Dynamic Contrast-enhanced Magnetic Resonance Research Articles

Multinodular and Vacuolating Neuronal Tumor in the Brain Depth - Atypical Presentation of a New Tumor: A Case Report

Introduction: Multinodular and Vacuolating Neuronal Tumors (MVNTs) are clinically benign lesions and was recently included in the last World Health Organization Classification of Tumors of the Central Nervous System (WHO – 2016). Recent studies have discovered specific immunomarkers that classifies the MVNTs as a new neoplasm group placed within the section covering "Neuronal and mixed neuronal-glial tumors” as a WHO grade I lesion. Case report: We present a case of a probable MVNT in a 55-year old woman, who presented with chronic pattern seizures. Conventional MRI revealed an infiltrative and multinodular pattern lesion localized in the deep left cerebral hemisphere compromising the ventral posteromedial and pulvinar surfaces of thalamus, lateral geniculate body, choroidal fissure, mammillothalamic tract, fornix and septum pellucidum, anterior commissure, extending to contralateral globus pallidus and internal capsule. Dynamic susceptibility contrast-enhanced MR perfusion and Arterial Spin Labeling (ASL) technique didn’t show any abnormalities. MR spectroscopy demonstrated a mild increase in Choline (Cho) and mild reduction in N-acetyl aspartate (NAA). No substantial difference both clinically and radiologically was observed on five years follow up. Conclusion: The diagnosis of MVNT was made considering the extremally benign biological behavior of this tumor and the typical imaging features, despite of deep localization of the lesion, and once this kind of lesion was previously described as a “don’t touch lesion” the histological, biopsy was not performed.

Pediatric Application of Dynamic Contrast-Enhanced MR Imaging (DCE-MR) in the Management of Extra-Cranial Tumor: Experience in Routine Clinical Practice

Background: Dynamic contrast-enhanced MR imaging (DCE-MR) is becoming a widely accepted complementary method for diagnosing breast cancer and other cancers in adults. It is useful to predict tumor response to anticancer therapy and monitor the tumor response to the therapy. This form of imaging techniques has not been adequately explored in pediatric oncology patients. Objective: To determine the potential role of dynamic contrast-enhanced MR imaging (DCE-MR) in the diagnosis and treatment response monitoring of childhood and young adult extra-cranial tumors in routine clinical setting. Methods: Children with suspected extra-cranial solid tumors, including newly diagnosed or follow-up cases of confirmed tumors, were recruited. DCE-MR was performed with intravenous injection of 0.1 mmol/kg contrast. The enhancement time curves were plotted and the enhancement patterns were categorized into type 1, 2 and 3 curves. Enhancement curve patterns and maximal enhancement intensity were compared with types of tumor in newly diagnosed cases. The preoperative percentiles of inactive area on the colour map were compared with the necrotic areas on histologic sections of the resected specimens in follow-up cases. Pearson Chi-square test and Unpaired two-sample t-test were used for statistical analysis. Results: There were 36 patients, involving 28 malignant and 8 benign cases. There were 14 type 3 curves, (all of them were malignant tumors), 6 type 2 curves and 16 type 1 curves. All the benign cases (n = 8) demonstrated type 1 curve (accuracy & negative predictive value = 100%). All the malignant cases after treatment showed type 2 or 1 curve. For those cases with operation done afterwards, the extent of tumor necrosis was correlated closely with pathology findings (accuracy = 93.3%). Conclusion: Type 1 curve was a good predictor of benign lesion. DEC-MR may have a role to play in the monitoring of the progress of treatment and extent of tumor necrosis.

Dynamic Contrast-Enhanced MR Imaging of Tietze's Syndrome: a Case Report

Dynamic Contrast-Enhanced MR Imaging of Tietze's Syndrome: a Case Report

Dynamic Contrast-Enhanced MR Imaging of Nonenhancing T2 High-Signal-Intensity Lesions in Baseline and Posttreatment Glioblastoma: Temporal Change and Prognostic Value.

