Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements

DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.

A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma

PURPOSE

This study aimed to build and evaluate a deep learning (DL) model to predict vessels encapsulating tumor clusters (VETC) and prognosis preoperatively in patients with hepatocellular carcinoma (HCC).

METHODS

320 pathologically confirmed HCC patients (58 women and 262 men) from two hospitals were included in this retrospective study. Institution 1 (n = 219) and Institution 2 (n = 101) served as the training and external test cohorts, respectively. Tumors were evaluated three-dimensionally and regions of interest were segmented manually in the arterial, portal venous, and delayed phases (AP, PP, and DP). Three ResNet-34 DL models were developed, consisting of three models based on a single sequence. The fusion model was developed by inputting the prediction probability of the output from the three single-sequence models into logistic regression. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs. Early recurrence (ER) was defined as recurrence within two years of surgery and early recurrence-free survival (ERFS) rate was evaluated by Kaplan-Meier survival analysis.

RESULTS

Among the 320 HCC patients, 227 were VETC- and 93 were VETC+ . In the external test cohort, the fusion model showed an AUC of 0.772, a sensitivity of 0.80, and a specificity of 0.61. The fusion model-based prediction of VETC high-risk and low-risk categories exhibits a significant difference in ERFS rates, akin to the outcomes observed in VETC + and VETC- confirmed through pathological analyses (p < 0.05).

CONCLUSIONS

A DL framework based on ResNet-34 has demonstrated potential in facilitating non-invasive prediction of VETC as well as patient prognosis.

Microvascular disease not type 2 diabetes is associated with increased cortical porosity: A study of cortical bone microstructure and intracortical vessel characteristics

Introduction

Fracture risk is elevated in type 2 diabetes (T2D) despite normal or even high bone mineral density (BMD). Microvascular disease (MVD) is a diabetic complication, but also associated with other diseases, for example chronic kidney disease. We hypothesize that increased fracture risk in T2D could be due to increased cortical porosity (Ct.Po) driven by expansion of the vascular network in MVD. The purpose of this study was to investigate associations of T2D and MVD with cortical microstructure and intracortical vessel parameters.

Methods

The study group consisted of 75 participants (38 with T2D and 37 without T2D). High-resolution peripheral quantitative CT (HR-pQCT) and dynamic contrast-enhanced MRI (DCE-MRI) of the ultra-distal tibia were performed to assess cortical bone and intracortical vessels (outcomes). MVD was defined as ≥1 manifestation including neuropathy, nephropathy, or retinopathy based on clinical exams in all participants. Adjusted means of outcomes were compared between groups with/without T2D or between participants with/without MVD in both groups using linear regression models adjusting for age, sex, BMI, and T2D as applicable.

Results

MVD was found in 21 (55 %) participants with T2D and in 9 (24 %) participants without T2D. In T2D, cortical pore diameter (Ct.Po.Dm) and diameter distribution (Ct.Po.Dm.SD) were significantly higher by 14.6 μm (3.6 %, 95 % confidence interval [CI]: 2.70, 26.5 μm, p = 0.017) and by 8.73 μm (4.8 %, CI: 0.79, 16.7 μm, p = 0.032), respectively. In MVD, but not in T2D, cortical porosity was significantly higher by 2.25 % (relative increase = 12.9 %, CI: 0.53, 3.97 %, p = 0.011) and cortical BMD (Ct.BMD) was significantly lower by -43.6 mg/cm3 (2.6 %, CI: -77.4, -9.81 mg/cm3, p = 0.012). In T2D, vessel volume and vessel diameter were significantly higher by 0.02 mm3 (13.3 %, CI: 0.004, 0.04 mm3, p = 0.017) and 15.4 μm (2.9 %, CI: 0.42, 30.4 μm, p = 0.044), respectively. In MVD, vessel density was significantly higher by 0.11 mm-3 (17.8 %, CI: 0.01, 0.21 mm-3, p = 0.033) and vessel volume and diameter were significantly lower by -0.02 mm3 (13.7 %, CI: -0.04, -0.004 mm3, p = 0.015) and - 14.6 μm (2.8 %, CI: -29.1, -0.11 μm, p = 0.048), respectively.

Conclusions

The presence of MVD, rather than T2D, was associated with increased cortical porosity. Increased porosity in MVD was coupled with a larger number of smaller vessels, which could indicate upregulation of neovascularization triggered by ischemia. It is unclear why higher variability and average diameters of pores in T2D were accompanied by larger vessels.

Evaluation of response to neoadjuvant chemotherapy in osteosarcoma using dynamic contrast-enhanced MRI: development and external validation of a model

OBJECTIVE

To identify which dynamic contrast-enhanced (DCE-)MRI features best predict histological response to neoadjuvant chemotherapy in patients with an osteosarcoma.

METHODS

Patients with osteosarcoma who underwent DCE-MRI before and after neoadjuvant chemotherapy prior to resection were retrospectively included at two different centers. Data from the center with the larger cohort (training cohort) was used to identify which method for region-of-interest selection (whole slab or focal area method) and which change in DCE-MRI features (time to enhancement, wash-in rate, maximum relative enhancement and area under the curve) gave the most accurate prediction of histological response. Models were created using logistic regression and cross-validated. The most accurate model was then externally validated using data from the other center (test cohort).

RESULTS

Fifty-five (27 poor response) and 30 (19 poor response) patients were included in training and test cohorts, respectively. Intraclass correlation coefficient of relative DCE-MRI features ranged 0.81-0.97 with the whole slab and 0.57-0.85 with the focal area segmentation method. Poor histological response was best predicted with the whole slab segmentation method using a single feature threshold, relative wash-in rate <2.3. Mean accuracy was 0.85 (95%CI: 0.75-0.95), and area under the receiver operating characteristic curve (AUC-index) was 0.93 (95%CI: 0.86-1.00). In external validation, accuracy and AUC-index were 0.80 and 0.80.

CONCLUSION

In this study, a relative wash-in rate of <2.3 determined with the whole slab segmentation method predicted histological response to neoadjuvant chemotherapy in osteosarcoma. Consistent performance was observed in an external test cohort.

Quantitative dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer: A systematic comparison of different modelling approaches

Background and purpose

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) describes tissue microvasculature and has prognostic and predictive potential in radiotherapy for head and neck cancer (HNC). However, lack in standardization of DCE-MRI hinders comparison of studies and clinical implementation. This study investigated the accuracy and robustness of the population arterial input function (AIF), correlations between pharmacokinetic parameters and their association to T stage and human papillomavirus (HPV) status for HNC.

Materials and methods

DCE-MRI was acquired for 44 HNC patients. Population AIFs were calculated with six different approaches. DCE-MRI was analysed in primary and lymph node tumours using Tofts model (TM) with population AIFs and individual AIFs, extended TM (ETM) with individual AIFs, Brix model (BM), and areas under the curve (AUCs). Intraclass correlation, concordance correlation, Pearson correlation and Whitney Mann U test helped examining the robustness and accuracy of population AIF, correlations between DCE-MRI parameters and their association to T stage and HPV status, respectively.

Results

The population AIF was robust but differed from individual AIFs. There was significant correlation between KtransTM/ETM and ve, TM/ETM, and KtransTM/ETM and Kep, TM/ETM. ABrix and AUCs correlated for lymph nodes. Kep, Brix correlated with ABrix, KtransTM/ETM and Kep, TM/ETM for primary tumours. Kep, TM significantly decreased with increasing T stage. Both the correlations and the parameters' association to T stage were stronger for HPV negative lesions.

Conclusions

Individual AIF was preferred for accurate pharmacokinetic modelling of DCE-MRI. DCE-MRI parameters and their correlations were affected by the lesion type, HPV status and T staging.

Molecular-subtype guided automatic invasive breast cancer grading using dynamic contrast-enhanced MRI

BACKGROUND AND OBJECTIVES

Histological grade and molecular subtype have presented valuable references in assigning personalized or precision medicine as the significant prognostic indicators representing biological behaviors of invasive breast cancer (IBC). To evaluate a two-stage deep learning framework for IBC grading that incorporates with molecular-subtype (MS) information using DCE-MRI.

