Parametric Response Map Analysis Research Articles

Interaction of smoking and aging on emphysema and small airways disease in asymptomatic healthy men by CT-based parametric response mapping analysis

To explore whether small airway disease and emphysema were affected by the interaction between smoking and aging on chest computed tomography (CT) images of asymptomatic healthy men analysed using a quantitative imaging tool parametric response mapping (PRM). In this retrospective study, 95 asymptomatic healthy men underwent biphasic chest CT. The PRM classifies lung as a percentage of normal (PRMNormal%), functional small airway disease (PRMfSAD%), and emphysema (PRMEmph%). The patients were divided into groups based on their age and smoking status. Multiple linear regression analysis was applied to explore the factors influencing lung injury. Simple effects analysis was performed to explore the interaction between different age groups and smoking status. The interaction between aging and smoking significantly affected PRMfSAD% and PRMEmph% (p<0.001). The age range 60-69 and smoking were associated with increased PRMfSAD% and PRMEmph% (p<0.05). Futher stratification into different age subgroups showed that smoking was associated with increased PRMfSAD% and PRMEmph% in the 50-59 year age group. Besides, smoking in the 50-59 and 60-69 years group was associated with decreased PRMNormal%, while smoking in the 60-69 years group did not significantly influence the prevalence of PRMfSAD% and PRMEmph% (p>0.05). PRM reveals the interplay between smoking and aging in the development of lung injury in asymptomatic healthy men. Aging and smoking are important factors of emphysema and small airway disease in the 50-69 years group. In the 60-69 years group, aging poses a greater risk of lung injury compared to smoking.

Comparing the predictive value of quantitative magnetic resonance imaging parametric response mapping and conventional perfusion magnetic resonance imaging for clinical outcomes in patients with chronic ischemic stroke.

Predicting clinical outcomes after stroke, using magnetic resonance imaging (MRI) measures, remains a challenge. The purpose of this study was to investigate the prediction of long-term clinical outcomes after ischemic stroke using parametric response mapping (PRM) based on perfusion MRI data. Multiparametric perfusion MRI datasets from 30 patients with chronic ischemic stroke were acquired at four-time points ranging from V2 (6 weeks) to V5 (7 months) after stroke onset. All perfusion MR parameters were analyzed using the classic whole-lesion approach and voxel-based PRM at each time point. The imaging biomarkers from each acquired MRI metric that was predictive of both neurological and functional outcomes were prospectively investigated. For predicting clinical outcomes at V5, it was identified that PRMTmax-, PRMrCBV-, and PRMrCBV+ at V3 were superior to the mean values of the corresponding maps at V3. We identified correlations between the clinical prognosis after stroke and MRI parameters, emphasizing the superiority of the PRM over the whole-lesion approach for predicting long-term clinical outcomes. This indicates that complementary information for the predictive assessment of clinical outcomes can be obtained using PRM analysis. Moreover, new insights into the heterogeneity of stroke lesions revealed by PRM can help optimize the accurate stratification of patients with stroke and guide rehabilitation.

Correlating Clinical Outcome with Quantitative MRI Parametric Response Mapping Analysis in Chronic Ischemic Stroke

Correlating Clinical Outcome with Quantitative MRI Parametric Response Mapping Analysis in Chronic Ischemic Stroke

Feasibility of function-guided lung treatment planning with parametric response mapping.

PurposeRecent advancements in functional lung imaging have been developed to improve clinicians’ knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)–based functional lung imaging method that utilizes a voxel‐wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning.Methods and materialsA retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric‐modulated arc therapy and PRM‐guided treatment plans were designed for each patient.ResultsPRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM‐guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity.ConclusionsPRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM‐guided treatment planning.

