Rim enhancement of pancreatic ductal adenocarcinoma: investigating the relationship with DCE-MRI-based radiomics and next-generation sequencing

Purpose

To identify the clinical and genetic variables associated with rim enhancement of pancreatic ductal adenocarcinoma (PDAC) and to develop a dynamic contrast-enhanced (DCE) MRI-based radiomics model for predicting the genetic status from next-generation sequencing (NGS).

Materials and methods

Patients with PDAC, who underwent pretreatment pancreatic DCE-MRI between November 2019 and July 2021, were eligible in this prospective study. Two radiologists evaluated presence of rim enhancement in PDAC, a known radiological prognostic indicator, on DCE MRI. NGS was conducted for the tissue from the lesion. The Mann-Whitney U and Chi-square tests were employed to identify clinical and genetic variables associated with rim enhancement in PDAC. For continuous variables predicting rim enhancement, the cutoff value was set based on the Youden's index from the receiver operating characteristic (ROC) curve. Radiomics features were extracted from a volume-of-interest of PDAC on four DCE maps (Ktrans, Kep, Ve, and iAUC). A random forest (RF) model was constructed using 10 selected radiomics features from a pool of 392 original features. This model aimed to predict the status of significant NGS variables associated with rim enhancement. The performance of the model was validated using test set.

Results

A total of 55 patients (32 men; median age 71 years) were randomly assigned to the training (n = 41) and test (n = 14) sets. In the training set, KRAS, TP53, CDKN2A, and SMAD4 mutation rates were 92.3%, 61.8%, 14.5%, and 9.1%, respectively. Tumor size and KRAS variant allele frequency (VAF) differed between rim-enhancing (n = 12) and nonrim-enhancing (n = 29) PDACs with a cutoff of 17.22%. The RF model's average AUC from 10-fold cross-validation for predicting KRAS VAF status was 0.698. In the test set comprising 6 tumors with low KRAS VAF and 8 with high KRAS VAF, the RF model's AUC reached 1.000, achieving a sensitivity of 75.0%, specificity of 100% and accuracy of 87.5%.

Conclusion

Rim enhancement of PDAC is associated with KRAS VAF derived from NGS-based genetic information. For predicting the KRAS VAF status in PDAC, a radiomics model based on DCE maps showed promising results.

Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: Added value of dynamic contrast-enhanced imaging

OBJECTIVES

To develop diagnostic algorithms of multisequence prostate magnetic resonance imaging for cancer detection and segmentation using deep learning and explore values of dynamic contrast-enhanced imaging in multiparametric imaging, compared with biparametric imaging.

METHODS

We collected 3227 multiparametric imaging sets from 332 patients, including 218 cancer patients (291 biopsy-proven foci) and 114 noncancer patients. Diagnostic algorithms of T2-weighted, T2-weighted plus dynamic contrast-enhanced, biparametric, and multiparametric imaging were built using 2578 sets, and their performance for clinically significant cancer was evaluated using 649 sets.

RESULTS

Biparametric and multiparametric imaging had following region-based performance: sensitivity of 71.9% and 74.8% (p = 0.394) and positive predictive value of 61.3% and 74.8% (p = 0.013), respectively. In side-specific analyses of cancer images, the specificity was 72.6% and 89.5% (p < 0.001) and the negative predictive value was 78.9% and 83.5% (p = 0.364), respectively. False-negative cancer on multiparametric imaging was smaller (p = 0.002) and more dominant with grade group ≤2 (p = 0.028) than true positive foci. In the peripheral zone, false-positive regions on biparametric imaging turned out to be true negative on multiparametric imaging more frequently compared with the transition zone (78.3% vs. 47.2%, p = 0.018). In contrast, T2-weighted plus dynamic contrast-enhanced imaging had lower specificity than T2-weighted imaging (41.1% vs. 51.6%, p = 0.042).

CONCLUSIONS

When using deep learning, multiparametric imaging provides superior performance to biparametric imaging in the specificity and positive predictive value, especially in the peripheral zone. Dynamic contrast-enhanced imaging helps reduce overdiagnosis in multiparametric imaging.

