Dynamic Contrast-enhanced Magnetic Resonance Imaging Data Research Articles

Evaluation of the Prognosis of Neuroglioma Based on Dynamic Magnetic Resonance Enhancement

This paper explores the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the prognosis of glioma, and judges the relevant factors affecting the prognosis of glioma. This paper used a Cox proportional hazards model to retrospectively analyze clinical data of 81 patients with complete neuroglioma from the same neurosurgery medical team from January 2012 to November 2018, including DCE-MRI data. To determine the prognostic factors, P < 0.05 was used as the statistical standard, and the survival curve of statistically significant factors was drawn by Kaplan-Meier method. The Cox proportional hazard model analysis showed the preoperative Karnofsky Performance Status Scale (KPS) score, age, tumor pathologic grade, postoperative radiotherapy, temozolomide use, and Ki-67 expression had an impact on the prognosis of patients with neuroglioma. Multivariate analysis and DCE-MRI data showed that age, tumor grade, preoperative KPS score, postoperative radiotherapy, and Ki-67 expression were prognostic factors for patients with glioma. The older the age, the higher the pathologic grade, the higher the Ki-67 expression level, and the lower the KPS score before surgery, the worse the prognosis. Postoperative radiotherapy and appropriate temozolomide chemotherapy will help improve the prognosis of patients with neuroglioma.

The Influence of Signal to Noise Ratio on the Pharmacokinetic Analysis in DCE-MRI Studies

Purpose: Recruiting the Pharmacokinetic (PK) parameters estimated from non-invasive methods such as Dynamic Contrast Enhanced MRI (DCE-MRI) to evaluate or plan treatment procedure is widely investigated in clinical practices. Interpretation of the DCE-MRI data is highly dependent to precision and accuracy of the estimated parameters. One of the most effective factors on the DCE-MR images and on the contrast concentration profile is the Signal to Noise Ratio (SNR). This work focuses on the analytical evaluation of the noise effect on accuracy of the estimated PK parameters in DCE-MRI studies. Materials and Methods: Tofts model as a popular pharmacokinetic model and model selection technique was used to simulate 3470 time curves of contrast concentration. Maximum likelihood estimator as a minimum variance unbiased estimator was recruited to estimate the PK parameters. Eleven levels of signal to noise ratios (SNR= 5, 8, 10, 13, 15, 20, 25, 30, 35, 50, Noiseless) were added to the simulated CA concentration profiles. The PK parameters were estimated for 11 series data and then Mean Percentage Error (MPE) was calculated for estimated parameters. Results: The results indicate that the most sensitive parameter to the SNR of the DCE-MR images is inverse transfer constant. A SNR greater than 25 was found to ensure a reasonable error (MPE &lt;5%) in all models parameters. Conclusion: Clinical decision based on the DCE-MRI data analysis and estimated PK parameters needs a good image quality (SNR&gt;25), an accurate and robust estimator and correct pharmacokinetic model selection

A Clifford Analytic Signal-Based Breast Lesion Segmentation Method for 4D Spatial-Temporal DCE-MRI Sequences

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used for lesion detection in breast cancer diagnosis for its capability to provide spatial-temporal information. However, the massive and complex 4D spatial-temporal DCE-MRI data make the diagnosis process lengthy and error-prone. Moreover, normal fibroglandular tissue is occasionally enhanced through background parenchymal enhancement (BPE), which can degrade the performance of current algorithms. We propose a new method using a 3D Clifford analytic signal (CAS) approach for breast lesion segmentation of DCE-MRI data. A 2D temporal image is constructed from all the 2D DCE-MRI slices at different scanning time points on a given transverse plane, according to the CAS approach. Then, a 3D Clifford temporal image (CTI) is constructed by successively stacking temporal images. The proposed CTI can distinguish lesion regions both visually and quantitatively compared to the traditional DCE-MRI subtraction image. Finally, we employ a fully convolutional network (FCN) model for breast lesion segmentation using the CTI as one of the inputs. Experimental results on an independent public dataset (TCIA QIN breast DCE-MRI) and a private household breast DCE-MRI dataset (TBD) show that the proposed method can achieve superior performance over current methods, both qualitatively and quantitatively.

