DCE-MRI Analysis Research Articles

Artificial Intelligence Apps for Medical Image Analysis using pyCERR and Cancer Genomics Cloud.

This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware dependencies. The components of our software framework include the Python-native Computational Environment for Radiological Research (pyCERR) platform for radiological image processing, Cancer Genomics Cloud (CGC) for accessing hardware resources and user management utilities for accessing images from data repositories and installing AI models and their dependencies. GNU-GPL copyright pyCERR was ported to Python from MATLAB-based CERR to enable researchers to organize, access, and transform metadata from high dimensional, multi-modal datasets to build cloud-compatible workflows for AI modeling in radiation therapy and medical image analysis. pyCERR provides an extensible data structure to accommodate metadata from commonly used medical imaging file formats and a viewer to allow for multi-modal visualization. Analysis modules are provided to facilitate cloud-compatible AI-based workflows for image segmentation, radiomics, DCE MRI analysis, radiotherapy dose-volume histogram-based features, and normal tissue complication and tumor control models for radiotherapy. Image processing utilities are provided to help train and infer convolutional neural network-based models for image segmentation, registration and transformation. The framework allows for round-trip analysis of imaging data, enabling users to apply AI models to their images on CGC and retrieve and review results on their local machine without requiring local installation of specialized software or GPU hardware. The deployed AI models can be accessed using APIs provided by CGC, enabling their use in a variety of programming languages. In summary, the presented framework facilitates end-to-end radiological image analysis and reproducible research, including pulling data from sources, training or inferring from an AI model, utilities for data management, visualization, and simplified access to image metadata.

Comparison of SHD-IBG and PVIBGT in ONFH including mechanical and pathological analysis of failure cases

Currently, there is a lack of relevant research on the efficacy difference between SHD combined with IBG and PVIBGT in the treatment of osteonecrosis of the femoral head(ONFH). Firstly, this study intends to compare the effectiveness of surgical hip dislocation combined with impacting bone grafts (SHD-IBG) and pedicled vascularised iliac bone graft transfer (PVIBGT) in treating ONFH. And the study investigates patients who suffered from hip preservation failures from both groups to better comprehend failure reasons. 30 patients (34 hips) with ARCO stage IIIA femoral head necrosis were selected between January 2012 and July 2022. They were divided into group A(SHD-IBG) and group B (PVIBGT) according to different surgical methods. Firstly, compared the 1-year effect between SHD-IBG and PVIBGT at 1 year postoperatively; Secondly, assessed the medium and long-term efficacy of SHD-IBG hip preservation treatment; Lastly, based on study of the femoral head removed from patients with hip preservation failure in the two groups, the reasons for the failure of hip preservation were comprehensively analyzed in the two groups. Group A: 11 males (13 hips), 4 females (4 hips);Group B: 9 males (11 hips), 6 females (6 hips).Firstly, the average Harris scores of the two groups at 1 year after surgery: preoperative: 70.7, 1 year after surgery: 78.9 in group A; preoperative: 69.5, 1 year after surgery: 81.5 in group B. The differences were statistically significant (P < 0.05).Compared to the preoperative period, quantitative analysis by DCE-MRI showed an increase in perfusion in the necroticarea and an improvement in hyperperfusion in the repair-responsive area one year after the surgery. Secondly, in group A, the hip preservation rate was 88.2% at 2.5–11 (average of 77 months) years of follow-up, and the mean Harris score at the last follow-up was 73.2.Semi-quantitative analysis of postoperative DCE-MRI showed that the perfusion curves of necrotic and repaired areas were similar to those of the normal area. This suggests the instability within the femoral head had been effectively improved, and the perfusion had partially recovered. Thirdly, according to Micro-CT and pathologica studies of patients with hip preservation failure in these two groups, all these patients' femoral head was significantly collapsed and deformed. Their trabeculae was thin and partially disorganized, with fractures in the subchondral bone and separation of the cartilage from the subchondral bone. The necrotic areas had sparse trabeculae, disorganized arrangement, loss of continuity, and disappearance of cells in the trabecular traps. The necrotic area was covered with fibrous tissue, and partial restoration was observed in the repair area. Mechanical finite element analysis showed that the maximum equivalent force was observed in the weight- bearing area and the cortical bone surrounding the shaft of femurand. The result of DCE-MRI showed that the repair reaction area exhibited abnormal hyperperfusion. In this study, the efficacy of SHD-IBG and PVIBGT was compared at 1 year after operation, and the long-term follow-up of SHD-IBG was 2.5–11 (mean 77 months) years, combined with DCE-MRI results, we found that the short-term effect of PVIBGT was more significant than that of SHD-IBG. SHD-IBG can achieve satisfactory hip preservation in the medium and long term follow-up.