METHODS

In Stage I, an innovative neural network called IOS2-DA is developed, which includes a dense atrous-spatial pyramid pooling block with a pooling layer (DA) and inception-octconved blocks with double kernel squeeze-and-excitations (IOS2). This method focuses on the imaging manifestation of IBC grades and performs preliminary prediction using a novel class F1-score loss function. In Stage II, a MS attention branch is introduced to fine-tune the integrated deep vectors from IOS2-DA via Kullback-Leibler divergence. The MS-guided information is weighted with preliminary results to obtain classification values, which are analyzed by ensemble learning for tumor grade prediction on three MRI post-contrast series. Objective assessment is quantitatively evaluated by receiver operating characteristic curve analysis. DeLong test is applied to measure statistical significance (P < 0.05).

RESULTS

The molecular-subtype guided IOS2-DA performs significantly better than the single IOS2-DA in terms of accuracy (0.927), precision (0.942), AUC (0.927, 95% CI: [0.908, 0.946]), and F1-score (0.930). The gradient-weighted class activation maps show that the feature representations extracted from IOS2-DA are consistent with tumor areas.

CONCLUSIONS

IOS2-DA elucidates its potential in non-invasive tumor grade prediction. With respect to the correlation between MS and histological grade, it exhibits remarkable clinical prospects in the application of relevant clinical biomarkers to enhance the diagnostic effectiveness of IBC grading. Therefore, DCE-MRI tends to be a feasible imaging modality for the thorough preoperative assessment of breast biological behavior and carcinoma prognosis.

Assessment of LI-RADS efficacy in classification of hepatocellular carcinoma and benign liver nodules using DCE-MRI features and machine learning

Purpose

The current study aimed to evaluate the efficiency of dynamic contrast-enhanced (DCE) MRI visual features in classifying benign liver nodules and hepatocellular carcinoma (HCC) using a machine learning model.

Methods

115 LI-RADS3, 137 LI-RADS4, and 140 LI-RADS5 nodules were included (392 nodules from 245 patients), which were evaluated by follow-up imaging for LR-3 and pathology results for LR-4 and LR-5 nodules. Data was collected retrospectively from 3 T and 1.5 T MRI scanners. All the lesions were categorized into 124 benign and 268 HCC lesions. Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model.

Results

For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001.

Conclusions

The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. For future work, this study suggests developing a deep-learning algorithm for automatic lesion segmentation and feature assessment to reduce lesion categorization errors.

Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists' interpretations at various levels

PURPOSE

To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (ResNet50) neural network constructed from different segmentations for distinguishing malignant and benign non-mass enhancement (NME) on breast magnetic resonance imaging (MRI) and conduct a comparison with radiologists with various levels of experience.

MATERIALS AND METHODS

A total of 84 consecutive patients with 86 lesions (51 malignant, 35 benign) presenting NME on breast MRI were analyzed. Three radiologists with different levels of experience evaluated all examinations, based on the Breast Imaging-Reporting and Data System (BI-RADS) lexicon and categorization. For the deep learning method, one expert radiologist performed lesion annotation manually using the early phase of dynamic contrast-enhanced (DCE) MRI. Two segmentation methods were applied: a precise segmentation was carefully set to include only the enhancing area, and a rough segmentation covered the whole enhancing region, including the intervenient non-enhancing area. ResNet50 was implemented using the DCE MRI input. The diagnostic performance of the radiologists' readings and deep learning were then compared using receiver operating curve analysis.

RESULTS

The ResNet50 model from precise segmentation achieved diagnostic accuracy equivalent [area under the curve (AUC) = 0.91, 95% confidence interval (CI) 0.90, 0.93] to that of a highly experienced radiologist (AUC = 0.89, 95% CI 0.81, 0.96; p = 0.45). Even the model from rough segmentation showed diagnostic performance equivalent to a board-certified radiologist (AUC = 0.80, 95% CI 0.78, 0.82 vs. AUC = 0.79, 95% CI 0.70, 0.89, respectively). Both ResNet50 models from the precise and rough segmentation exceeded the diagnostic accuracy of a radiology resident (AUC = 0.64, 95% CI 0.52, 0.76).

CONCLUSION

These findings suggest that the deep learning model from ResNet50 has the potential to ensure accuracy in the diagnosis of NME on breast MRI.

Role of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters and Extracellular Volume Fraction as Predictors of Lung Cancer Subtypes and Lymph Node Status in Non-Small-Cell Lung Cancer Patients

Objective: The aim of this study is to determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based quantitative parameters and the extracellular volume fraction (ECV) can differentiate small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC), squamous-cell carcinoma (SCC) from adenocarcinoma (Adeno-Ca), and NSCLC with lymph node metastasis from NSCLC without lymph node metastasis. Materials and methods: We prospectively enrolled patients with lung cancer (41 Adeno-Ca, 29 SCC, and 23 SCLC) who underwent DCE-MRI and enhanced T1 mapping prior to histopathological confirmation. Quantitative parameters based on DCE-MRI and ECV based on T1 mapping were compared between SCLC and NSCLC patients, between SCC and Adeno-Ca patients, and between NSCLC patients with and without lymph node metastasis. The area under the receiver-operating characteristic curve (AUC) was used to evaluate the diagnostic performance of each parameter. Spearman rank correlation was used to clarify the associations between ECV and DCE-MRI-derived parameters. Results: Ktrans, Kep, Ve, and ECV all performed well in differentiating SCLC from NSCLC (AUC > 0.729). Ktrans showed the best performance in differentiating SCC from Adeno-Ca (AUC = 0.836). ECV could differentiate NSCLCs with and without lymph node metastases (AUC = 0.764). ECV showed a significant positive correlation with both Ktrans and Ve. Conclusions: Ktrans is the most promising imaging parameter to differentiate SCLC from NSCLC, and Adeno-Ca from SCC. ECV was helpful in detecting lymph node metastasis in NSCLC. These imaging parameters may help guide the selection of lung cancer treatment.

Dynamic Contrast-Enhanced MRI Assessing Antifibrotic Therapeutic Effects of Pancreatic Fibrosis with Curcumin - An Experimental Study at 11.7 T

RATIONALE AND OBJECTIVES

Pancreatic fibrosis is the hallmark of chronic pancreatitis (CP), which is associated with microcirculatory disturbance. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess the perfusion and permeability of the pancreas by providing information about microcirculation. We hypothesize that DCE-MRI parameters can be utilized to assess pancreatic fibrosis and may furthermore provide an opportunity to evaluate response to antifibrotic treatment with curcumin. Our study was to evaluate the feasibility of quantitative DCE-MRI in assessing pancreatic fibrosis and the antifibrotic effect of curcumin in a rat model of CP.

MATERIALS AND METHODS

Pancreatic fibrosis was induced by injecting dibutyltin dichloride (DBTC). Seventy rats were randomized to five groups: the control group (n = 10); DBTC for 2 weeks (n = 15); DBTC for 4 weeks (n = 15); DBTC + curcumin for 2 weeks (n = 15); DBTC + curcumin for 4 weeks (n = 15). DCE-MRI was performed at an 11.7 T MR scanner. DCE-MRI quantitative parameters (Ktrans, Ve, and Vp) were derived from an extended Tofts model. Fibrosis content and DCE-MRI parameters were compared among the above groups (one-way analysis of variance). The correlations between DCE-MRI parameters and pancreatic fibrosis content as well as the expression of α-SMA were computed by Spearman correlation coefficients.

RESULTS

Fifty-three rats survived and underwent MR imaging. Ktrans in rats 4 weeks after DBTC injection was significantly lower than DBTC 2 weeks rats and control rats (0.30 ± 0.06 min vs 0.49 ± 0.09 vs 0.62 ± 0.09, respectively). Vp in DBTC 4 weeks rats was also significantly lower than control rats (0.048 ± 0.010 min-1 vs 0.065 ± 0.011 min-1, respectively). Ktrans and Vp significantly correlated with fibrosis content of pancreas (r = -0.619 and -0.450, all P < 0.001), and the expression of α-SMA (r = -0.688 and -0.402, all P < 0.01). Ktrans and Vp in rats with daily curcumin treatment for 4 weeks were significantly higher than DBTC 4 weeks rats (Ktrans, 0.51 ± 0.09 vs 0.30 ± 0.06; Vp, 0.064 ± 0.015 vs 0.048 ± 0.010).

