Contrast-enhanced T1-weighted Magnetic Resonance Imaging Research Articles

Radiation dose to circumscribed brain regions and neurocognitive function in patients with meningioma.

Although radiation (RT) is standard treatment for many brain tumors, it may contribute to neurocognitive decline. The objective of this study was to investigate associations between RT dose to circumscribed brain regions and specific neurocognitive domains in patients with meningioma. We undertook a retrospective study of 40 patients with meningioma who received RT and underwent an in-depth clinical neurocognitive assessment. Radiation dosimetry characteristics were delineated based on treatment planning computerized tomography co-registered with contrast-enhanced 3D T1-weighted magnetic resonance imaging. Principal components analysis was applied to organize neurocognitive test scores into factors, and multivariate multiple linear regression models were undertaken to examine if RT dose to circumscribed brain regions is associated with specific neurocognitive outcomes. Radiation dose to brain regions was associated with neurocognitive functions across a number of domains. High dose to the parietal-occipital region was associated with slower visuomotor processing speed (mean dose, β = -1.100, P = .017; dose to 50% of the region [D50], β = -0.697, P = .049). In contrast, high dose to the dorsal frontal region was associated with faster visuomotor processing speed (mean dose, β = 0.001, P = .036). These findings suggest that RT delivered to brain regions (ie, parietal-occipital areas) may contribute to poor neurocognitive outcomes. Given that modern radiotherapy techniques allow for precise targeting of dose delivered to brain regions, prospective trials examining relations between dose and neurocognitive functions are warranted to confirm these preliminary results.

Understanding nanomedicine treatment in an aggressive spontaneous brain cancer model at the stage of early blood brain barrier disruption

Personalised nanomedicine is an advancing field which has developed significant improvements for targeting therapeutics to aggressive cancer and with fewer side effects. The treatment of gliomas such as glioblastoma (or other brain tumours), with nanomedicine is complicated by a commonly poor accumulation of drugs in tumour tissue owing to the partially intact blood-brain barrier (BBB). Nonetheless, the BBB becomes compromised following surgical intervention, and gradually with disease progression. Increased vasculature permeability generated by a tumour, combined with decreased BBB integrity, offers a mechanism to enhance therapeutic outcomes. We monitored a spontaneous glioma tumour model in immunocompetent mice with ongoing T2-weighted and contrast-enhanced T1-weighted magnetic resonance imaging gradient echo and spin echo sequences to predict an optimal “leakiness” stage for nanomedicine injections. To ascertain the effectiveness of targeted nanomedicines in treating brain tumours, subsequent systemic administration of targeted hyperbranched polymers was then utislised, to deliver the therapeutic payload when both the tumour and brain vascularity had become sufficiently susceptible to allow drug accumulation. Treatment with either doxorubicin-loaded hyperbranched polymer, or the same nanomedicine targeted to an ephrin receptor (EphA2) using a bispecific antibody, resulted in uptake of chemotherapeutic doxorubicin in the tumour and in reduced tumour growth. Compared to vehicle and doxorubicin only, nanoparticle delivered doxorubicin resulted in increased tumour apoptosis, while averting cardiotoxicity. This suggests that polyethylene based (PEGylated)-nanoparticle delivered doxorubicin could provide a more efficient treatment in tumours with a disrupted BBB, and that treatment should commence immediately following detection of gadolinium permeability, with early detection and ongoing ‘leakiness’ monitoring in susceptible patients being a key factor.

Abstract P1-08-20: In silico analysis of a novel mathematical model integrating in vitro and in vivo imaging data reveals driving mechanisms of breast cancer response to NAT for personalized tumor forecasting