Triple-negative breast cancer: Pretreatment magnetic resonance imaging features and clinicopathological factors associated with recurrence

PurposeWe aimed to investigate the magnetic resonance imaging (MRI) features and clinicopathologic factors with recurrence of triple-negative breast cancer (TNBC). Patients and methodsWe identified 281 patients with 288 surgically confirmed TNBC lesions who underwent pretreatment MRI between 2009 and 2015. The presence of intratumoral high signal on T2-weighted images, high-signal rim on diffusion-weighted images (DWI), and rim enhancement on the dynamic contrast-enhanced MRI and clinicopathological data were collected. Cox proportional analysis was performed. ResultsOf the 288 lesions, 36 (12.5%) recurred after a median follow-up of 18 months (range, 3.6–68.3 months). Rim enhancement (hazard ratio [HR] = 3.15; 95% confidence interval [CI] = 1.01, 9.88; p = .048), and lymphovascular invasion (HR = 2.73, 95% CI = 1.20, 6.23; p = .016) were independently associated with disease recurrence. While fibroglandular volume, background parenchymal enhancement, intratumoral T2 high signal, and high-signal rim on DWI, were not found to be risk factors for recurrence. ConclusionPretreatment MRI features may help predict a high risk of recurrence in patients with TNBC.

Accelerated dynamic contrast enhanced MRI based on region of interest compressed sensing

Magnetic Resonance Imaging (MRI) provides excellent soft tissue contrast with one significant limitation of slow data acquisition. Dynamic Contrast Enhanced MRI (DCE-MRI) is one of the widely employed techniques to estimate tumor tissue physiological parameters using contrast agents. DCE-MRI data acquisition and reconstruction requires high spatiotemporal resolution, especially during the post-contrast phase. The region of Interest Compressed Sensing (ROICS) is based on Compressed Sensing (CS) framework and works on the hypothesis that limiting CS to an ROI can achieve superior CS performance. In this work, ROICS has been demonstrated on breast DCE-MRI data at chosen acceleration factors and the results are compared with conventional CS implementation. Normalized Root Mean Square Error (NRMSE) was calculated to compare ROICS with CS quantitatively. CS and ROICS reconstructed images were used to compare Ktrans and ve values derived using standard Tofts Model (TM). This also validated the superior performance of ROICS over conventional CS. ROICS generated Concentration Time Curves (CTC's) at chosen acceleration factors follow similar trend as the ground truth data as compared to CS. Both qualitative and quantitative analyses show that ROICS outperforms CS particularly at acceleration factors of 5× and above.

Exercise-induced pain changes associate with changes in muscle perfusion in knee osteoarthritis: exploratory outcome analyses of a randomised controlled trial

BackgroundExercise therapy is recommended for knee osteoarthritis (OA), but the underlying mechanisms of pain relief are not fully understood. The purpose of this study was to explore the effects of exercise on muscle perfusion assessed by dynamic contrast enhanced MRI (DCE-MRI) and its association with changes in pain in patients with knee OA.MethodsExploratory outcome analyses of a randomised controlled study with per-protocol analyses (ClinicalTrials.gov: NCT01545258) performed at an outpatient clinic at a public hospital in Denmark. We compared 12 weeks of supervised exercise therapy 3 times per week (ET) with a no attention control group (CG). Analyses of covariance (ANCOVA) were used to assess group mean differences in changes from baseline to week 12 in knee muscle perfusion quantified by DCE-MRI, patient-reported pain and function using the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire, knee extensor and flexor muscle strength tests, and the six-minute walking test (6MWT). Spearman’s correlation coefficients were used to determine the correlation between changes in DCE-MRI variables, KOOS, muscle strength, and 6MWT. The potential effect mediation of the DCE-MRI perfusion variables was investigated in a post-hoc mediation analysis.ResultsOf 60 participants randomised with knee osteoarthritis, 33 (ET, n = 16, CG, n = 17) adhered to the protocol and had complete DCE-MRI data. At follow-up, there were significant group differences in muscle perfusion changes and clinically relevant group differences in KOOS pain changes (10.7, 95% CI 3.3 to 18.1, P = 0.006) in favor of ET. There were no significant between-group differences on muscle strength and function. The changes in pain and muscle perfusion were significantly correlated (highest Spearman’s rho = 0.42, P = 0.014). The mediation analyses were generally not statistically significant.ConclusionThe pain-reducing effects of a 12-week exercise program are associated with changes in knee muscle perfusion quantified by DCE-MRI in individuals with knee OA, but whether the effects are mediated by muscle perfusion changes remains unclear.Trial registrationClinicalTrials.gov: NCT01545258, first posted March 6, 2012.

