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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Unifying simulation and inference with normalizing flows

There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration. Published by the American Physical Society 2025

Hadronic calorimiters↗
DOE OSTI2023

A Reconfigurable Planar Capacitor Sensor for Thru the Jacket Dielectric Constant Measurement of Cable Insulation

Planar capacitor sensors have been proposed to measure the aging-related dielectric constant variation of the jacket or insulation materials of cables. Although capacitor sensors have been used to monitor the aging of cable insulation by removing the jacket, through the jacket measurement of cable insulation dielectric constant variation is difficult because once the sensor is placed on the jacket it would either measure the aging of the jacket or that of the jacket and insulation together. A reconfigurable sensor along with innovative cable-specific models could be used to determine the jacket and insulation aging using the same sensor. For the proposed novel reconfigurable sensor, the electric field penetration depth is changed by activating and deactivating PIN diode switches. In one instance, the sensor measures the dielectric constant variation of the jacket, while in the next, it measures the dielectric constant variation of both the jacket and the insulation. Here, by leveraging previously developed capacitance estimation models from large-scale finite element simulations for a specific cable, these two sets of measurement data are used to evaluate the aging-related dielectric constant variation of the insulation. Experimental data from aged cable specimens are presented to demonstrate the operation of the sensor.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Segmentation of Deposition Periods: An Opportunity to Improve PV Soiling Extraction

Soiling profiles are commonly assumed to have sawtooth shapes, made of alternating cleaning events and soiling deposition periods. The rates at which soiling deposit on the PV modules are considered to be constant for each period. In reality, events such as changes in climatic conditions can lead to a sudden variation in soiling deposition rate. These changes cannot be reproduced if cleanings are the only events modelled to extract soiling profiles directly from PV performance data. For this reason, in this work, the use of change points and segmented regression is proposed to improve the extraction of soiling profiles through the model of up to two deposition rates per period in between cleanings. The results show that the quality of soiling extraction can be enhanced compared to a cleanings-only identification approach if both cleanings and change points are considered.

monitoring↗
DOE OSTI2016

Distribution system model calibration with big data from AMI and PV inverters

Efficient management and coordination of distributed energy resources with advanced automation schemes requires accurate distribution system modeling and monitoring. Big data from smart meters and photovoltaic (PV) micro-inverters can be leveraged to calibrate existing utility models. This paper presents computationally efficient distribution system parameter estimation algorithms to improve the accuracy of existing utility feeder radial secondary circuit model parameters. The method is demonstrated using a real utility feeder model with advanced metering infrastructure (AMI) and PV micro-inverters, along with alternative parameter estimation approaches that can be used to improve secondary circuit models when limited measurement data is available. Lastly, the parameter estimation accuracy is demonstrated for both a three-phase test circuit with typical secondary circuit topologies and single-phase secondary circuits in a real mixed-phase test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Thermokarst rates intensify due to climate change and forest fragmentation in an Alaskan boreal forest lowland

Abstract Lowland boreal forest ecosystems in Alaska are dominated by wetlands comprised of a complex mosaic of fens, collapse‐scar bogs, low shrub/scrub, and forests growing on elevated ice‐rich permafrost soils. Thermokarst has affected the lowlands of the Tanana Flats in central Alaska for centuries, as thawing permafrost collapses forests that transition to wetlands. Located within the discontinuous permafrost zone, this region has significantly warmed over the past half‐century, and much of these carbon‐rich permafrost soils are now within ~0.5 °C of thawing. Increased permafrost thaw in lowland boreal forests in response to warming may have consequences for the climate system. This study evaluates the trajectories and potential drivers of 60 years of forest change in a landscape subjected to permafrost thaw in unburned dominant forest types (paper birch and black spruce) associated with location on elevated permafrost plateau and across multiple time periods (1949, 1978, 1986, 1998, and 2009) using historical and contemporary aerial and satellite images for change detection. We developed (i) a deterministic statistical model to evaluate the potential climatic controls on forest change using gradient boosting and regression tree analysis, and (ii) a 30 × 30 m land cover map of the Tanana Flats to estimate the potential landscape‐level losses of forest area due to thermokarst from 1949 to 2009. Over the 60‐year period, we observed a nonlinear loss of birch forests and a relatively continuous gain of spruce forest associated with thermokarst and forest succession, while gradient boosting/regression tree models identify precipitation and forest fragmentation as the primary factors controlling birch and spruce forest change, respectively. Between 1950 and 2009, landscape‐level analysis estimates a transition of ~15 km² or ~7% of birch forests to wetlands, where the greatest change followed warm periods. This work highlights that the vulnerability and resilience of lowland ice‐rich permafrost ecosystems to climate changes depend on forest type.