Magnetic Resonance Imaging Techniques in Peripheral Arterial Disease

Significance: Peripheral arterial disease (PAD) leads to a significant burden of morbidity and impaired quality of life globally. Diabetes is a significant risk factor accelerating the development of PAD with an associated increase in the risk of chronic wounds, tissue, and limb loss. Various magnetic resonance imaging (MRI) techniques are being increasingly acknowledged as useful methods of accurately assessing PAD. Recent Advances: Conventionally utilized MRI techniques for assessing macrovascular disease have included contrast enhanced magnetic resonance angiography (MRA), noncontrast time of flight MRA, and phase contrast MRI, but have significant limitations. In recent years, novel noncontrast MRI methods assessing skeletal muscle perfusion and metabolism such as arterial spin labeling (ASL), blood-oxygen-level dependent (BOLD) imaging, and chemical exchange saturation transfer (CEST) have emerged. Critical Issues: Conventional non-MRI (such as ankle-brachial index, arterial duplex ultrasonography, and computed tomographic angiography) and MRI based modalities image the macrovasculature. The underlying mechanisms of PAD that result in clinical manifestations are, however, complex, and imaging modalities that can assess the interaction between impaired blood flow, microvascular tissue perfusion, and muscular metabolism are necessary. Future Directions: Further development and clinical validation of noncontrast MRI methods assessing skeletal muscle perfusion and metabolism, such as ASL, BOLD, CEST, intravoxel incoherent motion microperfusion, and techniques that assess plaque composition, are advancing this field. These modalities can provide useful prognostic data and help in reliable surveillance of outcomes after interventions.

A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study

Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%.

Differentiation of various salivary gland tumours using diffusion-weighted MRI and dynamic contrast-enhanced MRI

Purpose

To determine the role of functional magnetic resonance imaging techniques (diffusion-weighted magnetic resonance imaging [DW-MRI] and dynamic contrast-enhanced magnetic resonance imaging [DCE-MRI]) in the differentiation of various salivary gland tumours.

Material and methods

In this prospective study, we evaluated 32 patients with salivary gland tumours using functional MRI. Diffusion parameters (mean apparent diffusion coefficient [ADC], normalized ADC and homogeneity index [HI]),semiquantitative DCE parameters (time signal intensity curves [TICs]) and quantitative DCE parameters (K_ep, K_trans and V_e) were analysed. Diagnostic efficiencies of all these parameters were determined to differentiate benign and malignant tumours as well as to characterize 3 major subgroups of salivary gland tumours, namely pleomorphic adenoma, Warthin tumour, and malignant tumours.

Results

Mean ADC, normalized ADC and HI were insignificant in differentiating benign and malignant tumours but were significant in differentiating pleomorphic adenomas, Warthin tumours, and malignant tumours. Mean ADC was the best parameter in predicting both pleomorphic adenomas and Warthin tumours (AUC: 0.95 and 0.89, respectively). Amongst DCE parameters, only TIC pattern could differentiate between benign and malignant tumours, with an accuracy of 93.75% (AUC: 0.94). The quantitative perfusion parameters aided greatly in characterizing pleomorphic adenomas, Warthin tumours and malignant tumours. For predicting pleomorphic adenomas, the accuracy of K_ep and K_trans was 96.77% (AUC: 0.98) and 93.55% (AUC: 0.95), respectively and for predicting Warthin tumours, the accuracy of both K_ep and K_trans was 96.77% (AUC: 0.97).

Conclusions

DCE parameters (particularly TIC, K_ep and K_trans) had higher accuracy in characterizing various tumour subgroups (pleomorphic adenomas, Warthin tumours, and malignant tumours) than DWI parameters. Hence, dynamic contrast-enhanced imaging adds immense value with only a minimum time penalty to the examination.

A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.

Evaluation of the Effects of Anti-PD-1 Therapy on Triple-Negative Breast Cancer in Mice by Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Imaging

BACKGROUND

The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity.

PURPOSE

To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model.

STUDY TYPE

Prospective.

ANIMAL MODEL

A total of 54 BALB/c mouse subcutaneous 4 T1 transplantation models of TNBC.

FIELD STRENGTH/SEQUENCE

A 3.0-T; turbo spin echo (TSE) T2-weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm² ) and T1-twist DCE acquisition series.

ASSESSMENT

DKI and DCE-MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross-sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists.

STATISTICAL TESTS

The two-tailed unpaired t-test, Mann-Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P < 0.05 was considered statistically significant.

RESULTS

The apparent diffusion coefficient (ADC), mean diffusivity (MD), K_trans and K_ep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and V_e values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P = 0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r = -0.833; MD, r = 0.890) and the CD45 level (ADC, r = 0.720; MD, r = 0.718). The K_trans and K_ep values were significantly correlated with the CD31 level (K_trans , r = 0.820; K_ep , r = 0.683).

DATA CONCLUSION

DKI and DCE-MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti-PD-1 therapy, respectively.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 4.

Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma

Objectives

To assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma.

Methods

Twenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance.

Results

Fifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p < 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24–84.93) and a specificity of 78.6% (95% CI 67.13–87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819–0.914).

Conclusions

DCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy.

