Today molecular imaging technologies play a central role in clinical oncology. The use of imaging techniques in early cancer detection, treatment response and new therapy development is steadily growing and has already significantly impacted clinical management of cancer. In this chapter we will overview three different molecular imaging technologies used for the understanding of disease biomarkers, drug development, or monitoring therapeutic outcome. They are (1) optical imaging (bioluminescence and fluorescence imaging) (2) magnetic resonance imaging (MRI), and (3) nuclear imaging (e.g, single photon emission computed tomography (SPECT) and positron emission tomography (PET)). We will review the use of molecular reporters of biological processes (e.g. apoptosis and protein kinase activity) for high throughput drug screening and new cancer therapies, diffusion MRI as a biomarker for early treatment response and PET and SPECT radioligands in oncology.
Glioblastomas (GBMs) are the most common and malignant primary brain tumors and are aggressively treated with surgery, chemotherapy, and radiotherapy. Despite this treatment, recurrence is inevitable and survival has improved minimally over the last 50 years. Recent studies have suggested that GBMs exhibit both heterogeneity and instability of differentiation states and varying sensitivities of these states to radiation. Here, we employed an iterative combined theoretical and experimental strategy that takes into account tumor cellular heterogeneity and dynamically acquired radioresistance to predict the effectiveness of different radiation schedules. Using this model, we identified two delivery schedules predicted to significantly improve efficacy by taking advantage of the dynamic instability of radioresistance. These schedules led to superior survival in mice. Our interdisciplinary approach may also be applicable to other human cancer types treated with radiotherapy and, hence, may lay the foundation for significantly increasing the effectiveness of a mainstay of oncologic therapy.
Vascular-targeted therapies have shown promise as adjuvant cancer treatment. As these agents undergo clinical evaluation, sensitive imaging biomarkers are need to assess drug target interaction and treatment response. In this study, dynamic contrast enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) were evaluated for detecting response of intracerebral 9L gliosarcomas to the antivascular agent VEGF-Trap, a fusion protein designed to bind all forms of Vascular Endothelial Growth Factor-A (VEGF-A) and Placental Growth Factor (PGF). Rats with 9L tumors were treated twice weekly for two weeks with vehicle or VEGF-Trap. DCE- and DW-MRI were performed one day prior to treatment initiation and one day following each administered dose. Kinetic parameters (Ktrans: volume transfer constant, kep: efflux rate constant from extravascular/extracellular space to plasma, and vp: blood plasma volume fraction) and the apparent diffusion coefficient (ADC) over the tumor volumes were compared between groups. A significant decrease in kinetic parameters was observed 24 hours following the first dose of VEGF-Trapin treated versus control animals (p<0.05) and was accompanied by a decline in ADC values. In addition to the significant hemodynamic effect, VEGF-Trap treated animals exhibited significantly longer tumor doubling times (p<0.05) compared to the controls. Histological findings were found to support imaging response metrics. In conclusion, kinetic MRI parameters and change in ADC have been found to serve as sensitive and early biomarkers of VEGF-Trapanti-vascular targeted therapy.
anti-angiogenic therapy; VEGF-Trap; glioma; DCE-MRI; DW-MRI; hemodynamics; diffusion; preclinical
Rats were given unilateral kainate injection into hippocampal CA3 region,
and the effect of chronic electrographic seizures on extracellular glutamine
(GLNECF) was examined in those with low and steady levels of
extracellular glutamate (GLUECF). GLNECF, collected by
microdialysis in awake rats for 5 h, decreased to 62 ± 4.4% of the
initial concentration (n = 6). This change correlated with the
frequency and magnitude of seizure activity, and occurred in the ipsilateral but
not in contralateral hippocampus, nor in kainate-injected rats that did not
undergo seizure (n = 6). Hippocampal intracellular GLN did not
differ between the Seizure and No-Seizure Groups. These results suggested an
intriguing possibility that seizure-induced decrease of GLNECF
reflects not decreased GLN efflux into the extracellular fluid, but increased
uptake into neurons. To examine this possibility, neuronal uptake of
GLNECF was inhibited in vivo by intrahippocampal perfusion of
2-(methylamino)isobutyrate, a competitive and reversible inhibitor of the
sodium-coupled neutral amino acid transporter (SNAT) subtypes 1 and 2, as
demonstrated by 1.8 ± 0.17 fold elevation of GLNECF
(n = 7). The frequency of electrographic seizures during
uptake inhibition was reduced to 35 ± 7% (n = 7) of the
frequency in pre-perfusion period, and returned to 88 ± 9% in the
post-perfusion period. These novel in vivo results strongly suggest that, in
this well-established animal model of temporal-lobe epilepsy, the observed
seizure-induced decrease of GLNECF reflects its increased uptake into
neurons to sustain enhanced glutamatergic epileptiform activity, thereby
demonstrating a possible new target for anti-seizure therapies.