Lara, Mark J.↗
DOE OSTI2019

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (T a ) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to T a and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought-induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to T a anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Reference air kerma and kerma-area product as estimators of peak skin dose for fluoroscopically guided interventions

Purpose: To determine more accurate regression formulas for estimating peak skin dose (PSD) from reference air kerma (RAK) or kerma-area product (KAP). Methods: After grouping of the data from 21 procedures into 13 clinically similar groups, assessments were made of optimal clustering using the Bayesian information criterion to obtain the optimal linear regressions of (log-transformed) PSD vs RAK, PSD vs KAP, and PSD vs RAK and KAP. Results: Three clusters of clinical groups were optimal in regression of PSD vs RAK, seven clusters of clinical groups were optimal in regression of PSD vs KAP, and six clusters of clinical groups were optimal in regression of PSD vs RAK and KAP. Prediction of PSD using both RAK and KAP is significantly better than prediction of PSD with either RAK or KAP alone. The regression of PSD vs RAK provided better predictions of PSD than the regression of PSD vs KAP. The partial-pooling (clustered) method yields smaller mean squared errors compared with the complete-pooling method.Conclusion: PSD distributions for interventional radiology procedures are log-normal. Estimates of PSD derived from RAK and KAP jointly are most accurate, followed closely by estimates derived from RAK alone. Estimates of PSD derived from KAP alone are the least accurate. Using a stochastic search approach, it is possible to cluster together certain dissimilar types of procedures to minimize the total error sum of squares.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

An empirical model of diagnostic x-ray attenuation under narrow-beam geometry

Purpose: The purpose of this study was to develop and validate a mathematical model to describe narrow-beam attenuation of kilovoltage x-ray beams for the intended applications of half-value layer (HVL) and quarter-value layer (QVL) estimations, patient organ shielding, and computer modeling. Methods: An empirical model, which uses the Lambert W function and represents a generalized Lambert-Beer law, was developed. To validate this model, transmission of diagnostic energy x-ray beams was measured over a wide range of attenuator thicknesses [0.49-33.03 mm Al on a computed tomography (CT) scanner, 0.09-1.93 mm Al on two mammography systems, and 0.1-0.45 mm Cu and 0.49-14.87 mm Al using general radiography]. Exposure measurements were acquired under narrow-beam geometry using standard methods, including the appropriate ionization chamber, for each radiographic system. Nonlinear regression was used to find the best-fit curve of the proposed Lambert W model to each measured transmission versus attenuator thickness data set. In addition to validating the Lambert W model, we also assessed the performance of two-point Lambert W interpolation compared to traditional methods for estimating the HVL and QVL [i.e., semilogarithmic (exponential) and linear interpolation]. Results: The Lambert W model was validated for modeling attenuation versus attenuator thickness with respect to the data collected in this study (R{sup 2} > 0.99). Furthermore, Lambert W interpolation was more accurate and less sensitive to the choice of interpolation points used to estimate the HVL and/or QVL than the traditional methods of semilogarithmic and linear interpolation. Conclusions: The proposed Lambert W model accurately describes attenuation of both monoenergetic radiation and (kilovoltage) polyenergetic beams (under narrow-beam geometry).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2011