Blood-brain barrier dysfunction and decreased transcription of tight junction proteins in epileptic dogs.

Epilepsy in dogs and humans is associated with blood-brain barrier (BBB) dysfunction (BBBD), which may involve dysfunction of tight junction (TJ) proteins, matrix metalloproteases, and astrocytes. Imaging techniques to assess BBB integrity, to identify potential treatment strategies, have not yet been evaluated in veterinary medicine. Some dogs with idiopathic epilepsy (IE) will exhibit BBBD. Identifying BBBD may improve antiepileptic treatment in the future. Twenty-seven dogs with IE and 10 healthy controls. Retrospective, prospective cohort study. Blood-brain barrier permeability (BBBP) scores were calculated for the whole brain and piriform lobe of all dogs by using dynamic contrast enhancement (DCE) magnetic resonance imaging (MRI) and subtraction enhancement analysis (SEA). Matrix metalloproteinase-9 (MMP9) activity in serum and cerebrospinal fluid (CSF) was measured and its expression in the piriform lobe was examined using immunofluorescent staining. Gene expression of TJ proteins and astrocytic transporters was analyzed in the piriform lobe. The DCE-MRI analysis of the piriform lobe identified higher BBBP score in the IE group when compared with controls (34.5% vs 26.5%; P = .02). Activity and expression of MMP9 were increased in the serum, CSF, and piriform lobe of IE dogs as compared with controls. Gene expression of Kir4.1 and claudin-5 in the piriform lobe of IE dogs was significantly lower than in control dogs. Our findings demonstrate BBBD in dogs with IE and were supported by increased MMP9 activity and downregulation of astrocytic potassium channels and some TJ proteins. Blood brain barrier dysfunction may be a novel antiepileptic therapy target.

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods.

To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of the field inhomogeneity on estimated parameters. The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy of and increase to about 33% and 51% without correction for field. We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

Exploring the Impact of Irradiation on Glioblastoma Blood-Brain-Barrier Permeability: Insights from Dynamic-Contrast-Enhanced-MRI and Histological Analysis.

(1) Background: Glioblastoma (GB) presents a formidable challenge in neuro-oncology due to its aggressive nature, limited treatment options, and poor prognosis. The blood-brain barrier (BBB) complicates treatment by hindering drug delivery to the tumor site, particularly to the infiltrative cells in the margin of the tumor, which are mainly responsible for tumor recurrence. Innovative strategies are therefore needed to enhance drug delivery in the margins of the tumor. This study explores whether irradiation can enhance BBB permeability by assessing hemodynamic changes and the distribution of contrast agents in the core and the margins of GB tumors. (2) Methods: Mice grafted with U-87MG cells were exposed to increasing irradiation doses. The distribution of contrast agents and hemodynamic parameters was evaluated using both non-invasive magnetic resonance imaging (MRI) techniques with gadolinium-DOTA as a contrast agent and invasive histological analysis with Evans blue, a fluorescent vascular leakage marker. Diffusion-MRI was also used to assess cytotoxic effects. (3) Results: The histological study revealed a complex dose-dependent effect of irradiation on BBB integrity, with increased vascular leakage at 5 Gy but reduced leakage at higher doses (10 and 15 Gy). However, there was no significant increase in the diffusion of Gd-DOTA outside the tumor area by MRI. (4) Conclusions: The increase in BBB permeability could be an interesting approach to enhance drug delivery in glioblastoma margins for low irradiation doses. In this model, DCE-MRI analysis was of limited value in assessing the BBB opening in glioblastoma after irradiation.

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize measurement. A framework was created to evaluate values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' values, the applied software, and a standard operating procedure. These were evaluated using the proposed score defined with accuracy, repeatability, and reproducibility components. Across the 10 received submissions, the score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review.

The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis.

This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the 'responder' and 'non-responder' tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response.

Assessment of Hyperosmolar Blood-Brain Barrier Opening in Glioblastoma via Histology with Evans Blue and DCE-MRI.