SFFS–SVM based prostate carcinoma diagnosis in DCE-MRI via ACM segmentation

The prostate carcinoma is amongst the most commonly occurring cancers in Taiwanese males. Moreover, it is one of the chief reasons for cancer deaths among Taiwanese men, and early diagnosis of prostate cancer is vital for effective treatment. In this work, a diagnosis model for identifying the prostate carcinoma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. The urologists utilize the DCE-MRI as a support mechanism for better diagnosis of the carcinoma development in the prostate. Gadolinium is utilized as the contrast agent for the DCE-MRI data, and it was injected once and the time series data were captured at distinct time intervals of 0, 20, 60, and 100 s correspondingly. Primarily, after pre-processing the DCE-MRI information, the prostate data is segmented by employing the active contour model. Subsequently, 136 features are extracted from the segmented prostrate expanse of the DCE-MRI data, and the relative intensity change curve is computed. Afterward, Fisher’s discriminant ratio and sequential forward floating selection is deployed for choosing ten highly discriminative features. Lastly, the segmented prostate regions are classified into two groups, namely: tumor and normal classes by employing the support vector machine classifier. The experimental results elucidate that the proposed system is superior on the subject of accuracy, sensitivity, and specificity when compared with specific existing methods. Additionally, the proposed system also demonstrates a 94.75% accuracy. Moreover, this signifies the fact that the proposed method for analyzing the DCE data has shown prodigious prospects in the prostate carcinoma diagnosis.

Validating an Empirical Mathematical Model for Dynamic Contrast-enhanced MR Imaging of Hand and Wrist Synovitis in Rheumatoid Arthritis: Correlation of Model Parameters with Clinical Disease Activity.

Purpose:To evaluate the feasibility of an empirical mathematical model (EMM) to fit dynamic contrast-enhanced MRI (DCE-MRI) data of hand and wrist synovitis and whether parameters of EMM are significantly correlated with clinical disease activity in patients with rheumatoid arthritis (RA).Methods:Thirty-one consecutive patients with RA prospectively underwent Institutional Review Board (IRB)- approved DCE-MRI scans with temporal resolution of 20 s using a 1.5T system. ROIs were placed where the highest signal increase was observed and the kinetic curves were analyzed using an EMM: ΔS(t) = A(1 − e−αt) e−βt, where ΔS is relative enhancement, t is time from when the signal increase was first observed, starting from baseline (ΔS = 0), A is the upper limit of signal intensity, α (s−1) is the rate of signal increase, and β (s−1) is the rate of signal decrease during washout. The initial slope of the kinetic curve (Aα), the initial area under the curve (AUC30), the time at which the kinetic curve reached its peak (Tpeak) and the signal enhancement ratio (SER) defined as the change in signal intensity between the initial and delayed time points (t = 60 and 300 s, respectively) were calculated. RA magnetic resonance imaging scores (RAMRIS) with and without contrast media were evaluated. These parameters or scores were compared with the Disease Activity Score (DAS) 28-erythrocyte sedimentation rate (ESR).Results:A showed a significant correlation with DAS28-ESR (r = 0.58; P = 0.0005). β, AUC30 and Tpeak were also significantly correlated with DAS28-ESR with a lesser degree (r = 0.49; P = 0.0051, r = 0.50; P = 0.0038 and r = −0.51; P = 0.0028, respectively), whereas α, Aα, SER and RAMRIS were not.Conclusion:EMM could fit the DCE-MRI data of hand and wrist synovitis. AUC30 obtained from the uptake phase of the kinetic curve as well as A, β and Tpeak obtained throughout the kinetic curve might be effective to predict the clinical disease activity.

Assessment of Hepatic Perfusion Using GRASP MRI: Bringing Liver MRI on a New Level.