The prognostic value of dynamic contrast-enhanced MR imaging on nonenhancing T2 high-signal-intensity lesions in patients with glioblastoma has not been thoroughly elucidated to date. We evaluated the temporal change and prognostic value for progression-free survival of dynamic contrast-enhanced MR imaging-derived pharmacokinetic parameters on nonenhancing T2 high-signal-intensity lesions in patients with glioblastoma before and after standard treatment, including gross total surgical resection. This retrospective study included 33 patients who were newly diagnosed with glioblastoma and treated with gross total surgical resection followed by concurrent chemoradiation therapy and adjuvant chemotherapy with temozolomide in a single institution. All patients underwent dynamic contrast-enhanced MR imaging before surgery as a baseline and after completion of maximal surgical resection and concurrent chemoradiation therapy. On the whole nonenhancing T2 high-signal-intensity lesion, dynamic contrast-enhanced MR imaging-derived pharmacokinetic parameters (volume transfer constant [K trans], volume of extravascular extracellular space [v e], and blood plasma volume [vp ]) were calculated. The Cox proportional hazards regression model analysis was performed to determine the histogram features or percentage changes of pharmacokinetic parameters related to progression-free survival. Baseline median K trans, baseline first quartile K trans, and posttreatment median K trans were significant independent variables, as determined by univariate analysis (P < .05). By multivariate Cox regression analysis including methylation status of O6-methylguanine-DNA methyltransferase, baseline median K trans was determined to be the significant independent variable and was negatively related to progression-free survival (hazard ratio = 1.48, P = .003). Baseline median K trans from nonenhancing T2 high-signal-intensity lesions could be a potential prognostic imaging biomarker in patients undergoing gross total surgical resection followed by standard therapy for glioblastoma.

CT and MRI Findings of Glomangiopericytoma in the Head and Neck: Case Series Study and Systematic Review.

Glomangiopericytoma is a rare sinonasal mesenchymal tumor of borderline or low malignant potential. We reviewed the CT and MR imaging findings of head and neck glomangiopericytoma via a retrospective case series study and systematic review. Our study revealed that glomangiopericytoma is a well-defined lobulated avidly enhancing soft-tissue mass with erosive bony remodeling that is most commonly found in the sinonasal cavity. Typically, it is hyperintense on T2-weighted images with vascular signal voids, has a high mean ADC value, and a wash-in and washout pattern on dynamic contrast-enhanced MR imaging. Although the CT findings are nonspecific, typical MR imaging findings, including those on the ADC map and dynamic contrast-enhanced MR imaging, may be helpful for differentiating glomangiopericytomas from other hypervascular tumors in the head and neck.

Prognostic Predictions for Patients with Glioblastoma after Standard Treatment: Application of Contrast Leakage Information from DSC-MRI within Nonenhancing FLAIR High-Signal-Intensity Lesions.

Attempts have been made to quantify the microvascular leakiness of glioblastomas and use it as an imaging biomarker to predict the prognosis of the tumor. The purpose of our study was to evaluate whether the extraction fraction value from DSC-MR imaging within nonenhancing FLAIR hyperintense lesions was a better prognostic imaging biomarker than dynamic contrast-enhanced MR imaging parameters for patients with glioblastoma. A total of 102 patients with glioblastoma who received a preoperative dynamic contrast-enhanced MR imaging and DSC-MR imaging were included in this retrospective study. Patients were classified into the progression (n = 87) or nonprogression (n = 15) groups at 24 months after surgery. We extracted the means and 95th percentile values for the contrast leakage information parameters from both modalities within the nonenhancing FLAIR high-signal-intensity lesions. The extraction fraction 95th percentile value was higher in the progression-free survival group of >24 months than at ≤24 months. The median progression-free survival of the group with an extraction fraction 95th percentile value of >13.32 was 17 months, whereas that of the group of ≤13.32 was 12 months. In addition, it was an independent predictor variable for progression-free survival in the patients regardless of their ages and genetic information. The extraction fraction 95th percentile value was the only independent parameter for prognostic prediction in patients with glioblastoma among the contrast leakage information, which has no statistically significant correlations with the DCE-MR imaging parameters.

Hierarchical Bayesian myocardial perfusion quantification.

Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

Quantitative dynamic contrast-enhanced MR imaging for the preliminary prediction of the response to gemcitabine-based chemotherapy in advanced pancreatic ductal carcinoma

PurposeTo investigate the role of the quantitative parameters of dynamic contrast-enhanced MR imaging (DCE-MRI) in the prediction of the response to chemotherapy in pancreatic ductal carcinoma (PDC). MethodForty patients with histologically confirmed PDC who underwent quantitative DCE-MRI were retrospectively analyzed. All patients were divided into groups of responders and nonresponders. DCE-MRI parameters, including the volume transfer constant (Ktrans), the extracellular extravascular volume fraction (ve), the rate constant (kep) and the initial area under the concentration curve in 60 s (iAUC60), were measured and compared. DCE-MRI parameters were obtained from different ROIs. ResultsThe values of Ktrans in responders with peripheral, whole tumor slice, and adjacent non-tumorous region ROIs were significantly higher than those in nonresponders (P = 0.015, 0.043, and 0.025, respectively). Responders showed a significantly higher kep with peripheral area ROI compared with nonresponders (P = 0.013). Ve and iAUC60 with all ROIs were not significantly different between responders and nonresponders (P = 0.140-0.968). Kep with periphery ROI showed the highest area under the ROC curve (AUC) of 0.806, but there were no statistical differences when compared with values of Ktrans.There were statistically significant differences for DCE-MRI parameters among four ROIs (all P < 0.05). All parameters showed good to excellent intra and interobserver agreement. ConclusionsQuantitative parameters derived from DCE-MRI might be a potential predictor of response to gemcitabine in patients with PDC. Perfusion parameters were diverse depending on the location of the ROI on different tumoral and peritumoral areas.

Topics on quantitative liver magnetic resonance imaging.

Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.

Value of Dynamic Contrast-Enhanced (DCE) MR Imaging in Peripheral Lesions in PI-RADS-4 Patients.

To assess the impact of dynamic contrast-enhanced imaging (DCE) in mp-MRI on prostate cancer (PCa) detection in a large patient cohort assigned to PI-RADS category 4. This retrospective, single center cohort study includes 193 consecutive patients with PI-RADS assessment category 4 in mp-MRI (T2WI, DWI, DCE) at 3 T with targeted plus systematic biopsy combined as the reference standard. The detection of prostate cancer with and without the use of DCE was compared. Overall, the PCa detection rate in PI-RADS-4 patients was 62 % (119/193) with DCE and 52 % (101/193) without the inclusion of lesions upgraded on the basis of DCE. 48 % (92/193) had clinically significant PCa (csPCa; Gleason score ≥ 3 + 4 = 7) and 40 % (78/193) without use of DCE. 38 of the 193 patients (20 %) had peripheral lesions upgraded from PI-RADS category 3 to an overall PI-RADS category 4 due to focal positive DCE findings. Of these 38 patients, 18 had PCa including 14 with csPCa. Thus, 15 % (18/119) of the patients with PCa and 15 % (14/92) of the patients with csPCa were detected only based on additional DCE information. DCE prevents underestimation and misclassification of a significant number of cases of peripheral csPCa and might improve detection rates in PI-RADS-4 patients. The current PI-RADS decision rules regarding upgrading PI-RADS-3 lesions to category 4 due to positive DCE imaging are useful for PCa detection. · Positive peripheral DCE upgraded 20 % of patients in PI-RADS category 4 from category 3.. · Clinically significant PCa was found in almost 40 % of upgraded, peripheral PIRADS-3-lesions.. · 15 % of all csPCa in PI-RADS-4-patients was detected in DCE-upgraded lesions.. · In 7 % of all PI-RADS-4-cases csPCa would had been underestimated without DCE upgrade.. · Ullrich T, Quentin M, Arsov C et al. Value of Dynamic Contrast-Enhanced (DCE) MR Imaging in Peripheral Lesions in PI-RADS-4 Patients. Fortschr Röntgenstr 2020; 192: 441 - 447.

Liver segmentation and metastases detection in MR images using convolutional neural networks.