Epileptic seizure; Extracellular glutamine; Neuronal uptake; 2-(Methylamino) isobutyrate; Rat hippocampus; Kainate
Gradient nonlinearity of MRI systems leads to spatially-dependent b-values and consequently high non-uniformity errors (10–20%) in ADC measurements over clinically relevant field-of-views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements.
All-inclusive bias analysis considers spatial and time-domain cross-terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b-matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations.
Spatial dependence of nonlinearity correction terms accounts for the bulk (75–95%) of ADC bias for FA = 0.3–0.9. Residual ADC non-uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems.
The proposed simplified correction algorithm appears sufficient to control ADC non-uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non-lab-based diffusion gradients.
diffusion MRI; gradient nonlinearity; ADC systematic bias
Imaging biomarkers capable of early quantification of tumor response to therapy would provide an opportunity to individualize patient care. Image registration of longitudinal scans provides a method of detecting treatment associated changes within heterogeneous tumors by monitoring alterations in the quantitative value of individual voxels over time, which is unattainable by traditional volumetric-based histogram methods. The concepts involved in the use of image registration for tracking and quantifying breast cancer treatment response using parametric response mapping (PRM), a voxel-based analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) scans, are presented. Application of PRM to breast tumor response detection is described, wherein robust registration solutions for tracking small changes in water diffusivity in breast tumors during therapy are required. Methodologies that employ simulations are presented for measuring expected statistical accuracy of PRM for response assessment. Test-retest clinical scans are used to yield estimates of system noise to indicate significant changes in voxel-based changes in water diffusivity. Overall, registration-based PRM image analysis provides significant opportunities for voxel-based image analysis to provide the required accuracy for early assessment of response to treatment in breast cancer patients receiving neoadjuvant chemotherapy.
PURPOSE: In the current study we examined the ability of diffusion MRI (dMRI) to predict pathologic response in pancreatic cancer patients receiving neoadjuvant chemoradiation. METHODS: We performed a prospective pilot study of dMRI in patients with resectable pancreatic cancer. Patients underwent dMRI prior to neoadjuvant chemoradiation. Surgical specimens were graded according to the percent tumor cell destruction. Apparent diffusion coefficient (ADC) maps were used to generate whole-tumor derived ADC histogram distributions and mean ADC values. The primary objective of the study was to correlate ADC parameters with pathologic and CT response. RESULTS: Ten of the 12 patients enrolled on the study completed chemoradiation and had surgery. Three were found to be unresectable at the time of surgery and no specimen was obtained. Out of the 7 patients who underwent pancreaticoduodenectomy, 3 had a grade III histopathologic response (> 90% tumor cell destruction), 2 had a grade IIB response (51% to 90% tumor cell destruction), 1 had a grade IIA response (11% to 50% tumor cell destruction), and 1 had a grade I response (> 90% viable tumor). Median survival for patients with a grade III response, grade I-II response, and unresectable disease were 25.6, 18.7, and 6.1 months, respectively. There was a significant correlation between pre-treatment mean tumor ADC values and the amount of tumor cell destruction after chemoradiation with a Pearson correlation coefficient of 0.94 (P = .001). Mean pre-treatment ADC was 161 × 10− 5 mm2/s (n = 3) in responding patients (> 90% tumor cell destruction) compared to 125 × 10− 5 mm2/s (n = 4) in non-responding patients (> 10% viable tumor). CT imaging showed no significant change in tumor size in responders or non-responders. CONCLUSIONS: dMRI may be useful to predict response to chemoradiation in pancreatic cancer. In our study, tumors with a low ADC mean value at baseline responded poorly to standard chemoradiation and would be candidates for intensified therapy.