A Potential Biomarker from Diffusion Weighted Imaging and Parametric Response Map Analysis for Treatment Response Prediction in Nasopharyngeal Cancer

Diffusion-weighted imaging (DWI) is an MRI technique which provides functional information of tissue by detecting microscopic motion of water molecules. The change of apparent diffusion coefficient (ADC) derived from DWI was used as an imaging biomarker for treatment response prediction in cancers [1]. However, it was based on whole-tumor analysis which did not reflect heterogeneity within the tumor. To overcome this limitation, a new method called parametric response map (PRM) analysis was proposed to evaluate response by quantifying voxel-wise changes in ADC [2]. Here we investigated the use of PRM analysis on ADC from DWI as an imaging biomarker for treatment response prediction in nasopharyngeal cancer (NPC) patients. We collected thirteen patient datasets including ten complete response (CR) patients and three partial response (PR) patients at King Chulalongkorn Memorial Hospital where one patient dataset consisted of DWI and ADC data acquired before (i.e. pre-treatment) and at five weeks after (i.e. mid-treatment) initiation of chemoradiation therapy. For each dataset, we compared pre-treatment ADC image with co-registered mid-treatment ADC image, and calculated the percentage of voxels with increased ADC values with respect to total voxels within the tumor ROI, defined as PRM+. To validate the feasibility of the PRM biomarker, we computed the mean and standard deviation (SD) of percentage change in tumor volume (%AVol ) and in ADC (%AADC) and PRM+ across CR and PR patients, where tumor response was from 6-month follow-up data using RECIST1.1 guideline. The results showed that %AVol as well as %AADC between both groups was not significantly different. In contrast, PRM+ was significantly different between both groups (p < 0.05, 82.7±7.8% in CR vs 66.7±6.5% in PR). Our results implied that the proposed PRM+ biomarker could be a potential biomarker for early treatment response prediction in NPC patients.

Noninvasive Imaging Biomarker Identifies Small Airway Damage in Severe Chronic Obstructive Pulmonary Disease.

Rationale: Evidence suggests damage to small airways is a key pathologic lesion in chronic obstructive pulmonary disease (COPD). Computed tomography densitometry has been demonstrated to identify emphysema, but no such studies have been performed linking an imaging metric to small airway abnormality.Objectives: To correlate ex vivo parametric response mapping (PRM) analysis to in vivo lung tissue measurements of patients with severe COPD treated by lung transplantation and control subjects.Methods: Resected lungs were inflated, frozen, and systematically sampled, generating 33 COPD (n = 11 subjects) and 22 control tissue samples (n = 3 subjects) for micro-computed tomography analysis of terminal bronchioles (TBs; last generation of conducting airways) and emphysema.Measurements and Main Results: PRM analysis was conducted to differentiate functional small airways disease (PRMfSAD) from emphysema (PRMEmph). In COPD lungs, TB numbers were reduced (P = 0.01); surviving TBs had increased wall area percentage (P < 0.001), decreased circularity (P < 0.001), reduced cross-sectional luminal area (P < 0.001), and greater airway obstruction (P = 0.008). COPD lungs had increased airspace size (P < 0.001) and decreased alveolar surface area (P < 0.001). Regression analyses demonstrated unique correlations between PRMfSAD and TBs, with decreased circularity (P < 0.001), decreased luminal area (P < 0.001), and complete obstruction (P = 0.008). PRMEmph correlated with increased airspace size (P < 0.001), decreased alveolar surface area (P = 0.003), and fewer alveolar attachments per TB (P = 0.01).Conclusions: PRMfSAD identifies areas of lung tissue with TB loss, luminal narrowing, and obstruction. This is the first confirmation that an imaging biomarker can identify terminal bronchial pathology in established COPD and provides a noninvasive imaging methodology to identify small airway damage in COPD.

A generalized parametric response mapping method for analysis of multi-parametric imaging: A feasibility study with application to glioblastoma.

Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well-suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub-volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single-parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi-parametric data while maintaining the key advantages of the original PRM method. MRI-derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3-months post-RT for 19 patients with high-grade glioma were used to demonstrate the algorithm. Images were first co-registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four-dimensional Cartesian space with coordinate values equal to a voxel's image intensity in each of the image volumes and an origin defined as the multi-parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre-determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non-responding patients within a training dataset. Voxel classifications were visualized via familiar three-class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast-enhancing lesion (CEL) and a 1 cm shell of surrounding peri-tumoral tissue were performed. Prediction using tumor volume metrics was also investigated. Leave-one-out cross validation (LOOCV) was used in combination with permutation testing to assess preliminary predictive efficacy and estimate statistically robust P-values. The predictive endpoint was overall survival (OS) greater than or equal to the median OS of 18.2 months. Single-parameter PRM and multi-parametric response maps (MPRMs) were generated for each patient and used to predict OS via the LOOCV. Tumor volume metrics (P ≥ 0.071 ± 0.01) and single-parameter PRM analyses (P ≥ 0.170 ± 0.01) were not found to be predictive of OS within this study. MPRM analysis of the peri-tumoral region but not the CEL was found to be predictive of OS with a classification sensitivity, specificity and accuracy of 80%, 100%, and 89%, respectively (P = 0.001 ± 0.01). The feasibility of a generalized MPRM analysis framework was demonstrated with improved prediction of overall survival compared to the original single-parameter method when applied to a glioblastoma dataset. The proposed algorithm takes the spatial heterogeneity in multi-parametric response into consideration and enables visualization. MPRM analysis of peri-tumoral regions was shown to have predictive potential supporting further investigation of a larger glioblastoma dataset.

Parametric response mapping of dynamic CT: enhanced prediction of survival in hepatocellular carcinoma patients treated with transarterial chemoembolization.

The aim of this study was to evaluate the prognostic significance of parametric response mapping (PRM) analysis for hepatocellular carcinoma (HCC) patients undergoing transarterial chemoembolization (TACE). We recruited 65 HCC patients who underwent TACE. These patients underwent longitudinal multiphasic CT before and after TACE. We applied PRM analysis to the baseline CT before TACE and first/second follow-up CTs. The results of PRM analyses were used to stratify patients into responders and non-responders. Overall survival was compared between the two groups. An independent survival analysis using conventional radiological assessments was performed, and the results were compared with PRM results. Univariate and multivariate analyses were performed to identify clinical factors affecting survival. The PRM analyses demonstrated that the responding group had a median survival of 529days, while the non-responding group had a median survival of 263days [hazard ratio (HR) 12.9, p<0.05 for differences in survival]. The manual analyses indicated median survivals of 491 and 329days for the responding and non-responding groups, respectively (HR 2.7, p<0.05). Tumor size, albumin level, and PRM values were found to be significantly related to overall survival after univariate and multivariate analyses. The PRM analysis could be a better predictor of overall survival for patients with HCC undergoing TACE than conventional radiological assessments.

Age and Small Airway Imaging Abnormalities in Subjects with and without Airflow Obstruction in SPIROMICS.

Aging is associated with reduced FEV1 to FVC ratio (FEV1/FVC), hyperinflation, and alveolar enlargement, but little is known about how age affects small airways. To determine if chest computed tomography (CT)-assessed functional small airway would increase with age, even among asymptomatic individuals. We used parametric response mapping analysis of paired inspiratory/expiratory CTs to identify functional small airway abnormality (PRMFSA) and emphysema (PRMEMPH) in the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) cohort. Using adjusted linear regression models, we analyzed associations between PRMFSA and age in subjects with or without airflow obstruction. We subdivided participants with normal spirometry based on respiratory-related impairment (6-minute-walk distance <350 m, modified Medical Research Council ≥2, chronic bronchitis, St. George's Respiratory Questionnaire >25, respiratory events requiring treatment [antibiotics and/or steroids or hospitalization] in the year before enrollment). Among 580 never- and ever-smokers without obstruction or respiratory impairment, PRMFSA increased 2.7% per decade, ranging from 3.6% (ages 40-50 yr) to 12.7% (ages 70-80 yr). PRMEMPH increased nonsignificantly (0.1% [ages 40-50 yr] to 0.4% [ages 70-80 yr]; P = 0.34). Associations were similar among nonobstructed individuals with respiratory-related impairment. Increasing PRMFSA in subjects without airflow obstruction was associated with increased FVC (P = 0.004) but unchanged FEV1 (P = 0.94), yielding lower FEV1/FVC ratios (P < 0.001). Although emphysema was also significantly associated with lower FEV1/FVC (P = 0.04), its contribution relative to PRMFSA in those without airflow obstruction was limited by its low burden. In never- and ever-smokers without airflow obstruction, aging is associated with increased FVC and CT-defined functional small airway abnormality regardless of respiratory symptoms.

Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping.

Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging. Development of the parametric response mapping (PRM) applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor. Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for patient management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans. The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice.

A Pilot Study of Quantitative MRI Parametric Response Mapping of Bone Marrow Fat for Treatment Assessment in Myelofibrosis.

Myelofibrosis (MF) is a hematologic neoplasm arising as a primary disease or secondary to other blood malignancies. Both primary and secondary MF develop progressive fibrosis of bone marrow, displacing normal hematopoietic cells to other organs and disrupting normal production of mature blood cells. Activation of JAK2 signaling in hematopoietic stem cells commonly causes MF, and ruxolitinib, a drug targeting this pathway, is the preferred treatment for many patients. However, current measures of disease status in MF do not necessarily predict response to treatment with ruxolitinib or other drugs. Bone marrow biopsies are invasive and prone to sampling error, while measurements of spleen volume only indirectly reflect status of bone marrow. Toward the goal of developing an imaging biomarker for treatment response in MF, we present preliminary results from a prospective clinical study evaluating parametric response mapping (PRM) of quantitative Dixon MRI bone marrow fat fraction maps in four MF patients treated with ruxolitinib. PRM allows voxel-wise identification of temporal changes in quantitative imaging readouts, in this case bone marrow fat. We identified heterogeneous responses of bone marrow fat among patients and within different bone marrow sites in the same patient. Changes in bone marrow fat fraction also were discordant with reductions in spleen volume, the standard imaging metric for treatment response. This study provides initial support for PRM analysis of quantitative MRI of bone marrow fat to monitor therapy in MF, setting the stage for larger studies to further develop and validate this method as a complementary imaging biomarker.

Potential for Early Fracture Risk Assessment in Patients with Metastatic Bone Disease using Parametric Response Mapping of CT Images.

Pathologic vertebral compression fractures (PVCFs) cause significant morbidity in patients with metastatic bone disease. Limitations in existing clinical biomarkers leave clinicians without reliable metrics for predicting PVCF, thus impeding efforts to prevent this severe complication. To establish the feasibility of a new method for defining the risk of a PVCF, we retrospectively analyzed serial computed tomography (CT) scans from 5 breast cancer patients using parametric response mapping (PRM) to quantify dynamic bone miniral density (BMD) changes that preceded an event. Vertebrae segmented from each scan were registered to the same spatial frame and voxel classification was accomplished using a predetermined threshold of change in Hounsfield units (HU), resulting in relative volumes of increased (PRMHU+), decreased (PRMHU−), or unchanged (PRMHU0) attenuation. A total of 7 PVCFs were compared to undiseased vertebrae in each patient serving as controls. A receiver operator curve (ROC) analysis identified optimal imaging times for group stratification. BMD changes were apparent by an elevated PRMHU+ as early as 1 year before fracture. ROC analysis showed poor performance of PRMHU− in stratifying PVCFs versus controls. As early as 6 months before PVCF, PRMHU+ was significantly larger (12.9 ± 11.6%) than control vertebrae (2.3 ± 2.5%), with an area under the curve of 0.918 from an ROC analysis. Mean HU changes were also significant between PVCF (26.8 ± 26.9%) and control (−2.2 ± 22.0%) over the same period. A PRM analysis of BMD changes using standard CT imaging was sensitive for spatially resolving changes that preceded structural failure in these patients.

Parametric response mapping of dynamic CT for predicting intrahepatic recurrence of hepatocellular carcinoma after conventional transcatheter arterial chemoembolization.

