DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy.

Objective. Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients.Approach. Denoising diffusion probabilistic models (DDPMs) were developed to generate fraction-specific anatomical changes based on a reference cone-beam CT (CBCT), the fraction number and the dose distribution delivered. Three distinct DDPMs were developed: (1) theimage modelwas trained to directly generate likely future CBCTs, (2) the deformable vector field (DVF) model was trained to generate DVFs that deform a reference CBCT and (3) thehybrid modelwas trained similarly to the DVF model, but without relying on an external deformable registration algorithm. The models were trained on 9 patients with longitudinal CBCT images (224 CBCTs) and evaluated on 5 patients (152 CBCTs).Results. The generated images mainly exhibited random positioning shifts and small anatomical changes for early fractions. For later fractions, all models predicted weight losses in accordance with the training data. The distributions of volume and position changes of the body, esophagus, and parotids generated with the image and hybrid models were more similar to the ground truth distribution than the DVF model, evident from the lower Wasserstein distance achieved with the image (0.33) and hybrid model (0.30) compared to the DVF model (0.36). Generating several images for the same fraction did not yield the expected variability since the ground truth anatomical changes were only in 76% of the fractions within the 95% bounds predicted with the best model. Using the generated images for robust optimization of simplified proton therapy plans improved the worst-case clinical target volume V95 with 7% compared to optimizing with 3 mm set-up robustness while maintaining a similar integral dose.Significance. The newly developed DDPMs generate distributions similar to the real anatomical changes and have the potential to be used for robust anatomical optimization.

Optimal treatment plan adaptation using mid-treatment imaging biomarkers.

Previous studies on personalized radiotherapy (RT) have mostly focused on baseline patient stratification, adapting the treatment plan according to mid-treatment anatomical changes, or dose boosting to selected tumor subregions using mid-treatment radiological findings. However, the question of how to find the optimal adapted plan has not been properly tackled. Moreover, the effect of information uncertainty on the resulting adaptation has not been explored. In this paper, we present a framework to optimally adapt radiation therapy treatments to early radiation treatment response estimates derived from pre- and mid-treatment imaging data while considering the information uncertainty. The framework is based on the optimal stopping in radiation therapy (OSRT) framework. Biological response is quantified using tumor control probability (TCP) and normal tissue complication probability (NTCP) models, and these are directly optimized for in the adaptation step. Two adaptation strategies are discussed: (1) uniform dose adaptation and (2) continuous dose adaptation. In the first strategy, the original fluence-map is simply scaled upwards or downwards, depending on whether dose escalation or de-escalation is deemed appropriate based on the mid-treatment response observed from the radiological images. In the second strategy, a full NTCP-TCP-based fluence map re-optimization is performed to achieve the optimal adapted plans. We retrospectively tested the performance of these strategies on 14 canine head and neck cases treated with tomotherapy, using as response biomarker the change in the 3'-deoxy-3'[(18)F]-fluorothymidine (FLT)-PET signals between the pre- and mid-treatment images, and accounting for information uncertainty. Using a 10% uncertainty level, the two adaptation strategies both yield a noteworthy average improvement in guaranteed (worst-case) TCP.

Recent advances of PET imaging in clinical radiation oncology.

Radiotherapy and radiation oncology play a key role in the clinical management of patients suffering from oncological diseases. In clinical routine, anatomic imaging such as contrast-enhanced CT and MRI are widely available and are usually used to improve the target volume delineation for subsequent radiotherapy. Moreover, these modalities are also used for treatment monitoring after radiotherapy. However, some diagnostic questions cannot be sufficiently addressed by the mere use standard morphological imaging. Therefore, positron emission tomography (PET) imaging gains increasing clinical significance in the management of oncological patients undergoing radiotherapy, as PET allows the visualization and quantification of tumoral features on a molecular level beyond the mere morphological extent shown by conventional imaging, such as tumor metabolism or receptor expression. The tumor metabolism or receptor expression information derived from PET can be used as tool for visualization of tumor extent, for assessing response during and after therapy, for prediction of patterns of failure and for definition of the volume in need of dose-escalation. This review focuses on recent and current advances of PET imaging within the field of clinical radiotherapy / radiation oncology in several oncological entities (neuro-oncology, head & neck cancer, lung cancer, gastrointestinal tumors and prostate cancer) with particular emphasis on radiotherapy planning, response assessment after radiotherapy and prognostication.