Mass and non-mass breast MRI patterns: a radiologic approach to sick lobe theory

BACKGROUND

According to sick lobe theory, one or more lobes of the breast are more prone to the development of carcinoma. However, the implications of this theory in breast magnetic resonance imaging (MRI) are unknown.

PURPOSE

To evaluate the MRI appearance of mass type (multifocal and multicentric diseases) and non-mass type (non-mass enhancements) sick lobe patterns, together with the histopathology results.

MATERIAL AND METHODS

MRI reports of 2015 patients in two tertiary breast imaging centers between June 2012 and June 2018 were retrospectively reviewed for multifocal-multicentric diseases and segmental, linear, and regional enhancements. A total of 113 patients were included. The specimens obtained by thick needle, vacuum, excisional biopsy/lumpectomy or mastectomy after breast MRI scans were pathologically assessed. The pathologic results were categorized as invasive carcinoma, precursor, and benign proliferative lesions according to the 2012 World Health Organization Classification of Tumors.

RESULTS

The percentage of underlying benign and precursor invasive lesions was significantly different in patients with mass and non-mass MRI patterns. While the pathology results of mass type patterns were premalignant and malignant in all cases, nearly half of the underlying histologies were benign proliferative subtypes in patients with non-mass type patterns.

CONCLUSION

In this study, the mass and non-mass patterns derived from sick lobe theory were related to different risks of malignancy in the pathological examinations.

MRI of the Pancreas

MRI has played a critical role in the evaluation of patients with pancreatic pathologies, from screening of patients at high risk for pancreatic cancer to the evaluation of pancreatic cysts and indeterminate pancreatic lesions. The high mortality associated with pancreatic adenocarcinomas has spurred much interest in developing effective screening tools, with MRI using magnetic resonance cholangiopancreatography (MRCP) playing a central role in the hopes of identifying cancers at earlier stages amenable to curative resection. Ongoing efforts to improve the resolution and robustness of imaging of the pancreas using MRI may thus one day reduce the mortality of this deadly disease. However, the increasing use of cross-sectional imaging has also generated a concomitant clinical conundrum: How to manage incidental pancreatic cystic lesions that are found in over a quarter of patients who undergo MRCP. Efforts to improve the specificity of MRCP for patients with pancreatic cysts and with indeterminate pancreatic masses may be achieved with continued technical advances in MRI, including diffusion-weighted and T₁ -weighted dynamic contrast-enhanced MRI. However, developments in quantitative MRI of the pancreas remain challenging, due to the small size of the pancreas and its upper abdominal location, adjacent to bowel and below the diaphragm. Further research is needed to improve MRI of the pancreas as a clinical tool, to positively affect the lives of patients with pancreatic abnormalities. This review focuses on various MR techniques such as MRCP, quantitative imaging, and dynamic contrast-enhanced imaging and their clinical applicability in the imaging of the pancreas, with an emphasis on pancreatic malignant and premalignant lesions. Level of Evidence 5 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:347-359.

Response of the cerebral vasculature to systemic carbon monoxide administration-Regional differences and sexual dimorphism

Previous studies about the modulation of the vasculature by CO were performed exclusively in male or sexually immature animals. Understanding the sex differences regarding systemic drug processing and pharmacodynamics is an important feature for safety assessment of drug dosing and efficacy. In this work, we used CORM-A1 as source of CO to examine the effects of this gasotransmitter on brain perfusion and the sex-dependent differences. Dynamic contrast-enhanced imaging (DCE)-based analysis was used to characterize the properties of CO in the modulation of cerebral vasculature in vivo, in adult C57BL/6 healthy mice. Perfusion of the temporal muscle, maxillary vein and in hippocampus, cortex and striatum was analysed for 108 min following CORM-A1 administration of 3 or 5 mg/kg. Under control conditions, brain perfusion was lower in females when compared with males. Under CO treatment, females showed a surprisingly overall reduced perfusion compared with controls (F = 3.452, p = .0004), while no major alterations (or even the expected increase) were observed in males. Cortical structures were only modulated in females. A striking female-dominated vasoconstriction effect was observed in the hippocampus and striatum following administration of CO, in this mixed-sex cohort. As these two regions are implicated in episodic and procedural memory formation, CO may have a relevant impact in learning and memory.

Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer

Objective: To investigate whether machine learning analysis of multiparametric MR radiomics can help classify immunohistochemical (IHC) subtypes of breast cancer.

Study design: One hundred and thirty-four consecutive patients with pathologically-proven invasive ductal carcinoma were retrospectively analyzed. A total of 2,498 features were extracted from the DCE and DWI images, together with the new calculated images, including DCE images changing over six time points (DCE_sequential) and DWI images changing over three b-values (DWI_sequential). We proposed a novel two-stage feature selection method combining traditional statistics and machine learning-based methods. The accuracies of the 4-IHC classification and triple negative (TN) vs. non-TN cancers were assessed.

