilization of these 'grey' lipids. Furthermore, both types of lipid droplets in porcine oocytes changed morphologically during cooling: they changed into a spherical form with lucent streaks. Lipid droplets in bovine GV oocytes revealed no visible morphological changes after cooling.

Prognostic factors of radiographic progression in early rheumatoid arthritis: a two year prospective study after a structured therapeutic strategy using DMARDs and very low doses of glucocorticoids

The objective of the study was to analyze the prognostic factors of radiographic progression in a series of patients with early rheumatoid arthritis (RA) after 2 years of therapy with a structured algorithm using disease-modifying antirheumatic drugs (DMARDs) and very low doses of oral glucocorticoids. One hundred and five patients (81% female) with early RA (disease duration <2 years) treated with the same therapeutic protocol using gold salts and methotrexate in a step-up strategy, together with methylprednisolone (4 mg/day), were followed up for 2 years. The outcome variable was radiographic progression after 2 years of DMARD therapy using the modified Larsen method. Clinical, biological, immunogenetic, and radiographic data were analyzed at study entry and after 1 and 2 years of follow-up. Radiographic progression (increase of four or more units in the Larsen score) was observed in 32% of patients after 2 years of follow-up. The percentage of erosive disease increased from 18.3% at baseline to 28.9% at 12 months and 44.6% at 24 months, in spite of a significant improvement in disease activity. New erosions appeared in 33% of patients after 2 years. Several baseline parameters were associated with radiographic progression in the univariate analysis: shared epitope (SE) homozygozity, HLA-DRB*04 alleles, female gender, hemoglobin, erythrocyte sedimentation rate, and anticyclic citrullinated peptide antibodies (anti-CCP). In the multivariate analysis, female gender [odds ratio (OR) 5.5, 95% confidence interval (CI): 1.1-28.2, p = 0.04], DRB1*04 alleles (OR 3.1, 95% CI 1.1-9, p = 0.03) and, marginally, anti-CCP antibodies (OR 3.6, 95% CI 0.9-14.5, p = 0.06), were associated with progression. Female patients with both DRB1*04 alleles and anti-CCP antibodies showed the highest scores in radiographic progression. The presence, but not the titer, of anti-CCP antibodies predicted progression. The positive predictive value of the multivariate model for progression was only 53.9% whereas the negative predictive value was 80.3%. In a series of early RA patients treated with a structured algorithm using DMARDs and very low doses of glucocorticoids, radiographic progression was observed in one third of patients after 2 years. Female gender, DRB1*04 alleles (rather than the SE), and the presence of anti-CCP antibodies at baseline (independently of the titer) were the most important predictors of progression. The utility of these parameters in clinical practice is limited by their relatively low positive predictive value.

Bartsocas-Papas syndrome: three familial cases from Spain

We report on 3 Spanish sibs with the Bartsocas-Papas syndrome. This appears to be the seventh reported family; all but one of them are of Mediterranean origin. We propose that a generalized vascular disruption is the most likely pathogenetic mechanism for the anomalies found in this syndrome.

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

PURPOSE

This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans.

METHODS AND MATERIALS

A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists.

RESULTS

The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%,D95%, and D99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs.

CONCLUSIONS

Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.

Multi-element survey of allotment produce and soil in the UK

In 2004, a survey of 12 metals was carried out in six rural and six urban allotment sites across the UK. A total of 215 samples of vegetables and 36 samples of soft fruit were analysed using ICP-MS, and 51 samples of soil using ICP-OES. On a fresh weight basis, Cu levels in produce ranged 0.119-2.271 mg kg(-1). The highest levels measured (in mg kg(-1)) were Pb 0.164 in a sample of blackcurrants, Cd 0.039 in spinach, Hg 0.003 in curly kale and As 0.025 in raspberries. The lowest concentrations for these metals were below the level of detection (LOD) in numerous samples. The majority of Pt samples were below LOD and all were under the limit of quantification (LOQ). The results of the survey showed that the concentrations of the metals in vegetables, fruit and soils were consistent with previous studies and, generally, at low levels.

