Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify the blood-brain barrier (BBB) permeability-surface area product. Serial measurements can indicate changes in BBB health, of interest to the study of normal physiology, neurological disease, and the effect of therapeutics. We performed a scan-rescan study to inform both sample size calculation for future studies and an appropriate reference change value for patient care. The final dataset included 28 healthy individuals (mean age 53.0 years, 82% female) scanned twice with mean interval 9.9 weeks. DCE-MRI was performed at 3T using a 3D gradient echo sequence with whole brain coverage, T1 mapping using variable flip angles, and a 16-min dynamic sequence with a 3.2-s time resolution. Segmentation of white and grey matter (WM/GM) was performed using a 3D magnetization-prepared gradient echo image. The influx constant Ki was calculated using the Patlak method. The primary outcome was the within-subject coefficient of variation (CV) of Ki in both WM and GM. Ki values followed biological expectations in relation to known GM/WM differences in cerebral blood volume (CBV) and consequently vascular surface area. Subject-derived arterial input functions showed marked within-subject variability which were significantly reduced by using a venous input function (CV of area under the curve 46 vs. 12%, p < 0.001). Use of the venous input function significantly improved the CV of Ki in both WM (30 vs. 59%, p < 0.001) and GM (21 vs. 53%, p < 0.001). Further improvement was obtained using motion correction, scaling the venous input function by the artery, and using the median rather than the mean of individual voxel data. The final method gave CV of 27% and 17% in WM and GM, respectively. No further improvement was obtained by replacing the subject-derived input function by one standard population input function. CV of Ki was shown to be highly sensitive to dynamic sequence duration, with shorter measurement periods giving marked deterioration especially in WM. In conclusion, measurement variability of 3D brain DCE-MRI is sensitive to analysis method and a large precision improvement is obtained using a venous input function.

Critical limb ischemia (CLI) is a severe disease which affects about 2 million people in the US. Its prevalence is assessed at 800/100,000 population. However, no reliable tools are currently available to assess perfusion defects at the muscle tissue level. DCE-MRI is a technique that holds the potential to be effective in achieving this goal. However, preclinical studies performed with DCE-MRI have indicated low sensitivity assessing perfusion at resting state. To improve these previous results, in this work we propose new methodologies for data acquisition and analysis and we also revisit the biological model used for evaluation. Eleven rabbits underwent embolization of a lower limb. They were imaged at day 7 after embolization using DCE-MRI, performed on a 4.7 T small imaging device. Among them, n = 4 rabbits were used for MRI sequence optimization and n = 6 for data analysis after one exclusion. Normalized Areas under the curve (AUCn), and kinetic parameters such as K_trans and V_d resulting from the Tofts-Kety modeling (KTM) were calculated on the embolized and contralateral limbs. Average and heterogeneity features, consisting on standard-deviation and quantiles, were calculated on muscle groups and whole limbs. The Wilcoxon and Fisher-tests were performed to compare embolized and contralateral regions of interests. The Wilcoxon test was also used to compare features of parametric maps. Quantiles of 5 and 95% in the contralateral side were used to define low and high outliers. A P-value <0.05 was considered statistically significant. Average features were inefficient to identify injured muscles, in agreement with the low sensitivity of the technique previously reported by the literature. However, these findings were dramatically improved by the use of additional heterogeneity features (97% of total accuracy for group muscles, P < 0.01 and 100% of total accuracy for the total limbs). The mapping analysis and automatic outlier detection quantification improvement was explained by the presence of local hyperemia that impair the average calculations. The analysis with KTM did not provide any additional information compared to AUCn. The DCE technique can be effective in detecting embolization-induced disorders of limb muscles in a CLI model when heterogeneity is taken into account in the data processing, even without vascular stimulation. The simultaneous presence of areas of ischemia and hyperemia appeared as a signature of the injured limbs. These areas seem to reflect the simultaneous presence of infarcted areas and viable peripheral areas, characterized by a vascular response that is visible in DCE.

We aimed to investigate the effect of T2* correction on estimation of kinetic parameters from T1-weighted dynamic contrast enhanced (DCE) MRI data when a reference-tissue arterial input function (AIF) is used.