Abstract The early assessment of neoadjuvant therapy (NAT) response in triple-negative breast cancer (TNBC) would enable a treating oncologist to adjust a therapeutic plan of a non-responding patient, and thereby enhance outcomes while preventing unnecessary toxicities. To address this challenge, we propose leveraging personalized, in silico forecasts of tumor response to therapeutic regimens via a mechanistic mathematical model calibrated with patient-specific longitudinal magnetic resonance imaging (MRI) data acquired early during NAT. Here, we focus on identifying the driving mechanisms involved in the model formulation through a global sensitivity analysis. Our model describes the dynamics of TNBC cell density as a combination of mobility, which is formulated as a diffusion process constrained by local tumor-induced mechanical stress, and net proliferation, which is represented with a logistic term. To model TNBC response to NAT drug combinations, we adjust the tumor cell proliferation rate with a recent pharmacodynamic model, MuSyC, which also accounts for synergy of potency and efficacy. Tumor cell density is estimated from diffusion-weighted MRI data, while tissue mechanical properties are defined from segmented contrast-enhanced T1-weighted MRI data. To model the heterogeneous intratumoral delivery of drugs, we use perfusion maps estimated from dynamic contrast-enhanced MRI data. NAT drug pharmacokinetics are approximated with a linear model, which reasonably represents their temporal decay during NAT. Sobol’s method is used for the global sensitivity analysis of the NAT response of two different tumors (one well-perfused and one poorly-perfused) on a 3D, tissue-scale domain. This allows us to assess the total effect (ST) of each model parameter on tumor volume and global cellularity. Here, we focus on two standard NAT regimens: doxorubicin plus cyclophosphamide, and paclitaxel plus carboplatin. The parameter space is constructed by integrating three approaches. First, we use prior patient-specific in silico estimates of tumor cell mobility and proliferation. Second, we experimentally constrain the parameters accounting for potential synergistic drug activity by using time-resolved, high-throughput, automated microscopy assays that capture drug-induced changes in proliferation rates of various TNBC lines (HCC1143, SUM149, MDAMB231, and MDAMB468; perfosfamide was used in lieu of the pro-drug cyclophosphamide). Third, we scale the resulting in vitro parameter ranges to be clinically-relevant through an in silico study with our mechanistic model. Our results show that out of the 15 model parameters considered in the sensitivity analysis only a minority exhibited a driving role (ST > 0.1) in representing the dynamics of TNBC response to NAT, namely: the baseline tumor cell proliferation rate along with the effect and ratio of peak concentration to EC50 of doxorubicin, paclitaxel, and carboplatin. The other parameters have a marginal effect and can thus be fixed to any value within the parameter space. We select these constant parameters such that they contribute to simplifying the model formulation, leading to a surrogate reduced model. We further show that the reduced and the original model produce distributions of tumor volume and global cellularity that are in remarkable agreement both qualitatively (Dice similarity coefficient > 0.90 and > 0.94, respectively) and parameter-wise (concordance correlation coefficient > 0.85 and > 0.89, respectively). Thus, we conclude that our reduced model constitutes a feasible surrogate for future clinical calibration-forecasting studies, thereby facilitating personalized NAT response forecasting with patient-specific imaging datasets acquired in vivo. Citation Format: Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Darren R. Tyson, Vito Quaranta, Thomas E. Yankeelov. In silico analysis of a novel mathematical model integrating in vitro and in vivo imaging data reveals driving mechanisms of breast cancer response to NAT for personalized tumor forecasting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-20.

A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas.

Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging. A total of 141 transitional meningioma and 101 atypical meningioma cases between January 2014 and December 2018 with a histopathologically confirmed diagnosis were retrospectively reviewed. All patients underwent magnetic resonance imaging before surgery. For each patient, 1227 radiomic features were extracted from contrast-enhanced T1-weighted and T2-weighted images each. Least absolute shrinkage and selection operator regression analysis was performed to select the most informative features of different modalities. Subsequently, stepwise multivariate logistic regression was chosen to further select strongly correlated features and build classification models that can distinguish transitional from atypical meningioma. The diagnostic abilities were evaluated by receiver operating characteristic analysis. Furthermore, a nomogram was built by incorporating clinical characteristics, radiological features, and radiomic signatures, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sex, tumor shape, brain invasion, and four radiomic features differed significantly between transitional meningioma and atypical meningioma. The clinicoradiomic model derived by fusing the above features resulted in the best discrimination ability, with areas under the curves of 0.809 (95% confidence interval, 0.743-0.874) and 0.795 (95% confidence interval, 0.692-0.899) and sensitivity values of 74.0% and 71.4% in the training and validation cohorts, respectively. The clinicoradiomic model demonstrated good performance for the differentiation between transitional and atypical meningioma. It is a quantitative tool that can potentially aid the selection of surgical techniques and the prognosis prediction and can thus be applied in patients with these two meningioma subtypes.

Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN

Background Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN).MethodsThe dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively.ResultsThe average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes.ConclusionAn automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.

Zwitterion-Coated Ultrasmall MnO Nanoparticles Enable Highly Sensitive T1-Weighted Contrast-Enhanced Brain Imaging.

Manganese oxide nanoparticles (NPs) have attracted increasing attention recently as contrast agents (CAs) for magnetic resonance imaging (MRI). However, the clinical translation and popularization of conventional MnO NPs are hampered by their relatively poor imaging performance. Herein, we report the construction of ultrasmall MnO NPs (USMnO) via a one-pot synthetic approach that show a much better capability of T1-weighted contrast enhancement for MRI (r1 = 15.6 ± 0.4 mM-1 s-1 at 0.5 T) than MnCl2 and conventional large-sized MnO NPs (MnO-22). These USMnO are further coated with zwitterionic dopamine sulfonate (ZDS) molecules, which improves their biocompatibility and prevents nonspecific binding of serum albumins. Interestingly, USMnO@ZDS are capable of passing through the blood-brain barrier (BBB), which enables the acquisition of clear images showing brain anatomic structures with T1-weighted contrast-enhanced MRI. Therefore, our USMnO@ZDS could be used as a promising MRI CA for the flexible and accurate diagnosis of brain diseases, which is also instructive for the construction of manganese-based CA with a high MRI performance.

Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion.

BackgroundDetection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable.ObjectiveTo train and validate a multi-parametric magnetic resonance imaging (mpMRI)-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies.Materials and MethodsThe model was developed from the tumor volume of interest (VOI) of 125 patients with CC. A total of 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, as well as axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator method on the training set. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set.ResultsFeature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC]: 0.940) reached a significantly higher performance (better than the clinical parameters [AUC: 0.730]), including any single-layer model using sFOV HR-T2WI (AUC: 0.840), T2WI (AUC: 0.770), FS-T2WI (AUC: 0.710), ADC maps (AUC: 0.650), sagittal, and axial T1c values (AUC: 0.710, 0.680) in the validation set.ConclusionBiomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.

Evaluation of clinical applicability of automated liver parenchyma segmentation of multi-center magnetic resonance images

PurposeAutomated algorithms for liver parenchyma segmentation can be used to create patient-specific models (PSM) that assist clinicians in surgery planning. In this work, we analyze the clinical applicability of automated deep learning methods together with level set post-processing for liver segmentation in contrast-enhanced T1-weighted magnetic resonance images. MethodsUNet variants with/without attention gate, multiple loss functions, and level set post-processing were used in the workflow. A multi-center, multi-vendor dataset from Oslo laparoscopic versus open liver resection for colorectal liver metastasis clinical trial is used in our study. The dataset of 150 volumes is divided as 81:25:25:19 corresponding to train:validation:test:clinical evaluation respectively. We evaluate the clinical use, time to edit automated segmentation, tumor regions, boundary leakage, and over-and-under segmentations of predictions. ResultsThe deep learning algorithm shows a mean Dice score of 0.9696 in liver segmentation, and we also examined the potential of post-processing to improve the PSMs. The time to create clinical use segmentations of level set post-processed predictions shows a median time of 16 min which is 2 min less than deep learning inferences. The intra-observer variations between manually corrected deep learning and level set post-processed segmentations show a 3% variation in the Dice score. The clinical evaluation shows that 7 out of 19 cases of both deep learning and level set post-processed segmentations contain all required anatomy and pathology, and hence these results could be used without any manual corrections. ConclusionsThe level set post-processing reduces the time to create clinical standard segmentations, and over-and-under segmentations to a certain extent. The time advantage greatly supports clinicians to spend their valuable time with patients.