Growth hormone plus high protein diet promotes adaptation after massive bowel resection in aged rats

OBJECTIVE

To determine whether GH improves adaptation following massive bowel resection in the aged rat fed on a high protein-content diet.

MATERIAL AND METHODS

Seventy-seven male Wistar rats aged 22+/-1 months underwent 80% bowel resection or laparotomy (sham-operation). They were randomly placed into one of eight groups, treated with either growth hormone (1mg/kg/day) or saline, and fed a liquid diet containing either a high or a normal protein content. Intestinal tissue and blood samples were taken seven days after surgery and analysed to measure intestinal mucosal proliferation and mucosal height, as well as plasma levels of IGF-1 and somatostatin.

RESULTS

Resection of the small bowel in aged rats remarkably increased villous height and crypt proliferation. Growth hormone did not potentiate the increase in mucosal height and crypt proliferation observed after intestinal resection in aged rats fed a normal protein content diet, but did in those receiving a high-protein diet. Plasma levels of IGF-1 and somatostatin were not modified by surgery or treatment.

CONCLUSION

Growth hormone may increase the adaptation of intestinal mucosa in aged rats undergoing massive intestinal resection, but requires an adequate nutritional support with increased amounts of high quality protein.

Modified sequential multiplex PCR for determining capsular serotypes of invasive pneumococci recovered from Seville

The heptavalent pneumococcal vaccine's introduction resulted in a decline in invasive disease caused by Streptococcus pneumoniae, but was accompanied by an increase in non-vaccine serotypes. We evaluated a modified scheme of the sequential multiplex PCRs adapted to the prevalence of serotypes in Seville (Spain) for determining capsular serotypes of S. pneumoniae invasive clinical isolates. In adults, the modified scheme allowed us to type 73% with the first three reactions, and 92% with two additional PCRs. In paediatric patients, it allowed us to type 73.5% with the first three reactions, and 90% with the two additional PCRs. The multiplex PCR approach was successfully adapted to target the serotypes most prevalent in Seville.

Growth hormone effects in intestinal adaptation after massive bowel resection in the suckling rat

BACKGROUND

Massive small bowel resection provokes intestinal malabsorption that leads to diminished growth in the suckling rat. Growth hormone is one of the several factors that can enhance the adaptive response of the intestines in the adult rat; however, whether it also enhances postresection intestinal adaptation in the suckling rat, thus reducing the adverse effects of resection on growth, is still unclear.

METHODS

Seventy-four 30-day-old suckling Wistar rats underwent 80% midgut bowel resection, laparotomy (sham operation), or no surgery. They were treated with either growth hormone or saline for 15 days and studied 15 or 45 days after surgery. Body weight was monitored and samples of bone and intestinal mucosa were obtained at the end of the study period for analysis.

RESULTS

Resected rats lost body and bone weight regardless of growth hormone administration. Bowel resection provoked significant increases in the proliferation and size of the intestinal mucosa. Growth hormone significantly, but just barely, increased crypt height and mucosal mass at day 15 after surgery, but not at day 45. Lengthening of the intestines was the main effect of growth hormone.

CONCLUSIONS

The relatively small adaptive response of intestines to growth hormone is insufficient to promote body growth after intestinal resection in the suckling rat. This response is lower than that in older rats and may reflect an age-related differential response to growth hormone.

Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers

BACKGROUND

In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated.

PURPOSE

To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements.

METHODS

A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U-Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5-point scale (5, acceptable as-is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions.

RESULTS

The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel-wise dose difference was -0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics ofD 1 % ${D}_{1{\mathrm{\% }}}$ andD 98 % ${D}_{98{\mathrm{\% }}}$ were -1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were -0.30 ± 1.66 Gy and -0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions.

CONCLUSIONS

Deep-learning dose prediction can be used to predict high-quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.