RATIONALE: Treatment of glioblastoma (GBM) remains challenging due in part to its histologic intratumoral heterogeneity that contributes to its overall poor treatment response. Our goal was to evaluate a voxel-based biomarker, the functional diffusion map (fDM), as an imaging biomarker to detect heterogeneity of tumor response in a radiation dose escalation protocol using a genetically engineered murine GBM model. EXPERIMENTAL DESIGN: Twenty-four genetically engineered murine GBM models [Ink4a-Arf-/-/Ptenloxp/loxp/Ntv-a RCAS/PDGF(+)/Cre(+)] were randomized in four treatment groups (n = 6 per group) consisting of daily doses of 0, 1, 2, and 4 Gy delivered for 5 days. Contrast-enhanced T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scans were acquired for tumor delineation and quantification of apparent diffusion coefficient (ADC) maps, respectively. MRI experiments were performed daily for a week and every 2 days thereafter. For each animal, the area under the curve (AUC) of the percentage change of the ADC (AUCADC) and that of the increase in fDM values (AUCfDM+) were determined within the first 5 days following therapy initiation. RESULTS: Animal survival increased with increasing radiation dose. Treatment induced a dose-dependent increase in tumor ADC values. The strongest correlation between survival and ADC measurements was observed using the AUCfDM+ metric (R2 = 0.88). CONCLUSION: This study showed that the efficacy of a voxel-based imaging biomarker (fDM) was able to detect spatially varying changes in tumors, which were determined to be a more sensitive predictor of overall response versus whole-volume tumor measurements (AUCADC). Finally, fDM provided for visualization of treatment-associated spatial heterogeneity within the tumor.
To determine the potential value of intravoxel water diffusion heterogeneity imaging for brain tumor characterization and evaluation of high-grade gliomas, by comparing an established heterogeneity indices (α value) measured in human high-grade gliomas to those of normal appearing white and grey matter landmarks.
Materials and Methods
Twenty patients with high-grade gliomas prospectively underwent diffusion-weighted magnetic resonance imaging using multiple b-values. The stretched-exponential model was used to generate α and distributed diffusion coefficient (DDC) maps. The α values and DDCs of the tumor and contralateral anatomic landmarks were measured in each patient. Differences between α values of tumors and landmark tissues were assessed using paired t tests. Correlation between tumor α and tumor DDC was assessed using Pearson’s correlation coefficient.
Mean α of tumors was significantly lower than that of contralateral frontal white matter (P = 0.0249), basal ganglia (P < 0.0001), cortical grey matter (P < 0.0001), and centrum semiovale (P = 0.0497). Correlation between tumor α and tumor DDC was strongly negative (Pearson correlation coefficient, −0.8493; P < 0.0001).
The heterogeneity index α of human high-grade gliomas is significantly different from those of normal brain structures, which potentially offers a new method for evaluating brain tumors. The observed negative correlation between tumor α and tumor DDC requires further investigation.
Diffusion-weighted magnetic resonance imaging; stretched-exponential; intravoxel water diffusion heterogeneity; brain tumor; glioma
Ataxia telangiectasia mutated (ATM) is a serine/threonine kinase critical to the cellular DNA-damage response, including from DNA double-strand breaks (DSBs). ATM activation results in the initiation of a complex cascade of events including DNA damage repair, cell cycle checkpoint control, and survival. We sought to create a bioluminescent reporter that dynamically and non-invasively measures ATM kinase activity in living cells and subjects.
Methods and Materials
Using the split luciferase technology we constructed a hybrid cDNA, ATM-reporter (ATMR), coding for a protein that quantitatively reports on changes in ATM kinase activity through changes in bioluminescence.