Primary tumors have a high likelihood of developing metastases in the liver, and early detection of these metastases is crucial for patient outcome. We propose a method based on convolutional neural networks to detect liver metastases. First, the liver is automatically segmented using the six phases of abdominal dynamic contrast-enhanced (DCE) MR images. Next, DCE-MR and diffusion weighted MR images are used for metastases detection within the liver mask. The liver segmentations have a median Dice similarity coefficient of 0.95 compared with manual annotations. The metastases detection method has a sensitivity of 99.8% with a median of two false positives per image. The combination of the two MR sequences in a dual pathway network is proven valuable for the detection of liver metastases. In conclusion, a high quality liver segmentation can be obtained in which we can successfully detect liver metastases.

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.

Dynamic contrast-enhanced MRI radiomic features predict axillary lymph node metastasis of breast cancer

Objective To investigate the prognostic value of radiomics analysis in predicting axillary lymph nodes (ALN) metastasis of breast cancer based on dynamic contrast-enhanced MR imaging (DCE-MRI). Methods One hundred and ninety-six patients with suspected breast cancer were prospectively collected for dynamic breast DCE-MRI. Enhanced MR imaging data of 72 axillary lymph nodes were evaluated separately by a chief radiologist and a resident, and the consistency analysis was performed. Lymph nodes were dichotomized according to the pathology results derived from operation or biopsy under real-time virtual sonography based on MRI data. Clinical and imaging data were also divided into corresponding groups. (Imaging) Data from both groups were respectively classified as training set and testing set by stratified sampling in proportion with 3∶1. AK software was applied to extract 6 major categories of 385 features (including histogram, morphology, texture parameters, gray level co-occurrence matrix, run-length matrix and grey level zone size matrix from imaging), and a set of statistically significant features were subsequently obtained by dimension reduction. The prediction model was established through binary classification logistic regression and employed to externally test the validation set by the method of confusion matrix. Meanwhile, ROC analysis was applied to assess the diagnostic performance of the model. Results Of the 72 axillary lymph nodes, 35 were metastatic negative and 37 were positive. The consistency of enhanced MRI radiomics features was good, between 0.841 and 0.980. Uniformity, ClusterProminence_AllDirection_offset1_SD, Correlation_AllDirection_offset1, LongRunEmphasis_angle90_offset7 and SurfaceVolumeRatio were statistically significant differences (P<0.01), the area under the ROC between 0.747 and 0.931. In the training and testing group, the areas under the ROC, sensitivity, specificity and accuracy of the model were 0.953, 0.893, 0.926, 92.6% (50/54) and 0.944, 0.900, 1.000, 88.9% (16/18) respectively. Conclusion The prediction model based on radiomic features may provide a non-invasive and effective approach to the assessment of the risk of ALN metastasis of breast cancer. Key words: Breast neoplasms; Axillary lymph nodes; Magnetic resonance imaging; Radiomics; Heterogeneity

A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma.

We aimed to develop and validate a nomogram combining bi-regional radiomics features from multimodal magnetic resonance imaging (MRI) and clinicoradiological characteristics to preoperatively predict microvascular invasion (MVI) of hepatocellular carcinoma (HCC). A total of 267 HCC patients were divided into training (n=194) and validation (n=73) cohorts according to MRI data. Bi-regional features were extracted from whole tumors and peritumoral regions in multimodal MRI. The minimum redundancy maximum relevance (mRMR) algorithm was applied to select features and build signatures. The predictive performance of the optimal radiomics signature was further evaluated within subgroups defined by tumor size and alpha fetoprotein (AFP) level. Then, a radiomics nomogram including the optimal radiomics signature, radiographic descriptors, and clinical variables was developed using multivariable regression. The nomogram performance was evaluated based on its discrimination, calibration, and clinical utility. The fusion radiomics signature derived from triphasic dynamic contrast-enhanced (DCE) MR images can effectively classify MVI and non-MVI HCC patients, with an AUC of 0.784 (95% CI: 0.719-0.840) in the training cohort and 0.820 (95% CI: 0.713-0.900) in the validation cohort. The fusion radiomics signature also performed well in the subgroups defined by the two risk factors, respectively. The nomogram, consisting of the fusion radiomics signature, arterial peritumoral enhancement, and AFP level, outperformed the clinicoradiological prediction model in the validation cohort (AUCs: 0.858 vs. 0.729; P=0.022), fitting well in the calibration curves (P>0.05). Decision curves confirmed the clinical utility of the nomogram. The radiomics nomogram can serve as a visual predictive tool for MVI in HCCs, and thus assist clinicians in selecting optimal treatment strategies to improve clinical outcomes.