Quantitative phase retrieval with low photon counts using an energy resolving quantum detector

X-ray phase contrast imaging (PCI) combined with phase retrieval has the potential to improve soft-material visibility and discrimination. This work examined the accuracy, image quality gains, and robustness of a spectral phase retrieval method proposed by our group. Spectroscopic PCI measurements of a physical phantom were obtained using state-of-the-art photon-counting detectors in combination with a polychromatic x-ray source. The phantom consisted of four poorly attenuating materials. Excellent accuracy was demonstrated in simultaneously retrieving the complete refractive properties (photoelectric absorption, attenuation, and phase) of these materials. Approximately 10 times higher SNR was achieved in retrieved images compared to the original PCI intensity image. These gains are also shown to be robust against increasing quantum noise, even for acquisition times as low as 1 s with a low-flux microfocus x-ray tube (average counts of 250 photons/pixels). We expect that this spectral phase retrieval method, adaptable to several PCI geometries, will allow significant dose reduction and improved material discrimination in clinical and industrial x-ray imaging applications.

A deep learning-based approach for statistical robustness evaluation in proton therapy treatment planning: a feasibility study

OBJECTIVE

Robustness evaluation is critical in particle radiotherapy due to its susceptibility to uncertainties. However, the customary method for robustness evaluation only considers a few uncertainty scenarios, which are insufficient to provide a consistent statistical interpretation. We propose an artificial intelligence-based approach that overcomes this limitation by predicting a set of percentile dose values at every voxel and allows for the evaluation of planning objectives at specific confidence levels. &#xD;Approach: We built and trained a deep learning (DL) model to predict the 5th and 95th percentile dose distributions, which corresponds to the lower and upper bounds of a two-tailed 90% confidence interval (CI), respectively. Predictions were made directly from the nominal dose distribution and planning computed tomography scan. The data used to train and test the model consisted of proton plans from 543 prostate cancer patients. The ground truth percentile values were estimated for each patient using 600 dose recalculations representing randomly sampled uncertainty scenarios. For comparison, we also tested whether a common worst-case scenario (WCS) robustness evaluation (voxel-wise minimum and maximum) corresponding to a 90% CI could reproduce the ground truth 5th and 95th percentile doses. &#xD;Main Results: The percentile dose distributions predicted by DL yielded excellent agreements with the ground truth dose distributions, with mean dose errors below 0.15Gy and average gamma passing rates (GPR) at 1 mm/1% above 93.9, which were substantially better than the WCS dose distributions (mean dose error above 2.2Gy and GPR at 1 mm/1% below 54). We observed similar outcomes in a dose-volume histogram error analysis, where the DL predictions generally yielded smaller mean errors and standard deviations than the WCS evaluation doses. &#xD;Significance: The proposed method produces accurate and fast predictions (~2.5s for one percentile dose distribution) for a given confidence level. Thus, the method has the potential to improve robustness evaluation.

Deep learning-based statistical robustness evaluation of intensity-modulated proton therapy for head and neck cancer

Objective. Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ∼8) or becomes unfeasible in daily clinical practice when the number is sufficiently large (e.g., >100). Our proposed deep learning (DL)-based method addressed this issue by avoiding the intermediate dose calculation step and instead directly predicting the percentile dose distribution from the nominal dose distribution using a DL model. In this study, we sought to validate this DL-based statistical robustness evaluation method for efficient and accurate robustness quantification in head and neck (H&N) intensity-modulated proton therapy with diverse beam configurations and multifield optimization.Approach. A dense, dilated 3D U-net was trained to predict the 5th and 95th percentile dose distributions of uncertainty scenarios using the nominal dose and planning CT images. The data set comprised proton therapy plans for 582 H&N cancer patients. Ground truth percentile values were estimated for each patient through 600 dose recalculations, representing randomly sampled uncertainty scenarios. The comprehensive comparisons of different models were conducted for H&N cancer patients, considering those with and without a beam mask and diverse beam configurations, including varying beam angles, couch angles, and beam numbers. The performance of our model trained based on a mixture of patients with H&N and prostate cancer was also assessed in contrast with models trained based on data specific for patients with cancer at either site.Results. The DL-based model's predictions of percentile dose distributions exhibited excellent agreement with the ground truth dose distributions. The average gamma index with 2 mm/2%, consistently exceeded 97% for both 5th and 95th percentile dose volumes. Mean dose-volume histogram error analysis revealed that predictions from the combined training set yielded mean errors and standard deviations that were generally similar to those in the specific patient training data sets.Significance. Our proposed DL-based method for evaluation of the robustness of proton therapy plans provides precise, rapid predictions of percentile dose for a given confidence level regardless of the beam arrangement and cancer site. This versatility positions our model as a valuable tool for evaluating the robustness of proton therapy across various cancer sites.