Treatment of ATMR expressing cells with ATM inhibitors resulted in a dose dependent increase in bioluminescence activity. In contrast, induction of ATM kinase activity upon irradiation resulted in a decrease in reporter activity that correlated with ATM and Chk2 activation by immunoblotting in a time-dependent fashion. Nuclear targeting improved ATMR sensitivity to both ATM inhibitors and radiation, while a mutant ATMR (lacking the target phosphorylation site) displayed a muted response. Treatment with ATM inhibitors and siRNA-targeted knockdown of ATM confirm the specificity of the reporter. Using reporter expressing xenografted tumors demonstrated the ability of ATMR to report in ATM activity in mouse models which correlated in a time-dependent fashion with changes in Chk2 activity.
We describe the development and validation of a novel, specific, non-invasive bioluminescent reporter that enables monitoring of ATM activity in real-time in vitro and in vivo. Potential applications of this reporter include the identification and development of novel ATM inhibitors or ATM-interacting partners through high-throughput screens, and in vivo pharmacokinetic/pharmacodynamic studies of ATM inhibitors in pre-clinical models.
Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a highly heterogeneous disorder, composed of varying pathobiology. Accurate detection of COPD subtypes by image biomarkers are urgently needed to enable individualized treatment thus improving patient outcome. We adapted the Parametric Response Map (PRM), a voxel-wise image analysis technique, for assessing COPD phenotype. We analyzed whole lung CT scans of 194 COPD individuals acquired at inspiration and expiration from the COPDGene Study. PRM identified the extent of functional small airways disease (fSAD) and emphysema as well as provided CT-based evidence that supports the concept that fSAD precedes emphysema with increasing COPD severity. PRM is a versatile imaging biomarker capable of diagnosing disease extent and phenotype, while providing detailed spatial information of disease distribution and location. PRMs ability to differentiate between specific COPD phenotypes will allow for more accurate diagnosis of individual patients complementing standard clinical techniques.
PURPOSE: The inherent treatment resistance of glioblastoma (GBM) can involve multiple mechanisms including checkpoint kinase (Chk1/2)-mediated increased DNA repair capability, which can attenuate the effects of genotoxic chemotherapies and radiation. The goal of this study was to evaluate diffusion-weighted magnetic resonance imaging (DW-MRI) as a biomarker for Chk1/2 inhibitors in combination with radiation for enhancement of treatment efficacy in GBM. EXPERIMENTAL DESIGN: We evaluated a specific small molecule inhibitor of Chk1/2, AZD7762, in combination with radiation using in vitro human cell lines and in vivo using a genetically engineered GBM mouse model. DW-MRI and T1-contrast MRI were used to follow treatment effects on intracranial tumor cellularity and growth rates, respectively. RESULTS: AZD7762 inhibited clonal proliferation in a panel of GBM cell lines and increased radiosensitivity in p53-mutated GBM cell lines to a greater extent compared to p53 wild-type cells. In vivo efficacy of AZD7762 demonstrated a dose-dependent inhibitory effect on GBM tumor growth rate and a reduction in tumor cellularity based on DW-MRI scans along with enhancement of radiation efficacy. CONCLUSION: DW-MRI was found to be a useful imaging biomarker for the detection of radiosensitization through inhibition of checkpoint kinases. Chk1/2 inhibition resulted in antiproliferative activity, prevention of DNA damage-induced repair, and radiosensitization in preclinical GBM tumor models, both in vitro and in vivo. The effects were found to be maximal in p53-mutated GBM cells. These results provide the rationale for integration of DW-MRI in clinical translation of Chk1/2 inhibition with radiation for the treatment of GBM.