The aim of our study was to determine the diagnostic value of a novel image analysis method called parametric response mapping (PRM) for prediction of intrahepatic recurrence of hepatocellular carcinoma (HCC) treated with conventional transcatheter arterial chemoembolization (TACE). This retrospective study was approved by the IRB. We recruited 55 HCC patients who achieved complete remission (CR) after TACE and received longitudinal multiphasic liver computed tomography (CT). The patients fell into two groups: the recurrent tumour group (n = 29) and the non-recurrent tumour group (n = 26). We applied the PRM analysis to see if this technique could distinguish between the two groups. The results of the PRM analysis were incorporated into a prediction algorithm. We retrospectively removed data from the last time point and attempted to predict the response to therapy of the removed data. The PRM analysis was able to distinguish between the non-recurrent and recurrent groups successfully. The prediction algorithm detected response to therapy with an area under the curve (AUC) of 0.76, while the manual approach had AUC 0.64. Adopting PRM analysis can potentially distinguish between recurrent and non-recurrent HCCs and allow for prediction of response to therapy after TACE. Parametric response mapping (PRM) could help assess patients with recurrent HCCs after TACE. Parametric response mapping could direct patients to individualized therapy. Longitudinal CT images were analyzed with advanced image analysis method.

Feasibility analysis of the parametric response map as an early predictor of treatment efficacy in head and neck cancer.

Estimating changes in the volume transfer constant, normalized area under the contrast-enhancement time curve at 60 seconds, and fractional blood plasma volume by using dynamic contrast-enhanced MR imaging may be useful in predicting tumor response to chemoradiation. We hypothesized that the parametric response map, a voxel-by-voxel analysis of quantitative dynamic contrast-enhanced MR imaging maps, predicts survival in patients with head and neck cancer. Ten patients with locoregionally advanced head and neck squamous cell carcinoma underwent definitive concurrent chemoradiation therapy. For each patient, dynamic contrast-enhanced MR imaging data were collected before and 2 weeks after treatment initiation. Change in perfusion parameters within the primary tumor volume with time was analyzed by parametric response mapping and by whole-tumor mean percentage change. Outcome was defined as overall survival. The perfusion parameter and metric most predictive of outcome were identified. Overall survival was estimated by the log-rank test and Kaplan-Meier survival curve. The volume transfer constant and normalized area under the contrast-enhancement time curve at 60 seconds were predictive of survival both in parametric response map analysis (volume transfer constant, P = .002; normalized area under the contrast-enhancement time curve at 60 seconds, P = .02) and in the percentage change analysis (volume transfer constant, P = .04; normalized area under the contrast-enhancement time curve at 60 seconds, P = .02). Blood plasma volume predicted survival in neither analysis. Parametric response mapping of MR perfusion biomarkers could potentially guide treatment modification in patients with predicted treatment failure. Larger studies are needed to determine whether parametric response map analysis or percentage signal change in these perfusion parameters is the stronger predictor of survival.

An augmented parametric response map with consideration of image registration error: towards guidance of locally adaptive radiotherapy

Parametric response map (PRM) analysis is a voxel-wise technique for predicting overall treatment outcome, which shows promise as a tool for guiding personalized locally adaptive radiotherapy (RT). However, image registration error (IRE) introduces uncertainty into this analysis which may limit its use for guiding RT. Here we extend the PRM method to include an IRE-related PRM analysis confidence interval and also incorporate multiple graded classification thresholds to facilitate visualization. A Gaussian IRE model was used to compute an expected value and confidence interval for PRM analysis. The augmented PRM (A-PRM) was evaluated using CT-perfusion functional image data from patients treated with RT for glioma and hepatocellular carcinoma. Known rigid IREs were simulated by applying one thousand different rigid transformations to each image set. PRM and A-PRM analyses of the transformed images were then compared to analyses of the original images (ground truth) in order to investigate the two methods in the presence of controlled IRE. The A-PRM was shown to help visualize and quantify IRE-related analysis uncertainty. The use of multiple graded classification thresholds also provided additional contextual information which could be useful for visually identifying adaptive RT targets (e.g. sub-volume boosts). The A-PRM should facilitate reliable PRM guided adaptive RT by allowing the user to identify if a patient’s unique IRE-related PRM analysis uncertainty has the potential to influence target delineation.