Interfraction patient motion and implant displacement in prostate high dose rate brachytherapy

Purpose: To quantify movement of prostate cancer patients undergoing treatment, using an in-house developed motion sensor in order to determine a relationship between patient movement and high dose rate (HDR) brachytherapy implant displacement. Methods: An electronic motion sensor was developed based on a three axis accelerometer. HDR brachytherapy treatment for prostate is delivered at this institution in two fractions 24 h apart and 22 patients were monitored for movement over the interval between fractions. The motion sensors functioned as inclinometers, monitoring inclination of both thighs, and the inclination and roll of the abdomen. The implanted HDR brachytherapy catheter set was assessed for displacement relative to fiducial markers in the prostate. Angle measurements and angle differences over a 2 s time base were binned, and the standard deviations of the resulting frequency distributions used as a metric for patient motion in each monitored axis. These parameters were correlated to measured catheter displacement using regression modeling. Results: The mean implant displacement was 12.6 mm in the caudal direction. A mean of 19.95 h data was recorded for the patient cohort. Patients generally moved through a limited range of angles with a mean of the exception of two patients who spent in excess of 2 h lying on their side. When tested for a relationship between movement in any of the four monitored axes and the implant displacement, none was significant. Conclusions: It is not likely that patient movement influences HDR prostate implant displacement. There may be benefits to patient comfort if nursing protocols were relaxed to allow patients greater freedom to move while the implant is in situ.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

The dosimetric impact of inversely optimized arc radiotherapy plan modulation for real-time dynamic MLC tracking delivery

Purpose: Real-time dynamic multileaf collimator (MLC) tracking for management of intrafraction tumor motion can be challenging for highly modulated beams, as the leaves need to travel far to adjust for target motion perpendicular to the leaf travel direction. The plan modulation can be reduced by using a leaf position constraint (LPC) that reduces the difference in the position of adjacent MLC leaves in the plan. The purpose of this study was to investigate the impact of the LPC on the quality of inversely optimized arc radiotherapy plans and the effect of the MLC motion pattern on the dosimetric accuracy of MLC tracking delivery. Specifically, the possibility of predicting the accuracy of MLC tracking delivery based on the plan modulation was investigated. Methods: Inversely optimized arc radiotherapy plans were created on CT-data of three lung cancer patients. For each case, five plans with a single 358 deg. arc were generated with LPC priorities of 0 (no LPC), 0.25, 0.5, 0.75, and 1 (highest possible LPC), respectively. All the plans had a prescribed dose of 2 Gy x 30, used 6 MV, a maximum dose rate of 600 MU/min and a collimator angle of 45 deg. or 315 deg. To quantify the plan modulation, an average adjacent leaf distance (ALD) was calculated by averaging the mean adjacent leaf distance for each control point. The linear relationship between the plan quality [i.e., the calculated dose distributions and the number of monitor units (MU)] and the LPC was investigated, and the linear regression coefficient as well as a two tailed confidence level of 95% was used in the evaluation. The effect of the plan modulation on the performance of MLC tracking was tested by delivering the plans to a cylindrical diode array phantom moving with sinusoidal motion in the superior-inferior direction with a peak-to-peak displacement of 2 cm and a cycle time of 6 s. The delivery was adjusted to the target motion using MLC tracking, guided in real-time by an infrared optical system. The dosimetric results were evaluated using gamma index evaluation with static target measurements as reference. Results: The plan quality parameters did not depend significantly on the LPC (p {>=} 0.066), whereas the ALD depended significantly on the LPC (p < 0.001). The gamma index failure rate depended significantly on the ALD, weighted to the percentage of the beam delivered in each control point of the plan (ALD{sub w}) when MLC tracking was used (p < 0.001), but not for delivery without MLC tracking (p {>=} 0.342). The gamma index failure rate with the criteria of 2% and 2 mm was decreased from > 33.9% without MLC tracking to <31.4% (LPC 0) and <2.2% (LPC 1) with MLC tracking. Conclusions: The results indicate that the dosimetric robustness of MLC tracking delivery of an inversely optimized arc radiotherapy plan can be improved by incorporating leaf position constraints in the objective function without otherwise affecting the plan quality. The dosimetric robustness may be estimated prior to delivery by evaluating the ALD{sub w} of the plan.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Brain necrosis after fractionated radiation therapy: Is the halftime for repair longer than we thought?

