Dynamic contrast-enhanced MRI perfusion for differentiating between melanoma and lung cancer brain metastases

Radiosurgery for Brain Metastases: Challenges in Imaging Interpretation after Treatment

Stereotactic radiosurgery (SRS) has transformed the management of brain metastases by achieving local tumor control, reducing toxicity, and minimizing the need for whole-brain radiation therapy (WBRT). This review specifically investigates radiation-induced changes in patients treated for metastasis, highlighting the crucial role of magnetic resonance imaging (MRI) in the evaluation of treatment response, both at very early and late stages. The primary objective of the review is to evaluate the most effective imaging techniques for assessing radiation-induced changes and distinguishing them from tumor growth. The limitations of conventional imaging methods, which rely on size measurements, dimensional criteria, and contrast enhancement patterns, are critically evaluated. In addition, it has been investigated the potential of advanced imaging modalities to offer a more precise and comprehensive evaluation of treatment response. Finally, an overview of the relevant literature concerning the interpretation of brain changes in patients undergoing immunotherapies is provided.

A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation

Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on "the role of the MPEs in clinical trials"-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

An Effective Approach to Improve the Automatic Segmentation and Classification Accuracy of Brain Metastasis by Combining Multi-phase Delay Enhanced MR Images

The objective of this study is to analyse the diffusion rule of the contrast media in multi-phase delayed enhanced magnetic resonance (MR) T1 images using radiomics and to construct an automatic classification and segmentation model of brain metastases (BM) based on support vector machine (SVM) and Dpn-UNet. A total of 189 BM patients with 1047 metastases were enrolled. Contrast-enhanced MR images were obtained at 1, 3, 5, 10, 18, and 20 min following contrast medium injection. The tumour target volume was delineated, and the radiomics features were extracted and analysed. BM segmentation and classification models in the MR images with different enhancement phases were constructed using Dpn-UNet and SVM, and differences in the BM segmentation and classification models with different enhancement times were compared. (1) The signal intensity for BM decreased with time delay and peaked at 3 min. (2) Among the 144 optimal radiomics features, 22 showed strong correlation with time (highest R-value = 0.82), while 41 showed strong correlation with volume (highest R-value = 0.99). (3) The average dice similarity coefficients of both the training and test sets were the highest at 10 min for the automatic segmentation of BM, reaching 0.92 and 0.82, respectively. (4) The areas under the curve (AUCs) for the classification of BM pathology type applying single-phase MRI was the highest at 10 min, reaching 0.674. The AUC for the classification of BM by applying the six-phase image combination was the highest, reaching 0.9596, and improved by 42.3% compared with that by applying single-phase images at 10 min. The dynamic changes of contrast media diffusion in BM can be reflected by multi-phase delayed enhancement based on radiomics, which can more objectively reflect the pathological types and significantly improve the accuracy of BM segmentation and classification.

Efficacy and safety of recombinant human endostatin combined with whole-brain radiation therapy in patients with brain metastases from non-small cell lung cancer

BACKGROUND AND PURPOSE

Brain metastasis (BM) is the leading cause of poor prognosis in non-small cell lung cancer (NSCLC) patients. To date, whole-brain radiation therapy (WBRT) is a standard treatment for patients with multiple BMs, while its effectiveness is currently unsatisfactory. This study aimed to investigate the effects of Rh-endostatin combined with WBRT on NSCLC patients with BMs.

MATERIALS AND METHODS

A total of 43 patients with BM were randomly divided into two groups. The Rh-endostatin combination group (n=19) received Rh-endostatin combined with WBRT, and the radiation group (n=24) received WBRT only. The primary endpoint of the study was progression-free survival (PFS), and the secondary endpoints were intracranial progression free survival (iPFS), overall survival (OS), objective response rate (ORR), and changes in the cerebral blood volume (CBV) and cerebral blood flow (CBF).