Advanced imaging provides insight into biophysical, physiologic, metabolic, or functional properties of tissues. Since water mobility is sensitive to cellular homeostasis, cellular density and microstructural organization, it is considered a valuable tool in the advanced imaging arsenal. This article briefly summarizes diffusion imaging concepts and highlights clinical applications of diffusion MRI for oncologic imaging. The inverse relationship between water mobility and density of cellular elements has been exploited in attempts to characterize and grade brain tumor based on apparent diffusion coefficient (ADC), as well as distinguish tumor from peritumoral edema. Diffusion tensor imaging and its derivative maps of diffusion anisotropy allow assessment of tumor compression or destruction of adjacent normal tissue anisotropy thus may aid to assess tumor infiltration and aid pre-surgical planning. A variety of preclinical studies on treated tumor models demonstrate ADC is sensitive to therapeutic alteration of tumor by effective cytotoxic agents, and that ADC changes are measurable before the lesion shrinks in size. In corresponding clinical studies, these ADC changes have been detected before completion of fractionated chemo-radiation schedules thus diffusion-based biomarkers of response have the potential to be used to intervene and individualize therapy delivery. Several methods to distill diffusion information into quantitative biomarkers have been proposed and include tumor summary statistics of baseline ADC/FA values and their change with time, as well as production of voxel-by-voxel response maps that reflect the relative volume of responding tumor. The voxel-based methods require coregistration of image volumes but this approach may also have value to guide spatially-directed therapies.
Quantitative quality control procedures were sought to evaluate technical variability in multi-center measurements of the diffusion coefficient of water as a prerequisite to use of the biomarker apparent diffusion coefficient (ADC) in multi-center clinical trials.
Materials and Methods
A uniform data acquisition protocol was developed and shared with 18 participating test sites along with a temperature-controlled diffusion phantom delivered to each site. Usable diffusion weighted imaging data of ice water at 5 b-values were collected on 35 clinical MRI systems from 3 vendors at 2 field strengths (1.5 and 3T) and analyzed at a central processing site.
Standard deviation of bore-center ADCs measured across 35 scanners was <2%; error range: −2% to +5% from literature value. Day-to-day repeatability of the measurements was within 4.5%. Intra-exam repeatability at the phantom center was within 1%. Excluding one outlier, inter-site reproducibility of ADC at magnet isocenter was within 3%, though variability increased for off-center measurements. Significant (>10%) vendor-specific and system-specific spatial non-uniformity ADC bias was detected for the off-center measurement that was consistent with gradient non-linearity.
Standardization of DWI protocol has improved reproducibility of ADC measurements and allowed identifying spatial ADC non-uniformity as a source of error in multi-site clinical studies.
diffusion; MRI; phantom; ice-water; quality control; gradient non-linearity
Recent clinical practice for the management for cancer patients has begun to change from a statistical “one-size fits all” approach to medicine to more individualized care. Pre-treatment biomarkers (i.e. genetically and histologically based) have a growing role in providing guidance related to the appropriate therapy and likelihood of response; they do not take into account heterogeneity within the tumor mass. Thus, a biomarker which could be utilized to measure actual tumor response early following treatment initiation would provide an important opportunity to evaluate treatment effects on an individual patient basis. Diffusion weighted magnetic resonance imaging (DW-MRI) offers the opportunity to monitor treatment-associated alterations in tumor microenvironment using quantification of changes in tumor water diffusion values as a surrogate imaging biomarker. Results obtained thus far using DW-MRI have shown that changes in tumor diffusion values can be detected early following treatment initiation which correlate with traditional outcome measures. Sensitive imaging biomarkers are providing for the first time a means of assessing 3 dimensional tumor response early in the treatment cycle which may also provide opportunities to visualize spatial heterogeneity of response within the tumor which could open up further opportunities for spatially modulating therapy. This review highlights the development of DW-MRI and its proposed usefulness in the clinical management of cancer patients. The utility of DW-MRI for assessing therapeutic-induced response is further evaluated on tumors residing in the brain, head and neck and bone.
DW-MRI; apparent diffusion coefficient; imaging biomarker; cancer; treatment response
Currently, radiologic response of brain tumors is assessed according to the Macdonald criteria 10 weeks from the start of therapy. There exists a critical need to identify non-responding patients early in the course of their therapy for consideration of alternative treatment strategies. Our study assessed the effectiveness of the Parametric Response Map (PRM) imaging biomarker to provide for an earlier measure of patient survival prediction.