While the blood-brain barrier (BBB) is often compromised in glioblastoma (GB), the perfusion and consequent delivery of drugs are highly heterogeneous. Moreover, the accessibility of drugs is largely impaired in the margins of the tumor and for infiltrating cells at the origin of tumor recurrence. In this work, we evaluate the value of methods to assess hemodynamic changes induced by a hyperosmolar shock in the core and the margins of a tumor in a GB model. Osmotic shock was induced with an intracarotid infusion of a hypertonic solution of mannitol in mice grafted with U87-MG cells. The distribution of fluorescent dye (Evans blue) within the brain was assessed via histology. Dynamic contrast-enhanced (DCE)-MRI with an injection of Gadolinium-DOTA as the contrast agent was also used to evaluate the effect on hemodynamic parameters and the diffusion of the contrast agent outside of the tumor area. The histological study revealed that the fluorescent dye diffused much more largely outside of the tumor area after osmotic shock than in control tumors. However, the study of tumor hemodynamic parameters via DCE-MRI did not reveal any change in the permeability of the BBB, whatever the studied MRI parameter. The use of hypertonic mannitol infusion seems to be a promising method to increase the delivery of compounds in the margins of GB. Nevertheless, the DCE-MRI analysis method using gadolinium-DOTA as a contrast agent seems of limited value for determining the efficacy of opening the BBB in GB after osmotic shock.

Delay of Aortic Arterial Input Function Time Improves Detection of Malignant Vertebral Body Lesions on Dynamic Contrast-Enhanced MRI Perfusion.

Dynamic contrast-enhanced MRI (DCE) is an emerging modality in the study of vertebral body malignancies. DCE-MRI analysis relies on a pharmacokinetic model, which assumes that contrast uptake is simultaneous in the feeding of arteries and tissues of interest. While true in the highly vascularized brain, the perfusion of the spine is delayed. This delay of contrast reaching vertebral body lesions can affect DCE-MRI analyses, leading to misdiagnosis for the presence of active malignancy in the bone marrow. To overcome the limitation of delayed contrast arrival to vertebral body lesions, we shifted the arterial input function (AIF) curve over a series of phases and recalculated the plasma volume values (Vp) for each phase shift. We hypothesized that shifting the AIF tracer curve would better reflect actual contrast perfusion, thereby improving the accuracy of Vp maps in metastases. We evaluated 18 biopsy-proven vertebral body metastases in which standard DCE-MRI analysis failed to demonstrate the expected increase in Vp. We manually delayed the AIF curve for multiple phases, defined as the scan-specific phase temporal resolution, and analyzed DCE-MRI parameters with the new AIF curves. All patients were found to require at least one phase-shift delay in the calculated AIF to better visualize metastatic spinal lesions and improve quantitation of Vp. Average normalized Vp values were 1.78 ± 1.88 for zero phase shifts (P0), 4.72 ± 4.31 for one phase shift (P1), and 5.59 ± 4.41 for two phase shifts (P2). Mann-Whitney U tests obtained p-values = 0.003 between P0 and P1, and 0.0004 between P0 and P2. This study demonstrates that image processing analysis for DCE-MRI in patients with spinal metastases requires a careful review of signal intensity curve, as well as a possible adjustment of the phase of aortic AIF to increase the accuracy of Vp.

Diagnostic efficacy of dynamic contrast-enhanced magnetic resonance perfusion imaging in detecting local tumor recurrence in patients with head and neck malignancies after definitive treatment.

Accurate interpretation of post-treatment imaging in head and neck malignancies poses a challenge due to treatment sequelae. Magnetic resonance (MR) perfusion helps in this scenario by evaluating the hemodynamic characteristics of lesions. This study aimed to elucidate the diagnostic efficacy of dynamic contrast-enhanced (DCE)-MR perfusion imaging in detecting recurrence in patients after they underwent definitive treatment for head and neck tumors. Thirty patients who had received definitive curative-intent treatment for histopathology-proven malignant head and neck tumors and in whom recurrent tumor was detected on precontrast MR imaging (MRI) were accrued in the study. Patients underwent DCE-MR perfusion imaging. Time to peak (TTP), relative maximum enhancement (RME), and relative washout (RWO) ratio were calculated by using time-intensity curve (TIC). The diagnostic accuracy was compared with histopathology. A cut-off value of ≥125.3 for RME showed a sensitivity of 76.2% and specificity of 66.7% for differentiating post-radiation changes and recurrence. The optimal cut-off for RWO ratio was ≥-6.24 with a sensitivity of 76.2% and specificity of 55.6%. The optimal cut-off of TTP was ≤45.8 s with a sensitivity of 61.9% and specificity of 77.8%. Diagnostic accuracies of RME, RWO, and TTP were 73.3%, 70%, and 66.7%, respectively. DCE-MRI had significant diagnostic accuracy in detecting and differentiating recurrences. TIC analysis of high-temporal resolution DCE-MRI can provide information regarding microcirculation of tumors, and hence can be considered as an imaging modality of choice for assessment of early local tumor recurrence in head and neck tumors.