RESULTS

Median PFS (mPFS) was 8.1 months in the Rh-endostatin combination group and 4.9 months in the radiation group (95%CI 0.2612-0.9583, p=0·0428). Besides, the median iPFS was 11.6 months in the Rh-endostatin combination group and 4.8 months in the radiation group (95%CI 0.2530-0.9504, p=0·0437). OS was 14.2 months in the Rh-endostatin combination group and 6.4 months in the radiation group (95%CI 0.2508-1.026, p=0·0688). CBV and CBF in the Rh-endostatin combination group were better improved than that in the radiation group, which indicated that Rh-endostatin might improve local blood supply and microcirculation.

CONCLUSION

Rh-endostatin showed better survival and improved cerebral perfusion parameters, which may provide further insights into the application of Rh-endostatin for NSCLC patients with BMs.

Reproducibility of dynamic contrast enhanced MRI derived transfer coefficient Ktrans in lung cancer

Dynamic contrast enhanced MRI (DCE-MRI) is a useful method to monitor therapy assessment in malignancies but must be reliable and comparable for successful clinical use. The aim of this study was to evaluate the inter- and intrarater reproducibility of DCE-MRI in lung cancer. At this IRB approved single centre study 40 patients with lung cancer underwent up to 5 sequential DCE-MRI examinations. DCE-MRI were performed using a 3.0T system. The volume transfer constant Ktrans was assessed by three readers using the two-compartment Tofts model. Inter- and intrarater reliability and agreement was calculated by wCV, ICC and their 95% confident intervals. DCE-MRI allowed a quantitative measurement of Ktrans in 107 tumors where 91 were primary carcinomas or intrapulmonary metastases and 16 were extrapulmonary metastases. Ktrans showed moderate to good interrater reliability in overall measurements (ICC 0.716-0.841; wCV 30.3-38.4%). Ktrans in pulmonary lesions ≥ 3 cm showed a good to excellent reliability (ICC 0.773-0.907; wCV 23.0-29.4%) compared to pulmonary lesions < 3 cm showing a moderate to good reliability (ICC 0.710-0.889; wCV 31.6-48.7%). Ktrans in intrapulmonary lesions showed a good reliability (ICC 0.761-0.873; wCV 28.9-37.5%) compared to extrapulmonary lesions with a poor to moderate reliability (ICC 0.018-0.680; wCV 28.1-51.8%). The overall intrarater agreement was moderate to good (ICC 0.607-0.795; wCV 24.6-30.4%). With Ktrans, DCE MRI offers a reliable quantitative biomarker for early non-invasive therapy assessment in lung cancer patients, but with a coefficient of variation of up to 48.7% in smaller lung lesions.

An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors

This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning–based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.

Advanced MRI to differentiate schwannomas and metastases in the cerebellopontine angle/internal auditory canal

BACKGROUND AND PURPOSE

Differentiating schwannomas and metastases in the cerebellopontine angles (CPA)/internal auditory canals (IAC) can be challenging. This study aimed to assess the role of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) to differentiate schwannomas and metastases in the CPA/IAC.

METHODS

We retrospectively reviewed 368 patients who were diagnosed with schwannomas or metastases in the CPA/IAC between April 2017 and February 2022 in a single academic center. Forty-three patients had pretreatment DWI and DCE-MRI along with conventional MRI. Normalized mean apparent diffusion coefficient ratio (nADCmean) and DCE-MRI parameters of fractional plasma volume (Vp), flux rate constant (Kep), and forward volume transfer constant were compared along with patients' demographics and conventional imaging features between schwannomas and metastases as appropriate. The diagnostic performances and multivariate logistic regression analysis were performed using the significantly different values.

RESULTS

Between 23 schwannomas (15 males; median 48 years) and 20 metastases (9 males; median 61 years), nADCmean (median: 1.69 vs. 1.43; p = .002), Vp (median: 0.05 vs. 0.20; p < .001), and Kep (median: 0.41 vs. 0.81 minute^-1 ; p < .001) were significantly different. The diagnostic performances of nADCmean, Vp, and Kep were 0.77, 0.90, and 0.83 area under the curves, with cutoff values of 1.68, 0.12, and 0.53, respectively. Vp was identified as the most significant parameter for the tumor differentiation in the multivariate logistic regression analysis (p < .001).