DCE-MRI data were acquired from seven mice with 4T1 mouse mammary tumors using a double gradient echo sequence at 7 T. The AIF was estimated from a region of interest in the muscle. The extended Tofts model was used to estimate pharmacokinetic parameters in the enhancing part of the tumor, with and without T2* correction of the lesion and AIF. The parameters estimated with T2* correction of both the AIF and lesion time-intensity curve were assumed to be the reference standard.

For the whole population, there was significant difference (p < 0.05) in transfer constant (K(trans)) between T2* corrected and not corrected methods, but not in interstitial volume fraction (ve). Individually, no significant differences were found in K(trans) and ve of four and six tumors, respectively, between the T2* corrected and not corrected methods. In contrast, K(trans) was significantly underestimated, if the T2* correction was not used, in other tumors for which the median K(trans) was larger than 0.4 min(-1).

T2* effect on tumors with high K(trans) may not be negligible in kinetic model analysis, even if AIF is estimated from reference tissue where the concentration of contrast agent is relatively low.

The blood-brain barrier (BBB) is essential for normal function of the brain, and its role in many brain pathologies has been the focus of numerous studies during the last decades. Dysfunction of the BBB is not only being shown in numerous brain diseases, but animal studies have indicated that it plays a direct key role in the genesis of neurovascular dysfunction and associated neurodegeneration. As such evidence accumulates, the need for robust and clinically applicable methods for minimally invasive assessment of BBB integrity is becoming urgent. This review provides an introduction to BBB imaging methods in the clinical scenario. First, imaging modalities are reviewed, with a focus on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We then proceed to review image analysis methods, including quantitative and semi-quantitative methods. The advantages and limitations of each approach are discussed, and future directions and questions are highlighted.

Dynamic contrast-enhanced computed tomography (DCE-CT) and magnetic resonance imaging (DCE-MRI) are functional imaging techniques. They aim to characterise the microcirculation by applying the principles of tracer-kinetic analysis to concentration-time curves measured in individual image pixels. In this paper, we review the basic principles of DCE-MRI and DCE-CT, with a specific emphasis on the use of tracer-kinetic modeling. The aim is to provide an introduction to the field for a broader audience of pharmacokinetic modelers. In a first part, we first review the key aspects of data acquisition in DCE-CT and DCE-MRI, including a review of basic measurement strategies, a discussion on the relation between signal and concentration, and the problem of measuring reference data in arterial blood. In a second part, we define the four main parameters that can be measured with these techniques and review the most common tracer-kinetic models that are used in this field. We first discuss the models for the capillary bed and then define the most general four-parameter models used today: the two-compartment exchange model, the tissue-homogeneity model, the "adiabatic approximation to the tissue-homogeneity model" and the distributed-parameter model. In simpler tissue types or when the data quality is inadequate to resolve all the features of the more complex models, it is often necessary to resort to simpler models, which are special cases of the general models and hence have less parameters. We discuss the most common of these special cases, i.e. the uptake models, the extended Tofts model, and the one-compartment model. Models for two specific tissue types, liver and kidney, are discussed separately. We conclude with a review of practical aspects of DCE-CT and DCE-MRI data analysis, including the problem of identifying a suitable model for any given data set, and a brief discussion of the application of tracer-kinetic modeling in the context of drug development. Here, an important application of DCE techniques is the derivation of quantitative imaging biomarkers for the assessment of effects of targeted therapeutics on tumors.

Renal function is characterized by different physiologic aspects, including perfusion, glomerular filtration, interstitial diffusion, and tissue oxygenation. Magnetic resonance imaging (MRI) shows great promise in assessing these renal tissue characteristics noninvasively. The last decade has witnessed a dramatic progress in MRI techniques for renal function assessment. This article briefly describes relevant renal anatomy and physiology, reviews the applications of functional MRI techniques for the diagnosis of renal diseases, and lists unresolved issues that will require future work.

To assess baseline reproducibility and compare performance of dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging versus DCE computed tomographic (CT) measures of early vascular response in the same patients treated with cediranib (30 or 45 mg daily).