Purpose: To derive a radiobiological model that enables the estimation of brain necrosis and spinal cord myelopathy rates for a variety of fractionation schemes, and to compare repair effects between brain and spinal cord. Methods: Sigmoidal dose response relationships for brain radiation necrosis and spinal cord myelopathy are derived from clinical data using nonlinear regression. Three different repair models are considered and the repair halftimes are included as regression parameters. Results: For radiation necrosis, a repair halftime of 38.1 (range 6.9-76) h is found with monoexponential repair, while for spinal cord myelopathy, a repair halftime of 4.1 (range 0-8) h is found. The best-fit alpha beta ratio is 0.96 (range 0.24-1.73)Conclusions: A radiobiological model that includes repair corrections can describe the clinical data for a variety of fraction sizes, fractionation schedules, and total doses. Modeling suggests a relatively long repair halftime for brain necrosis. This study suggests that the repair halftime for late radiation effects in the brain may be longer than is currently thought. If confirmed in future studies, this may lead to a re-evaluation of radiation fractionation schedules for some CNS diseases, particularly for those diseases where fractionated stereotactic radiation therapy is used.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications?

Purpose: To investigate the incorporation of pretherapy regional ventilation function in predicting radiation fibrosis (RF) in stage III nonsmall cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. Methods: Thirty-seven patients with stage III NSCLC were retrospectively studied. Patients received one cycle of cisplatin–gemcitabine, followed by two to three cycles of cisplatin–etoposide concurrently with involved-field thoracic radiotherapy (46–66 Gy; 2 Gy/fraction). Pretherapy regional ventilation images of the lung were derived from 4D computed tomography via a density change–based algorithm with mass correction. In addition to the conventional dose–volume metrics (V{sub 20}, V{sub 30}, V{sub 40}, and mean lung dose), dose–function metrics (fV{sub 20}, fV{sub 30}, fV{sub 40}, and functional mean lung dose) were generated by combining regional ventilation and radiation dose. A new class of metrics was derived and referred to as dose–subvolume metrics (sV{sub 20}, sV{sub 30}, sV{sub 40}, and subvolume mean lung dose); these were defined as the conventional dose–volume metrics computed on the functional lung. Area under the receiver operating characteristic curve (AUC) values and logistic regression analyses were used to evaluate these metrics in predicting hallmark characteristics of RF (lung consolidation, volume loss, and airway dilation). Results: AUC values for the dose–volume metrics in predicting lung consolidation, volume loss, and airway dilation were 0.65–0.69, 0.57–0.70, and 0.69–0.76, respectively. The respective ranges for dose–function metrics were 0.63–0.66, 0.61–0.71, and 0.72–0.80 and for dose–subvolume metrics were 0.50–0.65, 0.65–0.75, and 0.73–0.85. Using an AUC value = 0.70 as cutoff value suggested that at least one of each type of metrics (dose–volume, dose–function, dose–subvolume) was predictive for volume loss and airway dilation, whereas lung consolidation cannot be accurately predicted by any of the metrics. Logistic regression analyses showed that dose–function and dose–subvolume metrics were significant (P values ≤ 0.02) in predicting volume airway dilation. Likelihood ratio test showed that when combining dose–function and/or dose–subvolume metrics with dose–volume metrics, the achieved improvements of prediction accuracy on volume loss and airway dilation were significant (P values ≤ 0.04). Conclusions: The authors’ results demonstrated that the inclusion of regional ventilation function improved accuracy in predicting RF. In particular, dose–subvolume metrics provided a promising method for preventing radiation-induced pulmonary complications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Thermal modulation of nonlinear ultrasonic wave for concrete damage evaluation