CONCLUSION

DCE-MRI parameters (Ktrans and Vp) have the potential to noninvasively assess pancreatic fibrosis and the antifibrotic treatment response of curcumin.

Dynamic contrast-enhanced MRI and sequential CT findings of metaplastic breast carcinoma in neurofibromatosis type 1: A case report

Metaplastic breast carcinoma in neurofibromatosis type 1 is extremely rare. There are few reports about dynamic contrast-enhanced MRI findings and sequential CT findings of metaplastic breast carcinoma in neurofibromatosis type 1. Herein, we report imaging findings, including dynamic contrast-enhanced MRI and sequential CT, of metaplastic breast carcinoma in an 82-year-old woman with neurofibromatosis type 1. Short tau inversion recovery image revealed an oval mass with a circumscribed margin that exhibited moderate intensity with partially hyperintense area inside, and T1-weighted imaging revealed a spotty hyperintense area. The solid component of the mass showed heterogeneous enhancement and the time-intensity curve had a fast/washout pattern with restricted diffusion. In addition, multiple neurofibromas were observed. Sequential CT revealed that the diameter of the mass doubled in 3 months without apparent lymph node metastasis. Because detection of metaplastic breast carcinoma in neurofibromatosis type 1 tends to be delayed due to multiple neurofibromas, characteristic MRI findings suggestive of metaplastic breast carcinoma and sequential CT findings are important for early treatment of metaplastic breast carcinoma in patients with neurofibromatosis type 1.

Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI

BACKGROUND

Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC.

METHODS

This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (K^trans, K_ep and V_e) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived.

RESULTS

The binary logistic regression analysis revealed that K_{ep_Range} (p = 0.032) and V_{e_SumVariance} (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of K_{ep_Compactness2} (p = 0.049), K_{ep_SphericalDisproportion} (p = 0.049), and V_{e_GlcmEntropy} (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group.

CONCLUSION

Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.

Effect of pharmacologic anti-atherosclerotic therapy on carotid intraplaque neovascularization: A systematic review

Intraplaque neovascularization (IPN), a key feature of vulnerable carotid plaque, is associated with adverse cardiovascular (CV) events. Statin therapy has been shown to diminish and stabilize atherosclerotic plaque, but its effect on IPN is uncertain. This review investigated the effects of common pharmacologic anti-atherosclerotic therapies on carotid IPN. Electronic databases (MEDLINE, EMBASE and Cochrane Library) were searched from inception until July 13, 2022. Studies evaluating the effect of anti-atherosclerotic therapy on carotid IPN among adults with carotid atherosclerosis were included. Sixteen studies were eligible for inclusion. Contrast-enhanced ultrasound (CEUS) was the most common IPN assessment modality (n=8), followed by dynamic contrast-enhanced MRI (DCE-MRI) (n=4), excised plaque histology (n=3) and superb microvascular imaging (n=2). In fifteen studies, statins were the therapy of interest and one study assessed PCSK9 inhibitors. Among CEUS studies, baseline statin use was associated with a lower frequency of carotid IPN (median OR = 0.45). Prospective studies showed regression of IPN after 6-12 months of lipid-lowering therapy, with more regression observed in treated participants compared to untreated controls. Our findings suggest that lipid-lowering therapy with statins or PCSK9 inhibitors is associated with IPN regression. However, there was no correlation between change in IPN parameters and change in serum lipids and inflammatory markers in statin-treated participants, so it is unclear whether these factors are mediators in the observed IPN changes. Lastly, this review was limited by study heterogeneity and small sample sizes, so larger trials are needed to validate findings.

High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4

BACKGROUND

BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions.

METHODS

This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including K^trans, K_ep, V_e, and V_p, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_K^trans, K_ep, and V_p; Peri_K^trans, K_ep, and V_p) and from L_DCE-MRI (Lesion_K_ep, Peri_V_p, BPE_K^trans and BPE_V_p) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_K^trans (AUC = 0.866), Lesion_K_ep (AUC = 0.929), Lesion_V_p (AUC = 0.872), Peri_K^trans (AUC = 0.733), Peri_K_ep (AUC = 0.810), and Peri_V_p (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_K_ep (AUC = 0.767), Peri_V_p (AUC = 0.726), and BPE_K^trans and BPE_V_p (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_K_ep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_K_ep in the H_DCE-MRI group and the senior radiologist (P = 0.04).

CONCLUSIONS

Pharmacokinetic parameters (K^trans, K_ep and V_p) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional K_ep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.

Bone marrow MR perfusion imaging and potential for tumor evaluation

The physiology of bone perfusion is reviewed outlining how it can be measured with dynamic contrast-enhanced MRI as well as intravoxel incoherent imaging. Evaluation of bone perfusion provides a potential means of assessing tumor activity and treatment response beyond that possible with standard MR imaging.

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models

OBJECTIVES

Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI.

MATERIALS AND METHODS

Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models.

RESULTS

Radiomics analysis showed that T2-W images were more effective in PCa detection compare to DCE images. Local binary pattern features and speeded up robust features had the highest ability for prediction in T2-W and DCE images, respectively. The classifier fusion using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100%.

DISCUSSION

The findings of this framework provide researchers on PCa with a promising method for reliable detection of prostate lesions in MR images by fused model.

Erdheim-Chester disease: diffusion-weighted imaging and dynamic contrast-enhanced MRI provide useful information

This is, to our knowledge, the first case report with in-depth analysis of bone marrow and bone lesions with diffusion-weighted imaging and dynamic contrast-enhanced MRI in Erdheim-Chester disease to date. We present a case of a 70-year-old woman who was referred for an X-ray of the pelvis, right femur and right knee after complaints of migratory arthralgia in hip and knee five months after an initial hip and knee trauma. Bone lesions on X-ray were identified. This case report highlights the strength and complementary use of modern multimodality multiparametric imaging techniques in the clinical radiological manifestations of Erdheim-Chester disease, in the differential diagnosis and in treatment response assessment, which is classically performed using ¹⁸FDG PET-CT. Erdheim-Chester disease is a rare form of non-Langerhans' cell histiocytosis, mainly affecting individuals in their fifth-seventh decade of life and without sex predominance. Apart from the typical bilateral symmetric lesions in long bone diaphyseal and metaphyseal regions and classically sparing the epiphyses, this multisystemic disease causes significant morbidity by infiltrating critical organs (the central nervous system, cardiovascular system, retroperitoneum, lungs and skin). With non-traumatic bone pain being the most common complaint, Erdheim-Chester disease is diagnosed most often in an incidental setting on imaging. The imaging workup classically consists of a multimodality approach using conventional radiography, CT, MRI, bone scintigraphy and ¹⁸FDG PET-CT. This case report extends this evaluation with diffusion-weighted imaging and dynamic contrast-enhanced imaging techniques.

Changes in hippocampal volume during a preceding 10-year period do not correlate with cognitive performance and hippocampal blood‒brain barrier permeability in cognitively normal late-middle-aged men

Hippocampal blood-brain barrier (BBB) permeability may increase in normal healthy ageing and contribute to neurodegenerative disease. To examine this hypothesis, we investigated the correlation between blood-brain barrier (BBB) permeability, regional brain volume, memory functions and health and lifestyle factors in The Metropolit 1953 Danish Male Birth Cohort. We used dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with a gadolinium-based contrast agent to assess BBB permeability in 77 participants in the cohort. BBB permeability was measured as K_i values in the hippocampus, thalamus and white matter. Over a 10-year period, we observed progressive atrophy of both the left and right hippocampus (p = 0.001). There was no significant correlation between current BBB permeability and hippocampal volume, prior atrophy or cognition. The hippocampus volume ratio was associated with better visual and verbal memory scores (p < 0.01). Regional BBB differences revealed higher K_i values in the hippocampus and white matter than in the thalamus (p < 0.001). Participants diagnosed with type II diabetes had significantly higher BBB permeability in the white matter (p = 0.015) and thalamus (p = 0.016), which was associated with a higher Fazekas score (p = 0.024). We do not find evidence that BBB integrity is correlated with age-related hippocampal atrophy or cognitive functions. The association between diabetes, white matter hyperintensities and increased BBB permeability is consistent with the idea that cerebrovascular disease compromises BBB integrity. Our findings suggest that the hippocampus is particularly prone to age-related atrophy, which may explain some of the cognitive changes that accompany older age, but this prior atrophy is not correlated with current BBB permeability.

