Dynamic Contrast-enhanced Curves Research Articles

Value for combination of T1WI star-VIBE with TWIST-VIBE dynamic contrast-enhanced MRI in distinguishing lung nodules.

With the increasing detection rate of lung nodules, the qualitative problem of lung nodules has become one of the key clinical issues. This study aims to evaluate the value of combining dynamic contrast-enhanced (DCE) MRI based on time-resolved imaging with interleaved stochastic trajectories-volume interpolated breath hold examination (TWIST-VIBE) with T1 weighted free-breathing star-volumetric interpolated breath hold examination (T1WI star-VIBE) in identifying benign and malignant lung nodules. We retrospectively analyzed 79 adults with undetermined lung nodules before the operation. All nodules of patients included were classified into malignant nodules (n=58) and benign nodules (n=26) based on final diagnosis. The unenhanced T1WI-VIBE, the contrast-enhanced T1WI star-VIBE, and the DCE curve based on TWIST-VIBE were performed. The corresponding qualitative [wash-in time, wash-out time, time to peak (TTP), arrival time (AT), positive enhancement integral (PEI)] and quantitative parameters [volume transfer constant (Ktrans), interstitium-to-plasma rate constant (Kep), and fractional extracellular space volume (Ve)] were evaluated. Besides, the diagnostic efficacy (sensitivity and specificity) of enhanced CT and MRI were compared. There were significant differences in unenhanced T1WI-VIBE hypo-intensity, and type of A, B, C DCE curve type between benign and malignant lung nodules (all P<0.001). Pulmonary malignant nodules had a shorter wash-out time than benign nodules (P=0.001), and the differences of the remaining parameters were not statistically significant (all P>0.05). After T1WI star-VIBE contrast-enhanced MRI, the image quality was further improved. Compared with enhanced CT scan, the sensitivity (82.76% vs 80.50%) and the specificity (69.23% vs 57.10%) based on MRI were higher than that of CT (both P<0.001). T1WI star-VIBE and dynamic contrast-enhanced MRI based on TWIST-VIBE were helpful to improve the image resolution and provide more information for clinical differentiation between benign and malignant lung nodules.

Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment.

Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable. To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images. Retrospective. A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results. A 3.0 T/multi-b-value DWI (b values=0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence. In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort. Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant. For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P=0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635. Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions. 4. Stage 2.

Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma.

Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI. The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome. Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases. The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.

Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging-based differential diagnoses of benign and malignant breast lesions.

To compare the diagnostic performance of models based on a combination of contrast-enhanced (CE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) or time-intensity curves (TIC) in diagnosing malignancies of breast lesions. A double-blind retrospective study was conducted in 328 patients (254 for training and the following 74 for validation) who underwent dynamic contrast-enhanced MRI (DCE-MRI) of the breast with pathological results. Two score models, the DWI model (apparent diffusion coefficient (ADC) + morphology + enhanced information) and the TIC model (TIC + morphology + enhanced information), were established with binary logistic regression for mass and non-mass enhancements (NMEs) in the training set. The sensitivity, specificity, and area under the curve (AUC) were compared between the two models (DWI model vs. TIC model); p < 0.05 was considered as statistically different. External validation was used. In the training set, the sensitivities, specificities, and AUCs of the DWI/TIC model were 95.2%/95.8%, 70.8%/47.9%, and 0.932/0.891 for masses, and 94.2%/90.4%, 47.4%/47.4%, and 0.798 (95% CI, 0.686-0.884)/0.802 (95% CI, 0.691-0.887) for NMEs, respectively. The AUC of the DWI model was significantly higher than that of the TIC model (p < 0.05) for masses. In the validation set, the AUCs of the DWI/TIC model were 0.896/0.861 for masses (p < 0.05) and 0.936/0.836 for NMEs (p > 0.05). Combined with CE MRI, the DWI model was superior or equal to the TIC model in differentiating benign and malignant breast lesions. • Diffusion magnetic resonance imaging played an important role in the diagnosis of breast neoplasms. • On the basis of contrast-enhanced MRI, the DWI model had significantly higher diagnostic ability than the TIC model in distinguishing benign and malignant masses. • It would be reasonable to replace the time-consuming TIC with DWI for less scan time and similar diagnostic efficiency.