A study of dynamic contrast-enhanced MR imaging features and influence factors of pelvic bone marrow in adult females.

To investigate the effect of menstrual status and age on the perfusion of pelvic bone marrow in adult females using quantitative dynamic contrast-enhanced MRI (DCE-MRI). In total, 96 adult females who underwent DCE-MRI between September 2017 and December 2017 were included. All the subjects' quantitative DCE-MRI parameters of pelvic bone marrow were measured and retrospectively analyzed, including Ktrans (volume transfer constant), Kep (efflux rate constant), and Ve (interstitial volume). According to their menstrual status, the subjects were divided into a premenopausal group (n = 39) and a postmenopausal group (n = 57), and the two groups were then divided into four subgroups according to age. The intraobserver reliability was assessed by the intraclass correlation coefficient (ICC). The parameters were compared between different menstrual status groups and age subgroups by Mann-Whitney test, and Spearman correlation analysis was used to evaluate the correlation between the age and the quantitative parameters. The ICCs of the Ktrans, Kep, and Ve values were 0.989, 0.974, and 0.920, respectively. Ktrans, Kep, and Ve of the premenopausal group were significantly higher than those of the postmenopausal group (P < 0.05). The overall age was negatively correlated with Ktrans, Kep, and Ve (r = - 0.590, - 0.357, and - 0.381, respectively, P < 0.05). In the premenopausal group, Ktrans and Ve were significantly higher in subgroup 1 (≤ 40years) compared with subgroup 2 (> 40years) (P < 0.05), and age showed a negative correlation with Ktrans and Ve (r = - 0.344 and - 0.334, respectively, P < 0.05). In the postmenopausal group, Ktrans and Kep were significantly higher in subgroup 3 (≤ 60years) compared with subgroup 4 (> 60years) (P < 0.05), and age showed a negative correlation with Ktrans and Kep (r = - 0.460 and - 0.303, respectively, P < 0.05). Menstrual status and age have significant effects on the perfusion of the pelvic bone marrow microenvironment in adult females and that the microenvironment of the pelvic bone marrow displays different changes at different age stages. Quantitative DCE-MRI has contributed to the interpretation of the pelvic bone marrow perfusion status.

A self-supervised strategy for fully automatic segmentation of renal dynamic contrast-enhanced magnetic resonance images.

An automated accurate segmentation for dynamic contrast-enhanced magnetic resonance (DCE-MR) image sequences is essential for quantification of renal function. A self-supervised strategy is proposed for fully automatic segmentation of the renal DCE-MR images without using manually labeled data. The proposed strategy employed both temporal and spatial information of the DCE-MR image sequences. First, the kidney area, the seed regions of the cortex, the medulla, and the pelvis were automatically detected in the spatial domain. Subsequently, all the pixels in the kidney were automatically labeled as the cortex, the medulla, or the pelvis based on their time-intensity signal and spatial position using a supervised classifier. The feasibility of the proposed strategy was verified on a dataset of renal DCE-MR images of 14 subjects without history of kidney diseases. Furthermore, the self-supervised strategy and the commonly used traditional unsupervised method were quantitatively compared with a reference manual segmentation by an experienced radiologist, using similarity indexes. The average Dice coefficient (ADC) for the segmentations of the proposed self-supervised method is 0.92 using a ransom walker model as the classifier or 0.86 using a K-nearest neighbor model as the classifier. The ADC of the Kmeans-based unsupervised methods with three and six clusters were 0.65 and 0.79, respectively. The Dice coefficients of the self-supervised method were remarkably higher than that of the unsupervised method (one-tailed paired-sample t-test, P-values <10-3 ). The results indicate that the proposed self-supervised approach yields a satisfactory similarity with the reference manual segmentation. Compared with the traditional unsupervised clustering method, the new strategy does not require manual intervention during the segmentation process and achieves better results for the segmentation of renal DCE-MR images.