Patient and scanner-specific variable acquisition times for whole-body PET/CT imaging.

Image quality in positron emission tomography (PET) is limited by the number of detected photons. Heavier patients present higher photon attenuation levels, thus increasing image noise. In this work, we propose a new method that uses the combined patient attenuation/system matrix together with a tracer uptake prediction model to optimize scan times for different bed positions in whole body scans. Our main goal is to achieve consistent noise levels across patients and anatomical regions. We propose to optimize scan times for individual bed positions, for patients of any size, based on the scanner sensitivity and patient-specific attenuation. Variable scan times for every bed position were determined by combining the system matrix, derived from the computed tomography (CT) and the scanner-specific geometric sensitivity profiles, and estimations of the global tracer uptake for each patient. The method was validated with anthropomorphic phantoms and whole-body patient 18F-FDG PET/CT scans, where variable and fixed times were compared. Phantom experiments showed that the proposed method was successful in keeping noise level constant for different attenuation setups. In real patients, image noise variability was reduced to less than one-half compared with conventional fixed-time scans at the expense of a four-fold increase in scan times between the biggest and smallest patients. Our method can homogenize image quality not only across patients of different sizes but also across different bed positions of the same patient.

The impact of linac output variations on dose distributions in helical tomotherapy.

It has been suggested for quality assurance purposes that linac output variations for helical tomotherapy (HT) be within +/-2% of the long-term average. Due to cancellation of systematic uncertainty and averaging of random uncertainty over multiple beam directions, relative uncertainties in the dose distribution can be significantly lower than those in linac output. The sensitivity of four HT cases with respect to linac output uncertainties was assessed by scaling both modeled and measured systematic and random linac output uncertainties until a dose uncertainty acceptance criterion failed. The dose uncertainty acceptance criterion required the delivered dose to have at least a 95% chance of being within 2% of the planned dose in all of the voxels in the treatment volume. For a random linac output uncertainty of 5% of the long-term mean, the maximum acceptable amplitude of the modeled, sinusoidal, systematic component of the linac output uncertainty for the four cases was 1.8%. Although the measured linac output variations represented values that were outside of the +/-2% tolerance, the acceptance criterion did not fail for any of the four cases until the measured linac output variations were scaled by a factor of almost three. Thus, the +/-2% tolerance in linac output variations for HT is a more conservative tolerance than necessary.

A novel approach for quantitative harmonization in PET.

Positron emission tomography (PET) imaging allows for measurement of activity concentrations of a given radiotracer in vivo. The quantitative capabilities of PET imaging are particularly important in the context of monitoring response to treatment, where quantitative changes in tracer uptake could be used as a biomarker of treatment response. Reconstruction algorithms and settings have a significant impact on PET quantification. In this work we introduce a novel harmonization methodology requiring only a simple cylindrical phantom and show that it can match the performance of more complex harmonization approaches based on phantoms with spherical inserts. Resolution and noise measurements from cylindrical phantoms are used to simulate the spherical inserts from NEMA image quality phantoms. An optimization algorithm was used to find the optimal smoothing filters for the simulated NEMA phantom images to identify those that best harmonized the PET scanners. Our methodology was tested on seven different PET models from two manufacturers installed at five institutions. Our methodology is able to predict contrast recovery coefficients (CRCs) from NEMA phantoms with errors within ±5.2% for CRCmax and ±3.7% for CRCmean (limits of agreement = 95%). After applying the proposed harmonization protocol, all the CRC values were within the tolerances from EANM. Quantitative harmonization in compliance with the EARL FDG-PET/CT accreditation program is achieved in a simpler way, without the need of NEMA phantoms. This may lead to simplified scanner harmonization workflows more accessible to smaller institutions.

Dual-component model of respiratory motion based on the periodic autoregressive moving average (periodic ARMA) method.