A nonlinear ultrasonic test is proposed for material damage evaluation using thermal modulation. Temperature change excites and modulates nonlinear behavior of ultrasonic waves in concrete. Coda wave interferometry was used to analyze the relative velocity change of ultrasonic wave with temperature variations. On concrete samples with different levels of thermal damage, experimental results indicate that the samples with a higher damage level demonstrated higher sensitivity to temperature variations. In addition, a slow dynamics nonlinear behavior was observed. When the temperature changed and then remained constant, the wave velocity gradually approached to its equilibrium value.

Acoustic spectroscopy↗
DOE OSTI2019

Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3–5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Interactions between Climate Change and Complex Topography Drive Observed Streamflow Changes in the Colorado River Basin

Abstract The Colorado River basin (CRB) is one of the most important watersheds for energy, water, and food security in the United States. CRB water supports 15% of U.S. food production, more than 50 GW of electricity capacity, and one of the fastest growing populations in the United States. Energy–water–food nexus impacts from climate change are projected to increase in the CRB. These include a higher incidence of extreme events, widespread snow-to-rain regime shifts, and a higher frequency and magnitude of climate-driven disturbances. Here, we empirically show how the historical annual streamflow maximum and hydrograph centroid timing relate to temperature, precipitation, and snow. In addition, we show how these hydroclimatic relationships vary with elevation and how the elevation dependence has changed over this historical observational record. We find temperature and precipitation have a relatively weak relation (|r| < 0.3) to interannual variations in streamflow timing and extremes at low elevations (<1500 m), but a relatively strong relation (|r| > 0.5) at high elevations (>2300 m) where more snow occurs in the CRB. The threshold elevation where this relationship is strongest (|r| > 0.5) is moving uphill at a rate of up to 4.8 m yr−1 (p = 0.11) and 6.1 m yr−1 (p = 0.01) for temperature and precipitation, respectively. Based on these findings, we hypothesize where warming and precipitation-related streamflow changes are likely to be most severe using a watershed-scale vulnerability map to prioritize areas for further research and to inform energy, water, and food resource management in the CRB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude?

Abstract Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s −1 ) and damaging (≥25.7 m s −1 ) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein. Significance Statement Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Outcome of Primary Desmoid Tumors at All Anatomic Locations Initially Managed with Active Surveillance

Background: The behavior of desmoid tumors is unpredictable and varies from spontaneous remission to symptomatic and radiologic progression. This study aimed to evaluate the radiologic and symptomatic course of the disease in patients initially managed with active surveillance. Methods: Patients with a primary desmoid tumor at any anatomic location diagnosed between 1998 and 2016 were identified in a prospectively maintained database from a single sarcoma reference center in the United Kingdom. Inverse univariate Cox proportional hazard regression analyses were conducted to evaluate the course of the disease and indications for initiating treatment. Results: The study identified 168 patients with a primary desmoid tumor initially managed with active surveillance. The tumors were located in the abdominal wall (n = 61, 36%), an extremity (n = 51, 30%), chest wall (n = 30, 18%), intra-abdominal site (n = 15, 9%), or elsewhere (n = 11, 6%). Of all the patients, 36% experienced radiologic progressive disease, 36% had stable disease, and 27% regressed. The patients younger than 50 years were more likely to progress (p = 0.046), whereas the patients with chest wall or upper-extremity tumors reported significantly more pain (p = 0.01). Eventually, 46% of the patients proceeded to treatment. The median time to start of treatment after initial surveillance was 31 months, whereas the median follow-up time for the patients not receiving any treatment was 40.5 months. The indications for initiation of treatment were pain (32%), progression (31%), or both (13%). Conclusions: Patients with desmoid tumors can be managed with initial active surveillance, although almost half of patients may eventually need treatment. Pain, tumor progression, or both are the most common indications for the initiation of treatment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