MRI brain segmentation using correlation based adaptively regularized kernel-based fuzzy C-means clustering

Brain tumour detection is the most challenging aspect in the field of medical image processing and analysis. Whereas, the traditional techniques used in MRI brain tumour segmentation are extremely time consuming tasks. In this paper, the proposed approach contains two major steps: image acquisition and segmentation. Initially, the brain tumour detection was assessed by employing T1-weighted contrast enhanced magnetic resonance imaging (T1-WCEMRI) database. After image acquisition, segmentation was carried-out by using correlation based adaptively regularised kernel-based fuzzy C-means (ARKFCM) clustering along with Otsu thresholding. In conventional clustering methodologies, it was very hard to detect the ill-defined masses that highly decrease the segmentation accuracy. To address this concern, the kernel function in ARKFCM was replaced by a correlation function for localising the object in a complex template. In experimental analysis, the proposed approach distinguishes the normal brain region and brain tumour region by means of dice coefficient, Jaccard coefficient, true positive rate (TPR), false positive rate (FPR) and accuracy. The experimental outcome shows that the proposed methodology delivered average accuracy of 99.213% in brain tumour detection. The proposed methodology improved accuracy in brain tumour segmentation up to 3-3.2% compared to the existing methods: FCM and ARKFCM.

Sonobiopsy for minimally invasive, spatiotemporally-controlled, and sensitive detection of glioblastoma-derived circulating tumor DNA.

Though surgical biopsies provide direct access to tissue for genomic characterization of brain cancer, they are invasive and pose significant clinical risks. Brain cancer management via blood-based liquid biopsies is a minimally invasive alternative; however, the blood-brain barrier (BBB) restricts the release of brain tumor-derived molecular biomarkers necessary for sensitive diagnosis.Methods: A mouse glioblastoma multiforme (GBM) model was used to demonstrate the capability of focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) to improve the diagnostic sensitivity of brain tumor-specific genetic mutations compared with conventional blood-based liquid biopsy. Furthermore, a pig GBM model was developed to characterize the translational implications of sonobiopsy in humans. Magnetic resonance imaging (MRI)-guided FUS sonication was performed in mice and pigs to locally enhance the BBB permeability of the GBM tumor. Contrast-enhanced T1-weighted MR images were acquired to evaluate the BBB permeability change. Blood was collected immediately after FUS sonication. Droplet digital PCR was used to quantify the levels of brain tumor-specific genetic mutations in the circulating tumor DNA (ctDNA). Histological staining was performed to evaluate the potential for off-target tissue damage by sonobiopsy.Results: Sonobiopsy improved the detection sensitivity of EGFRvIII from 7.14% to 64.71% and TERT C228T from 14.29% to 45.83% in the mouse GBM model. It also improved the diagnostic sensitivity of EGFRvIII from 28.57% to 100% and TERT C228T from 42.86% to 71.43% in the porcine GBM model.Conclusion: Sonobiopsy disrupts the BBB at the spatially-targeted brain location, releases tumor-derived DNA into the blood circulation, and enables timely collection of ctDNA. Converging evidence from both mouse and pig GBM models strongly supports the clinical translation of sonobiopsy for the minimally invasive, spatiotemporally-controlled, and sensitive molecular characterization of brain cancer.

Brain penetration and efficacy of tepotinib in orthotopic patient-derived xenograft models of MET-driven non-small cell lung cancer brain metastases

Central nervous system-penetrant therapies with intracranial efficacy against non-small cell lung cancer (NSCLC) brain metastases are urgently needed. We report preclinical studies investigating brain penetration and intracranial activity of the MET inhibitor tepotinib. After intravenous infusion of tepotinib in Wistar rats (n = 3), mean (±standard deviation) total tepotinib concentration was 2.87-fold higher in brain (505 ± 22 ng/g) than plasma (177 ± 20 ng/mL). In equilibrium dialysis experiments performed in triplicate, mean tepotinib unbound fraction was 0.35% at 0.3 and 3.0 µM tepotinib in rat brain tissue, and 4.0% at 0.3 and 1.0 µM tepotinib in rat plasma. The calculated unbound brain-to-plasma ratio was 0.25, indicating brain penetration sufficient for intracranial target inhibition. Of 20 screened subcutaneous patient-derived xenograft (PDX) models from lung cancer brain metastases (n = 1), two NSCLC brain metastases models (LU5349 and LU5406) were sensitive to the suboptimal dose of tepotinib of 30 mg/kg/qd (tumor volume change [%TV]: –12% and –88%, respectively). Molecular profiling (nCounter®; NanoString) revealed high-level MET amplification in both tumors (mean MET gene copy number: 11.2 and 24.2, respectively). Tepotinib sensitivity was confirmed for both subcutaneous models at a clinically relevant dose (125 mg/kg/qd; n = 5). LU5349 and LU5406 were orthotopically implanted into brains of mice and monitored by magnetic resonance imaging (MRI). Tepotinib 125 mg/kg/qd induced pronounced tumor regression, including complete or near-complete regressions, compared with vehicle in both orthotopic models (n = 10; median %TV: LU5349, –84%; LU5406, –63%). Intracranial antitumor activity of tepotinib did not appear to correlate with blood–brain barrier leakiness assessed in T1-weighted gadolinium contrast-enhanced MRI.