Dynamic contrast-enhanced (DCE) MR imaging: the role of qualitative and quantitative parameters for evaluating prostate tumors stratified by Gleason score and PI-RADS v2.

To investigate the role of qualitative and quantitative DCE-MRI parameters in prostate cancer (PCa) stratified by whole-mount histopathology (WMHP) Gleason score (GS) and PI-RADSv2. This retrospective study included 323 PCa tumors in 254 men, who underwent 3T MRI prior to prostatectomy, 7/2009-12/2016. Qualitative DCE curve types included type 1 (progressive), type 2 (plateau) and type 3 (washout). Quantitative DCE-MRI pharmacokinetic (PK) parameters included Ktrans (influx volume transfer coefficient), Kep (efflux reflux rate constant) and iAUC (initial area under the curve). DCE-MRI features of true positive lesions were evaluated for overall, index, transition zone (TZ) and peripheral zone (PZ), based on GS grade (low = 6, high > 6) and PI-RADSv2 score using SPSSv24. There were 57 (17.6%) low-grade and 266 (82.4%) high-grade PCa lesions. PI-RADSv2 3, 4 and 5 included 106, 120 and 97 lesions, respectively. 251 (77.7%) and 72 (22.3%) lesions were located in PZ and TZ, respectively. High-grade lesions had significantly higher proportion of Type 3 curves compared to low-grade lesions in overall (70.3% vs. 54.4%) and TZ (73.5% vs. 43.5%). As PI-RADSv2 increased, the proportion of type 3 curve significantly increased for overall (80.4-51.9%), index (80.4-54.7%) and PZ (78.7-52.1%) lesions. Among PK parameters, Ktrans (0.43 vs 0.32) and iAUC (8.99 vs 6.9) for overall PCa, Ktrans (0.43 vs 0.31) and iAUC (9 vs 6.67) for PZ PCa, and iAUC (8.94 vs 7.42) for index PCa were significantly higher for high-grade versus low-grade lesions. Also, Ktrans (0.51-0.34), Kep (1.75-1.29) and iAUC (9.79-7.6) for overall PCa, Ktrans (0.53-0.32), Kep (1.81-1.26) and iAUC (9.83-7.34) for PZ PCa; and Kep (1.79-1.17) and iAUC (11.3-8.45) for index PCa increased significantly with a higher PI-RADSv2 score. The results of study show the possible utility of qualitative and quantitative DCE-MRI parameters for assessment of PCa GS and PI-RADSv2 categorization.

Alterations in Blood-Brain Barrier Permeability in Patients with Systemic Lupus Erythematosus.

Neuropsychiatric systemic lupus erythematosus refers to central and peripheral nervous system involvement, which may occur secondary to antineuronal antibodies crossing the blood-brain barrier that preferentially target cells in the hippocampus leading to abnormal hypermetabolism and atrophy. Thus, we hypothesized that alterations in BBB permeability, detected on dynamic contrast-enhanced MR imaging, occur in the hippocampus in patients with systemic lupus erythematosus before development of neuropsychiatric systemic lupus erythematosus. Six patients with systemic lupus erythematosus without neuropsychiatric systemic lupus erythematosus and 5 healthy controls underwent dynamic contrast-enhanced MR imaging with postprocessing into BBB permeability parameters (K trans and Ve) and CBF. Standardized methods selected ROI sampling of the abnormal brain regions detected on FDG-PET. The mean and SD of K trans, Ve, and CBF were calculated. Linear regression and nonparametric Spearman rank correlation analyses of K trans and Ve with CBF were performed. Dynamic contrast-enhanced curves and the area under the curve were generated for each brain region. Student t test comparisons were performed. Quantitative data revealed that patients with systemic lupus erythematosus have statistically increased K trans (P < .001) and Ve (P < .001) compared with controls. In patients with systemic lupus erythematosus, statistically significant positive correlations were seen between K trans (P < .001) and Ve (P < .001) with CBF. Furthermore, the mean area under the curve revealed statistically increased BBB permeability in the hippocampus (P = .02) compared with other brain regions in patients with systemic lupus erythematosus compared with controls. These initial findings are proof-of-concept to support the hypothesis that patients with systemic lupus erythematosus have increased BBB permeability, specifically in the hippocampus, compared with other brain regions. These findings may advance our understanding of the underlying pathophysiology affecting the brain in autoimmune diseases.