Breast Cancer: Early Prediction of Response to Neoadjuvant Chemotherapy Using Parametric Response Maps for MR Imaging

To prospectively compare the performance of dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging using parametric response map (PRM) analysis with that using pharmacokinetic parameters (transfer constant [K(trans)], rate constant [kep ], and relative extravascular extracellular space [ve]) in the early prediction of pathologic responses to neoadjuvant chemotherapy (NAC) in breast cancer patients. The institutional review board approved this study; informed consent was obtained. Between August 2010 and December 2012, 48 women (mean age, 46.4 years; range, 29-65 years) with breast cancer were enrolled and treated with an anthracycline-taxane regimen. DCE MR imaging was performed before and after the first cycle of chemotherapy, and the pathologic response was assessed after surgery. Tumor size and volume, PRM characteristics, and pharmacokinetic parameters (K(trans), kep, and ve) on MR images were assessed and compared according to the pathologic responses by using the Fisher exact test or the independent-sample t test. Six of 48 (12%) patients showed pathologic complete response (CR) (pCR) and 42 (88%) showed nonpathologic CR (npCR). Thirty-eight (79%) patients showed a good response (Miller-Payne score of 3, 4, or 5), and 10 (21%) showed a minor response (Miller-Payne score of 1 or 2). The mean proportion of voxels with increased signal intensity (PRMSI+) in the pCR or good response group was significantly lower than that in the npCR or minor response group (14.0% ± 6.5 vs 40.7% ± 27.2, P < .001; 34.3% ± 26.4 vs 52.8% ± 24.9, P = .041). Area under the receiver operating characteristic curve for PRMSI+ in the pCR group was 0.770 (95% confidence interval: 0.626, 0.879), and that for the good response group was 0.716 (95% confidence interval: 0.567, 0.837). No difference in tumor size, tumor volume, or pharmacokinetic parameters was found between groups. PRM analysis of DCE MR images may enable the early identification of the pathologic response to NAC after the first cycle of chemotherapy, whereas pharmacokinetic parameters (K(trans), kep, and ve) do not.

Effects of registration error on parametric response map analysis: a simulation study using liver CT-perfusion images

Purpose: To investigate the effects of registration error (RE) on parametric response map (PRM) analysis of pre and post-radiotherapy (RT) functional images.Methods: Arterial blood flow maps (ABF) were generated from the CT-perfusion scans of 5 patients with hepatocellular carcinoma. ABF values within each patient map were modified to produce seven new ABF maps simulating 7 distinct post-RT functional change scenarios. Ground truth PRMs were generated for each patient by comparing the simulated and original ABF maps. Each simulated ABF map was then deformed by different magnitudes of realistic respiratory motion in order to simulate RE. PRMs were generated for each of the deformed maps and then compared to the ground truth PRMs to produce estimates of RE-induced misclassification.Main findings: The percentage of voxels misclassified as decreasing, no change, and increasing, increased with RE For all patients, increasing RE was observed to increase the number of high post-RT ABF voxels associated with low pre-RT ABF voxels and vice versa. 3 mm of average tumour RE resulted in 18-45% tumour voxel misclassification rates.Conclusions: RE induced misclassification posed challenges for PRM analysis in the liver where registration accuracy tends to be lower. Quantitative understanding of the sensitivity of the PRM method to registration error is required if PRMs are to be used to guide radiation therapy dose painting techniques.

Impact of Perfusion Map Analysis on Early Survival Prediction Accuracy in Glioma Patients

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.