Results: For the 4-IHC classification task, the best accuracy of 72.4% was achieved based on linear discriminant analysis (LDA) or subspace discrimination of assembled learning in conjunction with 20 selected features, and only small dependent emphasis of Kendall-tau-b for sequential features, based on the DWI_sequential with the LDA model, yielding an accuracy of 53.7%. The linear support vector machine (SVM) and medium k-nearest neighbor using eight features yielded the highest accuracy of 91.0% for comparing TN to non-TN cancers, and the maximum variance for DWI_sequential alone, together with a linear SVM model, achieved an accuracy of 83.6%.

Conclusions: Whole-tumor radiomics on MR multiparametric images, DCE images changing over time points, and DWI images changing over different b-values provide a non-invasive analytical approach for breast cancer subtype classification and TN cancer identification.

Patient-based 4D digital breast phantom for perfusion contrast-enhanced breast CT imaging

Purpose

The purpose of this study was to develop a realistic patient‐based 4D digital breast phantom including time‐varying contrast enhancement for simulation of dedicated breast CT perfusion imaging.

Methods

A 3D static phantom is first created by segmenting a breast CT image from a healthy patient into skin, fibroglandular tissue, adipose tissue, and vasculature. For the creation of abnormal cases, a breast lesion model was developed and can be added to the phantom. After defining the necessary perfusion parameters for each tissue (e.g., arterial input function for vasculature, blood volume and blood flow for the other normal tissues) based on contrast‐enhanced dynamic breast MRI data, the corresponding time‐enhancement curves are computed for each voxel in the phantom, according to tissue type. These curves are calculated by convolution between the arterial input function and a shifted exponential function. This exponential depends on the perfusion parameters associated with each tissue voxel, and, to incorporate normal biological variability, a uniform random distribution is used to vary the perfusion parameters on a voxel‐basis. Finally, a 4D array is produced by sampling the continuous time‐enhancement curves at the desired sampling rate. Beside modeling different enhancement dynamics according to the given input perfusion parameters, the phantom also includes the possibility to realistically simulate different spatial enhancement patterns for the breast parenchyma, taking into account the arterial sources supplying the breast. Finally, different patterns of contrast medium uptake can also be simulated for the tumor models (homogeneous and rim enhancement).

Results

As an example, a typical 4D phantom has dimensions of 426 × 421 × 260 × 559 (x, y, z, t), with a voxel size of 273 μm and a sampling time of 1 s. The characteristics of the tumor model can be modified at will to evaluate perfusion in different types of breast lesions. Results show the expected enhancement of tissues, consistent with the given input parameters. Moreover, the tumor models evaluated in this work show different enhancement dynamics according to the tumor type (defined by different input perfusion parameters), and also present a higher enhancement compared to the other healthy tissues, as expected.

Conclusions

The proposed digital phantom can model the breast tissue perfusion during 4D breast CT image acquisition, displaying the different enhancement dynamics that could be found in a real patient breast. This phantom can be used during the development of dynamic contrast‐enhanced dedicated breast CT imaging, for optimization of image acquisition, image reconstruction, and image analysis. This modality could provide functional information of the breast, resulting in detection, diagnosis, and treatment improvements of breast cancer with breast CT.

Associations Between Histogram Analysis DCE MRI Parameters and Complex 18F-FDG-PET Values in Head and Neck Squamous Cell Carcinoma

AIM

To analyze associations between parameters of positron-emission tomography (PET) and histogram analysis values of dynamic contrast-enhanced (DCE) imaging in patients with head and neck squamous cell carcinoma (HNSCC).

PATIENTS AND METHODS

Overall, 28 patients with primary HNSCC of different localizations were involved. ¹⁸F-FDG-PET/CT and DCE MR imaging were performed for all patients. Histogram analysis parameters of DCE MRI were calculated. Spearman's correlation coefficient was used to analyze the associations between investigated parameters.

RESULTS

In the overall cohort, SUV_max correlated with K_ep P10 (ρ=0.43, p=0.027), K_ep P25 (0.40, p=0.035), and had a tendency to correlate with median K_ep (ρ=0.33, p=0.098). TLG tended to correlate with K_ep P25 (0.33, p=0.09) and P10 K_trans (ρ=0.35, p=0.07). In G1/2 tumors, SUV_max correlated with K_ep P10 (ρ=0.645, p=0.032), and tended to correlate with K_trans mean (ρ=0.54, p=0.089), K_trans min (ρ=0.58, p=0.06), K_trans P10 (ρ=0.56, p=0.07), K_trans P25 (ρ=0.59, p=0.056), and K_trans median (ρ=0.054, p=0.089), as well with K_ep min (ρ=0.56, p=0.07). SUVmean tended to correlate with K_ep kurtosis (ρ=0.56, p=0.07). In G3 tumors, no tendencies or statistically significant correlations between the PET and DCE MRI parameters were identified.