Dynamic Contrast-Enhanced MRI Reveals Unique Blood-Brain Barrier Permeability Characteristics in the Hippocampus in the Normal Brain.

We report a prospective dynamic contrast-enhanced MR imaging analysis of region-specific blood-brain barrier permeability in 5 healthy subjects. By means of standardized postprocessing and ROI sampling methods, the hippocampi revealed significantly elevated area under the dynamic contrast-enhanced curve and significantly increased blood-brain barrier permeability metrics (volume transfer constant and volume in the extravascular extracellular space) from model-based quantitation. These findings suggest unique blood-brain barrier permeability characteristics in the hippocampus, which are concordant with previous animal studies, potentially laying the groundwork for future studies assessing patient populations in which hippocampal pathology plays a role.

Multi-parametric (mp) MRI for the diagnosis of abdominal wall desmoid tumors

IntroductionDesmoid tumors are benign myofibroblastic neoplasms, originating from the muscle aponeurosis and classified as deep fibromatoses. The aim of this study was to evaluate the utility of multi-parametric (mp)-MRI for the diagnosis of abdominal wall desmoid tumor (awdt). Material and methodsThis Institutional review board approved retrospective study compared 10 patients (mean age±SD; 38.2±13years; 9 females and 1 male) with awdt to 14 subjects (mean age±SD; 45.6±14.7years; 9 females and 5 males) with non-desmoid abdominal wall tumors (ndawt). All included subjects underwent mp-MRI, which included conventional, diffusion weighted and dynamic contrast-enhanced (DCE) MRI. Two blinded experienced fellowship trained radiologists (MK and SR) evaluated each lesion characteristics qualitatively and quantitatively which included margin, homogeneity, T2W/T1W signal intensity (SI), T2 dark strands, and fascial tail together with measurements of apparent diffusion coefficient (ADC) and semi-quantitative DCE analysis. Inter-observer agreement was assessed using Cohen’s kappa and data were compared between groups using independent sample t-tests and Chi-square tests. ResultsNo significant differences in age or gender appeared between groups. On qualitative analysis, T2 dark strands were identified in 90% by both radiologist (K=0.82) of awdt, while fascial tail was identified in 70% by radiologist 1 and 80% by second radiologist (k=0.91) of awdt; however no other lesions showed these findings. Other subjective imaging findings did not significantly differ between groups with moderate-to-strong agreements (k=0.7–1.0). On quantitative measurements, diffusion imaging awdt lesions showed higher mean ADC value compared to other lesions, although it did not reached at the level of significance. While on DCE MRI, all awdt lesions showed type 1 (progressive) DCE curve, however no significant difference was observed between groups. ConclusionsT2 dark strands and fascial tail are characteristic features of awdt, whereas other subjective/qualitative findings are not useful. Quantitative findings such as ADC measurements and DCE curve analysis may have additional value to differentiate awdt from ndawt, but will require further analysis.

Two-compartment modeling of tissue microcirculation revisited.