Forty-five high grade glioma patients received concurrent chemoradiation. Quantitative MRI including apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps were acquired pre-treatment and 3 weeks mid-treatment on a prospective institutional-approved study. PRM, a voxel-by-voxel image analysis method, was evaluated as an early prognostic biomarker of overall survival. Clinical and conventional MR parameters were also evaluated.
Multivariate analysis showed that PRMADC+ in combination with PRMrCBV- obtained at week 3 had a stronger correlation to one-year and overall survival rates than any baseline clinical or treatment response imaging metric. The composite biomarker identified three distinct patient groups, non-responders (median survival (MS) of 5.5 months CI: 4.4-6.6) months, partial responders (MS of 16 months CI: 8.6-23.4) and responders (MS has not yet been reached.)
Inclusion of PRMADC+ and PRMrCBV- into a single imaging biomarker metric provided early identification of patients resistant to standard chemoradiation. In comparison to the current standard of assessment of response at 10 weeks (MacDonald Criteria) the composite PRM biomarker potentially provides a useful opportunity for clinicians to identify patients who may benefit from alternative treatment strategies.
DW-MRI; DSC-MRI; glioma; prospective trial; treatment response
Here we describe the Parametric Response Map (PRM), a voxel-wise approach for image analysis and quantification of hemodynamic alterations during treatment for 44 patients with high-grade glioma. Relative cerebral blood volume (rCBV) and flow (rCBF) maps were acquired before treatment and after 1 and 3 weeks of therapy. We compared the standard approach using region-of-interest analysis for change in rCBV or rCBF to the change in perfusion parameters on the basis of PRM (PRMrCBV and PRMrCBF) for their accuracy in predicting overall survival. Neither the percentage change of rCBV or rCBF predicted survival, whereas the regional response evaluations based upon PRM were highly predictive of survival. Even when accounting for baseline rCBV, which is prognostic, PRMrCBV proved more predictive of overall survival.
PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create “variable signal,” whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.
Ovarian cancer is the fifth leading cause of cancer deaths among American women. Platinum-based chemotherapy, such as cisplatin, represents the standard of care for ovarian cancer. However, toxicity and acquired resistance to cisplatin have proven challenging in the treatment of ovarian cancer patients.
Using a genetically engineered mouse (GEM) model of ovarian endometrioid adenocarcinoma (OEA) in combination with molecular imaging technologies, we studied the activation of the AKT serine/threonine kinase in response to long-term cisplatin therapy.
Treatment of cells in culture and tumor-bearing animals with cisplatin resulted in activation of AKT, a key mediator of cell survival. Based on these results we investigated the therapeutic utility of AKT inhibition in combination with cisplatin, which resulted in enhanced and prolonged induction of apoptosis and in significantly improved tumor control compared to either agent alone.
These results provide an impetus for clinical trials using combination therapy. To facilitate these trials, we also demonstrate the utility of diffusion-weighted MRI as an imaging biomarker for evaluation of therapeutic efficacy in OEA.
bioluminescence imaging; diffusion-weighted MR imaging; ovarian carcinoma; AKT; cisplatin
Studies investigating dynamic susceptibility contrast magnetic resonance imaging-determined relative cerebral blood volume (rCBV) maps as a metric of treatment response assessment have generated conflicting results. We evaluated the potential of various analytical techniques to predict survival of patients with glioma treated with chemoradiation. rCBV maps were acquired in patients with high-grade gliomas at 0, 1, and 3 weeks into chemoradiation therapy. Various analytical techniques were applied to the same cohort of serial rCBV data for early assessment of survival. Three different methodologies were investigated: 1) percentage change of whole tumor statistics (i.e., mean, median, and percentiles), 2) physiological segmentation (low rCBV, medium rCBV, or high rCBV), and 3) a voxel-based approach, parametric response mapping (PRM). All analyses were performed using the same tumor contours, which were determined using contrast-enhanced T1-weighted and fluid attenuated inversion recovery images. The predictive potential of each response metric was assessed at 1-year and overall survival. PRM was the only analytical approach found to generate a response metric significantly predictive of patient 1-year survival. Time of acquisition and contour volume were not found to alter the sensitivity of the PRM approach for predicting overall survival. We have demonstrated the importance of the analytical approach in early response assessment using serial rCBV maps. The PRM analysis shows promise as a unified early and robust imaging biomarker of treatment response in patients diagnosed with high-grade gliomas.