After institutional review board approval, written informed consent was obtained from 29 patients with advanced solid tumors who had lesions 3 cm or larger and in whom simultaneous imaging of an adjacent artery was possible. Two baseline DCE MR acquisitions and two baseline DCE CT acquisitions 7 days or fewer apart (within 14 days of starting treatment) and two posttreatment acquisitions with each modality at day 7 and 28 (±3 days) were obtained. Nonmodeled and modeled parameters were derived (measured arterial input function [AIF] for CT, population-based AIF for MR imaging; temporal sampling rate of 0.5 second for CT, 3-6 seconds for MR imaging). Baseline variability was assessed by using intra- and intersubject analysis of variance and Bland-Altman analysis; a paired t test assessed change from baseline to after treatment.

The most reproducible parameters were DCE MR imaging enhancement fraction (baseline intrapatient coefficient of variation [CV]=8.6%), volume transfer constant (CV=13.9%), and integrated area under the contrast agent uptake curve at 60 seconds (CV=15.5%) and DCE CT positive enhancement integral (CV=16.0%). Blood plasma volume was highly variable and the only parameter with CV greater than 30%. Average reductions (percentage change) from baseline were consistently observed for all DCE MR imaging and DCE CT parameters at day 7 and 28 for both starting-dose groups (45 and 30 mg), except for DCE CT mean transit time. Percentage change from baseline for parameters reflecting blood flow and permeability were comparable, and reductions from baseline at day 7 were maintained at day 28.

DCE MR imaging and DCE CT can depict vascular response to antiangiogenic agents with response evident at day 7. Improved reproducibility with MR imaging favors its use in trials with small patient numbers.

The quantification of cerebral blood flow (CBF), cerebral blood volume (CBV), and blood-brain barrier permeability in scattered lesions in the brain is a methodological challenge. We aimed to investigate the feasibility of a 3D T1-weighted dynamic contrast-enhanced (DCE) MRI acquisition in combination with a 2-compartment modeling approach for the quantification of CBF, CBV and permeability surface area product (PS) in lesions, and normal-appearing white matter (NAWM) in patients with multiple sclerosis (MS).

In all, 19 MS patients (mean age 35 years, 12 female) underwent DCE-MRI with a 3D T1-weighted spoiled gradient-echo sequence on a 3T MRI scanner. A total of 44 slices (thickness 3 mm) with an in-plane resolution of 1.7 × 1.7 mm(2) (matrix size 128 × 104), providing coverage of the whole brain, were acquired every 2.1 seconds over a total measurement time of 420 s. Data postprocessing was performed using a set of 2-compartment models with automated model selection; CBF, CBV, and PS as a measure of blood-brain barrier leakage were determined in contrast-enhancing (CE) and nonenhancing lesions as well as in NAWM.

Perfusion quantification produced reasonable values in lesions as well as in NAWM. In CE lesions, CBF (22.9 (22.7) vs. 15.8 (6.7) mL/100 mL/min), CBV (1.18 (0.48) vs. 0.76 (0.19) mL/100 mL), and PS (0.98 (0.46) vs. 0.04 (0.03) mL/100 mL/min) were significantly (P < 0.001) higher than in NAWM. In nonenhancing lesions, a weakly (P < 0.05) significantly increased CBV of 1.00 (0.35) mL/100 mL, compared with NAWM, was observed.

Our study demonstrates the feasibility of 3D T1-weighted DCE-MRI for the quantitative assessment of CBF, CBV, and PS in NAWM as well as in multiple MS lesions scattered throughout the brain, even without previous knowledge of their location. Quantification on the region level produced reasonable values both in lesions and in NAWM, but parameter maps would benefit from an increase in contrast-to-noise ratio. The increased values of CBF, CBV, and PS in CE lesions may reflect inflammatory activity, the heterogeneity of parameter estimates suggests a potential for lesion characterization. NAWM appears hypoperfused, this is in accordance with previous studies, but requires validation with a control group.

Hepatic perfusion imaging with magnetic resonance (MR) imaging is an emerging technique for quantitative assessment of diffuse hepatic disease and hepatic lesion blood flow. The principal method that has been used is based on T1 dynamic contrast-enhanced MR imaging. Perfusion imaging shows promise in the assessment of tumor therapy response, staging of liver fibrosis, and evaluation of hepatocellular carcinoma. The future standardization of imaging protocols and MR imaging pulse sequences will allow for broader clinical applications.