Limited value of multiparametric MRI with dynamic contrast-enhanced and diffusion-weighted imaging in non-mass enhancing breast tumors

PURPOSE

To investigate the diagnostic value of multiparametric MRI (mpMRI) including dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in non-mass enhancing breast tumors.

METHOD

Patients who underwent mpMRI, who were diagnosed with a suspicious non-mass enhancement (NME) on DCE-MRI (BI-RADS 4/5), and who subsequently underwent image-guided biopsy were retrospectively included. Two radiologists independently evaluated all NMEs, on both DCE-MR images and high-b-value DW images. Different mpMRI reading approaches were evaluated: 1) with a fixed apparent diffusion coefficient (ADC) threshold (<1.3 malignant, ≥1.3 benign) based on the recommendation by the European Society of Breast Imaging (EUSOBI); 2) with a fixed ADC threshold (<1.5 malignant, ≥1.5 benign) based on recently published trial data; 3) with an ADC threshold adapted to the assigned BI-RADS classification using a previously published reading method; and 4) with individually determined best thresholds for each reader.

RESULTS

The final study sample consisted of 66 lesions in 66 patients. DCE-MRI alone had the highest sensitivity for breast cancer detection (94.8-100 %), outperforming all mpMRI reading approaches (R1 74.4-87.1 %, R2 71.7-94.8 %) and DWI alone (R1 74.4 %, R2 79.4 %). The adapted approach achieved the best specificity for both readers (85.1 %), resulting in the best diagnostic accuracy for R1 (86.5 %) but a moderate diagnostic accuracy for R2 (77.2 %).

CONCLUSION

mpMRI has limited added diagnostic value to DCE-MRI in the assessment of NME.

Dynamic contrast enhanced MRI for characterization of blood-brain-barrier dysfunction after traumatic brain injury

BACKGROUND AND PURPOSE

Dysfunction of the blood-brain-barrier (BBB) is a recognized pathological consequence of traumatic brain injury (TBI) which may play an important role in chronic TBI pathophysiology. We hypothesized that BBB disruption can be detected with dynamic contrast-enhanced (DCE) MRI not only in association with focal traumatic lesions but also in normal-appearing brain tissue of TBI patients, reflecting microscopic microvascular injury. We further hypothesized that BBB integrity would improve but not completely normalize months after TBI.

MATERIALS AND METHODS

DCE MRI was performed in 40 adult patients a median of 23 days after hospitalized TBI and in 21 healthy controls. DCE data was analyzed using Patlak and linear models, and derived metrics of BBB leakage including the volume transfer constant (Ktrans) and the normalized permeability index (NPI) were compared between groups. BBB metrics were compared with focal lesion distribution as well as with contemporaneous measures of symptomatology and cognitive function in TBI patients. Finally, BBB metrics were examined longitudinally among 18 TBI patients who returned for a second MRI a median of 204 days postinjury.

RESULTS

TBI patients exhibited higher mean Ktrans (p = 0.0028) and proportion of suprathreshold NPI voxels (p = 0.001) relative to controls. Tissue-based analysis confirmed greatest TBI-related BBB disruption in association with focal lesions, however elevated Ktrans was also observed in perilesional (p = 0.011) and nonlesional (p = 0.044) regions. BBB disruption showed inverse correlation with quality of life (rho = -0.51, corrected p = 0.016). Among the subset of TBI patients who underwent a second MRI several months after the initial evaluation, metrics of BBB disruption did not differ significantly at the group level, though variable longitudinal changes were observed at the individual subject level.

CONCLUSIONS

This pilot investigation suggests that TBI-related BBB disruption is detectable in the early post-injury period in association with focal and diffuse brain injury.

Analysis of Volumetric Perfusion Quantitative Parameters Using CS-VIBE Breast Dynamic Contrast Enhanced MR Imaging

PURPOSE

To evaluate the diagnostic performance of three-dimensional volume of interest (3D-VOI) perfusion quantitative parameters using CS-VIBE DCE-MRI, and investigate the relationship of the prognostic factors.

PATIENTS AND METHODS

The volumetric perfusion quantitative parameters of Ktrans, Kep, Ve, Vp, of 124 pathologically proven breast masses in 93 patients were obtained using the two-compartment extended Tofts model. Also, the perfusion parameters of AUC, TTP, Emax, wash-in, and washout were automatically calculated using post-processing software. The relationship between the perfusion quantitative parameters and lesion size, pathology, and prognostic factors of malignancy was evaluated.

RESULTS

Ktrans and Kep were significantly higher in the malignant than the benign lesions (p < 0.001), and the AUROC of Ktrans and Kep were 0.802 and 0.815, respectively. The area under DCE curve, TTP, Emax, wash-in, and wash-out were significantly different between the benign and malignant lesion (p < 0.05). In multiple linear regression analysis, Ktrans and Kep were significantly different between benign and malignant tumors. Malignant tumors larger than 2cm were significantly different from those smaller than 2cm in Ktrans, Kep, Vp, area under DCE curve, TTP, Emax, and wash-in values (p < 0.05). TTP was significantly lower in higher Ki-67 index (p < 0.05).

CONCLUSION

Perfusion quantitative parameters may be applied as a feasible imaging biomarker to discriminate malignant from benign tumors. In malignant lesions, perfusion parameters were not associated with histopathological results, but only in tumor size.

The role of MRI in prostate cancer: current and future directions

There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI

PURPOSE

To investigate if the pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics machine learning predicts the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

Seventy-eight breast cancer patients who underwent DCE-MRI before NAC and confirmed as pCR or non-pCR were enrolled. Early enhancement mapping images of pretreatment DCE-MRI were created using subtraction formula as follows: Early enhancement mapping = (Signal _{1 min} - Signal _pre)/Signal _pre. Images of the whole tumors were manually segmented and radiomics features extracted. Five prediction models were built using five scenarios that included clinical information, subjective radiological findings, first order texture features, second order texture features, and their combinations. In texture analysis workflow, the corresponding variables were identified by mutual information for feature selection and random forest was used for model prediction. In five models, the area under the receiver operating characteristic curves (AUC) to predict the pCR and several metrics for model evaluation were analyzed.

RESULTS

The best diagnostic performance based on F-score was achieved when both first and second order texture features with clinical information and subjective radiological findings were used (AUC = 0.77). The second best diagnostic performance was achieved with an AUC of 0.76 for first order texture features followed by an AUC of 0.76 for first and second order texture features.

CONCLUSIONS

Pretreatment DCE-MRI can improve the prediction of pCR in breast cancer patients when all texture features with clinical information and subjective radiological findings are input to build the prediction model.

Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI

This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF-the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of [Formula: see text] named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference-that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors-combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching-learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs.

Multiparametric evaluation of bone tumors utilising diffusion weighted imaging and dynamic contrast enhanced magnetic resonance imaging

AIM

This study aimed to use multiparametric magnetic resonance imaging (MRI) techniques, namely, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate bone tumors.

METHODS

Thirty-three patients with primary untreated bone tumors were assessed utilizing DWI and DCE-MRI. Various parameters like ADC values from DWI and percentage peak signal intensity (%PSI), the maximum slope of increase (MSI), and time to peak signal intensity (TTP) values were assessed in different cases, and the final correlation was drawn with histopathological findings.

RESULT

Parameters of semi-quantitative DCE-MRI, i.e., %PSI, MSI and, TTP, correlated significantly with the histopathological characteristics of the tumor (p values < 0.001). Minimum ADC value in the tumor also showed a strong correlation with the tumor characteristic (p values < 0.001). Also, the correlation between parameters of DWI and DCI-MRI is well correlated with each other.

CONCLUSION

The results of this study provide grounds for the integration of multiparametric pre-treatment evaluation of bone tumors. In our study, we not only tried to utilize different parameters of functional MRI in bone tumors as well as re-explored the semi-quantitative analysis of DCE-MRI.

Endometrial carcinoma: use of tracer kinetic modeling of dynamic contrast-enhanced MRI for preoperative risk assessment

Background

To compare two tracer kinetic models in predicting of preoperative risk types in endometrial carcinoma (EC) using DCE-MRI.