CONCLUSION

Tumor metabolism and perfusion show complex associations in HNSCC. These associations depend on tumor grading.

Imaging Facilities' Adherence to PI-RADS v2 Minimum Technical Standards for the Performance of Prostate MRI

PURPOSE

This study aimed to assess variability in imaging facilities' adherence to the minimum technical standards for prostate magnetic resonance imaging acquisition established by Prostate Imaging-Reporting and Data System (PI-RADS) version 2 (v2).

METHODS

A total of 107 prostate magnetic resonance imaging examinations performed at 107 unique imaging facilities after the release of PI-RADS v2 and that were referred to a tertiary care center for secondary interpretation were included. Image sets, DICOM headers, and outside reports were reviewed to assess adherence to 21 selected PI-RADS v2 minimum technical standards.

RESULTS

Hardware arrangements were 23.1%, 1.5T without endorectal coil; 7.7%, 1.5T with endorectal coil; 63.5%, 3T without endorectal coil; and 5.8%, 3T with endorectal coil. Adherence to minimum standards was lowest on T2 weighted imaging (T2WI) for frequency resolution ≤0.4 mm (16.8%) and phase resolution ≤0.7 mm (48.6%), lowest on diffusion-weighted imaging (DWI) for field of view (FOV) 120-220 mm (30.0%), and lowest on dynamic contrast-enhanced (DCE) imaging for slice thickness 3 mm (33.3%) and temporal resolution <10 s (31.5%). High b-value (≥1400 s/mm²) images were included in 58.0% (calculated in 25.9%). Adherence to T2WI phase resolution and DWI inter-slice gap were greater (P < .05) at 3T than at 1.5T. Adherence did not differ (P > .05) for any parameter between examinations performed with and without an endorectal coil. Adherence was greater for examinations performed at teaching facilities for T2WI slice thickness and DCE temporal resolution (P < .05). Adherence was not better for examinations performed in 2016 than in 2015 for any parameter (P > .05).

CONCLUSION

Facilities' adherence to PI-RADS v2 minimum technical standards was variable, being particularly poor for T2WI frequency resolution and DCE temporal resolution. The standards warrant greater community education. Certain technical standards may be too stringent, and revisions should be considered.

Characterization and stratification of prostate lesions based on comprehensive multiparametric MRI using detailed whole-mount histopathology as a reference standard

The purpose of this study was to characterize prostate cancer (PCa) based on multiparametric MR (mpMR) measures derived from MRI, diffusion, spectroscopy, and dynamic contrast-enhanced (DCE) MRI, and to validate mpMRI in detecting PCa and predicting PCa aggressiveness by correlating mpMRI findings with whole-mount histopathology. Seventy-eight men with untreated PCa received 3 T mpMR scans prior to radical prostatectomy. Cancerous regions were outlined, graded, and cancer amount estimated on whole-mount histology. Regions of interest were manually drawn on T₂ -weighted images based on histopathology. Logistic regression was used to identify optimal combinations of parameters for the peripheral zone and transition zone to separate: (i) benign from malignant tissues; (ii) Gleason score (GS) ≤3 + 3 disease from ≥GS3 + 4; and (iii) ≤ GS3 + 4 from ≥GS4 + 3 cancers. The performance of the models was assessed using repeated fourfold cross-validation. Additionally, the performance of the logistic regression models created under the assumption that one or more modality has not been acquired was evaluated. Logistic regression models yielded areas under the curve (AUCs) of 1.0 and 0.99 when separating benign from malignant tissues in the peripheral zone and the transition zone, respectively. Within the peripheral zone, combining choline, maximal enhancement slope, apparent diffusion coefficient (ADC), and citrate measures for separating ≤GS3 + 3 from ≥GS3 + 4 PCa yielded AUC = 0.84. Combining creatine, choline, and washout slope yielded AUC = 0.81 for discriminating ≤GS3 + 4 from ≥GS4 + 3 disease. Within the transition zone, combining washout slope, ADC, and creatine yielded AUC = 0.93 for discriminating ≤GS3 + 3 and ≥GS3 + 4 cancers. When separating ≤GS3 + 4 from ≥GS4 + 3 PCa, combining choline and washout slope yielded AUC = 0.92. MpMRI provides excellent separation between benign tissues and PCa, and across PCa tissues of different aggressiveness. The final models prominently feature spectroscopy and DCE-derived metrics, underlining their value within a comprehensive mpMRI examination.