Functional imaging biomarkers of cancer treatment response offer the potential for early determination of outcome through assessment of biochemical, physiological, and micro-environmental readouts. Cell death may result in an immunological response thus complicating interpretation of biomarker readouts. This study evaluated the temporal impact of treatment-associated inflammatory activity on diffusion-MRI and FDG-PET imaging biomarkers to delineate the effects of the inflammatory response on imaging readouts.
Rats with intracerebral 9L gliosarcomas were separated into four groups consisting of control, an immunosuppressive agent dexamethasone (Dex), 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU), and BCNU+Dex (BCNU+Dex). Animals were imaged using diffusion-weighted MRI and FDG-PET at 0, 3 and 7 days post-treatment.
In the BCNU and BCNU+Dex treated animal groups, diffusion values increased progressively over the 7 day study period to about 23% over baseline. FDG %SUV decreased at day 3 (−30.9%) but increased over baseline levels at day 7 (+20.1%). FDG-PET of BCNU+Dex treated animals were found to have %SUV reductions of −31.4% and −24.7% at days 3 and 7, respectively following treatment. Activated macrophages were observed on day 7 in the BCNU treatment group with much fewer found in the BCNU+Dex group.
Results revealed treatment-associated inflammatory response following tumor therapy resulted in accentuation of tumor diffusion response along with a corresponding increase in tumor FDG uptake due to the presence of glucose-consuming activated macrophages. The dynamics and magnitude of potential inflammatory response should be considered when interpreting imaging biomarker results.
Diffusion; MRI; ADC; positron emission tomography; FDG; brain tumor
Early treatment of Alzheimer’s disease may reduce its devastating effects. By focusing research on asymptomatic individuals with Alzheimer’s disease pathology (the preclinical stage), earlier indicators of disease may be discovered. Decreasing cerebrospinal fluid beta-amyloid42 is the first indicator of preclinical disorder, but it is not known which pathology causes the first clinical effects. Our hypothesis is that neuropsychological changes within the normal range will help to predict preclinical disease and locate early pathology.
Methods and Findings
We recruited adults with probable Alzheimer’s disease or asymptomatic cognitively healthy adults, classified after medical and neuropsychological examination. By logistic regression, we derived a cutoff for the cerebrospinal fluid beta amyloid42/tau ratios that correctly classified 85% of those with Alzheimer’s disease. We separated the asymptomatic group into those with (n = 34; preclinical Alzheimer’s disease) and without (n = 36; controls) abnormal beta amyloid42/tau ratios; these subgroups had similar distributions of age, gender, education, medications, apolipoprotein-ε genotype, vascular risk factors, and magnetic resonance imaging features of small vessel disease. Multivariable analysis of neuropsychological data revealed that only Stroop Interference (response inhibition) independently predicted preclinical pathology (OR = 0.13, 95% CI = 0.04–0.42). Lack of longitudinal and post-mortem data, older age, and small population size are limitations of this study.
Our data suggest that clinical effects from early amyloid pathophysiology precede those from hippocampal intraneuronal neurofibrillary pathology. Altered cerebrospinal fluid beta amyloid42 with decreased executive performance before memory impairment matches the deposits of extracellular amyloid that appear in the basal isocortex first, and only later involve the hippocampus. We propose that Stroop Interference may be an additional important screen for early pathology and useful to monitor treatment of preclinical Alzheimer’s disease; measures of executive and memory functions in a longitudinal design will be necessary to more fully evaluate this approach.