The aim of this study was to demonstrate the feasibility of hepatic perfusion imaging using dynamic contrast-enhanced (DCE) golden-angle radial sparse parallel (GRASP) magnetic resonance imaging (MRI) for characterizing liver parenchyma and hepatocellular carcinoma (HCC) before and after transarterial chemoembolization (TACE) as a potential alternative to volume perfusion computed tomography (VPCT). Between November 2017 and September 2018, 10 patients (male = 8; mean age, 66.5 ± 8.6 years) with HCC were included in this prospective, institutional review board-approved study. All patients underwent DCE GRASP MRI with high spatiotemporal resolution after injection of liver-specific MR contrast agent before and after TACE. In addition, VPCT was acquired before TACE serving as standard of reference. From the dynamic imaging data of DCE MRI and VPCT, perfusion maps (arterial liver perfusion [mL/100 mL/min], portal liver perfusion [mL/100 mL/min], hepatic perfusion index [%]) were calculated using a dual-input maximum slope model and compared with assess perfusion measures, lesion characteristics, and treatment response using Wilcoxon signed-rank test. To evaluate interreader agreement for measurement repeatability, the interclass correlation coefficient (ICC) was calculated. Perfusion maps could be successfully generated from all DCE MRI and VPCT data. The ICC was excellent for all perfusion maps (ICC ≥ 0.88; P ≤ 0.001). Image analyses revealed perfusion parameters for DCE MRI and VPCT within the same absolute range for tumor and liver tissue. Dynamic contrast-enhanced MRI further enabled quantitative assessment of treatment response showing a significant decrease (P ≤ 0.01) of arterial liver perfusion and hepatic perfusion index in the target lesion after TACE. Dynamic contrast-enhanced GRASP MRI allows for a reliable and robust assessment of hepatic perfusion parameters providing quantitative results comparable to VPCT and enables characterization of HCC before and after TACE, thus posing the potential to serve as an alternative to VPCT.

Localized Blood–Brain Barrier Opening in Ovine Model Using Image-Guided Transcranial Focused Ultrasound

Transcranial application of focused ultrasound (FUS) combined with vascular introduction of microbubble contrast agents (MBs) has emerged as a non-invasive technique that can temporarily create a localized opening in the blood–brain barrier (BBB). Under image-guidance, we administered FUS to sheep brain after intravenous injection of microbubbles. BBB opening was confirmed by performing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to detect the extravasated gadolinium-based magnetic resonance contrast agents. Through pharmacokinetic analysis as well as independent component analysis of the DCE-MRI data, we observed localized enhancement in BBB permeability at the area that subjected to acoustic pressure of 0.48 MPa (mechanical index = 0.96). On the other hand, application of a higher pressure at 0.58 MPa resulted in localized, minor cerebral hemorrhage. No animals exhibited abnormal behavior during the post-FUS survival periods up to 2 mo. Our data suggest that monitoring for excessive BBB disruption is important for safe translation of the method to humans.

A Phase II, Multicenter, Single-Arm Study of Mipsagargin (G-202) as a Second-Line Therapy Following Sorafenib for Adult Patients with Progressive Advanced Hepatocellular Carcinoma.

Background: Mipsagargin (G-202) is a thapsigargin-based prodrug with cytotoxic activity masked by a peptide that is cleaved by prostate-specific membrane antigen (PSMA), a protease expressed in prostate cancer cells and the endothelium of tumor vasculature. It was hypothesized that PSMA-mediated activation of mipsagargin would result in disruption of the tumor vasculature, leading to a decrease in blood flow, and in direct cytotoxic effects on tumor cells, resulting in anti-tumor activity. Method: In this open-label, Phase II study, mipsagargin was administered intravenously on Days 1, 2, and 3 of a 28-day cycle to patients with hepatocellular carcinoma (HCC) who progressed on or after treatment with sorafenib or intolerant of sorafenib. Assessments included time to disease progression (TTP), response rate, progression-free survival (PFS), overall survival (OS), and safety. Blood flow metrics in hepatic lesions were evaluated using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Results: Of 25 treated patients, 19 were evaluable for efficacy. None had an objective response, 12 (63.2%) had a best response of stable disease, and 12 (63.2%) showed radiologic progression; seven patients (36.8%) were censored. The median TTP was 134.0 days, median PFS was 129.0 days, and median OS was 205.0 days. Of five patients with DCE-MRI data for 11 HCC lesions, all demonstrated a reduced Ktrans (mean, 52%). The most common treatment-emergent AEs were Grade 1–2 and consisted of increased blood creatinine (68.0%), fatigue (56.0%), and nausea (44.0%). Conclusions: Mipsagargin is relatively well tolerated and promotes prolonged disease stabilization in patients with advanced HCC that had progressed on prior treatment with sorafenib. A significant decrease in Ktrans upon treatment suggests mipsagargin reduces blood flow in hepatic lesions.