A new approach to the problem of modelling and predicting respiration motion has been implemented. This is a dual-component model, which describes the respiration motion as a non-periodic time series superimposed onto a periodic waveform. A periodic autoregressive moving average algorithm has been used to define a mathematical model of the periodic and non-periodic components of the respiration motion. The periodic components of the motion were found by projecting multiple inhale-exhale cycles onto a common subspace. The component of the respiration signal that is left after removing this periodicity is a partially autocorrelated time series and was modelled as an autoregressive moving average (ARMA) process. The accuracy of the periodic ARMA model with respect to fluctuation in amplitude and variation in length of cycles has been assessed. A respiration phantom was developed to simulate the inter-cycle variations seen in free-breathing and coached respiration patterns. At +/-14% variability in cycle length and maximum amplitude of motion, the prediction errors were 4.8% of the total motion extent for a 0.5 s ahead prediction, and 9.4% at 1.0 s lag. The prediction errors increased to 11.6% at 0.5 s and 21.6% at 1.0 s when the respiration pattern had +/-34% variations in both these parameters. Our results have shown that the accuracy of the periodic ARMA model is more strongly dependent on the variations in cycle length than the amplitude of the respiration cycles.

Comparison of intensity modulated x-ray therapy and intensity modulated proton therapy for selective subvolume boosting: a phantom study.

Selective subvolume boosting can theoretically improve tumour control probability while maintaining normal tissue complication probabilities similar to those of uniform dose distributions. In this work the abilities of intensity-modulated x-ray therapy (IMXT) and intensity-modulated proton therapy (IMPT) to deliver boosts to multiple subvolumes of varying size and proximities are compared in a thorough phantom study. IMXT plans were created using the step-and-shoot (IMXT-SAS) and helical tomotherapy (IMXT-HT) methods. IMPT plans were created with the spot scanning (IMPT-SS) and distal gradient tracking (IMPT-DGT) methods. IMPT-DGT is a generalization of the distal edge tracking method designed to reduce the number of proton beam spots required to deliver non-uniform dose distributions relative to IMPT-SS. The IMPT methods were delivered over both 180 degrees and 360 degrees arcs. The IMXT-SAS and IMPT-SS methods optimally satisfied the non-uniform dose prescriptions the least and the most, respectively. The IMPT delivery methods reduced the normal tissue integral dose by a factor of about 2 relative to the IMXT delivery methods, regardless of the delivery arc. The IMPT-DGT method reduced the number of proton beam spots by a factor of about 3 relative to the IMPT-SS method.

Patient characteristics influencing the variability of distributed parameter-based models in DCE-CT kinetic analysis.

Kinetic parameter variability may be sensitive to kinetic model choice, kinetic model implementation or patient-specific effects. The purpose of this study was to assess their impact on the variability of dynamic contrast-enhanced computed tomography (DCE-CT) kinetic parameters. A total of 11 canine patients with sinonasal tumours received high signal-to-noise ratio, test-double retest DCE-CT scans. The variability for three distributed parameter (DP)-based models was assessed by analysis of variance. Mixed-effects modelling evaluated patient-specific effects. Inter-model variability (CVinter ) was comparable to or lower than intra-model variability (CVintra ) for blood flow (CVinter :[4-28%], CVintra :[28-31%]), fractional vascular volume (CVinter :[3-17%], CVintra :[16-19%]) and permeability-surface area product (CVinter :[5-12%], CVintra :[14-15%]). The kinetic models were significantly (P<0.05) impacted by patient characteristics for patient size, area underneath the curve of the artery and of the tumour. In conclusion, DP-based models demonstrated good agreement with similar differences between models and scans. However, high variability in the kinetic parameters and their sensitivity to patient size may limit certain quantitative applications.

The helical tomotherapy thread effect.

Inherent to helical tomotherapy is a dose variation pattern that manifests as a "ripple" (peak-to-trough relative to the average). This ripple is the result of helical beam junctioning, completely unique to helical tomotherapy. Pitch is defined as in helical CT, the couch travel distance for a complete gantry rotation relative to the axial beam width at the axis of rotation. Without scattering or beam divergence, an analytical posing of the problem as a simple integral predicts minima near a pitch of 1/n where n is an integer. A convolution-superposition dose calculator (TomoTherapy, Inc.) included all the physics needed to explore the ripple magnitude versus pitch and beam width. The results of the dose calculator and some benchmark measurements demonstrate that the ripple has sharp minima near p=0.86(1/n). The 0.86 factor is empirical and caused by a beam junctioning of the off-axis dose profiles which differ from the axial profiles as well as a long scatter tail of the profiles at depth. For very strong intensity modulation, the 0.86 factor may vary. The authors propose choosing particular minima pitches or using a second delivery that starts 180 deg off-phase from the first to reduce these ripples: "Double threading." For current typical pitches and beam widths, however, this effect is small and not clinically important for most situations. Certain extremely large field or high pitch cases, however, may benefit from mitigation of this effect.