Differentiation of focal indeterminate marrow abnormalities with multiparametric MRI

PURPOSE

To explore magnetic resonance imaging (MRI) parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), multiecho Dixon imaging (ME-Dixon), and dynamic contrast-enhanced imaging (DCE) for differentiating focal indeterminate marrow abnormalities MATERIALS AND METHODS: Forty-two patients with 14 benign and 28 malignant focal marrow abnormalities were included. The following were independently analyzed by two readers: signal intensity (SI), contour, and margin on conventional MR images; SI on b-800 images (SI_b-800 ), apparent diffusion coefficient (ADC), IVIM parameters (D_slow, D_fast , and f), fat fraction (Ff), and DCE parameters (time-to-signal intensity curve pattern, iAUC, K^trans , k_ep , and v_e ). The MR characteristics and parameters from benign and malignant lesions were compared with a chi-squared test and the Mann-Whitney U-test, respectively. The area under receiver operating characteristic (ROC) curves (AUC) of each sequence were also compared. Interobserver agreements were assessed with Cohen's κ, and intraclass correlation coefficient (ICC).

RESULTS

ADC, D_slow , and Ff demonstrated a significant difference between benign and malignant marrow abnormalities for both readers (P < 0.001). SI_b-800 and perfusion-related parameters from IVIM-DWI and DCE were not significantly different between the two groups (P = 0.145, 0.439, and 0.337 for reader 1, P = 0.378, 0.368, and 0.343 for reader 2, respectively). The AUCs of ADC, D_slow , and Ff were significantly higher for differentiating indeterminate marrow abnormalities in both readers (P < 0.001). Interobserver agreements were substantial in SI_b-800 , and ICCs were almost perfect for ADC, D_slow , f, and Ff, and substantial for iAUC, k_ep , K^trans , v_e , and D_fast .

CONCLUSION

ADC, D_slow , and Ff may provide information for differentiating focal indeterminate abnormalities.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:49-60.

Optimization of saturation-recovery dynamic contrast-enhanced MRI acquisition protocol: monte carlo simulation approach demonstrated with gadolinium MR renography

Dynamic contrast-enhanced (DCE) MRI is widely used for the measurement of tissue perfusion and to assess organ function. MR renography, which is acquired using a DCE sequence, can measure renal perfusion, filtration and concentrating ability. Optimization of the DCE acquisition protocol is important for the minimization of the error propagation from the acquired signals to the estimated parameters, thus improving the precision of the parameters. Critical to the optimization of contrast-enhanced T1 -weighted protocols is the balance of the T1 -shortening effect across the range of gadolinium (Gd) contrast concentration in the tissue of interest. In this study, we demonstrate a Monte Carlo simulation approach for the optimization of DCE MRI, in which a saturation-recovery T1 -weighted gradient echo sequence is simulated and the impact of injected dose (D) and time delay (TD, for saturation recovery) is tested. The results show that high D and/or high TD cause saturation of the peak arterial signals and lead to an overestimation of renal plasma flow (RPF) and glomerular filtration rate (GFR). However, the use of low TD (e.g. 100 ms) and low D leads to similar errors in RPF and GFR, because of the Rician bias in the pre-contrast arterial signals. Our patient study including 22 human subjects compared TD values of 100 and 300 ms after the injection of 4 mL of Gd contrast for MR renography. At TD = 100 ms, we computed an RPF value of 157.2 ± 51.7 mL/min and a GFR of 33.3 ± 11.6 mL/min. These results were all significantly higher than the parameter estimates at TD = 300 ms: RPF = 143.4 ± 48.8 mL/min (p = 0.0006) and GFR = 30.2 ± 11.5 mL/min (p = 0.0015). In conclusion, appropriate optimization of the DCE MRI protocol using simulation can effectively improve the precision and, potentially, the accuracy of the measured parameters. Copyright © 2016 John Wiley & Sons, Ltd.

Medullary hemangioblastoma in a child with von Hippel-Lindau disease: vascular tumor perfusion depicted by arterial spin labeling and dynamic contrast-enhanced imaging

Medullary hemangioblastoma is very rare in children. Based on small nodular enhancement with peritumoral edema and without dilated feeding arteries on conventional MRI, hemangioblastoma, pilocytic astrocytoma, oligodendroglioma, and ganglioglioma were included in the differential diagnosis of the medullary tumor. In this case report, the authors emphasize the diagnostic value of arterial spin labeling and dynamic contrast-enhanced MRI in demonstrating vascular tumor perfusion of hemangioblastoma in a 12-year-old boy who was later found to have von Hippel-Lindau disease.