Methods

A prospective study of patients with EC was conducted with institutional ethics approval and written informed consent. DCE-MRI data was analyzed using the extended Tofts (ET) and the distributed parameter (DP) models. DCE parameters blood flow (F), mean transit time, blood volume (Vp), extravascular extracellular volume (Ve), permeability surface area product (PS), extraction fraction, transfer constant (Ktrans), and efflux rate (Kep) between high- and low-risk EC were compared using the Mann–Whitney test. Bland–Altman analysis was utilized to compare parameter consistency and Spearman test to assess parameter correlation. Diagnostic performance of DCE parameters was analyzed by receiver-operating characteristic curve and compared with traditional MRI assessment.

Results

Fifty-one patients comprised the study group. Patients with high-risk EC exhibited significantly lower Ktrans, Kep, F, Vp and PS (P < 0.001). ET-derived Ktrans and DP-derived F attained AUC of 0.92 and 0.91, respectively. Bland–Altman analysis showed that the consistency of Ve or Vp between the two models was low (P < 0.001) while Spearman test showed a strong correlation (r = 0.719, 0.871). Both Ktrans and F showed higher accuracy in predicting EC risk types than traditional MRI assessment.

Conclusions

Kinetic parameters derived from DCE-MRI revealed a more hypovascular microenvironment for high risk EC than to low- risk ones, providing potential imaging biomarkers in preoperative risk assessment that might improve individualized surgical planning and management of EC.

Quantitative evaluation of simultaneous spatial and temporal regularization in dynamic contrast-enhanced MRI of the liver using Gd-EOB-DTPA

The purpose of this study was to quantitatively evaluate the usefulness of simultaneous spatial and temporal regularization using total variation (TV), total generalized variation (TGV), a combination of low-rank decomposition (LRD) and TV (LRD+TV), a combination of LRD and TGV (LRD+TGV), and nuclear norm (NN) when applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in rats with concanavalin A (ConA)-induced acute hepatic injury. The rats were divided into three groups: normal control (NC) (n = 10), ConA10 (n = 8), and ConA20 (n = 7). Rats in the ConA10 and ConA20 groups were intravenously injected with 10 and 20 mg/kg of ConA, respectively; those in the NC group were intravenously injected with the same volume of saline. DCE-MRI studies were performed using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA; 0.025 mmol Gd/kg) as a contrast agent (CA), 24 h after the ConA or saline injection. After the DCE-MRI study, we generated zero-filled and undersampled k-space data from the original images using a pseudoradial sampling scheme with 4 to 64 spokes. We subsequently reconstructed images from these data using the above regularizers and calculated the signal-to-error ratio (SER_img) and structural similarity index (SSIM) using the original and reconstructed images. We also calculated the area under the curve (AUC), rate of CA washout (λ_w), maximum relative enhancement (RE_max), and time to RE_max (T_max) from time-intensity curves using an empirical mathematical model (EMM) and the signal-to-error ratio for curve fitting (SER_fit) from the original and fit curves. We also compared the parameters obtained using the pseudoradial and Cartesian sampling schemes in the NC group. When using LRD+TV and LRD+TGV, both SER_img and SSIM were greater than those for the other regularizers at all spoke numbers studied; the SER_fit for TGV was the greatest. When using TGV and LRD+TGV, in the majority of cases the AUCs did not significantly differ from those obtained from the original images, whereas those for LRD+TV and NN were significantly less at several spoke numbers. The λ_w for NN was significantly greater at numerous spoke numbers in the NC group; the RE_max values for LRD+TV and NN were significantly less at several spoke numbers in all groups. The T_max values for TV, TGV, and LRD+TGV were significantly greater at numerous spoke numbers in the NC group. Although there were significant differences in SER_img and SSIM between the pseudoradial and Cartesian sampling schemes, the kinetic parameters obtained by the EMM did not significantly differ between the two sampling schemes, with certain exceptions. In conclusion, our results suggest that simultaneous spatial and temporal regularization using TGV or LRD+TGV is useful for accelerating DCE-MRI without significant reduction in the accuracy of the kinetic parameter estimation, even at extremely low sampling factors.

Quantitative analysis of macroscopic solute transport in the murine brain

Background

Understanding molecular transport in the brain is critical to care and prevention of neurological disease and injury. A key question is whether transport occurs primarily by diffusion, or also by convection or dispersion. Dynamic contrast-enhanced (DCE-MRI) experiments have long reported solute transport in the brain that appears to be faster than diffusion alone, but this transport rate has not been quantified to a physically relevant value that can be compared to known diffusive rates of tracers.

Methods

In this work, DCE-MRI experimental data is analyzed using subject-specific finite-element models to quantify transport in different anatomical regions across the whole mouse brain. The set of regional effective diffusivities (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{eff}$$\end{document}Deff), a transport parameter combining all mechanisms of transport, that best represent the experimental data are determined and compared to apparent diffusivity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{app}$$\end{document}Dapp), the known rate of diffusion through brain tissue, to draw conclusions about dominant transport mechanisms in each region.

Results

In the perivascular regions of major arteries, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{eff}$$\end{document}Deff for gadoteridol (550 Da) was over 10,000 times greater than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{app}$$\end{document}Dapp. In the brain tissue, constituting interstitial space and the perivascular space of smaller blood vessels, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{eff}$$\end{document}Deff was 10–25 times greater than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{app}$$\end{document}Dapp.

Conclusions

The analysis concludes that convection is present throughout the brain. Convection is dominant in the perivascular space of major surface and branching arteries (Pe > 1000) and significant to large molecules (> 1 kDa) in the combined interstitial space and perivascular space of smaller vessels (not resolved by DCE-MRI). Importantly, this work supports perivascular convection along penetrating blood vessels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12987-021-00290-z.

MRI and CT features of a malignant myoepithelioma of the scrotum: A case report and literature review

Malignant myoepithelioma of the scrotum is extremely rare. We report the case of a 51-year-old man with malignant myoepithelioma of the scrotum, wherein computed tomography and magnetic resonance imaging revealed a lobulated soft tissue mass with calcification, cystic component, and solid component with gradual contrast enhancement on dynamic contrast-enhanced scans. The patient presented with scrotal induration, and there was no elevation of tumor markers and no evidence of a metastatic lesion on computed tomography and magnetic resonance imaging. Histopathological examination of the resected scrotal specimen confirmed a well-circumscribed solid tumor with septa, a small area of hemorrhage, and necrosis. The subsequent diagnosis was malignant myoepithelioma of the scrotum. This case shows that scrotal malignant myoepithelioma might appear as a well-defined lobulated mass with cystic regions. We conjecture that the enhancement pattern and apparent diffusion coefficient values can be potential markers for scrotal myoepithelial tumors.

Operator dependency of arterial input function in dynamic contrast-enhanced MRI

Objective

To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas.

Methods

The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in K^trans was assessed by Fleiss’ kappa (κ).

Results

There was a moderate to substantial agreement (ICC 0.51–0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77–0.95 for K^trans, k_ep and v_e and ICC 0.70 for v_p. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in K^trans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement.

Discussion

AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

Imaging biomarkers in the diagnosis of salivary gland tumors: the value of lesion/parenchyma ratio of perfusion-MR pharmacokinetic parameters

BACKGROUND AND PURPOSE

Morphologic magnetic resonance imaging (MRI) for characterization of salivary gland tumors has limited utility, and the use of perfusion MRI data in the clinical setting is controversial. We examined the potential of tissue-normalized dynamic contrast-enhanced (DCE) MRI pharmacokinetic parameters of salivary gland tumors as imaging biomarkers for characterization and differentiation between benign and malignant lesions.

MATERIALS AND METHODS

DCE-MR images acquired from 60 patients with parotid and submandibular gland tumors were retrospectively reviewed. Pharmacokinetic parameters as transfer constant (Ktrans), rate constant (Kep), extracellular space volume (Ve), fractional plasma volume (Vp), and AEC (area of all times enhancement curve) were measured on both the lesion and the normal contralateral salivary gland parenchyma. Lesion/parenchyma ratio (L/P) for each parameter was calculated.