Comparison of contrast-enhanced MRI features of the (teno)synovium in the wrist of patients with juvenile idiopathic arthritis and pediatric controls

To directly compare and describe the differences between juvenile idiopathic arthritis (JIA) patients and pediatric controls regarding features of the synovial and tenosynovial membrane on contrast-enhanced magnetic resonance imaging (MRI) of the wrist. T1-weighted contrast-enhanced MRI scans of 25 JIA patients with clinically active wrist arthritis and 25 children without a history of joint complaints nor any clinical signs of joint inflammation were evaluated by two readers blinded to clinical data. The synovium was scored at five anatomical sites based on thickening of the synovium (0–3 scale) and synovial enhancement (0–2 scale). Thickening and/or enhancement of the tenosynovium was scored at four anatomical sites using a 0–3 scale. Significantly higher scores for synovial thickening (median 4 vs. 1, p < 0.001) and synovial enhancement (median 4 vs. 1, p < 0.001) are found in the wrist of JIA patients as compared to controls. JIA patients experienced the highest synovial scores at the mid-/inter-carpal, 2nd –5th carpometacarpal, and radiocarpal joints. No significant difference in tenosynovial scores is found between both groups (median 0 vs. 0, p = 0.220). This study highlights the higher synovial thickening/enhancement scores on contrast-enhanced MRI of the wrist in JIA patients compared to pediatric controls. Tenosynovial thickening and/or enhancement was rarely present in both groups. In JIA patients, synovial thickening and enhancement were particularly present at three anatomical sites. These results substantially support rheumatologists and radiologists when navigating through MRI of the wrist in search for JIA disease activity.

Virtual Contrast-Enhanced Magnetic Resonance Images Synthesis for Patients With Nasopharyngeal Carcinoma Using Multimodality-Guided Synergistic Neural Network

To investigate a novel deep-learning network that synthesizes virtual contrast-enhanced T1-weighted (vceT1w) magnetic resonance images (MRI) from multimodality contrast-free MRI for patients with nasopharyngeal carcinoma (NPC). This article presents a retrospective analysis of multiparametric MRI, with and without contrast enhancement by gadolinium-based contrast agents (GBCAs), obtained from 64 biopsy-proven cases of NPC treated at Hong Kong Queen Elizabeth Hospital. A multimodality-guided synergistic neural network (MMgSN-Net) was developed to leverage complementary information between contrast-free T1-weighted and T2-weighted MRI for vceT1w MRI synthesis. Thirty-five patients were randomly selected for model training, whereas 29 patients were selected for model testing. The synthetic images generated from MMgSN-Net were quantitatively evaluated against real GBCA-enhanced T1-weighted MRI using a series of statistical evaluating metrics, which include mean absolute error (MAE), mean squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Qualitative visual assessment between the real and synthetic MRI was also performed. Effectiveness of our MMgSN-Net was compared with 3 state-of-the-art deep-learning networks, including U-Net, CycleGAN, and Hi-Net, both quantitatively and qualitatively. Furthermore, a Turing test was performed by 7 board-certified radiation oncologists from 4 hospitals for assessing authenticity of the synthesized vceT1w MRI against the real GBCA-enhanced T1-weighted MRI. Results from the quantitative evaluations demonstrated that our MMgSN-Net outperformed U-Net, CycleGAN and Hi-Net, yielding the top-ranked scores in averaged MAE (44.50 ± 13.01), MSE (9193.22 ± 5405.00), SSIM (0.887 ± 0.042), and PSNR (33.17 ± 2.14). Furthermore, the mean accuracy of the 7 readers in the Turing tests was determined to be 49.43%, equivalent to random guessing (ie, 50%) in distinguishing between real GBCA-enhanced T1-weighted and synthetic vceT1w MRI. Qualitative evaluation indicated that MMgSN-Net gave the best approximation to the ground-truth images, particularly in visualization of tumor-to-muscle interface and the intratumor texture information. Our MMgSN-Net was capable of synthesizing highly realistic vceT1w MRI that outperformed the 3 comparable state-of-the-art networks.