Antinociceptive and motor-blocking action of epidurally administered IQB-9302 and bupivacaine in the dog

BACKGROUND AND OBJECTIVES

The aim of this study was to compare the antinociceptive and motor-blocking effects of epidurally administered IQB-9302 (C18H26N2O.HCl) and bupivacaine in the dog.

METHODS

Twelve adult female Beagle dogs were used. Each animal received 3 concentrations (0.25%, 0.50%, and 0.75%) of either IQB-9302 (n = 6) or bupivacaine (n = 6) by means of a chronic epidural catheter. The nocifensive and motor-blocking status were determined at regular intervals before (baseline) and after drug administration.

RESULTS

Epidurally administered IQB-9302 caused a more potent nocifensive and motor-blocking action than bupivacaine. The duration of complete nocifensive block was the longest with IQB-9302, whereas the duration of dermatome nocifensive block was similar for both drugs. The nocifensive to motor block ratio was significantly higher with IQB-9302.

CONCLUSIONS

IQB-9302 produced an anesthetic action similar to that of bupivacaine, although the former drug induced a slightly more potent nocifensive block. Nocifensive and motor block duration are very similar with IQB-9302, whereas bupivacaine induces a more prolonged motor block without nocifensive block.

Deep learning techniques for proton dose prediction across multiple anatomical sites and variable beam configurations

Objective.To evaluate the impact of beam mask implementation and data aggregation on artificial intelligence-based dose prediction accuracy in proton therapy, with a focus on scenarios involving limited or highly heterogeneous datasets.Approach.In this study, 541 prostate and 632 head and neck (H&N) proton therapy plans were used to train and evaluate convolutional neural networks designed for the task of dose prediction. Datasets were grouped by anatomical site and beam configuration to assess the impact of beam masks-graphical depictions of radiation paths-as a model input. We also evaluated the effect of combining datasets. Model performance was measured using dose-volume histograms (DVHs) scores, mean absolute error, mean absolute percent error, dice similarity coefficients (DSCs), and gamma passing rates.Main results.DSC analysis revealed that the inclusion of beam masks improved dose prediction accuracy, particularly in low-dose regions and for datasets with diverse beam configurations. Data aggregation alone produced mixed results, with improvements in high-dose regions but potential degradation in low-dose areas. Notably, combining beam masks and data aggregation yielded the best overall performance, effectively leveraging the strengths of both strategies. Additionally, the magnitude of the improvements was larger for datasets with greater heterogeneity, with the combined approach increasing the DSC score by as much as 0.2 for a subgroup of H&N cases characterized by small size and heterogeneity in beam arrangement. DVH scores reflected these benefits, showing statistically significant improvements (p< 0.05) for the more heterogeneous H&N datasets.Significance.Artificial intelligence-based dose prediction models incorporating beam masks and data aggregation significantly improve accuracy in proton therapy planning, especially for complex cases. This technique could accelerate the planning process, enabling more efficient and effective cancer treatment strategies.

Disability measured by the modified health assessment questionnaire in early rheumatoid arthritis: prognostic factors after two years of follow-up

OBJECTIVE

To analyze the rate and baseline prognostic factors of disability measured by the modified HAQ (MHAQ), in a series of patients with early rheumatoid arthritis (RA) after two years of therapy with a structured algorithm using disease-modifying anti-rheumatic drugs (DMARDs).

METHODS

One hundred and five patients (81% female) with early RA (disease duration <2 years) treated with the same therapeutic protocol using gold salts and methotrexate in a step-up strategy, together with methylprednisolone (4 mg/day), were followed up for two years. The outcome was the absence of disability (MHAQ=0) after two years of DMARD therapy. Clinical, biological, immunogenetic and radiographic data (Larsen score) were analyzed at study entry and at 12 and 24 months of follow-up.