RESULTS

Five groups of lesions were identified (reference: histopathology): pleomorphic adenomas(n = 20), Warthin tumors(n = 16), other benign entities(n = 4), non-Hodgkin lymphomas(n = 4), and malignancies(n = 16). Significant differences were seen for mean values of L/PKtrans (higher in malignancies), L/PKep (lower in adenomas than Warthin tumors), L/PVe (lower in Warthin tumors and lymphomas), L/PVp (higher in Warthin tumors and malignancies than adenomas), and L/PAEC (higher in malignancies). Significant differences were found between benign and malignant (non-lymphoproliferative) lesions in mean value of L/PKtrans (0.485 and 1.581), L/PVp (1.288 and 2.834), and L/PAEC (0.682 and 1.910). ROC analysis demonstrated the highest AUC (0.96) for L/PAEC, with sensitivity and specificity for malignancy of 93.8% and 97.5% (cutoff value = 1.038).

CONCLUSION

Lesion/parenchyma ratio of DCE-MRI pharmacokinetic data could be helpful for recognizing the principal types of salivary gland tumors; L/PAEC seems a valuable biomarker for differentiating benign from malignant tumors.

Motion correction of free-breathing magnetic resonance renography using model-driven registration

INTRODUCTION

Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR).

MATERIALS AND METHODS

MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data).

RESULTS

DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HD_unregistered = 9.98 ± 9.76, HD_registered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses.

DISCUSSION

MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.

Novel Thoracic MRI Approaches for the Assessment of Pulmonary Physiology and Inflammation

Excessive pulmonary inflammation can lead to damage of lung tissue, airway remodelling and established structural lung disease. Novel therapeutics that specifically target inflammatory pathways are becoming increasingly common in clinical practice, but there is yet to be a similar stepwise change in pulmonary diagnostic tools. A variety of thoracic magnetic resonance imaging (MRI) tools are currently in development, which may soon fulfil this emerging clinical need for highly sensitive assessments of lung structure and function. Given conventional MRI techniques are poorly suited to lung imaging, alternate strategies have been developed, including the use of inhaled contrast agents, intravenous contrast and specialized lung MR sequences. In this chapter, we discuss technical challenges of performing MRI of the lungs and how they may be overcome. Key thoracic MRI modalities are reviewed, namely, hyperpolarized noble gas MRI, oxygen-enhanced MRI (OE-MRI), ultrashort echo time (UTE) MRI and dynamic contrast-enhanced (DCE) MRI. Finally, we consider potential clinical applications of these techniques including phenotyping of lung disease, evaluation of novel pulmonary therapeutic efficacy and longitudinal assessment of specific patient groups.

Predictive value of T2-weighted imaging and dynamic contrast-enhanced MRI for assessing cervical invasion in patients with endometrial cancer: a meta-analysis

PURPOSE

To obtain the diagnostic accuracy of T2-weighted imaging (T2WI), and dynamic contrast-enhanced MRI (DCE-MRI) in the preoperative assessment of cervical invasion in patients with endometrial cancer (EC).

METHODS

Databases including PubMed, Embase, Cochrane Library, Web of Science, and Clinical Trials were searched for relevant articles published from January 2000 to August 2020. Pooled estimation data were obtained by statistical analysis.

RESULTS

In total, 24 articles were included. For assessing cervical invasion of EC, the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) for T2WI were 0.70 (0.61-0.77), 0.92 (0.89-0.94), 8.7 (6.5-11.6), 0.33 (0.25-0.43), 26 (17-41), and 0.92 (0.89-0.94), respectively. For DCE-MRI, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.75 (0.60-0.85), 0.95 (0.89-0.98), 14.7 (6.6-32.9), 0.27 (0.16-0.44), 55 (18-165), and 0.92 (0.89-0.94), respectively; for T2WI combined with DCE-MRI, they were 0.58 (0.41-0.73), 0.98 (0.95-0.99), 28.1 (12.8-62.1), 0.43 (0.30-0.63), 65 (29-146), and 0.94 (0.91-0.96), respectively.

CONCLUSIONS

DCE-MRI demonstrated higher diagnostic performance than T2WI in the prediction of cervical invasion in patients with EC. T2WI combined with DCE-MRI improved the pooled specificity, PLR, DOR, and AUC compared to T2WI alone or DCE-MRI alone.

Application of T1 Map Information Based on Synthetic MRI for Dynamic Contrast-Enhanced Imaging: A Comparison Study with the Fixed Baseline T1 Value Method

Objective

For an accurate dynamic contrast-enhanced (DCE) MRI analysis, exact baseline T1 mapping is critical. The purpose of this study was to compare the pharmacokinetic parameters of DCE MRI using synthetic MRI with those using fixed baseline T1 values.

Materials and Methods

This retrospective study included 102 patients who underwent both DCE and synthetic brain MRI. Two methods were set for the baseline T1: one using the fixed value and the other using the T1 map from synthetic MRI. The volume transfer constant (K^trans), volume of the vascular plasma space (v_p), and the volume of the extravascular extracellular space (v_e) were compared between the two methods. The interclass correlation coefficients and the Bland-Altman method were used to assess the reliability.

Results

In normal-appearing frontal white matter (WM), the mean values of K^trans, v_e, and v_p were significantly higher in the fixed value method than in the T1 map method. In the normal-appearing occipital WM, the mean values of v_e and v_p were significantly higher in the fixed value method. In the putamen and head of the caudate nucleus, the mean values of K^trans, v_e, and v_p were significantly lower in the fixed value method. In addition, the T1 map method showed comparable interobserver agreements with the fixed baseline T1 value method.

Conclusion

The T1 map method using synthetic MRI may be useful for reflecting individual differences and reliable measurements in clinical applications of DCE MRI.

Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol. 2021 epub ahead of print. [PMID: 33839938 DOI: 10.1007/s00432-021-03617-3]

PURPOSE

Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient's prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters.

METHODS

HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction.

RESULTS

Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients.

CONCLUSION

The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient's prognosis.

Quantitative kinetic parameters of primary tumor can be used to predict pelvic lymph node metastasis in early-stage cervical cancer

PURPOSE

To investigate the role of kinetic parameters of primary tumor derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer.

METHODS

66 women with newly diagnosed cervical cancer were included between July 2017 and August 2019. All patients had a FIGO stage IB-IIA cancer and treated with hysterectomy and bilateral lymphadenectomy. Kinetic parameters of the primary tumor were derived from DCE-MRI data. The tumor diameter, ADC value, kinetic parameters, and nodal short-axis diameter were compared between patients with or without PLNM. Logistic regression analysis was used to determine the independent predictors for PLNM and receiver operator characteristic curve was used to evaluate the predictive performance.

RESULTS

There were 20 patients with PLNM and 46 patients without PLNM. Tumor diameter, the efflux rate constant (K_ep), and nodal short-axis diameter were significantly higher in patients with PLNM (P < 0.01). Multivariate logistic regression analysis showed that K_ep and short-axis diameter were independent predictors for PLNM. Combining K_ep and nodal short-axis diameter yielded the highest area under the curve (AUC) of 0.839. Combined with K_ep, the sensitivity, specificity, negative predictive value, and positive predictive value of nodal short-axis diameter increased from 0.500, 0.957, 0.815, and 0.833 to 0.600, 0.978, 0.923, and 0.849, respectively. With 1.113 min^-1 as threshold, the sensitivity and specificity values of K_ep in predicting PLNM in patients with normal-sized lymph nodes were 0.909 and 0.667, respectively.

CONCLUSIONS

K_ep of primary tumor can be used as a surrogate marker to predict PLNM in cervical cancer.

Qualitative characterization of breast tumors with diffusion-weighted imaging has comparable accuracy to quantitative analysis

PURPOSE

To evaluate the applicability and accuracy of a new qualitative diffusion-weighted imaging (DWI) assessment method in the characterization of breast tumors compared to quantitative ADC measurement and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

After review board approval, MRIs of 216 consecutive women with final diagnoses (131 malignant, 85 benign) were retrospectively analyzed. Two radiologists independently scored DWI and dynamic contrast-enhanced MRI (DCE-MRI) according to malignancy probability. Qualitative assessments were performed by combined analysis of tumor morphology and diffusion signal. Quantitative data was obtained from apparent diffusion coefficient (ADC) measurements. Lastly, descriptive DWI features were evaluated and recorded. Cohen's kappa, receiver operating characteristic and multivariate analyzes were applied.