Value of diffusion-weighted and dynamic contrast-enhanced MR in predicting parametrial invasion in cervical stromal ring focally disrupted stage IB-IIA cervical cancers.

To compare the effectiveness of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging in detecting parametrial invasion (PMI) in cervical stromal ring focally disrupted stage IB-IIA cervical cancers. Eighty-one patients with cervical stromal ring focally disrupted stage IB-IIA cervical cancers (PMI positive, n = 35; PMI negative, n = 46) who underwent preoperative MRI and radical hysterectomy were included in this study. Preoperative clinical variables and MRI variables were analyzed and compared. The Ktrans (min, mean, 10%, 25%, 50%, 75%, 90%), Kep (min, 10%, 25%, 50%, 75%, 90%), and Ve (min, 10%, 25%, 50%, 75%, 90%) values of patients with PMI were significantly higher than patients without PMI. The apparent diffusion coefficient (ADC) value did not show statistical difference between the two groups (1.01 ± 0.21 vs. 0.97 ± 0.20 10-3 mm2/s, p = 0.360). Tumor craniocaudal planes were higher in PMI-positive group than PMI-negative group (35.84 ± 15.39 vs. 29.70 ± 11.78mm, p = 0.048). Tumor craniocaudal planes combined with Kepmin value showed the highest area under the curve (AUCs) of 0.775, with a sensitivity of 72.7% and a specificity of 71.1% (p = 0.000). DCE parameters combined tumor craniocaudal planes may represent a prognostic indicator for PMI in cervical stromal ring focally disrupted IB-IIA cervical cancers.

Utility of Percentage Signal Recovery and Baseline Signal in DSC-MRI Optimized for Relative CBV Measurement for Differentiating Glioblastoma, Lymphoma, Metastasis, and Meningioma.

The percentage signal recovery in non-leakage-corrected (no preload, high flip angle, intermediate TE) DSC-MR imaging is known to differ significantly for glioblastoma, metastasis, and primary CNS lymphoma. Because the percentage signal recovery is influenced by preload and pulse sequence parameters, we investigated whether the percentage signal recovery can still differentiate these common contrast-enhancing neoplasms using a DSC-MR imaging protocol designed for relative CBV accuracy (preload, intermediate flip angle, low TE). We retrospectively analyzed DSC-MR imaging of treatment-naïve, pathology-proved glioblastomas (n = 14), primary central nervous system lymphomas (n = 7), metastases (n = 20), and meningiomas (n = 13) using a protocol designed for relative CBV accuracy (a one-quarter-dose preload and single-dose bolus of gadobutrol, TR/TE = 1290/40 ms, flip angle = 60° at 1.5T). Mean percentage signal recovery, relative CBV, and normalized baseline signal intensity were compared within contrast-enhancing lesion volumes. Classification accuracy was determined by receiver operating characteristic analysis. Relative CBV best differentiated meningioma from glioblastoma and from metastasis with areas under the curve of 0.84 and 0.82, respectively. The percentage signal recovery best differentiated primary central nervous system lymphoma from metastasis with an area under the curve of 0.81. Relative CBV and percentage signal recovery were similar in differentiating primary central nervous system lymphoma from glioblastoma and from meningioma. Although neither relative CBV nor percentage signal recovery differentiated glioblastoma from metastasis, mean normalized baseline signal intensity achieved 86% sensitivity and 50% specificity. Similar to results for non-preload-based DSC-MR imaging, percentage signal recovery for one-quarter-dose preload-based, intermediate flip angle DSC-MR imaging differentiates most pair-wise comparisons of glioblastoma, metastasis, primary central nervous system lymphoma, and meningioma, except for glioblastoma versus metastasis. Differences in normalized post-preload baseline signal for glioblastoma and metastasis, reflecting a snapshot of dynamic contrast enhancement, may motivate the use of single-dose multiecho protocols permitting simultaneous quantification of DSC-MR imaging and dynamic contrast-enhanced MR imaging parameters.