Conventional two-compartment modeling of tissue microcirculation is used for tracer kinetic analysis of dynamic contrast-enhanced (DCE) computed tomography or magnetic resonance imaging studies although it is well-known that the underlying assumption of an instantaneous mixing of the administered contrast agent (CA) in capillaries is far from being realistic. It was thus the aim of the present study to provide theoretical and computational evidence in favor of a conceptually alternative modeling approach that makes it possible to characterize the bias inherent to compartment modeling and, moreover, to approximately correct for it. Starting from a two-region distributed-parameter model that accounts for spatial gradients in CA concentrations within blood-tissue exchange units, a modified lumped two-compartment exchange model was derived. It has the same analytical structure as the conventional two-compartment model, but indicates that the apparent blood flow identifiable from measured DCE data is substantially overestimated, whereas the three other model parameters (i.e., the permeability-surface area product as well as the volume fractions of the plasma and interstitial distribution space) are unbiased. Furthermore, a simple formula was derived to approximately compute a bias-corrected flow from the estimates of the apparent flow and permeability-surface area product obtained by model fitting. To evaluate the accuracy of the proposed modeling and bias correction method, representative noise-free DCE curves were analyzed. They were simulated for 36 microcirculation and four input scenarios by an axially distributed reference model. As analytically proven, the considered two-compartment exchange model is structurally identifiable from tissue residue data. The apparent flow values estimated for the 144 simulated tissue/input scenarios were considerably biased. After bias-correction, the deviations between estimated and actual parameter values were (11.2±6.4) % (vs. (105±21) % without correction) for the flow, (3.6±6.1) % for the permeability-surface area product, (5.8±4.9) % for the vascular volume and (2.5±4.1) % for the interstitial volume; with individual deviations of more than 20% being the exception and just marginal. Increasing the duration of CA administration only had a statistically significant but opposite effect on the accuracy of the estimated flow (declined) and intravascular volume (improved). Physiologically well-defined tissue parameters are structurally identifiable and accurately estimable from DCE data by the conceptually modified two-compartment model in combination with the bias correction. The accuracy of the bias-corrected flow is nearly comparable to that of the three other (theoretically unbiased) model parameters. As compared to conventional two-compartmentmodeling, this feature constitutes a major advantage for tracer kinetic analysis of both preclinical and clinical DCE imaging studies.

Comparative Investigation of Single Voxel Magnetic Resonance Spectroscopy and Dynamic Contrast Enhancement MR Imaging in Differentiation of Benign and Malignant Breast Lesions in a Sample of Iranian Women.

To make a comparison of single voxel magnetic resonance spectroscopy (SV-MRS) and dynamic contrast enhancement (DCE) MRI for differentiation of benign and malignant breast lesions in a sample of Iranian women. A total of 30 women with abnormal breast lesions detected in mammography, ultrasound, or clinical breast exam were examined with DCE and SV-MRS. tCho (total choline) resonance in MRS spectra was qualitatively evaluated and detection of a visible tCho peak at 3.2 ppm was defined as a positive finding for malignancy. Different types of DCE curves were persistent (type 1), plateau (type 2), and washout (type 3). At first, lesions were classified according to choline findings and types of DCE curve, finally being compared to pathological results as the standard reference. this study included 19 patients with malignant lesions and 11 patients with benign ones. While 63.6 % of benign lesions (7 of 11) showed type 1 DCE curves and 36.4% (4 of 11) showed type 2, 57.9% (11of 19) of malignant lesions were type 3 and 42.1% (8 of 19) type 2. Choline peaks were detected in 18 of 19 malignant lesions and in 3 of 11 benign counterparts. 1 malignant and 8 benign cases did not show any visible resonance at 3.2 ppm so SV-MRS featured 94.7% sensitivity, 72.7 % specificity and 86.7% accuracy. The present findings indicate that a combined approach using MRS and DCE MRI can improve the specificity of MRI for differentiation of benign and malignant breast lesions.

SU‐E‐J‐241: Wavelet‐Based Temporal Feature Extraction From DCE‐MRI to Identify Sub‐Volumes of Low Blood Volume in Head‐And‐Neck Cancer