Abstract 452: Novel MRI-based biomarker for early assessment of glioma recurrence

Abstract Gliomas continue to be the most common form of brain malignancy in adult patients. Even with advancements in the clinical management of these patients, assessment of tumor recurrence continues to be based on late or serial changes in tumor volume as measured by CT or MRI [1; 2]. The purpose of this study was to develop and evaluate a new voxel-based method, referred as parametric response mapping (PRM), for early detection of brain tumor progression using standard MR images (FLAIR and contrast-enhanced T1-weighted). PRM results were evaluated relative to standard MRI-based criteria of clinical progression assessment in patients with Grade III/IV gliomas. Fourteen patients with grades III/IV glioma underwent MRI before and during treatment (every 2 months). MRI scans, acquired on a 1.5T or a 3T clinical scanner, consisted of fluid-attenuated inversion recovery imaging (FLAIR) and contrast-enhanced T1-weighted (CE-T1w) images. All images were spatially registered to CE-T1w pre-treatment scans and tumors were manually contoured on the T1w-Gd images. Subsequent to PRM analysis, FLAIR images were normalized to the signal using the white matter (rFLAIR). PRM, applied to the whole brain volume on rFLAIR, was performed by first generating voxel-based difference maps at each longitudinal follow-up scan using the baseline as the subtrahend. Baseline was defined as either the pre-treatment or subsequent rFLAIR image. Individual voxels were classified based on the extent of change observed in the rFLAIR difference maps. Three classifications were used: voxels with a significant increase in rFLAIR values (PRMrFLAIR+), significant decrease (PRMrFLAIR-) or statistically unchanged (PRMrFLAIR0). Disease recurrence was defined by PRM as the sum of red voxels that exceed 20% of the tumor volume as delineated on T1w-Gd. PRMrFLAIR detected tumor recurrence on average 18±3 weeks earlier than the MacDonald criteria. It was also observed that PRMrFLAIR+ found outside the tumor CE-T1w regions revealed the spatial location at which the tumor would progress on subsequent scans based on current standard progression criteria. These results show that the PRM approach provides for the early detection and spatial depiction of brain tumor progression prior to detection by currently available conventional MRI-based criteria. While further validation of PRM for detection of tumor recurrence is currently underway, the ability to detect tumor progression significantly earlier than currently possible is anticipated to have a major impact on patient care. [1] Macdonald, D. R., et al. (1990). J Clin Oncol 8(7): 1277-1280. [2] Wen, P. Y., et al. (2010). J Clin Oncol 28(11): 1963-1972. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 452. doi:1538-7445.AM2012-452

Use of 11C-methionine PET parametric response map for monitoring WT1 immunotherapy response in recurrent malignant glioma

Immunotherapy targeting the Wilms tumor 1 (WT1) gene product is a promising treatment modality for patients with malignant gliomas, and there have been reports of encouraging results. It has become clear, however, that Gd-enhanced MR imaging does not reflect prognosis, thereby necessitating a more robust imaging evaluation system for monitoring response to WT1 immunotherapy. To meet this demand, the authors performed a voxel-wise parametric response map (PRM) analysis of (11)C-methionine PET (MET-PET) in WT1 immunotherapy and compared the data with the overall survival after initiation of WT1 immunotherapy (OS(WT1)). Fourteen patients with recurrent malignant glioma were included in the study, and OS(WT1) was compared with: 1) volume and length change in the contrast area of the tumor on Gd-enhanced MR images; 2) change in maximum uptake of (11)C-methionine; and 3) a more detailed voxel-wise PRM analysis of MET-PET pre- and post-WT1 immunotherapy. The PRM analysis was able to identify the following 3 areas within the tumor core: 1) area with no change in (11)C-methionine uptake pre- and posttreatment; 2) area with increased (11)C-methionine uptake posttreatment (PRM(+MET)); and 3) area with decreased (11)C-methionine uptake posttreatment. While the results of Gd-enhanced MR imaging volumetric and conventional MET-PET analysis did not correlate with OS(WT1) (p = 0.270 for Gd-enhanced MR imaging length, p = 0.960 for Gd-enhanced MR imaging volume, and p = 0.110 for MET-PET), the percentage of PRM(+MET) area showed excellent correlation (p = 0.008) with OS(WT1). This study describes the limited value of Gd-enhanced MR imaging and highlights the potential of voxel-wise PRM analysis of MET-PET for monitoring treatment response in immunotherapy for malignant gliomas. Clinical trial registration no.: UMIN000002001.