CONCLUSIONS

DWI and DCE-MRI can help differentiate CPA/IAC schwannomas and metastases, and Vp is the most significant parameter.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

Predictive effect of DCE-MRI and DWI in brain metastases from NSCLC

Non-small cell lung cancer (NSCLC), a commonly diagnosed lung cancer, is characterized by a high incidence of metastatic spread to the brain, which adversely impacts prognosis. The present study aimed to assess the value of combined dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in predicting the treatment outcomes of whole-brain radiotherapy (WBRT) and gefitinib in brain metastases from non-small cell lung cancer (NSCLC) from the perspectives of response rate and short- and long-term efficacy. These results suggested that the indicators measured by DCE-MRI combined with DWI can be used as key imaging-derived markers that predicted the efficacy of WBRT combined with gefitinib in NSCLC patients with brain metastases. Specifically, patients with higher ΔADC_mid and ΔADC_post values showed better treatment outcomes. ROC curve analysis indicated ADC_post, ΔADC_post, ΔADC_post (%), and tumor regression rate as the best predictors of efficacy of WBRT combined with gefitinib in these patients. The short-term and long-term effects noted were also significant. Taken together, the findings of this study reveal that tumor regression rate, ADC_post, ΔADC_post, and ΔADC_post (%) can be used as important imaging indicators that predict the therapeutic effect of WBRT combined with gefitinib in NSCLC patients with brain metastases.

Fully Integrated Quantitative Multiparametric Analysis of Non-Small Cell Lung Cancer at 3-T PET/MRI: Toward One-Stop-Shop Tumor Biological Characterization at the Supervoxel Level

INTRODUCTION

The aim of this study was to study the feasibility of a fully integrated multiparametric imaging framework to characterize non-small cell lung cancer (NSCLC) at 3-T PET/MRI.

PATIENTS AND METHODS

An 18F-FDG PET/MRI multiparametric imaging framework was developed and prospectively applied to 11 biopsy-proven NSCLC patients. For each tumor, 12 parametric maps were generated, including PET full kinetic modeling, apparent diffusion coefficient, T1/T2 relaxation times, and DCE full kinetic modeling. Gaussian mixture model-based clustering was applied at the whole data set level to define supervoxels of similar multidimensional PET/MRI behaviors. Taking the multidimensional voxel behaviors as input and the supervoxel class as output, machine learning procedure was finally trained and validated voxelwise to reveal the dominant PET/MRI characteristics of these supervoxels at the whole data set and individual tumor levels.

RESULTS

The Gaussian mixture model-based clustering clustering applied at the whole data set level (17,316 voxels) found 3 main multidimensional behaviors underpinned by the 12 PET/MRI quantitative parameters. Four dominant PET/MRI parameters of clinical relevance (PET: k2, k3 and DCE: ve, vp) predicted the overall supervoxel behavior with 97% of accuracy (SD, 0.7; 10-fold cross-validation). At the individual tumor level, these dimensionality-reduced supervoxel maps showed mean discrepancy of 16.7% compared with the original ones.

CONCLUSIONS

One-stop-shop PET/MRI multiparametric quantitative analysis of NSCLC is clinically feasible. Both PET and MRI parameters are useful to characterize the behavior of tumors at the supervoxel level. In the era of precision medicine, the full capabilities of PET/MRI would give further insight of the characterization of NSCLC behavior, opening new avenues toward image-based personalized medicine in this field.

The Value of Nomograms in Pre-Operative Prediction of Lymphovascular Invasion in Primary Breast Cancer Undergoing Modified Radical Surgery: Based on Multiparametric Ultrasound and Clinicopathologic Indicators