Accuracy of semiquantitative dynamic contrast-enhanced MRI for differentiating type II from type I endometrial carcinoma

PURPOSE

To investigate type II endometrial carcinoma characterization using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and evaluate the diagnostic accuracy of semiquantitative DCE-MRI in differentiating type II from type I tumors.

MATERIALS AND METHODS

Seventy-seven patients with endometrial carcinoma were retrospectively evaluated using 3T DCE-MRI. The maximum absolute enhancement of signal intensity (SImax), maximum relative enhancement (SIrel), wash-in rate (WIR), and the SImax/SI (piriformis) ratio were analyzed. To differentiate type I from type II tumors, optimal threshold criteria were established. The Mann-Whitney U-test was used for statistical comparison and receiver operating characteristic curve analysis was used to determine optimal cutoff values.

RESULTS

The SIrel (P < 0.001), WIR (P < 0.0001), and SImax/SI (piriformis) ratio (P < 0.0001), but not SImax, differed significantly between type I and type II carcinomas. Cutoff values of SIrel ≥58.8, WIR ≥37.0, and SImax/SI (piriformis) ratio ≥1.55 had sensitivities of 93%, 93%, and 67%, specificities of 60%, 60%, and 79%, accuracies of 66%, 66%, and 67%, respectively, for predicting type II endometrial carcinoma.

CONCLUSION

Endometrial carcinoma with strong (high level) enhancement on DCE-MRI is suggestive of type II endometrial carcinoma. Semiquantitative evaluation of DCE-MRI may be useful for differentiating type II from type I tumors.

Magnetic resonance imaging-based tumour perfusion parameters are biomarkers predicting response after radiation to brain metastases

PURPOSE

To investigate whether early relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF) and permeability (Ktrans(2)) measurements may serve as magnetic resonance imaging (MRI) biomarkers of radiation response or progression for brain metastases.

MATERIALS AND METHODS

Seventy brain metastases in 44 patients treated with either stereotactic radiosurgery or whole brain radiotherapy were imaged with dynamic susceptibility and dynamic contrast enhancement MRI at baseline, 1 week and 1 month after treatment. The final response status was determined according to volume criteria derived from a 1 year post-treatment MRI or last available follow-up MRI. Tumours were characterised as responders, non-responders, progressors and non-progressors and compared for Ktrans(2), rCBF and rCBV differences. Uni- and multivariate analysis evaluated factors associated with tumour response and progression at 1 week and 1 month. A generalised estimating equations (GEE) model accounted for multiple tumours per subject. Receiver operator characteristic (ROC) analysis identified optimal cut-off values, sensitivity and specificity for response or progression.

RESULTS

Tumour responders showed lower Ktrans(2) and reduced rCBF at 1 week (P < 0.05 each). Progressive disease showed lower rCBF and reduced rCBV at 1 month (P < 0.05 each). GEE and multivariate analysis revealed lower Ktrans(2) at 1 week, an absence of prior radiation predicted response. At 1 month only lower rCBV predicted progressive disease on GEE and multivariate analysis. Optimal cut-off points for Ktrans(2) and rCBV were 1.37 and 2.03 with sensitivity and specificity of 61.5 and 81.1% and 73.9 and 81.8%, respectively.

CONCLUSION

Lower Ktrans(2) at 1 week and rCBV at 1 month discriminated responders and progressive disease, respectively.

Dynamic contrast-enhanced magnetic resonance imaging: applications in oncology

Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) allows functional characterisation of tissue perfusion characteristics and acts as a biomarker for tumour angiogenesis. It involves serial acquisition of MRI images before and after injection of contrast, as such, tissue perfusion and permeability can be assessed based on the signal enhancement kinetics. The ability to evaluate whole tumour volumes in a non-invasive manner makes DCE MRI especially attractive for potential oncological applications. Here we provide an overview of the current research involving DCE MRI as a biomarker for the diagnosis and characterisation of malignancies, prediction of the therapeutic response and survival outcomes, as well as radiation therapy planning.

MRI findings of solitary fibrous tumours in the head and neck region

OBJECTIVES

To analyse the MRI findings of solitary fibrous tumours in the head and neck region.

METHODS

We retrospectively reviewed MR images in eight patients with solitary fibrous tumours proven on histological examination. All the patients underwent conventional MRI, and four patients also underwent dynamic contrast-enhanced MRI and diffusion-weighted imaging in five cases. Image characteristics were analysed.

RESULTS

All lesions were found as solitary well-defined masses ranging in size from 1.9 to 6.8 cm (mean, 4.1 cm). They were mostly homogeneous and isointense to the muscle on T1 weighted images and heterogeneous and mildly hyperintense on T2 weighted images. After gadolinium administration, areas that were mildly hyperintense on T2 weighted images were strongly enhanced. They were mildly hyperintense on diffusion-weighted imaging. The average tumour-apparent diffusion coefficient values were 0.001 157 ± 0.000 304 9 mm s(-2) compared with the muscle 0.000 760 ± 0.000 265 0 mm s(-2), and there was a statistical difference of p = 0.002. The time-intensity curves exhibited a rapidly enhancing and a slow washout pattern on dynamic contrast-enhanced MRI.