Dynamic contrast-enhanced MRI (DCE-MRI) is frequently used to provide response biomarkers in clinical trials of novel cancer therapeutics but assessment of their physiological accuracy is difficult. DCE-CT provides an independent probe of similar pharmacokinetic processes and may be modeled in the same way as DCE-MRI to provide purportedly equivalent physiological parameters. In this study, DCE-MRI and DCE-CT were directly compared in subjects with primary bladder cancer to assess the degree to which the model parameters report modeled physiology rather than artefacts of the measurement technique and to determine the interchangeability of the techniques in a clinical trial setting. The biomarker K(trans) obtained by fitting an extended version of the Kety model voxelwise to both DCE-MRI and DCE-CT data was in excellent agreement (mean across subjects was 0.085 ± 0.030 min(-1) for DCE-MRI and 0.087 ± 0.033 min(-1) for DCE-CT, intermodality coefficient of variation 9%). The parameter v(p) derived from DCE-CT was significantly greater than that derived from DCE-MRI (0.018 ± 0.006 compared to 0.009 ± 0.008, P = 0.0007) and v(e) was in reasonable agreement only for low values. The study provides evidence that the biomarker K(trans) is a robust parameter indicative of the underlying physiology and relatively independent of the method of measurement.

Dynamic contrast-enhanced (DCE) MRI is often used to measure the transfer constant (Ktrans) and distribution volume (ve) in pelvic tumors. For optimal accuracy and reproducibility, one must quantify the arterial input function (AIF). Unfortunately, this is challenging due to inflow and signal saturation. A potential solution is to use MR signal phase (ϕ), which is relatively unaffected by these factors. We hypothesized that phase-derived AIFs (AIFϕ) would provide more reproducible Ktrans and ve values than magnitude-derived AIFs (AIF|S|). We tested this in 27 prostate dynamic contrast-enhanced MRI studies (echo time=2.56 ms, temporal resolution=13.5 s), using muscle as a standard. AIFϕ peak amplitude varied much less as a function of measurement location (inferior-superior) than AIF|S| (5.6±0.6 mM vs. 2.6±1.5 mM), likely as a result of ϕ inflow insensitivity. However, our main hypothesis was not confirmed. The best AIF|S| provided similar reproducibility versus AIFϕ (interpatient muscle Ktrans=0.039±0.021 min(-1) vs. 0.037±0.025 min(-1), ve=0.090±0.041 vs. 0.062±0.022, respectively).

The purpose of this study was to determine the impact of water exchange on tracer kinetic parameter estimates derived from T(1)-weighted dynamic contrast-enhanced (DCE)-MRI data using a direct quantitative comparison with DCE-CT. Data were acquired from 12 patients with bladder cancer who underwent DCE-CT followed by DCE-MRI within a week. A two-compartment tracer kinetic model was fitted to the CT data, and two versions of the same model with modifications to account for the fast exchange and no exchange limits of water exchange were fitted to the MR data. The two-compartment tracer kinetic model provided estimates of the fractional plasma volume (v(p)), the extravascular extracellular space fraction (v(e)), plasma perfusion (F(p)), and the microvascular permeability surface area product. Our findings suggest that DCE-CT is an appropriate reference for DCE-MRI in bladder cancers as the only significant difference found between CT and MR parameter estimates were the no exchange limit estimates of v(p) (P = 0.002). These results suggest that although water exchange between the intracellular and extravascular-extracellular space has a negligible effect on DCE-MRI, vascular-extravascular-extracellular space water exchange may be more important.

The most common methods for measuring perfusion with MRI are arterial spin labelling (ASL), dynamic susceptibility contrast (DSC-MRI), and T(1)-weighted dynamic contrast enhancement (DCE-MRI). This review focuses on the latter approach, which is by far the most common in the body and produces measures of capillary permeability as well. The aim is to present a concise but complete overview of the technical issues involved in DCE-MRI data acquisition and analysis. For details the reader is referred to the references. The presentation of the topic is essentially generic and focuses on technical aspects that are common to all DCE-MRI measurements. For organ-specific problems and illustrations, we refer to the other papers in this issue. In Section 1 "Theory" the basic quantities are defined, and the physical mechanisms are presented that provide a relation between the hemodynamic parameters and the DCE-MRI signal. Section 2 "Data acquisition" discusses the issues involved in the design of an optimal measurement protocol. Section 3 "Data analysis" summarizes the steps that need to be taken to determine the hemodynamic parameters from the measured data.