The purpose of this study was to explore the value of pre-operative prediction of lymphovascular invasion (LVI) in primary breast cancer patients undergoing modified radical mastectomy and to develop a nomogram based on multiparametric ultrasound and clinicopathologic indicators. All patients with primary breast cancer confirmed by pre-operative biopsy underwent B-mode ultrasound and contrast-enhanced ultrasound examinations. Post-operative pathology was used as the gold standard to identify LVI. Lasso regression was used to select predictors most related to LVI. A nomogram was developed to calculate the diagnostic efficacy. We bootstrapped the data for 500 times to perform internal verification, drawing a calibration curve to verify prediction ability. A total of 244 primary breast cancer patients were included. LVI was observed in 77 patients. Ten predictors associated with LVI were selected by Lasso regression. The area under the curve, sensitivity, specificity and accuracy for the nomogram were 0.918, 92.2%, 76.7% and 81.6%, respectively. And the nomogram calibration curve showed good consistency between the predicted probability and the actual probability. The nomogram developed could be used to predict LVI in primary breast cancer patients undergoing modified radical mastectomy and to help in clinical decision-making.

A review of the role of MRI in diagnosis and treatment of early stage lung cancer

Despite magnetic resonance imaging (MRI) being a mainstay in the oncologic care for many disease sites, it has not routinely been used in early lung cancer diagnosis, staging, and treatment. While MRI provides improved soft tissue contrast compared to computed tomography (CT), an advantage in multiple organs, the physical properties of the lungs and mediastinum create unique challenges for lung MRI. Although multi-detector CT remains the gold standard for lung imaging, advances in MRI technology have led to its increased clinical relevance in evaluating early stage lung cancer. Even though positron emission tomography is used more frequently in this context, functional MR imaging, including diffusion-weighted MRI and dynamic contrast-enhanced MRI, are emerging as useful modalities for both diagnosis and evaluation of treatment response for lung cancer. In parallel with these advances, the development of combined MRI and linear accelerator devices (MR-linacs), has spurred the integration of MRI into radiation treatment delivery in the form of MR-guided radiotherapy (MRgRT). Despite challenges for MRgRT in early stage lung cancer radiotherapy, early data utilizing MR-linacs shows potential for the treatment of early lung cancer. In both diagnosis and treatment, MRI is a promising modality for imaging early lung cancer.

18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data

OBJECTIVES

To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI.

MATERIAL AND METHODS

Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations).

RESULTS

Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness.

CONCLUSION

A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

Improving dynamic contrast-enhanced MRI of the lung using motion-weighted sparse reconstruction: Initial experiences in patients

PURPOSE

The purpose of this study was to evaluate the performance of motion-weighted Golden-angle RAdial Sparse Parallel MRI (motion-weighted GRASP) for free-breathing dynamic contrast-enhanced MRI (DCE-MRI) of the lung.

METHODS

Motion-weighted GRASP incorporates a soft-gating motion compensation algorithm into standard GRASP reconstruction, so that motion-corrupted motion k-space (e.g., k-space acquired in inspiratory phases) contributes less to the final reconstructed images. Lung MR data from 20 patients (mean age = 57.9 ± 13.5) with known pulmonary lesions were retrospectively collected for this study. Each subject underwent a free-breathing DCE-MR scan using a fat-statured T₁-weighted stack-of-stars golden-angle radial sequence and a post-contrast breath-hold MR scan using a Cartesian volumetric-interpolated imaging sequence (BH-VIBE). Each radial dataset was reconstructed using GRASP without motion compensation and motion-weighted GRASP. All MR images were visually evaluated by two experienced radiologists blinded to reconstruction and acquisition schemes independently. In addition, the influence of motion-weighted reconstruction on dynamic contrast-enhancement patterns was also investigated.

RESULTS

For image quality assessment, motion-weighted GRASP received significantly higher visual scores than GRASP (P < 0.05) for overall image quality (3.68 vs. 3.39), lesion conspicuity (3.54 vs. 3.18) and overall artifact level (3.53 vs. 3.15). There was no significant difference (P > 0.05) between the breath-hold BH-VIBE and motion-weighted GRASP images. For assessment of temporal fidelity, motion-weighted GRASP maintained a good agreement with respect to GRASP.

CONCLUSION

Motion-weighted GRASP achieved better reconstruction performance in free-breathing DCE-MRI of the lung compared to standard GRASP, and it may enable improved assessment of pulmonary lesions.