CONCLUSIONS

Solitary fibrous tumours should be considered in cases of heterogeneous hypervascular tumours in the head and neck region. Areas of mild hyperintense intensity on T2 weighted images that are strongly enhanced after gadolinium injection are suggestive of this diagnosis. Non-restricted diffusion and rapidly enhancing and slow washout pattern time-intensity curves may be additional valuable features.

Role of high-field MR in studies of localized prostate cancer

Magnetic resonance imaging is attracting increasing attention from the uroradiological community as a modality to guide the management of prostate cancer. With the high incidence of prostate cancer it might come as a surprise that for a very long time (and in many places even at present) treatment decisions were being made without the use of detailed anatomical and functional imaging of the prostate gland at hand. Although T2 -weighted MRI can provide great anatomical detail, by itself it is not specific enough to discriminate cancer from benign disease, so other functional MRI techniques have been explored to aid in detection, localization, staging and risk assessment of prostate cancer. With the current evolution of clinical MR systems from 1.5 to 3 T it is important to understand the advantages and the challenges of the higher magnetic field strength for the different functional MR techniques most used in the prostate: T2 -weighted MRI, diffusion-weighted MRI, MR spectroscopic imaging and dynamic contrast-enhanced imaging. In addition to this, the use of the endorectal coil at different field strengths is discussed in this review, together with an outlook of the possibilities of ultra-high-field MR for the prostate.

Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease

RATIONALE AND OBJECTIVES

Magnetic resonance (MR) perfusion techniques and MR spectroscopy (MRS) provide specific physiological information that may allow distinction between recurrent glioma and progression from stable disease.

MATERIALS AND METHODS

Forty patients underwent conventional MR imaging, dynamic contrast-enhanced T1-weighted perfusion imaging, dynamic susceptibility contrast-enhanced perfusion imaging (DSC), and multivoxel MRS. Arterial spin labeling was available in 26 of these patients. Quantitative parameters were calculated in tumor recurrences and stable disease, which were retrospectively verified on clinical and radiological follow-up. Receiver operating characteristic curves for each parameter were generated for the differentiation between recurrent glioma and stable disease. A forward discriminant analysis was undertaken to assess the power of the conjunction of MR perfusion techniques and MRS.

RESULTS

Of the 40 patients, 23 were determined to have recurrent gliomas. Differences in arterial spin labeling between the two groups were not statistically significant (P = .063). Sensitivities and specificities for the detection of recurrent lesions in dynamic contrast-enhanced T1-weighted perfusion imaging and DSC were 61.9% and 80% transfer constant k(trans), 77.3% and 84.6% for cerebral blood flow, and 81% and 76.9% for cerebral blood volume, respectively. Among the parameters in MRS, the ratio of choline to normalized creatine showed the best diagnostic accuracy (P = .014; sensitivity 70%, specificity 78.6%). When considering all perfusion modalities, diagnostic accuracy could be increased to 82.5%, adding MRS to the multiparametric approach resulted in a diagnostic accuracy of 90.0%.

CONCLUSIONS

MR perfusion techniques and MRS are useful tools that enable improved differentiation between recurrent glioma and stable disease. Among the single parameters, DSC showed the best diagnostic performance. Multiparametric assessment substantially improved the ability to differentiate the two entities.

MRI in breast cancer therapy monitoring

Breast MRI has several roles in the clinical management of breast cancer, including as a screening method for high-risk women, as a diagnostic tool used as an adjunct to mammography and ultrasound, and for the staging of disease extent prior to treatment. In addition to these uses, MRI is also employed to track small changes in tumor size and microenvironment. MRI has produced several early indicators of treatment response in clinical trials over the last 10  years, including initial lesion pattern, changes in lesion size, kinetic parameters, apparent diffusion coefficient and T(2) value; the related technique of (1) H MRS has also shown that choline concentration, T(2) value and water-to-fat ratio are response indicators. In addition to measuring anatomical changes in the lesion size, as performed in traditional radiology, MRI has the ability to track vascular and cellular changes using dynamic contrast-enhanced MRI and diffusion-weighted MRI, respectively. By adding (1) H MRS to MRI examinations, metabolic changes can also be determined. These functional imaging techniques allow studies to focus on early time points relative to neoadjuvant treatment. Early treatment response predictors may allow therapy to be tailored to individual patients and thus aid in the realization of the goal of personalized medicine.