To validate a new method for converting MR arterial signal intensity versus time curves to arterial input functions (AIFs).

The method constrains AIF with patient's cardiac output (Q). Monte Carlo simulations of MR renography and tumor perfusion protocols were carried out for comparison with two alternative methods: direct measurement and population-averaged input function. MR renography was performed to assess the method's inter- and intraday reproducibility for renal parameters.

In simulations of tumor perfusion, the precision of the parameters (K(trans) and v(e)) computed using the proposed method was improved by at least a factor of three compared to direct measurement. Similar improvements were obtained in simulations of MR renography. Volunteer study for testing interday reproducibility confirmed the improvement of precision in renal parameters when using the proposed method compared to conventional methods. In another patient study (two injections within one session), the proposed method significantly increased the correlation coefficient (R) between GFR of the two exams (0.92 vs. 0.83) compared to direct measurement.

A new method significantly improves the precision of dynamic contrast-enhanced (DCE) parameters. The method may be especially useful for analyzing repeated DCE examinations, such as monitoring tumor therapy or angiotensin converting enzyme-inhibitor renography.

To assess the temporal sampling requirements needed for quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) data with a reference region (RR) model in human breast cancer.

Simulations were used to study errors in pharmacokinetic parameters (K(trans) and v(e)) estimated by the RR model using six DCE-MRI acquisitions over a range of pharmacokinetic parameter values, arterial input functions, and temporal samplings. DCE-MRI data were acquired on 12 breast cancer patients and parameters were estimated using the native resolution data (16.4 seconds) and compared to downsampled 32.8-second and 65.6-second data.

Simulations show that, in the majority of parameter combinations, the RR model results in an error less than 20% in the extracted parameters with temporal sampling as poor as 35.6 seconds. The experimental results show a high correlation between K(trans) and v(e) estimates from data acquired at 16.4-second temporal resolution compared to the downsampled 32.8-second data: the slope of the regression line was 1.025 (95% confidence interval [CI]: 1.021, 1.029), Pearson's correlation r = 0.943 (95% CI: 0.940, 0.945) for K(trans), and 1.023 (95% CI: 1.021. 1.025), r = 0.979 (95% CI: 0.978, 0.980) for v(e). For the 64-second temporal resolution data the results were: 0.890 (95% CI: 0.894, 0.905), r = 0.8645, (95% CI: 0.858, 0.871) for K(trans), and 1.041 (95% CI: 1.039, 1.043), r = 0.970 (95% CI: 0.968, 0.971) for v(e).

RR analysis allows for a significant reduction in temporal sampling requirements and this lends itself to analyze DCE-MRI data acquired in practical situations.

The present study determines the feasibility of generating an average arterial input function (Avg-AIF) from a limited population of patients with neck nodal metastases to be used for pharmacokinetic modeling of dynamic contrast-enhanced MRI (DCE-MRI) data in clinical trials of larger populations.

Twenty patients (mean age 50 years [range 27-77 years]) with neck nodal metastases underwent pretreatment DCE-MRI studies with a temporal resolution of 3.75 to 7.5 sec on a 1.5T clinical MRI scanner. Eleven individual AIFs (Ind-AIFs) met the criteria of expected enhancement pattern and were used to generate Avg-AIF. Tofts model was used to calculate pharmacokinetic DCE-MRI parameters. Bland-Altman plots and paired Student t-tests were used to describe significant differences between the pharmacokinetic parameters obtained from individual and average AIFs.

Ind-AIFs obtained from eleven patients were used to calculate the Avg-AIF. No overall significant difference (bias) was observed for the transfer constant (Ktrans) measured with Ind-AIFs compared to Avg-AIF (p = 0.20 for region-of-interest (ROI) analysis and p = 0.18 for histogram median analysis). Similarly, no overall significant difference was observed for interstitial fluid space volume fraction (ve) measured with Ind-AIFs compared to Avg-AIF (p = 0.48 for ROI analysis and p = 0.93 for histogram median analysis). However, the Bland-Altman plot suggests that as Ktrans increases, the Ind-AIF estimates tend to become proportionally higher than the Avg-AIF estimates.