Évaluation Chedoke-McMaster de l’AVC

Dynamic Contrast Enhanced (DCE) Magnetic Resonance Imaging (MRI) is an important diagnostic technique that can quantify the structure and function of microvasculature processes, using T1 relaxation times and tracer kinetic maps. However, a series of methodological limitations affect both the accuracy and standardisation of the quantified maps, and consequently their diagnostic ability. The main methodological challenge in the quantification of tracer kinetics is a multi-parameter optimization, with correlated parameters that have different scales, which results in local minima particularly when measurements are highly undersampled. This work suggests a novel data driven optimization scheme, based on a variation of the Stochastic Gradient Langevin dynamics (SGLD) Markov chain Monte Carlo algorithm, which combines stochastic gradient descent and Langevin dynamics. The proposed SGDL algorithm avoided local minima and accurately quantified proton density, T1 relaxation times and tracer kinetics. Joint direct parameterization significantly benefited the quantification of proton density, T1 relaxation times, and the selection of a suitable tracer kinetic model per tissue type. Model based arterial and portal vein input functions were automatically determined during the joint direct parameterization. Observations made on simulated fully and highly undersampled liver DCE MRI data were confirmed on acquired clinical data.

Golden-Angle Radial Sparse Parallel MR Image Reconstruction Using SC-GROG Followed by Iterative Soft Thresholding

Golden-angle radial sparse parallel (GRASP) magnetic resonance imaging (MRI) is a recent MR image reconstruction technique which integrates parallel imaging, compressed sensing and golden-angle radial scheme to reconstruct the dynamic contrast-enhanced MRI (DCE-MRI) data. Conventionally, GRASP exploits non-uniform fast Fourier transform to grid and de-grid the golden-angle radial data and employs nonlinear conjugate gradient method to recover the unaliased images. GRASP performs gridding and de-gridding operations of golden-angle radial data in every iteration which increases the computational complexity of the conventional GRASP and takes a long image reconstruction time. In this paper, self-calibrated GRAPPA operator gridding (SC-GROG) followed by iterative soft thresholding (IST) is proposed for faster GRASP reconstruction of the golden-angle radial DCE-MRI data. In the proposed method, firstly SC-GROG maps the undersampled golden-angle radial data to a Cartesian grid and then reconstructs the solution image using the IST technique. The proposed method does not require gridding and de-gridding in each iteration; therefore, it is computationally less expensive as compared to the conventional GRASP reconstruction approach. The proposed method is tested for undersampled DCE golden-angle radial liver perfusion data (at acceleration factors 11.8, 19.1 and 30.9). The reconstruction results are assessed visually as well as using mean square error, line profiles and reconstruction time. The reconstruction results are compared with the conventional GRASP reconstruction. The results show that the proposed method provides better quality reconstruction results in terms of reconstruction time and spatio-temporal resolution than the conventional GRASP approach.

Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI.

The spatiotemporal variations in tumor vasculature inevitably alters cell proliferation and treatment efficacy. Thus, rigorous characterization of tumor dynamics must include a description of this phenomenon. We have developed a family of biophysical models of tumor growth and angiogenesis that are calibrated with diffusion-weighted magnetic resonance imaging (DW-MRI) and dynamic contrast-enhanced (DCE-) MRI data to provide individualized tumor growth forecasts. Tumor and blood volume fractions were evolved using two, coupled partial differential equations consisting of proliferation, diffusion, and death terms. To evaluate these models, rats (n=8) with C6 gliomas were imaged seven times. The tumor volume fraction was estimated using DW-MRI, while DCE-MRI provided estimates of the blood volume fraction. The first three time points were used to calibrate model parameters, which were then used to predict growth at the remaining four time points and compared directly to the measurements. The best performing model predicted tumor growth with less than 10.3% error in tumor volume and with less than 9.4% error at the voxel-level at all prediction time points. The best performing model resulted in less than 9.3% error in blood volume at the voxel-level. This pre-clinical study demonstrates the potential for image-based, mechanistic modeling of tumor growth and angiogenesis.