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

BACKGROUND AND PURPOSE

Tumor recurrence is difficult to predict in patients receiving laser ablation for intracranial malignancy. We assessed the efficacy of the initial area under the time-to-signal intensity curve at 60 seconds (iAUC₆₀) from dynamic contrast-enhanced MR imaging in predicting progression-free survival in patients with brain metastases following laser interstitial thermal therapy.

MATERIALS AND METHODS

The study population was a consecutive series of patients undergoing laser interstitial thermal therapy for brain metastases. Patient demographics including age, sex, tumor histology, and Karnofsky Performance Scale were collected prospectively. Preoperative, postoperative, and 1-month follow-up dynamic contrast-enhanced MRIs were analyzed. Values of iAUC₆₀ were computed using a trapezoidal rule applied to the time history of contrast uptake over the first 60 seconds postenhancement. The change in iAUC₆₀ (ΔiAUC₆₀) was calculated by taking the difference between the values of iAUC₆₀ from 2 time points. Pearson correlation coefficients were calculated between progression-free survival, defined as the time from laser interstitial thermal therapy to tumor recurrence, and iAUC₆₀ or ΔiAUC₆₀ values.

RESULTS

Thirty-three cases of laser interstitial thermal therapy for 32 brain metastases in a cohort of 27 patients were prospectively analyzed. A significant relationship was observed between the values of iAUC₆₀ from postoperative dynamic contrast-enhanced MR imaging and progression-free survival with Pearson correlation (P = .03) and Cox univariate analysis (P = .01). The relationship between preoperative and 1-month follow-up dynamic contrast-enhanced MR imaging was not significantly correlated with progression-free survival. Similarly, no statistically significant relationship was observed with ΔiAUC₆₀ and progression-free survival between any time points.

CONCLUSIONS

Progression-free survival is difficult to predict in patients undergoing laser interstitial thermal therapy for brain metastases due to confounding with posttreatment change. iAUC₆₀ extracted from postoperative dynamic contrast-enhanced MR imaging shows promise for accurately prognosticating patients following this operative therapy.

Advanced Magnetic Resonance Imaging Techniques in Management of Brain Metastases

Brain metastases are the most common intracranial tumors and occur in 20–40% of all cancer patients. Lung cancer, breast cancer, and melanoma are the most frequent primary cancers to develop brain metastases. Treatment options include surgical resection, whole brain radiotherapy, stereotactic radiosurgery, and systemic treatment such as targeted or immune therapy. Anatomical magnetic resonance imaging (MRI) of the tumor (in particular post-Gadolinium T₁-weighted and T₂-weighted FLAIR) provide information about lesion morphology and structure, and are routinely used in clinical practice for both detection and treatment response evaluation for brain metastases. Advanced MRI biomarkers that characterize the cellular, biophysical, micro-structural and metabolic features of tumors have the potential to improve the management of brain metastases from early detection and diagnosis, to evaluating treatment response. Magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), quantitative magnetization transfer (qMT), diffusion-based tissue microstructure imaging, trans-membrane water exchange mapping, and magnetic susceptibility weighted imaging (SWI) are advanced MRI techniques that will be reviewed in this article as they pertain to brain metastases.

Contrast-enhanced magnetic resonance imaging with a novel nano-size contrast agent for the clinical diagnosis of patients with lung cancer

Recent studies have indicated that magnetic resonance imaging (MRI) efficiently diagnoses lung cancer. However, the efficacy of MRI in diagnosing lung cancer requires improving for patients in the early stage of the disease. In the present study, a novel nano-sized contrast agent of chistosan/Fe₃O₄-enclosed bispecific antibodies (BsAbCENS) was introduced, which targeted carcino-embryonic antigen (CEA) and neuron-specific enolase (NSE) in lung cancer cells. The diagnostic efficacy of contrast-enhanced MRI with BsAbCENS (CEMRI-BsAbCENS) was investigated in a total of 182 patients with suspected lung cancer who had high serum levels of CEA and NSE. BsAbCENS was administered by pulmonary inhalation prior to the MRI scan. The results revealed that CEA and NSE were overexpressed in human lung cancer cell lines. BsAbCENS bound with CEA and NSE on the surface of human lung cancer cells and produced a higher signal intensity than MRI alone for the diagnosis of patients with lung cancer. The diagnostic data revealed that CEMRI-BsAbCENS diagnosed 124/182 lung cancer cases, whereas CEMRI only diagnosed 98/182, which was significantly less (P<0.01). In addition, the survival rate of patients with lung cancer diagnosed by CEMRI-BsAbCENS was significantly higher than the mean 5-year survival rate (P<0.01). Furthermore, the pharmacodynamics demonstrated that BsAbCENS was metabolized within 24 h. The results of the present study indicate that the efficacy and accuracy of lung cancer diagnosis are improved by CEMRI-BsAbCENS. In conclusion, these results provide a potential novel protocol for the diagnosis of tumors in patients with suspected early stage lung cancer.

Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis

OBJECTIVES

Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters.

METHODS

A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity.

RESULTS

Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%).

CONCLUSIONS

Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis.

KEY POINTS

• Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.

Occipital blood-brain barrier permeability is an independent predictor of visual outcome in type 2 diabetes, irrespective of the retinal barrier: A longitudinal study

Blood-brain barrier (BBB) permeability in type 2 diabetic patients has been previously shown to be altered in certain brain regions such as the basal ganglia and the hippocampus. Because of the histological and functional similarities between the BBB) and the blood-retinal barrier (BRB), we aimed to investigate how the permeability of both barriers predicts visual outcome. We included 2 control groups (acute unilateral stroke patients, n = 9; type 2 diabetics without BRB leakage n = 10) and a case study group of type 2 diabetics with established BRB leakage (n = 17). We evaluated sex, age, disease duration, metabolic impairment, retinopathy grade and BBB permeability as predictors of visual acuity at baseline, 12  and 24 months in the type 2 diabetics without BRB leakage group and the case study group. We have also explored differences in BBB permeability in the occipital lobe and frontal lobe in the 3 different groups. K^trans (volume transfer coefficient) and V_p (fractional plasma volume) were estimated. The BBB permeability parameter V_p was higher in the case study group compared to the unaffected hemisphere of the stroke patient control group, suggesting vascular dynamics were changed in the occipital lobe of type 2 diabetics with established BRB leakage. These patients showed a significant correlation between glycated hemoglobin (HbA1C) levels and occipital and frontal K^trans . We report for the first time that occipital BBB permeability is an independent predictor of visual acuity at baseline, as well as at 12 and 24 months, in type 2 diabetics with established BRB leakage. Our results suggest that occipital BBB permeability might be an independent biomarker for visual impairment in patients with established BRB leakage.

Dynamic contrast-enhanced MRI perfusion for differentiating between melanoma and lung cancer brain metastases

Brain metastases originating from different primary sites overlap in appearance and are difficult to differentiate with conventional MRI. Dynamic contrast-enhanced (DCE)-MRI can assess tumor microvasculature and has demonstrated utility in characterizing primary brain tumors. Our aim was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (Ktrans ) derived from DCE-MRI in distinguishing between melanoma and nonsmall cell lung cancer (NSCLC) brain metastases. Forty-seven NSCLC and 23 melanoma brain metastases were retrospectively assessed with DCE-MRI. Regions of interest were manually drawn around the metastases to calculate Vpmean and Kmeantrans. The Mann-Whitney U test and receiver operating characteristic analysis (ROC) were performed to compare perfusion parameters between the two groups. The Vpmean of melanoma brain metastases (4.35, standard deviation [SD] = 1.31) was significantly higher (P = 0.03) than Vpmean of NSCLC brain metastases (2.27, SD = 0.96). The Kmeantrans values were higher in melanoma brain metastases, but the difference between the two groups was not significant (P = 0.12). Based on ROC analysis, a cut-off value of 3.02 for Vpmean (area under curve = 0.659 with SD = 0.074) distinguished between melanoma brain metastases and NSCLC brain metastases (P < 0.01) with 72% specificity. Our data show the DCE-MRI parameter Vpmean can differentiate between melanoma and NSCLC brain metastases. The ability to noninvasively predict tumor histology of brain metastases in patients with multiple malignancies can have important clinical implications.