RESULTS

Of malignant tumors, 97% were visible on DWI. Qualitative and quantitative DWI assessments provided comparable sensitivities of 89-94% and 88-92% and specificities of 51-61% and 59-67%, respectively. There was no statistical difference between the accuracies of qualitative and quantitative DWI (p ≥ 0.105). Best diagnostic values were obtained with DCE-MRI (sensitivity, 99-100%; specificity, 69-71%). Inter-reader agreement was moderate (kappa = 0.597) for qualitative DWI and substantial (kappa = 0.689) for DCE-MRI (p < 0.001). Agreement between qualitative DWI and DCE-MRI scores was moderate (kappa = 0.536 and 0.442). Visual diffusion signal, mass margin and shape were the most predictive features of malignancy on multivariate analysis of qualitative assessment.

CONCLUSION

Qualitative characterization of breast tumors on DWI has comparable accuracy to quantitative ADC analysis. This method might be used to make DWI more widely available with eliminating the need to a predetermined ADC threshold in tumor characterization. However, lower accuracy and inter-reader agreement of it compared to DCE-MRI should be considered.

Magnetic resonance imaging findings of a myxoid leiomyosarcoma of the uterus: A case report and literature review

Uterine myxoid leiomyosarcomas (MLMSs) are extremely rare. Here, we report a rare case of uterine MLMS with unique and bizarre magnetic resonance imaging (MRI) findings on diffusion-weighted images (DWIs) and dynamic contrast-enhanced (DCE) MRI scans. A 67-year-old woman presented with a uterine MLMS that had a multilocular cystic mass with a septum and solid components. The tumour demonstrated marked hyperintensity on T2-weighted images in a myxoid stroma with gradual partial contrast enhancement and diffusion restriction, which could be a characteristic feature suggestive of a myxoid malignant smooth muscle tumour of the uterus rather than a uterine leiomyoma with myxoid degeneration.

Current status of MR imaging of juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is the most common chronic arthropathy in the pediatric population. Although the diagnosis is essentially clinical for many affected joints, MR imaging has become an important tool for the assessment of joints that are difficult to evaluate clinically, such as temporomandibular and sacroiliac joints, and for screening of inflammatory changes in the entire body by whole body MRI (WBMRI) assessment. The utilization of MR imaging is challenging in the pediatric population given the need for discrimination between pathological and physiological changes in the growing skeleton. Several multicentric multidisciplinary organizations have made major efforts over the past decades to standardize, quantify, and validate scoring systems to measure joint changes both cross-sectionally and longitudinally according to rigorous methodological standards. In this paper, we (1) discuss current trends for the diagnosis and management of JIA, (2) review challenges for detecting real pathological changes in growing joints, (3) summarize the current status of standardization of MRI protocols for data acquisition and the quantification of joint pathology in JIA by means of scoring systems, and (4) outline novel MR imaging techniques for the evaluation of anatomy and function of joints in JIA. Optimizing the role of MRI as a robust biomarker and outcome measure remains a priority of future research in this field.

Differentiation of bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone with multiparametric MRI

PURPOSE

To investigate the diagnostic utilities of imaging parameters derived from T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.

MATERIALS AND METHODS

Thirty-six lesions from 36 patients with prostate cancer were analyzed with T1WI, DWI, and DCE-MRI. The lesions were classified in the bone metastases (n = 22) and benign red marrow depositions (n = 14). Lesion-muscle ratio (LMR), apparent diffusion coefficient (ADC), volume transfer constant (K^trans), reflux rate (Kep), and volume fraction of the extravascular extracellular matrix (Ve) values were obtained from the lesions. The imaging parameters of the both groups were compared using the Mann-Whitney U test, receiver operating characteristics (ROC) curves were analyzed. For the ROC curves, area under the curves (AUCs) were compared.

RESULTS

The ADC, K^trans, K_ep, and V_e values of bone metastases were significantly higher than those of benign red marrow depositions (Mann-Whitney U test, p < 0.05). However, there was no significant difference in LMR between the two groups (Mann-Whitney U test, p = 0.360). The AUCs of K^trans_, K_ep, ADC, V_e, and LMR were 0.896, 0.844, 0.812, 0.724, and 0.448, respectively. In the pairwise comparison of ROC curves, the AUCs of K^trans and K_ep was significantly higher than LMR.

CONCLUSIONS

K^trans, K_ep, V_e, and ADC values can be used as imaging tools to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.

Comparative study of imaging and histology of sacroiliac joint in normal rats based on IVIM-DWI and DCE-MRI

Background

Currently, few studies have described the relationship between functional MRI findings and histology of normal sacroiliac joint (SIJ). Besides, due to the difficulties in access to SIJ, authentic animal models are important in providing opportunities for quantitative parameter extraction on imaging.

Aims

This study aimed at exploring the parameters of Intravoxel Incoherent Motion Diffusion-Weighted Imaging (IVIM-DWI) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and comparing them with the histology of the SIJ in normal rats with different ages.

Methods

A total of thirty 7-week-old male Wistar rats were included in the study. The parameters of IVIM-DWI and DCE-MRI in the bone marrow and the joint space of SIJ were measured at 8, 13, 18, 23, 28, and 33 weeks. The histological analysis of the SIJ was examined using light microscopy. One-way ANOVA was used for statistical analysis.

Results

The D values in the sacral and iliac bone marrow of normal rats decreased with an increase in age. One-way ANOVA analysis indicated a significant difference in D values in different age groups (P<0.005). The normal values of D*, f, Fenh (%), Senh (%/s) in the sacral bone marrow, the iliac bone marrow, and the joint space in SIJ of normal rats were obtained. The results showed that in the six groups of rats of different ages, the histology of the SIJ surface was smooth and clear, the cartilage cells were intact, and no thickening or pannus formation was observed.

Conclusions

This study obtained the IVIM-DWI and DCE-MRI parameters of the sacral and iliac bone marrow and the synovial area of the joint space in normal rats. The parameters in normal rats can be used in future research to compare to similar parameters in animal models or patients with SIJ diseases. This study serves as a guide for future research in SIJ diseases.

Increase in blood-brain barrier leakage in healthy, older adults

Blood–brain barrier (BBB) breakdown can disrupt nutrient supply and waste removal, which affects neuronal functioning. Currently, dynamic contrast-enhanced (DCE) MRI is the preferred in-vivo method to quantify BBB leakage. Dedicated DCE MRI studies in normal aging individuals are lacking, which could hamper value estimation and interpretation of leakage rate in pathological conditions. Therefore, we applied DCE MRI to investigate the association between BBB disruption and age in a healthy sample. Fifty-seven cognitively and neurologically healthy, middle-aged to older participants (mean age: 66 years, range: 47–91 years) underwent MRI, including DCE MRI with intravenous injection of a gadolinium-based contrast agent. Pharmacokinetic modeling was applied to contrast concentration time-curves to estimate BBB leakage rate in each voxel. Subsequently, leakage rate was calculated in the white and gray matter, and primary (basic sensory and motor functions), secondary (association areas), and tertiary (higher-order cognition) brain regions. A difference in vulnerability to deterioration was expected between these regions, with especially tertiary regions being affected by age. Higher BBB leakage rate was significantly associated with older age in the white and gray matter, and also in tertiary, but not in primary or secondary brain regions. Even in healthy individuals, BBB disruption was stronger in older persons, which suggests BBB disruption is a normal physiologically aging phenomenon. Age-related increase in BBB disruption occurred especially in brain regions most vulnerable to age-related deterioration, which may indicate that BBB disruption is an underlying mechanism of normal age-related decline.

Netherlands Trial Register number: NL6358, date of registration: 2017-03-24.

Desmoid fibromatosis following surgical resection of spinal meningioma

A 42-year-old female patient with a history of cervicothoracic junction meningioma World Health Organization grade I, resected in early 2011, was admitted to the hospital with intractable headache and lower extremity weakness. Magnetic resonance imaging (MRI) showed an epidural mass compressing the spinal cord at the prior surgical site, which was interpreted as recurrent meningioma. Following surgical resection, histopathological analysis revealed desmoid fibromatosis (desmoid tumor). In retrospect, dynamic contrast-enhanced magnetic resonance imaging performed prior to surgery should have allowed for prospective exclusion of meningioma as the recurrent mass and suggested an alternative diagnosis such as post-traumatic desmoid fibromatosis or the need for biopsy to confirm diagnosis.