The parametric response map (PRM) was evaluated as an early surrogate biomarker for monitoring treatment-induced tissue alterations in patients with head and neck squamous cell carcinoma (HNSCC). Diffusion-weighted magnetic resonance imaging (DW-MRI) was performed on 15 patients with HNSCC at baseline and 3 weeks after treatment initiation of a nonsurgical organ preservation therapy (NSOPT) using concurrent radiation and chemotherapy. PRM was applied on serial apparent diffusion coefficient (ADC) maps that were spatially aligned using a deformable image registration algorithm to measure the tumor volume exhibiting significant changes in ADC (PRMADC). Pretherapy and midtherapy ADC maps, quantified from the DWIs, were analyzed by monitoring the percent change in whole-tumor mean ADC and the PRM metric. The prognostic values of percentage change in tumor volume and mean ADC and PRMADC as a treatment response biomarker were assessed by correlating with tumor control at 6 months. Pixel-wise differences as part of PRMADC analysis revealed regions where water mobility increased. Analysis of the tumor ADC histograms also showed increases in mean ADC as early as 3 weeks into therapy in patients with a favorable outcome. Nevertheless, the percentage change in mean ADC was found to not correlate with tumor control at 6 months. In contrast, significant differences in PRMADC and percentage change in tumor volume were observed between patients with pathologically different outcomes. Observations from this study have found that diffusion MRI, when assessed by PRMADC, has the potential to provide both prognostic and spatial information during NSOPT of head and neck cancer.
Loss of bone mass due to disease, such as osteoporosis and metastatic cancer to the bone, is a leading cause of orthopedic complications and hospitalization. Onset of bone loss resulting from disease increases the risk of incurring fractures and subsequent pain, increasing medical expenses while reducing quality of life. Although current standard CT-based protocols provide adequate prognostic information for assessing bone loss, many of the techniques for evaluating CT scans rely on measures based on whole-bone summary statistics. This reduces the sensitivity at identifying local regions of bone resorption, as well as formation. In this study, we evaluate the effectiveness of a voxel-based image post-processing technique, called the Parametric Response Map (PRM), for identifying local changes in bone mass in weight-bearing bones on CT scans using an established animal model of osteoporosis. Serial CT scans were evaluated weekly using PRM subsequent to ovariectomy or sham surgeries over the period of one month. For comparison, bone volume fraction and mineral density measurements were acquired and found to significantly differ between groups starting 3 weeks post-surgery. High resolution ex vivo measurements acquired four weeks post-surgery validated the extent of bone loss in the surgical groups. In contrast to standard methodologies for assessing bone loss, PRM results were capable of identifying local decreases in bone mineral by week 2, which were found to be significant between groups. This study concludes that PRM is able to detect changes in bone mineral with higher sensitivity and spatial differentiation than conventional techniques for evaluating CT scans, which may aid in clinical decision making for patients suffering from bone loss.
ovariectomy; osteoporosis; imaging; CT; response; biomarker
Assuming that early tumor volume change is a biomarker for response to therapy, accurate quantification of early volume changes could aid in adapting an individual patient’s therapy and lead to shorter clinical trials. We investigated an image registration–based approach for tumor volume change quantification that may more reliably detect smaller changes that occur in shorter intervals than can be detected by existing algorithms.
Methods and Materials
Variance and bias of the registration-based approach were evaluated using retrospective, in vivo, very-short-interval diffusion magnetic resonance imaging scans where true zero tumor volume change is unequivocally known and synthetic data, respectively. The interval scans were nonlinearly registered using two similarity measures: mutual information (MI) and normalized cross-correlation (NCC).
The 95% confidence interval of the percentage volume change error was (−8.93% to 10.49%) for MI-based and (−7.69%, 8.83%) for NCC-based registrations. Linear mixed-effects models demonstrated that error in measuring volume change increased with increase in tumor volume and decreased with the increase in the tumor’s normalized mutual information, even when NCC was the similarity measure being optimized during registration. The 95% confidence interval of the relative volume change error for the synthetic examinations with known changes over ±80% of reference tumor volume was (−3.02% to 3.86%). Statistically significant bias was not demonstrated.
A low-noise, low-bias tumor volume change measurement algorithm using nonlinear registration is described. Errors in change measurement were a function of tumor volume and the normalized mutual information content of the tumor.
Tumor volume change; Image registration; Dual baseline examination; Coffee-break examination; Linear mixed-effects model