RESULTS

The MHAQ decreased significantly at 6 months after initiation of DMARD therapy and the reduction was maintained at 24 months (mean+/-SD: 0.97+/-0.56 at baseline, 0.51+/- 0.57 at month 6 and 0.45+/-0.5 at month 24). No disability (MHAQ=0) was observed in 26.6% of patients after two years of follow-up. Age, MHAQ>0.5, DAS28>5.1, VAS pain, positive rheumatoid factor and ESR at baseline were associated with disability in the univariate analysis. In the logistic regression analysis, only age (OR: 1.058, 95%CI 1.017; 1.101 p<0.006), rheumatoid factor status (OR: 3.772 95%CI 1.204; 11.813, p<0.02) and MHAQ>0.5 (OR:4.023, 95%CI 1.373; 11.783, p<0.02) were associated with disability (MHAQ>0) at two years.

CONCLUSION

In a series of early RA patients treated with a structured algorithm using DMARDs and very low doses of glucocorticoids, no disability was observed in a quarter of patients after two years. Age, rheumatoid factor positivity and MHAQ>0.5 were independent predictors of disability at two years.

The genomic profiling of high-risk smoldering myeloma patients treated with an intensive strategy unveils potential markers of resistance and progression

Smoldering multiple myeloma (SMM) precedes multiple myeloma (MM). The risk of progression of SMM patients is not uniform, thus different progression-risk models have been developed, although they are mainly based on clinical parameters. Recently, genomic predictors of progression have been defined for untreated SMM. However, the usefulness of such markers in the context of clinical trials evaluating upfront treatment in high-risk SMM (HR SMM) has not been explored yet, precluding the identification of baseline genomic alterations leading to drug resistance. For this reason, we carried out next-generation sequencing and fluorescent in-situ hybridization studies on 57 HR and ultra-high risk (UHR) SMM patients treated in the phase II GEM-CESAR clinical trial (NCT02415413). DIS3, FAM46C, and FGFR3 mutations, as well as t(4;14) and 1q alterations, were enriched in HR SMM. TRAF3 mutations were specifically associated with UHR SMM but identified cases with improved outcomes. Importantly, novel potential predictors of treatment resistance were identified: NRAS mutations and the co-occurrence of t(4;14) plus FGFR3 mutations were associated with an increased risk of biological progression. In conclusion, we have carried out for the first time a molecular characterization of HR SMM patients treated with an intensive regimen, identifying genomic predictors of poor outcomes in this setting.

Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer

BACKGROUND

The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease.

PURPOSE

The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines.

METHODS

The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV-Expansion (CTV1 and CTV2) and CTV-Overall (the outer envelope of all the CTV contours), we used adjustable morphological geometric landmarks and mimicked physician interpretation of the protocol rules by partially or fully including select anatomic structures. The results were evaluated quantitatively using the dice similarity coefficient (DSC) and mean surface distance (MSD) and qualitatively by independent reviews by two H&N radiation oncologists.

RESULTS

The auto-contouring tool showed high accuracy for nasopharyngeal CTVs. Comparison between auto-contours and clinical contours for 19 patients with cancers of various stages showed a DSC of 0.94 ± 0.02 and MSD of 0.4 ± 0.4 mm for CTV-Expansion and a DSC of 0.83 ± 0.02 and MSD of 2.4 ± 0.5 mm for CTV-Overall. Upon independent review, two H&N physicians found the auto-contours to be usable without edits in 85% and 75% of cases. In 15% of cases, minor edits were required by both physicians. Thus, one physician rated 100% of the auto-contours as usable (use as is, or after minor edits), while the other physician rated 90% as usable. The second physician required major edits in 10% of cases.

CONCLUSIONS

The study demonstrates the ability of an auto-contouring tool to reliably delineate nasopharyngeal CTVs based on protocol guidelines. The tool was found to be clinically acceptable by two H&N radiation oncology physicians in at least 90% of the cases.