The Effect of Histogram Analysis of DCE-MRI Parameters on Differentiating Renal Tumors.

We aimed to assess the role of histogram analysis of DCE-MRI parameters for accurately distinguishing renal clear cell carcinoma from renal hamartoma with minimal fat. Patients with renal tumors were enrolled from January 2013 to December 2015, including renal clear cell carcinoma (n = 39) and renal hamartoma (n = 10). Preoperative DCE-MR Imaging was performed, and whole-tumor regions of interest were drawn to obtain the corresponding histogram parameters, including skewness, kurtosis, frequency size, energy, quartile, etc. Histogram parameters differences between renal clear cell car-cinoma and renal hamartoma with minimal fat were compared. The diagnostic value of each significant parameter in predicting malignant tumors was determined. Histogram parameters of the DCE map contributed to differentiating the benign from malignant renal tumor groups. Histogram analysis of DCE maps could effectively present the heterogeneity of renal tumors and aid in differentiating benign and malignant tumors. ROC analysis results indicated that when frequency size = 1,732 was set as the threshold value, favorable diagnostic performance in predicting malignant tumors was achieved (AUC - 0.964; sensitivity - 84.6%; specificity - 100%), followed by skewness, Energy, Entropy, Uniformity, quartile 5, quartile 50, and kurtosis. Histogram analysis of DCE-MRI shows promise for differentiating benign and malignant renal tumors. Frequency size was the most significant parameter for predicting renal clear cell carcinoma.

An Unsupervised Autoencoder Developed from Dynamic Contrast-Enhanced (DCE)-MRI Datasets for Classification of Acute Tumor Response in an Animal Model

<h3>Purpose/Objective(s)</h3> Recent studies have shown that vascular parameters of brain tumors derived from DCE-MRI may act as potential biomarkers for radiation-induced acute effects. However, accurate characterization of the spatial regions affected by radiation therapy (RT) remains challenging. Here, we introduce an unsupervised adaptive model for classification and ranking of the RT-affected regions in an animal model of cerebral U-251n tumors. <h3>Materials/Methods</h3> Twenty-three immune-compromised-RNU rats were implanted with human U251n cancer cells to form an orthotopic glioma (IACUC #1509). For each rat, 28 days after implantation, two DCE-MRI studies (Dual Gradient Echo, DGE, FOV: 32 × 32 mm², T_R/(T_E1-T_E2) = 24 ms/(2 ms-4 ms), flip angle = 18°, 400 acquisitions, 1.55 sec interval with Magnevist contrast agent, CA injection at ∼ 24 sec) were performed 24h apart using a 7T MRI scanner. A single 20 Gy stereotactic radiation exposure was performed before the second MRI, which was acquired 1-6.5 hrs after RT. DCE-MRI analysis was done using a model selection technique to distinguish three different brain regions as follows: Normal vasculature (Model 1: No leakage, only plasma volume, v_p, is estimated), leaky tumor tissues with no back-flux to the vasculature (Model 2: v_p and forward volumetric transfer constant, K^trans, are estimated), and leaky tumor tissues with back-flux (Model 3: v_p, K^trans, and interstitial volume fraction, v_e, are estimated). Normalized time traces of DCE-MRI information (24 pre, and 24 post-RT for each rat, total of 64108 training datasets) of tumors and their soft surrounding normal tissues were extracted from the 3 different model regions. To eliminate high-dimensional data similarity, an unsupervised autoencoder (AE) was trained to map out the model-derived data into a feature space (latent variables, N=10). For each model, the pre and post RT latent variables were compared (by appropriate tests of significance: ANOVA/Welch, CI=95%) to reveal RT-discriminant features. Pearson correlation coefficients were used to compare the decoded data to rank the effect of RT on different models. <h3>Results</h3> The time trace of DCE-MRI information of rat brain in normal (Model 1, non-leaky) and highly permeable (Model 3) regions are less impacted by RT (Higher correlation between pre and post RT: r=0.8518, p<0.0001 and r=0.9040, p<0.0001 for Model 1 and Model 3, respectively) compared to the peritumoral regions pertaining to Model 2 (r= 0.8077, p<0.0001). <h3>Conclusion</h3> This pilot study suggests that among different brain regions, peritumoral zones (infiltrative tumor borders with enhanced rim) are highly affected by RT. Spatial assessment of RT-affected brain regions can play a key role in optimization of treatment planning in cancer patients, but presents a challenging task in conventional DCE-MRI. This study represents an important step toward classification and ranking the RT-affected brain spatial regions according to their vascular response following hypofractionated RT.