Optimization of intensity-modulated radiation therapy with biological objectives.

IMRT treatment planning via biological objectives gives rise to constrained nonlinear optimization problems. We consider formulations with nonlinear objectives based on the equivalent uniform dose (EUD), with bound constraints on the beamlet weights, and describe fast, flexible variants of the two-metric gradient-projection approach for solving them efficiently and in a mathematically sound manner. We conclude that an approach that calculates the Newton component of the step iteratively, by means of the conjugate-gradient algorithm and an implicit representation of the Hessian matrix, is most effective. We also present an efficient heuristic for obtaining an approximate solution with a smoother distribution of beamlet weights. The effectiveness of the methods is verified by testing on a medium-scale clinical case.

Parameter dependence of the MCNP electron transport in determining dose distributions.

In this paper, a detailed study of the electron transport in MCNP is performed, separating the effects of the energy binning technique on the energy loss rate, the scattering angles, and the sub-step length as a function of energy. As this problem is already well known, in this paper we focus on the explanation as to why the default mode of MCNP can lead to large deviations. The resolution dependence was investigated as well. An error in the MCNP code in the energy binning technique in the default mode (DBCN 18 card = 0) was revealed, more specific in the updating of cross sections when a sub-step is performed corresponding to a high-energy loss. This updating error is not present in the ITS mode (DBCN 18 card = 1) and leads to a systematically lower dose deposition rate in the default mode. The effect is present for all energies studied (0.5-10 MeV) and depends on the geometrical resolution of the scoring regions and the energy grid resolution. The effect of the energy binning technique is of the same order of that of the updating error for energies below 2 MeV, and becomes less important for higher energies. For a 1 MeV point source surrounded by homogeneous water, the deviation of the default MCNP results at short distances attains 9% and remains approximately the same for all energies. This effect could be corrected by removing the completion of an energy step each time an electron changes from an energy bin during a sub-step. Another solution consists of performing all calculations in the ITS mode. Another problem is the resolution dependence, even in the ITS mode. The higher the resolution is chosen (the smaller the scoring regions) the faster the energy is deposited along the electron track. It is proven that this is caused by starting a new energy step when crossing a surface. The resolution effect should be investigated for every specific case when calculating dose distributions around beta sources. The resolution should not be higher than 0.85*(1-EFAC)*CSDA, where EFAC is the energy loss per energy step and CSDA a continuous slowing down approximation range. This effect could as well be removed by determining the cross sections for energy loss and multiple scattering at the average energy of an energy step and by sampling the cross sections for each sub-step. Overall, we conclude that MCNP cannot be used without a caution due to possible errors in the electron transport. When care is taken, it is possible to obtain correct results that are in agreement with other Monte Carlo codes.

The effect of dose calculation uncertainty on the evaluation of radiotherapy plans.

Monte Carlo dose calculations will potentially reduce systematic errors that may be present in currently used dose calculation algorithms. However, Monte Carlo calculations inherently contain random errors, or statistical uncertainty, the level of which decreases inversely with the square root of computation time. Our purpose in this study was to determine the level of uncertainty at which a lung treatment plan is clinically acceptable. The evaluation methods to decide acceptability were visual examination of both isodose lines on CT scans and dose volume histograms (DVHs), and reviewing calculated biological indices. To study the effect of systematic and/or random errors on treatment plan evaluation, a simulated "error-free" reference plan was used as a benchmark. The relationship between Monte Carlo statistical uncertainty and dose was found to be approximately proportional to the square root of the dose. Random and systematic errors were applied to a calculated lung plan, creating dose distributions with statistical uncertainties of between 0% and 16% (1 s.d.) at the maximum dose point and also distributions with systematic errors of -16% to 16% at the maximum dose point. Critical structure DVHs and biological indices are less sensitive to calculation uncertainty than those of the target. Systematic errors affect plan evaluation accuracy significantly more than random errors, suggesting that Monte Carlo dose calculation will improve outcomes in radiotherapy. A statistical uncertainty of 2% or less does not significantly affect isodose lines, DVHs, or biological indices.

Monte Carlo study of a highly efficient gas ionization detector for megavoltage imaging and image-guided radiotherapy.