Purpose:A previous study showed that large sub‐volumes of tumor with low blood volume (BV) (poorly perfused) in head‐and‐neck (HN) cancers are significantly associated with local‐regional failure (LRF) after chemoradiation therapy, and could be targeted with intensified radiation doses. This study aimed to develop an automated and scalable model to extract voxel‐wise contrast‐enhanced temporal features of dynamic contrastenhanced (DCE) MRI in HN cancers for predicting LRF.Methods:Our model development consists of training and testing stages. The training stage includes preprocessing of individual‐voxel DCE curves from tumors for intensity normalization and temporal alignment, temporal feature extraction from the curves, feature selection, and training classifiers. For feature extraction, multiresolution Haar discrete wavelet transformation is applied to each DCE curve to capture temporal contrast‐enhanced features. The wavelet coefficients as feature vectors are selected. Support vector machine classifiers are trained to classify tumor voxels having either low or high BV, for which a BV threshold of 7.6% is previously established and used as ground truth. The model is tested by a new dataset. The voxel‐wise DCE curves for training and testing were from 14 and 8 patients, respectively. A posterior probability map of the low BV class was created to examine the tumor sub‐volume classification. Voxel‐wise classification accuracy was computed to evaluate performance of the model.Results:Average classification accuracies were 87.2% for training (10‐fold crossvalidation) and 82.5% for testing. The lowest and highest accuracies (patient‐wise) were 68.7% and 96.4%, respectively. Posterior probability maps of the low BV class showed the sub‐volumes extracted by our model similar to ones defined by the BV maps with most misclassifications occurred near the sub‐volume boundaries.Conclusion:This model could be valuable to support adaptive clinical trials with further validation. The framework could be extendable and scalable to extract temporal contrastenhanced features of DCE‐MRI in other tumors.We would like to acknowledge NIH for funding support: UO1 CA183848

TH-CD-207-01: BEST IN PHYSICS (IMAGING): Correction of the First-Pass Distortion in Arterial Input Function of DCE MRI for Perfusion Quantification

Purpose: To quantify perfusion in an abdominal organ (liver, spleen or kidney) from dynamic contrast-enhanced (DCE) MRI, an arterial input function(AIF) is commonly sampled in a volume of interest (VOI) on the descending aorta. However, the peak of the first pass of the AIF could be corrupted, due to the T2* effect of high contrast-concentration, particularly at 3T MR, and other factors. This study aimed to correct this distortion in the AIF for DCE MRI perfusion quantification of an abdominal organ. Methods: A post-processing correction method was developed to automatically recognize the distortion portion in the first pass of an AIF and fit it with a gamma-variate function. The fitting of the first pass was incorporated in pharmacokinetic(PK) modeling of the DCE curves from VOIs in both spleen and kidney. The optimal solution was determined by balancing between best fitting the PK model and matching the fitted first pass with the measured one. The corrected AIFs were applied to quantify hepatic perfusion from DCE MRI of 6 patients, which were acquired on a 3T scanner using a VIBE sequence. Results: In the DCE-MRI of 6 patients, the original AIFs had 20%±6% saturation in the first-pass peak compared with the corrected AIFs. Using the corrected AIFs to quantify hepatic perfusion yielded average differences of-36% (p=0.002) and 17% (p=0.01) in respective arterial perfusion and portal venous perfusion on VOIs of normal hepatic tissue, compared to those estimated using the original uncorrected AIFs. Conclusion: The proposed method corrects the distortion in the first pass of the AIF sampled in the aorta, and could improve DCE-MRI perfusion quantification in the liver and other abdominal organs. The resultant perfusion may assess tissue function and tumor response to radiation and provide tools to support physiologically adaptive RT. The work is supported in part by RO1CA132834 and PO1CA59827 The work is supported in part by NIH RO1CA132834 and PO1CA59827

SU‐E‐I‐04: Texture Feature Based CAD for Breast Cancer Detection

Purpose: The purpose of this study was to retrospectively evaluate dynamic contrast enhanced (DCE) breast magnetic resonance imaging (MRI) data. Texture analysis is applied to extract features from this data and test the feasibility of using these texture features for creating a computer aided diagnostic system (CAD). Methods: Computation time for texture feature extraction for the entire 3D dataset precludes whole breast analysis. Reduction of data can be best accomplished through the generation of angiogenesis (parametric maps) based on contrast agent kinetics and the knowledge that malignancy exhibits rapid uptake and plateau or slow washout on the DCE curve. Breast image data that exhibits these high‐risk characteristics can be segmented into volumes‐of‐interest (VOIs). These volumes‐of‐interest can be projected on high resolution post contrast (T1) images using direction cosines information stored in the image header. The corresponding projected VOI on the high resolution, post contrast MR image can undergo feature extraction. Total 35 VOIs were extracted (16 benign + 19 malignant). In all 53 texture features such as grey level cooccurrence matrix (GLCM), second orientation pyramids (SOP), wavelets, gabor feature maps and 3 geometric features for each volume‐of‐interest was generated. Classification was performed using off the shelf classifiers and also with minimum enclosing ball (MEB) classifier designed by us. The efficacy of texture features to classify suspect VOIs as malignant or benign was evaluated.Results: Classification using the first 14 Fisher ranked coefficients using MEB classifier produces the best results with sensitivity of 88%, specificity of 83%, and accuracy of 86% Conclusions: Machine classification of breast cancer detection from DCE MRI is encouraging. Clearly additional work in this area is required. The hypothesis that feature extraction and machine learning alone has produced good sensitivity and specificity can be proven with 95% significance level only by the study of larger datasets.