Assessment of Early Response to Lung Cancer Chemotherapy by Semiquantitative Analysis of Dynamic Contrast-Enhanced MRI.

Objective To evaluate the early chemotherapy response in patients with lung cancer using semiquantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Methods Twenty-two patients with lung cancer treated with chemotherapy were subjected to DCE-MRI at two time points: before starting treatment and after one week of therapy. The image data were collected by DCE-MRI, and the semiquantitative parameters including positive enhancement integral (PEI), signal enhancement ratio (SER), maximum slope of increase (MSI), and time to peak (TTP) were calculated. After chemotherapy, the parameters and relevant variations between the responders and nonresponders were compared with Mann–Whitney U tests. Student's t-test for paired samples was used to evaluate the temporal changes between pre- and posttreatment images. Results The patients were categorized as 13 responders and 9 nonresponders based on the tumor response evaluation. After chemotherapy, the PEI, SER, and MSI were significantly increased in responders compared with the pretreatment values (P < 0.05), while no obvious decrease in TTP was observed (P > 0.05). However, 9 nonresponders showed no significant changes in PEI, SER, MSI, and TTP values, as compared with those of pretreatment (P > 0.05). Moreover, the increase of PEI was more dramatically in responders than in nonresponders (P < 0.05), but no significantly differences were observed in SER, MSI, and TTP (P > 0.05). Conclusion Semiquantitative analysis of DCE-MRI could provide a reliable noninvasive method for assessing early chemotherapy response in lung cancer patients.

A Finite Element Based Optimization Algorithm to Include Diffusion into the Analysis of DCE-MRI

DCE-MRI is a widly used technique to obtain a quantitative description of the tumour vasculature. Contrast agent diffusion has a great impact on the extraction of this descriptors. This work presents a new approach based on the FEM to account for this phenomenon. The proposed model outperforms the state-of-art models.

Humanized Anti-RGMa Antibody Treatment Promotes Repair of Blood-Spinal Cord Barrier Under Autoimmune Encephalomyelitis in Mice.

The lack of established biomarkers which reflect dynamic neuropathological alterations in multiple sclerosis (MS) makes it difficult to determine the therapeutic response to the tested drugs and to identify the key biological process that mediates the beneficial effect of them. In the present study, we applied high-field MR imaging in locally-induced experimental autoimmune encephalomyelitis (EAE) mice to evaluate dynamic changes following treatment with a humanized anti-repulsive guidance molecule-a (RGMa) antibody, a potential drug for MS. Based on the longitudinal evaluation of various MRI parameters including white matter, axon, and myelin integrity as well as blood-spinal cord barrier (BSCB) disruption, anti-RGMa antibody treatment exhibited a strong and prompt therapeutic effect on the disrupted BSCB, which was paralleled by functional improvement. The antibody’s effect on BSCB repair was also suggested via GeneChip analysis. Moreover, immunohistochemical analysis revealed that EAE-induced vascular pathology which is characterized by aberrant thickening of endothelial cells and perivascular type I/IV collagen deposits were attenuated by anti-RGMa antibody treatment, further supporting the idea that the BSCB is one of the key therapeutic targets of anti-RGMa antibody. Importantly, the extent of BSCB disruption detected by MRI could predict late-phase demyelination, and the predictability of myelin integrity based on the extent of acute-phase BSCB disruption was compromised following anti-RGMa antibody treatment. These results strongly support the concept that longitudinal MRI with simultaneous DCE-MRI and DTI analysis can be used as an imaging biomarker and is useful for unbiased prioritization of the key biological process that mediates the therapeutic effect of tested drugs.