The imaging characteristics of an arc-shaped xenon gas ionization chamber for the purpose of megavoltage CT imaging were investigated. The detector consists of several hundred 320 microm thick gas cavities separated by thin tungsten plates of the same thickness. Dose response, efficiency and resolution parameters were calculated using Monte Carlo simulations. The calculations were compared to measurements taken in a 4 MV photon beam, assuming that the measured signal in the chambers corresponds to the therein absorbed dose. The measured response profiles for narrow and broad incident photon beams could be well reproduced with the Monte Carlo calculations. They show, that the quantum efficiency is 29.2% and the detective quantum efficiency at zero frequency DQE(0) is 20.4% for the detector arc placed in focus with the photon source. For a detector placed out of focus, these numbers even increase. The efficiency of this kind of radiation detector for megavoltage radiation therefore surpasses the reported efficiency of existing detector technologies. The resolution of the detector is quantified with calculated and measured line spread functions. The corresponding modulation transfer functions were determined for different thicknesses of the tungsten plates. They show that the resolution is only slightly dependent on the plate thickness but is predominantly determined by the cell size of the detector. The optimal plate thickness is determined by a tradeoff between quantum efficiency, total signal generation and resolution. Thicker plates are more efficient but the total signal and the resolution decrease with plate thickness. In conclusion, a gas ionization chamber of the described type is a highly efficient megavoltage radiation detector, allowing to obtain CT images with very little dose for a sufficient image quality for anatomy verification. This kind of detector might serve as a model for a future generation of highly efficient radiation detectors.

The effect of statistical uncertainty on inverse treatment planning based on Monte Carlo dose calculation.

The effect of the statistical uncertainty, or noise, in inverse treatment planning for intensity modulated radiotherapy (IMRT) based on Monte Carlo dose calculation was studied. Sets of Monte Carlo beamlets were calculated to give uncertainties at Dmax ranging from 0.2% to 4% for a lung tumour plan. The weights of these beamlets were optimized using a previously described procedure based on a simulated annealing optimization algorithm. Several different objective functions were used. It was determined that the use of Monte Carlo dose calculation in inverse treatment planning introduces two errors in the calculated plan. In addition to the statistical error due to the statistical uncertainty of the Monte Carlo calculation, a noise convergence error also appears. For the statistical error it was determined that apparently successfully optimized plans with a noisy dose calculation (3% 1sigma at Dmax), which satisfied the required uniformity of the dose within the tumour, showed as much as 7% underdose when recalculated with a noise-free dose calculation. The statistical error is larger towards the tumour and is only weakly dependent on the choice of objective function. The noise convergence error appears because the optimum weights are determined using a noisy calculation, which is different from the optimum weights determined for a noise-free calculation. Unlike the statistical error, the noise convergence error is generally larger outside the tumour, is case dependent and strongly depends on the required objectives.

Monte Carlo-based inverse treatment planning.

A Monte Carlo based inverse treatment planning system (MCI) has been developed which combines arguably the most accurate dose calculation method (Monte Carlo particle transport) with a 'guaranteed' optimization method (simulated annealing). A distribution of photons is specified in the tumour volume; they are transported using an adjoint calculation method to outside the patient surface to build up an intensity distribution. This intensity distribution is used as the initial input into an optimization algorithm. The dose distribution from each beam element from a number of fields is pre-calculated using Monte Carlo transport. Simulated annealing optimization is then used to find the weighting of each beam element, to yield the optimal dose distribution for the given criteria and constraints. MCI plans have been generated in various theoretical phantoms and patient geometries. These plans show conformation of the dose to the target volume and avoidance of critical structures. To verify the code, an experiment was performed on an anthropomorphic phantom.

Comparisons between MCNP, EGS4 and experiment for clinical electron beams.

Understanding the limitations of Monte Carlo codes is essential in order to avoid systematic errors in simulations, and to suggest further improvement of the codes. MCNP and EGS4, Monte Carlo codes commonly used in medical physics, were compared and evaluated against electron depth dose data and experimental backscatter results obtained using clinical radiotherapy beams. Different physical models and algorithms used in the codes give significantly different depth dose curves and electron backscattering factors. The default version of MCNP calculates electron depth dose curves which are too penetrating. The MCNP results agree better with experiment if the ITS-style energy-indexing algorithm is used. EGS4 underpredicts electron backscattering for high-Z materials. The results slightly improve if optimal PRESTA-I parameters are used. MCNP simulates backscattering well even for high-Z materials. To conclude the comparison, a timing study was performed. EGS4 is generally faster than MCNP and use of a large number of scoring voxels dramatically slows down the MCNP calculation. However, use of a large number of geometry voxels in MCNP only slightly affects the speed of the calculation.