We found no statistically significant overall bias in Ktrans or ve estimates derived from Avg-AIF, generated from a limited population, as compared with Ind-AIFs.However, further study is needed to determine whether calibration is needed across the range of Ktrans. The Avg-AIF obtained from a limited population may be used for pharmacokinetic modeling of DCE-MRI data in larger population studies with neck nodal metastases. Further validation of the Avg-AIF approach with a larger population and in multiple regions is desirable.

For clinical dynamic contrast-enhanced (DCE) MRI studies, it is often not possible to obtain reliable arterial input function (AIF) in each measurement. Thus, it is important to find a representative AIF for pharmacokinetic modeling of DCE-MRI data when individual AIF (Ind-AIF) measurements are not available. A total of 16 patients with osteosarcomas in the lower extremity (knee region) underwent multislice DCE-MRI. Reliable Ind-AIFs were obtained in five patients with a contrast injection rate of 2 cc/s and another five patients with a 1 cc/s injection rate. Average AIF (Avg-AIF) for each injection rate was constructed from the corresponding five Ind-AIFs. For each injection rate there are no statistically significant differences between pharmacokinetic parameters of the five patients derived with Ind-AIFs and Avg-AIF. There are no statistically significant changes in pharmacokinetic parameters of the 16 patients when the two Avg-AIFs were applied in kinetic modeling. The results suggest that it is feasible, as well as practical, to use a limited-population-based Avg-AIF for pharmacokinetic modeling of osteosarcoma DCE-MRI data. Further validation with a larger population and multiple regions is desirable.

Models have been developed for the analysis of dynamic contrast-enhanced MRI (DCE-MRI) data that do not require direct measurements of the arterial input function; such methods are referred to as reference region models. These models typically return estimates of the volume transfer constant (K(trans)) and the extravascular extracellular volume fraction (v(e)). To date such models have assumed a linear relationship between the measured R(1) ( identical with 1/T(1)) and the concentration of contrast agent, a transformation referred to as the fast exchange limit, but this assumption is not valid for all concentrations of an agent. A theory for DCE-MRI reference region models which accounts for water exchange is presented, evaluated in simulations, and applied in tumor-bearing mice. Using reasonable parameter values, simulations show that the assumption of fast exchange can underestimate K(trans) and v(e) by up to 82% and 46%, respectively. By analyzing a large region of interest and a single voxel the new model can return parameters within approximately +/-10% and +/-25%, respectively, of their true values. Analysis of experimental data shows that the new approach returns K(trans) and v(e) values that are up to 90% and 73%, respectively, greater than conventional fast exchange analyses.

Dynamic contrast-enhanced MRI (DCE-MRI) of the hand and wrist was performed in 11 patients with rheumatoid arthritis twice before and once 2 weeks after treatment with anti-tumor necrosis factor (TNF)-alpha therapy. A rapid, T1-weighted 3D spoiled gradient echo (SPGR) sequence was used for the dynamic imaging. T1 estimation was performed using similar images obtained at different flip angles. The relative radiofrequency field was estimated from the known T1 of the periarticular fatty marrow. The arterial input function (AIF) was measured at each examination, and normalized to the expected plasma concentration to reduce partial volume effects. Synovial enhancement was modeled to yield values for Ktrans, ve, and vp. Ktrans and ve showed good reproducibility. There was a significant decrease of about 20% in Ktrans after 2 weeks of treatment. This study demonstrates the potential of DCE-MRI and pharmacokinetic modeling to study early changes in inflammatory activity in rheumatoid arthritis following treatment.

Rapid T(1)-weighted 3D spoiled gradient-echo (GRE) data sets were acquired in the abdomen of 23 cancer patients during a total of 113 separate visits to allow dynamic contrast-enhanced MRI (DCE-MRI) analysis of tumor microvasculature. The arterial input function (AIF) was measured in each patient at each visit using an automated AIF extraction method following a standardized bolus administration of gadodiamide. The AIFs for each patient were combined to obtain a mean AIF that is representative for any individual. The functional form of this general AIF may be useful for studies in which AIF measurements are not possible. Improvements in the reproducibility of DCE-MRI model parameters (K(trans), v(e), and v(p)) were observed when this new, high-temporal-resolution population AIF was used, indicating the potential for increased sensitivity to therapy-induced change.