Clinical Feasibility of High-Resolution Contrast-Enhanced Dynamic T1-Weighted Magnetic Resonance Imaging of the Upper Abdomen Using Compressed Sensing.

The objective of this study was to evaluate the clinical feasibility of high-resolution contrast-enhanced dynamic T1-weighted imaging (T1WI) using compressed sensing (CS) in magnetic resonance imaging. This study retrospectively included 35 patients who underwent dynamic T1WI using volumetric interpolated breath-hold examination (VIBE) with CS reconstruction (CS-VIBE) and 35 patients with conventional VIBE for comparison. Two observers assessed the liver and pancreas edges, hepatic artery, motion artifacts, and overall image quality. Quantitative analysis was performed by measuring signal intensity and image noise. The results showed that CS-VIBE achieved significantly better anatomic delineation of the liver and pancreas edges and hepatic artery clarity than VIBE (P < 0.001). There were no significant differences in motion artifacts in dynamic phases and overall image quality. The signal intensities and INs of CS-VIBE were higher than VIBE. High-resolution dynamic T1WI using CS provides better anatomic delineation with comparable or better overall image quality than conventional VIBE.

Hippocampus-Avoidance Whole-Brain Radiation Therapy Is Efficient in the Long-Term Preservation of Hippocampal Volume.

Background and PurposeWith improved life expectancy, preventing neurocognitive decline after cerebral radiotherapy is gaining more importance. Hippocampal damage has been considered the main culprit for cognitive deficits following conventional whole-brain radiation therapy (WBRT). Here, we aimed to determine to which extent hippocampus-avoidance WBRT (HA-WBRT) can prevent hippocampal atrophy compared to conventional WBRT.Methods and MaterialsThirty-five HA-WBRT and 48 WBRT patients were retrospectively selected, comprising a total of 544 contrast-enhanced T1-weighted magnetic resonance imaging studies, longitudinally acquired within 24 months before and 48 months after radiotherapy. HA-WBRT patients were treated analogously to the ongoing HIPPORAD-trial (DRKS00004598) protocol with 30 Gy in 12 fractions and dose to 98% of the hippocampus ≤ 9 Gy and to 2% ≤ 17 Gy. WBRT was mainly performed with 35 Gy in 14 fractions or 30 Gy in 10 fractions. Anatomical images were segmented and the hippocampal volume was quantified using the Computational Anatomy Toolbox (CAT), including neuroradiological expert review of the segmentations.ResultsAfter statistically controlling for confounding variables such as age, gender, and total intracranial volume, hippocampal atrophy was found after both WBRT and HA-WBRT (p < 10−6). However, hippocampal decline across time following HA-WBRT was approximately three times lower than following conventional WBRT (p < 10−6), with an average atrophy of 3.1% versus 8.5% in the first 2 years after radiation therapy, respectively.ConclusionHA-WBRT is a therapeutic option for patients with multiple brain metastases, which can effectively and durably minimize hippocampal atrophy compared to conventional WBRT.

Temporal Muscle Thickness is an Independent Prognostic Biomarker in Patients with Glioma: Analysis of 261 Cases.