MO-C-BRCD-01: Towards Personalized Medicine: Integration of Imaging into Therapy.

A significant advance in cancer therapy is currently underway with the evolution from a population-based to a personalized patient-based prescription. Rapid developments in imaging, particularly adoption of molecular imaging, offer unprecedented opportunities for accurate characterization of tumor biology, as well as early assessment of treatment response. Accurate characterization of tumor biology enables effective selection of appropriate therapy or even a design of purposefully non-uniform tumor-specific treatment plans, tailored to the spatial distribution of biological properties of each patient's tumor. Early assessment of treatment response enables treatment adaptation, potentially intensifying or reducing the treatment dose to provide more efficacious and less toxic therapies. However, integration of imaging into therapeutic applications requires a high level of image quantification, well beyond what is currently required in diagnostic imaging applications. This lecture will provide an overview of imaging applications in therapy, ranging from target selection totreatment response assessment. Potential roadblocks, as well as research opportunities on the path to personalization of cancer therapy, will be highlighted. LEARNING OBJECTIVES: 1. Understand the role of imaging in target definition and treatment response assessment 2. Understand the requirements for establishing imaging as a biomarker 3. Learn about research opportunities on the interface between imaging and therapy.

MO-A-BRCD-01: Unsettled Issues in the Radiobiology of Emergent Technology: Hypofractionation and PET-Guided Treatment Planning.

New technologies and innovative treatment techniques call into question long-standing radiobiological principles in RT. Reported apoptosis effects above 10 Gy/fraction challenge standard views of tumor cell kill as the only important path to tumor control. There are also reports of an immune-system response to large-dose per fraction. Repair-kinetics can also become important when comparing treatments given in less than 10 minutes to treatments given over 30 minutes or more. Modeling studies challenge the importance of hypoxia (and use of the linear quadratic model) in hypofractionation treatments. Critical questions regarding the use of FDG-PET guided boosts include the expected dose needed to achieve local control for FDG-PET positive tumors, and the relationship between FDG-PET images and underlying cellular parameters. This symposium will review our current scientific understanding of these important, unsettled issues. LEARNING OBJECTIVES: 1. Review non-classical radiobiology relevant to high-doses per fraction 2. Review dose-rate effects as they apply to SBRT/hypofx treatments 3. Review radiobiology of FDG-PET guided therapy.

SU-E-J-194: The Impact of Different Imaging Measures on Treatment Response Assessment of Bone Metastases.

PURPOSE: Several treatment response metrics, such as RECIST and PERCIST, have been established for assessing individual solid tumors. However, metastatic prostate cancer poses a unique challenge to these metrics because bone lesions are often numerous and non-measureable. This study investigated the impact of using different imaging measures for treatment response assessment in patients with metastatic prostate cancer. METHODS: Six patients with metastatic prostate cancer were treated with molecular targeted therapy and received whole body [18 F]NaF PET/CT scans pre-, mid-, and post-treatment. Lesions were segmented using a threshold of 20% the maximum SUV in bone and then manually adjusted with physician guidance. For each patient, SUVmax, SUVmean, SUVpeak, SUVtotal, number of lesions, and total volume of bone lesions were determined. For each measure, treatment response was calculated as the percent change relative to pre-treatment. The range of the different responses was calculated for each patient at each response time point. The population average of the patient ranges was calculated. RESULTS: The patient responses varied greatly for different imaging measures. The population-averaged range for all response measures was 50%. In general, SUVmax, SUVmean, and SUVpeak responses were negative, indicating good response to treatment, but the number of lesions, volume, and SUVtotal responses were positive, indicating disease progression. When the measures were separated into these two groups, the population-averaged range was only 25% among the number of lesions, volume, and SUVtotal responses and 10% among the SUVmax, SUVmean, and SUVpeak responses. CONCLUSIONS: Several treatment response metrics, such as RECIST and PERCIST, have been established for assessing individual solid tumors. However, metastatic prostate cancer poses a unique challenge to these metrics because bone lesions are often numerous and non-measureable. This study investigated the impact of using different imaging measures for treatment response assessment in patients with metastatic prostate cancer.