Dynamic Contrast-Enhanced MRI in Patients with Brain Metastases Undergoing Laser Interstitial Thermal Therapy: A Pilot Study.

Tumor recurrence is difficult to predict in patients receiving laser ablation for intracranial malignancy. We assessed the efficacy of the initial area under the time-to-signal intensity curve at 60 seconds (iAUC60) from dynamic contrast-enhanced MR imaging in predicting progression-free survival in patients with brain metastases following laser interstitial thermal therapy. The study population was a consecutive series of patients undergoing laser interstitial thermal therapy for brain metastases. Patient demographics including age, sex, tumor histology, and Karnofsky Performance Scale were collected prospectively. Preoperative, postoperative, and 1-month follow-up dynamic contrast-enhanced MRIs were analyzed. Values of iAUC60 were computed using a trapezoidal rule applied to the time history of contrast uptake over the first 60 seconds postenhancement. The change in iAUC60 (ΔiAUC60) was calculated by taking the difference between the values of iAUC60 from 2 time points. Pearson correlation coefficients were calculated between progression-free survival, defined as the time from laser interstitial thermal therapy to tumor recurrence, and iAUC60 or ΔiAUC60 values. Thirty-three cases of laser interstitial thermal therapy for 32 brain metastases in a cohort of 27 patients were prospectively analyzed. A significant relationship was observed between the values of iAUC60 from postoperative dynamic contrast-enhanced MR imaging and progression-free survival with Pearson correlation (P = .03) and Cox univariate analysis (P = .01). The relationship between preoperative and 1-month follow-up dynamic contrast-enhanced MR imaging was not significantly correlated with progression-free survival. Similarly, no statistically significant relationship was observed with ΔiAUC60 and progression-free survival between any time points. Progression-free survival is difficult to predict in patients undergoing laser interstitial thermal therapy for brain metastases due to confounding with posttreatment change. iAUC60 extracted from postoperative dynamic contrast-enhanced MR imaging shows promise for accurately prognosticating patients following this operative therapy.

Utility of Multiparametric MRI for Predicting Residual Clinically Significant Prostate Cancer After Focal Laser Ablation.

OBJECTIVE. The purpose of this study was to compare in a multireader manner the diagnostic accuracies of 3-T multiparametric MRI interpretation and serial prostate-specific antigen (PSA) measurement in predicting the presence of residual clinically significant prostate cancer after focal laser ablation. MATERIALS AND METHODS. Eighteen men had undergone focal laser ablation for low- or intermediate-risk prostate cancer as part of two National Cancer Institute-funded phase 1 clinical trials. Multiparametric MRI was performed immediately after and 6 and 12 months after focal laser ablation. Serial PSA measurements after focal laser ablation were recorded, and MRI-ultrasound fusion biopsy was performed 6 and 12 months after ablation and served as the reference standard. Multiparametric MRI was performed at 3 T with pelvic phased-array coils. T2-weighted, DW, and dynamic contrast-enhanced MR images were retrospectively assessed by two blinded radiologists using a 3-point Likert scale (0-2). Inter-reader agreement was assessed with the Cohen kappa statistic. The diagnostic accuracies of multiparametric MRI and PSA measurement were compared. RESULTS. Residual clinically significant prostate cancer was identified in 11 of 18 (61%) men. Logistic regression analysis of serial PSA measurements yielded a correct classification rate of 61.1% (p > 0.05). Using a multiparametric MRI threshold score of 4 or greater, both radiologists made correct classifications for 16 of 18 men (89%) at 6 months and 15 of 17 men (88%) at 12 months. Interreader agreement was substantial to excellent for T2-weighted imaging, DWI, and dynamic contrast-enhanced MRI and improved uniformly from 6 to 12 months. Logistic regression analysis of the retrospectively reviewed multiparametric MR images yielded AUCs greater than 0.90 for each radiologist 6 and 12 months after focal laser ablation (p < 0.001). CONCLUSION. Multiparametric MRI 6 and 12 months after focal laser ablation significantly outperformed serial PSA measurements for predicting the presence of residual clinically significant prostate cancer.