Blood-brain barrier imaging as a potential biomarker for bipolar disorder progression

Bipolar disorder affects approximately 2% of the population and is typically characterized by recurrent episodes of mania and depression. While some patients achieve remission using mood-stabilizing treatments, a significant proportion of patients show progressive changes in symptomatology over time. Bipolar progression is diverse in nature and may include a treatment-resistant increase in the frequency and severity of episodes, worse psychiatric and functional outcomes, and a greater risk of suicide. The mechanisms underlying bipolar disorder progression remain poorly understood and there are currently no biomarkers for identifying patients at risk. The objective of this study was to explore the potential of blood-brain barrier (BBB) imaging as such a biomarker, by acquiring the first imaging data of BBB leakage in bipolar patients, and evaluating the potential association between BBB dysfunction and bipolar symptoms. To this end, a cohort of 36 bipolar patients was recruited through the Mood Disorders Clinic (Nova Scotia Health Authority, Canada). All patients, along with 14 control subjects (matched for sex, age and metabolic status), underwent contrast-enhanced dynamic MRI scanning for quantitative assessment of BBB leakage as well as clinical and psychiatric evaluations. Outlier analysis has identified a group of 10 subjects with significantly higher percentages of brain volume with BBB leakage (labeled the "extensive BBB leakage" group). This group consisted exclusively of bipolar patients, while the "normal BBB leakage" group included the entire control cohort and the remaining 26 bipolar subjects. Among the bipolar cohort, patients with extensive BBB leakage were found to have more severe depression and anxiety, and a more chronic course of illness. Furthermore, all bipolar patients within this group were also found to have co-morbid insulin resistance, suggesting that insulin resistance may increase the risk of BBB dysfunction in bipolar patients. Our findings demonstrate a clear link between BBB leakage and greater psychiatric morbidity in bipolar patients and highlight the potential of BBB imaging as a mechanism-based biomarker for bipolar disorder progression.

Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma

AIMS

The aim of the study was to predict and assess treatment response by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to patients with locally advanced esophageal squamous cell carcinoma receiving chemoradiotherapy (CRT).

MATERIALS AND METHODS

Seventy-two patients with locally advanced esophageal squamous cell carcinoma who underwent DCE-MRI before and after chemoradiotherapy were enrolled and divided into the complete response (CR) group and the non-CR group based on RECIST. The histogram parameters (10th percentile, 90th percentile, median, mean, standard deviation, skewness, and kurtosis) of pre-CRT and post-CRT were compared using a paired Student's t test in the CR and non-CR groups, respectively. The histogram parameter differences between the CR and the non-CR groups were compared using an unpaired Student's t test. A receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance.

RESULTS

The histogram parameters of K^trans values were observed to have significantly decreased after chemoradiotherapy in the CR group. The CR responders showed significantly higher median, mean, and 10th and 90th percentile of pre-K^trans values than those of the non-CR group. The histogram analysis indicated the decreased heterogeneity in the CR group after CRT. Esophageal cancer with higher pre-K^trans and lower post-K^trans values indicated a good treatment response to CRT. Pre-K^trans-10th showed the best diagnostic performance in predicting the chemoradiotherapy response.

CONCLUSIONS

The histogram parameters of K^trans are useful in the assessment and prediction of the chemoradiotherapy response in patients with advanced esophageal squamous cell carcinoma. DCE-MRI could serve as an adjunctive imaging technique for treatment planning.

Ultrafast Dynamic Contrast-Enhanced Breast MRI: Kinetic Curve Assessment Using Empirical Mathematical Model Validated with Histological Microvessel Density

RATIONALE AND OBJECTIVES

To evaluate whether parameters from empirical mathematical model (EMM) for ultrafast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) correlate with histological microvessel density (MVD) in invasive breast cancer.

MATERIALS AND METHODS

Ninety-eight consecutive patients with invasive breast cancer underwent an institutional review board-approved ultrafast DCE-MRI including a pre- and 18 postcontrast whole breast ultrafast scans (3 seconds) followed by four standard scans (60 seconds) using a 3T system. Region of interest was placed within each lesion where the highest signal increase was observed on ultrafast DCE-MRI, and the increase rate of enhancement was calculated as follows: ΔS = (SIpost - SIpre)/SIpre. The kinetic curve obtained from ultrafast DCE-MRI was analyzed using a truncated EMM: ΔS(t) = A(1 - e^-αt), where A is the upper limit of the signal intensity, α (min^-1) is the rate of signal increase. The initial slope of the kinetic curve is given by Aα. Initial area under curve (AUC30) and time of initial enhancement was calculated. From the standard DCE-MRI, the initial enhancement rate (IER) and the signal enhancement ratio (SER) were calculated as follows: IER = (SIearly - SIpre)/SIpre, SER = (SIearly - SIpre)/(SIdelayed - SIpre). The parameters were compared to MVD obtained from surgical specimens.

RESULTS

A, α, Aα, AUC30, and time of initial enhancement significantly correlated with MVD (r = 0.29, 0.40, 0.51, 0.43, and -0.32 with p = 0.0027, p < 0.0001, p < 0.0001, p < 0.0001, and p = 0.0012, respectively), whereas IER and SER from standard DCE-MRI did not.

CONCLUSION

The parameters of the EMM, especially the initial slope or Aα, for ultrafast DCE-MRI correlated with MVD in invasive breast cancer.

MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer

INTRODUCTION

Longitudinal monitoring of breast tumor volume over the course of chemotherapy is informative of pathologic response. This study aims to determine whether axillary lymph node (aLN) volume by magnetic resonance imaging (MRI) could augment the prediction accuracy of treatment response to neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Level-2a curated data from the I-SPY-1 TRIAL (2002-2006) were used. Patients had stage 2 or 3 breast cancer. MRI was acquired pre-, during, and post-NAC. A subset with visible aLNs on MRI was identified (N = 132). Prediction of pathologic complete response (PCR) was made using breast tumor volume changes, nodal volume changes, and combined breast tumor and nodal volume changes with sub-stratification with and without large lymph nodes (3 mL or ∼1.79 cm diameter cutoff). Receiver operating characteristic curve analysis was used to quantify prediction performance.

RESULTS

The rate of change of aLN and breast tumor volume were informative of pathologic response, with prediction being most informative early in treatment (area under the curve (AUC), 0.57-0.87) compared with later in treatment (AUC, 0.50-0.75). Larger aLN volume was associated with hormone receptor negativity, with the largest nodal volume for triple negative subtypes. Sub-stratification by node size improved predictive performance, with the best predictive model for large nodes having AUC of 0.87.

CONCLUSION

aLN MRI offers clinically relevant information and has the potential to predict treatment response to NAC in patients with breast cancer.

Association between high-resolution MRI-detected extramural vascular invasion and tumour microcirculation estimated by dynamic contrast-enhanced MRI in rectal cancer: preliminary results

Background

To determine whether magnetic resonance imaging (MRI)-detected extramural vascular invasion (mrEMVI) status is associated with quantitative perfusion parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) in rectal cancer.

Methods

Seventy-two patients with rectal adenocarcinoma who underwent curative surgery alone within 2 weeks following rectal MRI were enrolled in this retrospective study. mrEMVI status was determined based on high-resolution MRI. The quantitative perfusion parameters (K^trans, k_ep and v_e) derived from DCE-MRI were calculated from all sections containing tumours. DCE-MRI parameters and clinicopathological variables in patients with different mrEMVI statuses were compared.

Results

For patients who were mrEMVI positive, the tumours demonstrated significantly lower k_ep values (P = 0.012) and higher v_e values (P = 0.021) than tumours of patients who were mrEMVI negative, while the K^trans value displayed no significant difference (P = 0.390). The patients who were mrEMVI positive had larger tumour size, higher pathological tumour stage and increased regional nodal metastases compared to those who were mrEMVI negative (2.9 cm vs. 3.5 cm, P = 0.011; 63.8% vs. 92.0%, P = 0.010; 36.2% vs. 76.0%, P = 0.001; respectively).

Conclusions

This study demonstrated for the first time that tumour microcirculation is altered in mrEMVI-positive patients with rectal adenocarcinoma, as evidenced by significantly lower k_ep and higher v_e values. In addition, these patients were more likely to have a larger tumour size, a higher pathological tumour stage and regional nodal metastases than mrEMVI-negative patients.