Heterogeneous Enhancement Pattern in DCE-MRI Reveals the Morphology of Normal Lymph Nodes: An Experimental Study.

Purpose To investigate the heterogeneous enhancement pattern in normal lymph nodes of healthy mice by different albumin-binding contrast agents. Methods The enhancement of normal lymph nodes was assessed in mice by dynamic contrast-enhanced MRI (DCE-MRI) after the administration of two contrast agents characterized by different albumin-binding properties: gadopentetate dimeglumine (Gd-DTPA) and gadobenate dimeglumine (Gd-BOPTA). To take into account potential heterogeneities of the contrast uptake in the lymph nodes, k-means cluster analysis was performed on DCE-MRI data. Cluster spatial distribution was visually assessed. Statistical comparison among clusters and contrast agents was performed on semiquantitative parameters (AUC, wash-in rate, and wash-out rate) and on the relative size of the segmented clusters. Results Cluster analysis of DCE-MRI data revealed at least two main clusters, localized in the outer portion and in the inner portion of each lymph node. With both contrast agents, AUC (p < 0.01) and wash-in (p < 0.05) rates were greater in the inner cluster, which also showed a steeper wash-out rate than the outer cluster (Gd-BOPTA, p < 0.01; Gd-DTPA, p=0.056). The size of the outer cluster was greater than that of the inner cluster by Gd-DTPA (p < 0.05) and Gd-BOPTA (p < 0.01). The enhancement pattern of Gd-DTPA was not significantly different from the enhancement pattern of Gd-BOPTA. Conclusion DCE-MRI in normal lymph nodes shows a characteristic heterogeneous pattern, discriminating the periphery and the central portion of the lymph nodes. Such a pattern deserves to be investigated as a diagnostic marker for lymph node staging.

Comparison of transport of chemotherapeutic drugs in voxelized heterogeneous model of human brain tumor

Systemic administration of chemotherapeutic drugs is widely used in the treatment of cancer. However, a good understanding of drug transport barriers that influence the treatment efficacy is still lacking. In this study, a voxelized numerical model based on dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) and computational fluid dynamics (CFD) is employed to study the transport and efficacy of three different chemotherapeutic drugs, namely methotrexate, doxorubicin and cisplatin in human brain tumors. DCE-MRI data provides realistic heterogeneous vasculature of the tumor, the permeability of tissue to contrast agent, interstitial volume fraction (porosity) of the tissue and patient-specific arterial input function (AIF). The permeability of tissue to aforementioned drugs is determined by correlating it with the permeability of tissue to the contrast agent. The model is employed to simulate drug concentration in the tissue and compare the effect of heterogeneous vasculature on the distribution of the drugs in the tumor. The drug accumulation is observed to be higher in high permeability areas initially, and in higher porosity areas at later times. Furthermore, it is observed that methotrexate remains in the interstitial space of the tumor in higher concentration for a longer duration as compared to other two drugs, facilitating more tumor cell killing.

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps.

We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (Ktrans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.

Abstract P1-01-02: Quantitative breast MRI to predict response to neoadjuvant therapy in community imaging centers: Preliminary results