The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T

The purpose of this work is to quantify the accuracy of pharmacokinetic parameter measurement in DCE-MRI of breast cancer at 3 T in relation to three sources of error. Individually, T1 measurement error, temporal resolution and transmitted RF field inhomogeneity are considered. Dynamic contrast enhancement curves were simulated using standard acquisition parameters of a DCE-MRI protocol. Errors on pre-contrast T1 due to incorrect RF spoiling were considered. Flip angle errors were measured and introduced into the fitting routine, and temporal resolution was also varied. The error in fitted pharmacokinetic parameters, Ktrans and ve, was calculated. Flip angles were found to be reduced by up to 55% of the expected value. The resultant errors in our range of Ktrans and ve were found to be up to 66% and 74%, respectively. Incorrect T1 estimation results in Ktrans and ve errors up to 531% and 233%, respectively. When the temporal resolution is reduced from 10 to 70 s Ktrans drops by up to 48%, while ve shows negligible variation. In combination, uncertainties in tissue T1 map and applied flip angle were shown to contribute to errors of up to 88% in Ktrans and 73% in ve. These results demonstrate the importance of high temporal resolution, accurate T1 measurement and good B1 homogeneity.

Predicting Control of Primary Tumor and Survival by DCE MRI During Early Therapy in Cervical Cancer

To assess the early predictive power of MRI perfusion and volume parameters, during early treatment of cervical cancer, for primary tumor control and disease-free-survival. Three MRI examinations were obtained in 101 patients before and during therapy (at 2-2.5 and 4-5 weeks) for serial dynamic contrast enhanced (DCE) perfusion MRI and 3-dimensional tumor volume measurement. Plateau Signal Intensity (SI) of the DCE curves for each tumor pixel of all 3 MRI examinations was generated, and pixel-SI distribution histograms were established to characterize the heterogeneous tumor. The degree and quantity of the poorly-perfused tumor subregions, which were represented by low-DCE pixels, was analyzed by using various lower percentiles of SI (SI%) from the pixel histogram. SI% ranged from SI2.5% to SI20% with increments of 2.5%. SI%, mean SI, and 3-dimensional volume of the tumor were correlated with primary tumor control and disease-free-survival, using Student t test, Kaplan-Meier analysis, and log-rank test. The mean post-therapy follow-up time for outcome assessment was 6.8 years (range: 0.2-9.4 years). Tumor volume, mean SI, and SI% showed significant prediction of the long-term clinical outcome, and this prediction was provided as early as 2 to 2.5 weeks into treatment. An SI5% of <2.05 and residual tumor volume of > or =30 cm(3) in the MRI obtained at 2 to 2.5 weeks of therapy provided the best prediction of unfavorable 8-year primary tumor control (73% vs. 100%, P = 0.006) and disease-free-survival rate (47% vs. 79%, P = 0.001), respectively. Our results show that MRI parameters quantifying perfusion status and residual tumor volume provide very early prediction of primary tumor control and disease-free-survival. This functional imaging based outcome predictor can be obtained in the very early phase of cytotoxic therapy within 2 to 2.5 weeks of therapy start. The predictive capacity of these MRI parameters, indirectly reflecting the heterogeneous delivery pattern of cytotoxic agents, tumor oxygenation, and the bulk of residual presumably therapy-resistant tumor, requires future study.