“DIFFERENTIATING BENIGN FROM MALIGNANT BREAST LESIONS- CLINICAL, HISTOPATHOLOGICAL AND IMAGING CORRELATION”

Introduction: Breast cancer is the second most common cancer in Indian women. Breast cancer is a signicant cause of worldwide morbidity and mortality. Mammography and ultrasound are used as the rst line of investigation for the early detection and localization of breast tumors. Mammography has high sensitivity in case of patients with fatty parenchyma however low sensitivity in case of patients with dense breasts, implants, and post-surgical scar, thus breast MRI with higher sensitivity and specicity due to its ability to depict excellent soft-tissue contrast has become increasingly important in the detection of breast cancer. DCE-MRI has improved specicity in characterizing breast lesions by analysis of time-intensity curves. Diffusion-weighted imaging can improve the sensitivity and specicity of MRI in the evaluation of breast lesions by calculating the ADC values a quantitative measure that is a useful tool for tumor detection and differentiating between benign and malignant breast lesions. To evaluate the role of diffusion-weighted MRI andObjective: dynamic contrast-enhanced MRI in differentiating benign from malignant breast lesions and to compare the ndings of diffusion-weighted MRI and dynamic enhanced MRI with histopathological or FNAC ndings. A totalMaterial And Methods: of 30 patients with palpable breast lumps with either positive or negative ndings on mammography and ultrasound were included in this study. All patients included in this study rst underwent lm-screen mammography. Ultrasonography was done with convex and linear probes. This was followed by MRI. T1W axial, T2W axial, SPAIR/ Fat Saturated T2 weighted axial images were acquired in appropriate imaging planes. Diffusion-weighted images were obtained using b values of 0 and 1000 and ADC values were calculated. Dynamic contrast-enhanced MR was performed using fat-suppressed 3D T1 weighted images after injection of gadolinium and time-intensity curves were generated. A single precontrast scan was followed by 4 post- contrast scans which were obtained for a total duration of 4 min 24 seconds. Findings of the MRI (Diffusion-weighted and dynamic contrast enhancement) were analyzed and correlated with histopathological and FNAC ndings to evaluate their use as a diagnostic modality. A total of 30 female patients presenting with palpable breast lumps were included in theResults: study. Out of 30 patients, 20 cases were malignant (66.67%) and 10 cases were benign (33.3%). Fibroadenoma accounted for a majority of benign lesions (4 out of 10 benign lesions) while IDC accounted for the majority of malignant lesions (15 out of 20 malignant lesions). Dynamic CE-MR is a reliable tool for differentiating between benign and malignant lesions based on kinetic curves. 7 out of 10 benign lesions showed a type I curve while the rest showed a type II curve while the majority (15/20) of malignant lesions showed a type III curve. 8 out of 10 benign breast lesions did not show restricted diffusion on DWI while all malignant lesions showed restricted diffusion on DWI. In our study, the mean ADC value for benign lesions was 1.59 x 10 mm/s while the mean ADC value for malignant lesions was 0.88 x 10 mm/s. Using the ROC curve, the cut-off value of ADC was calculated to be 1.19 x 10 mm/s which gives sensitivity and specicity of 95% and 90% respectively. The individual sensitivity for DCE-MRI and DWI was calculated to be 95% and 95% while the individual specicity for DCE-MRI and DWI was calculated to be 70% and 90% respectively. After a combined analysis of DCE-MRI and DWI using a positive result from any of the two techniques as malignancy, the sensitivity and specicity were 95% and 80% respectively.

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach.

To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI. A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.

Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

BackgroundThe purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer.MethodsPatients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves.ResultsTumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR.ConclusionsQuantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

Radiomic combination of spatial and temporal features extracted from DCE-MRI for prostate cancer detection.

Multi-parametric MRI is part of the standard prostate cancer (PCa) diagnostic protocol. Recent imaging guidelines (PI-RADS v2) downgraded the value of Dynamic Contrast-Enhanced (DCE)-MRI in the diagnosis of PCa. A purely qualitative analysis of the DCE-MRI time series, as it is generally done by radiologists, might indeed overlook information on the microvascular architecture and function. In this study, we investigate the discriminative power of quantitative imaging features derived from texture and pharmacokinetic analysis of DCE-MRI. In 605 regions of interest (benign and malignant tissue) delineated in 80 patients, we found through independent cross-validation that a subset of quantitative spatial and temporal features extracted from DCE-MRI and incorporated in machine learning classifiers obtains a good diagnostic performance (AUC = 0.80-0.86) in distinguishing malignant from benign regions.Clinical Relevance- These findings highlight the underlying potential of quantitative DCE-derived radiomic features in identifying PCa by MRI.