PurposeTemporal muscle thickness (TMT) has been proposed as a novel surrogate marker for skeletal muscle mass in head and neck malignancies. This study investigated the TMT prognostic relevance with gliomas and evaluated the influence of TMT values on survival in patients with gliomas of different grades and IDH subtypes.MethodsThe patients’ TMT was measured on contrast-enhanced T1-weighted magnetic resonance images before surgical treatment. Patients were divided into two cohorts based on their median TMT values. The Kaplan–Meier curve was used to compute the overall survival (OS) of different categories and all gliomas. Univariate and multivariate Cox regression analyses were conducted to assess the association between OS and TMT, hematological markers, and other clinical factors in glioma patients. Moreover, the clinical diagnostic efficiency of single and combination biomarkers was evaluated using receiver operating characteristic curve analysis.ResultsWe retrospectively analyzed 261 patients with newly diagnosed glioma between November 2016 and May 2020 at Hunan Cancer Hospital. Cox analysis indicated that higher TMT (HR 0.286, P< 0.001) and higher KPS score (HR 0.629, P= 0.012) were protective prognostic factors and IDH wildtype status (HR 2.946, P< 0.001), RDW > 12.6 (HR 1.513, P= 0.036), and NLR > 4 (HR 1.560, P= 0.042) were poor prognostic factors for gliomas. Subsequently, patients with thicker TMT were found to have significantly better overall survival (P<0.001) than patients with thinner TMT among WHO III and WHO IV grade and patients with or without IDH mutation. TMT was considered a better single biomarker than recently prevalent hematological biomarkers for predicting high-grade [0.856 (0.797–0.916)] and IDH- wild-type [0.864 (0.786–0.941)] gliomas.ConclusionThis study suggests that TMT is a positive biomarker for clinical prognosis in gliomas and that patients with thicker TMT have greater overall survival for gliomas of different grades and IDH subtypes.

Predicting local failure of brain metastases after stereotactic radiosurgery with radiomics on planning MR images and dose maps.

Stereotactic radiosurgery (SRS) has become an important modality in the treatment of brain metastases. The purpose of this study is to investigate the potential of radiomic features from planning magnetic resonance (MR) images and dose maps to predict local failure after SRS for brain metastases. Twenty-eight patients who received Gamma Knife (GK) radiosurgery for brain metastases were retrospectively reviewed in this IRB-approved study. 179 irradiated tumors included 42 that locally failed within one-year follow-up. Using SRS tumor volumes, radiomic features were calculated on T1-weighted contrast-enhanced MR images acquired for treatment planning and planned dose maps. 125 radiomic features regarding tumor shape, dose distribution, MR intensities and textures were extracted for each tumor. Logistic regression with automatic feature selection was built to predict tumor progression from local control after SRS. Feature selection and model evaluation using receiver operating characteristic (ROC) curves were performed in a nested cross validation (CV) scheme. The associations between selected radiomic features and treatment outcomes were statistically assessed by univariate analysis. The logistic model with feature selection achieved ROC AUC of 0.82±0.09 on 5-fold CV, providing 83% sensitivity and 70% specificity for predicting local failure. A total of 10 radiomic features including 1 shape feature, 6 MR images and 3 dose distribution features were selected. These features were significantly associated with treatment outcomes (p<0.05). The model was validated on independent holdout data with an AUC of 0.78. Radiomic features from planning MR images and dose maps provided prognostic information in SRS for brain metastases. A model built on the radiomic features shows promise for early prediction of tumor local failure after treatment, potentially aiding in personalized care for brain metastases.

Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging.

It is well-known that expanding glioblastomas typically induce significant deformations of the surrounding parenchyma (i.e., the so-called "mass effect"). In this study, we evaluate the performance of three mathematical models of tumor growth: 1) a reaction-diffusion-advection model which accounts for mass effect (RDAM), 2) a reaction-diffusion model with mass effect that is consistent only in the case of small deformations (RDM), and 3) a reaction-diffusion model that does not include the mass effect (RD). The models were calibrated with magnetic resonance imaging (MRI) data obtained during tumor development in a murine model of glioma (n = 9). We obtained T2-weighted and contrast-enhanced T1-weighted MRI at 6 time points over 10 days to determine the spatiotemporal variation in the mass effect and the volume fraction of tumor cells, respectively. We calibrated the three models using data 1) at the first four, 2) only at the first and fourth, and 3) only at the third and fourth time points. Each of these calibrations were run forward in time to predict the volume fraction of tumor cells at the conclusion of the experiment. The diffusion coefficient for the RDAM model (median of 10.65 × 10 -3 mm 2· d -1) is significantly less than those for the RD and RDM models (17.46 × 10 -3 mm 2· d -1 and 19.38 × 10 -3 mm 2· d -1, respectively). The error in the tumor volume fraction for the RD, RDM, and RDAM models have medians of 40.2%, 32.1%, and 44.7%, respectively, for the calibration using data from the first four time points. The RDM model most accurately predicts tumor growth, while the RDAM model presents the least variation in its estimates of the diffusion coefficient and proliferation rate. This study demonstrates that the mathematical models capture both tumor development and mass effect observed in experiments.

Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder

The use of machine learning algorithms and modern technologies for automatic segmentation of brain tissue increases in everyday clinical diagnostics. One of the most commonly used machine learning algorithms for image processing is convolutional neural networks. We present a new convolutional neural autoencoder for brain tumor segmentation based on semantic segmentation. The developed architecture is small, and it is tested on the largest online image database. The dataset consists of 3064 T1-weighted contrast-enhanced magnetic resonance images. The proposed architecture’s performance is tested using a combination of two different data division methods, and two different evaluation methods, and by training the network with the original and augmented dataset. Using one of these data division methods, the network’s generalization ability in medical diagnostics was also tested. The best results were obtained for record-wise data division, training the network with the augmented dataset. The average accuracy classification of pixels is 99.23% and 99.28% for 5-fold cross-validation and one test, respectively, and the average dice coefficient is 71.68% and 72.87%. Considering the achieved performance results, execution speed, and subject generalization ability, the developed network has great potential for being a decision support system in everyday clinical practice.

Qualitative and Quantitative Analysis of a Spiral Gradient Echo Sequence for Contrast-Enhanced Fat-Suppressed T1-Weighted Spine Magnetic Resonance Imaging.

Pulse sequences with non-Cartesian k-space sampling enable improved imaging in anatomical areas with high degrees of motion artifacts. We analyzed a novel spiral 3-dimensional (3D) gradient echo (GRE) magnetic resonance imaging (MRI) sequence ("spiral," 114.7 ± 11 seconds) and compared it with a radial 3D GRE ("vane," 216.7 ± 2 seconds) and a conventional Cartesian 2D turbo spin echo (TSE) sequence ("TSE," 266.7 ± 82 seconds) for contrast-enhanced fat-suppressed T1-weighted spine imaging. Forty consecutive patients referred for contrast-enhanced MRI were prospectively scanned with all 3 sequences. A qualitative analysis was performed by 3 readers using 4- or 5-point Likert scales to independently grade images in terms of overall image quality, occurrence of artifacts, lesion conspicuity, and conspicuity of nerve roots. The numbers of visible nerve roots per sequence and patient were counted in consensus. Coefficient of variation measurements were performed for the paravertebral musculature (CVPM) and the spinal cord (CVSC). Spiral (median [interquartile range], 5 [4-5]) exhibited improved overall image quality in comparison to TSE (3 [3-4]) and vane (4 [4-5]; both P < 0.001). Vane surpassed TSE in terms of overall image quality (P < 0.001). Spiral (4 [3.75-4]) and vane (3.5 [3-4]) presented with less artifacts than TSE (3 [2.75-3.25]; both P < 0.001). Spiral (4 [4-5]) outperformed vane (4 [3-5]; P = 0.01) and TSE (4 [3-4]; P = 0.04) in terms of lesion conspicuity. Conspicuity of nerve roots was superior on spiral (3 [3-4]) and vane (4 [3-4]) when compared with TSE (1.5 [1-2]; both P < 0.001). Readers discerned significantly more nerve roots on spiral (4 [2.75-8]) and vane (4 [3.75-7.25]) images when compared with TSE (2 [0-4]; both P < 0.001). Interreader agreement ranged from moderate (α = 0.639) to almost perfect (α = 0.967). CVPM and CVSC were significantly lower on spiral as compared with vane and TSE (P < 0.001, P = 0.04). Vane exhibited lower CVPM and CVSC than TSE (P < 0.001, P = 0.01). A novel spiral 3D GRE sequence improves contrast-enhanced fat-suppressed T1-weighted spinal imaging qualitatively and quantitatively in comparison with a conventional Cartesian 2D TSE sequence and to a lesser extent with a radial 3D GRE sequence at shorter scan times.