Abstract Introduction: Early response assessment to neoadjuvant therapy (NAT) for locally advanced breast cancer would allow for more accurate prognosis and provide the opportunity to replace an ineffective treatment with an alternative regimen. This could potentially increase systemic treatment efficacy and avoid unnecessary side effects from ineffective therapies. Quantitative MRI has been shown to be beneficial in predicting breast tumor response to treatment early during the course of NAT within many academic environments. Importantly, integrating quantitative imaging techniques into the community-based setting has the potential to reach a large percentage of breast cancer patients, as most patients receive their care at private practice or community hospitals. This study evaluated the ability to implement quantitative dynamic contrast enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) data in the community setting to predict the eventual response of breast tumors to NAT. Experimental Design: Women undergoing NAT for breast cancer (N=16) were scanned with DCE-MRI and DW-MRI at baseline (prior to beginning therapy, t1) and three longitudinal time points during the course of NAT to evaluate early response to therapy (t2, t3, t4). MRI was performed at two community imaging centers using a 3T Siemens Skyra scanners equipped with 8- or 16-channel breast coils. DW-MRI was acquired with a spin echo sequence with TR/TE = 3000/52 ms, b-values of 200, 800 s/mm2 and used to compute the apparent diffusion coefficient (ADC) values for every voxel. DCE-MRI data was collected (following a pre-contrast T1 map) with TR/TE/α = 7.02 ms/4.60 ms/6o, and a temporal resolution of 7.27 sec for eight minutes. A catheter placed within an antecubital vein delivered gadolinium-based contrast agent (0.1 mmol/kg of Multihance or 10 mL of Gadovist) at 2 mL/sec via a power injector after the acquisition of the first minute of dynamic scans (baseline).Quantitative measures of ADC (evaluating cellularity from DW-MRI) and Ktrans(evaluating vascular perfusion and permeability from DCE-MRI) were calculated for the segmented tumor volume. Imaging was compared to pathology reports at the conclusion of NAT. Results:The patients (n = 6) achieving a pathological complete response (pCR) revealed a 12.9% ± 19.1% increase in the mean ADC values of the tumor from t1 and t2. Conversely, patients (n = 10) that had residual disease burden after NAT (i.e., a non-pCR) had a decreased ADC, revealing a -8.0% ± 19.2% change between t1 and t2 (p = 0.06). The mean Ktrans values of the tumor decreased showing a change of -61.4% ± 18.2% from t1 to t2 in the pCR patients. Conversely, non-pCR patients had a 10.2% ± 80.4% increase in Ktrans between t1 and t2 (p = 0.14) Conclusion:Preliminary evidence reveals that quantitative DCE-MRI and DW-MRI can be implemented in community-based imaging settings to predict the response of breast tumors to NAT. Thus, our results provide evidence that quantitative DW-MRI and DCE-MRI can be disseminated across community imaging facilities, thereby dramatically increasing the patient population for which these techniques can serve. We acknowledge the support of CPRIT RR160005. Citation Format: Sorace AG, Virostko J, Wu C, Jarrett AM, Barnes SL, Ekrut D, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative breast MRI to predict response to neoadjuvant therapy in community imaging centers: Preliminary results [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-01-02.

Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated. Existing methods for BAT estimation are tailored to tissue concentration curves, which have a fast upslope to the peak as frequently observed in patient data. However, they may give poor results for curves that do not have this characteristic shape such as tissue concentration curves of small animals. In this paper, we propose a method for BAT estimation of signals that do not have a fast upslope to their peak. The model is based on splines which are able to adapt to a large variety of concentration curves. Furthermore, the method estimates BATs on a continuous time scale. All relevant model parameters are automatically determined by generalized cross validation. We use simulated concentration curves of small animal and patient settings to assess the accuracy and robustness of our approach. The proposed method outperforms a state-of-the-art method for small animal data and it gives competitive results for patient data. Finally, it is tested on in vivo acquired rat data where accuracy of BAT estimation was also improved upon the state-of-the-art method. The results indicate that the proposed method is suitable for accurate BAT estimation of DCE-MRI data, especially for small animals.

Effect of Tumor Volume on Drug Delivery in Heterogeneous Vasculature of Human Brain Tumors

Drug distribution in tumors is strongly dependent on tumor biological properties such as tumor volume, vasculature, and porosity. An understanding of the drug distribution pattern in tumors can help in enhancing the effectiveness of anticancer treatment. A numerical model is employed to study the distribution of contrast agent in the heterogeneous vasculature of human brain tumors of different volumes. Dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) has been done for a number of patients with different tumor volumes. Leaky tracer kinetic model (LTKM) is employed to obtain perfusion parameters from the DCE-MRI data. These parameters are used as input in the computational fluid dynamics (CFD) model to predict interstitial fluid pressure (IFP), interstitial fluid velocity (IFV), and distribution of the contrast agent in different tumors. Numerical results demonstrate that the IFP is independent of tumor volume. On the other hand, the IFV increases as the tumor volume increases. Further, the concentration of contrast agent also increases with the tumor volume. The results obtained in this work are in line with the experimental DCE-MRI data. It is observed that large volume tumors tend to retain a higher concentration of contrast agent for a longer duration of time because of large extravasation flux and slow washout as compared to smaller tumors. These results may be qualitatively extrapolated to chemotherapeutic drug delivery, implying faster healing in large volume tumors. This study helps in understanding the effect of tumor volume on the treatment outcome for a wide range of human tumors.