Analysis Of Dynamic Contrast Enhanced MRI Research Articles

Detecting residual soft tissue sarcoma after unplanned excision; model-free analysis of dynamic contrast-enhanced MRI at short-term follow-up.

To evaluate diagnostic utility of additional DCE-MRI for detecting residual soft tissue sarcomas (STS) after unplanned excision (UPE). We retrospectively evaluated 32 patients with UPE of STS, followed by conventional MRI with DCE-MRI and wide excision (WE), between November 2019 and January 2022. Residual tumors on conventional MRI were categorized into three groups: Lesion-type-0, no abnormal enhancement, Lesion-type-1, an indeterminate lesion, and Lesion-type-2, a definite enhancing nodule. On DCE-MRI, ROIs were manually placed on enhancing areas of suspected residual tumor. The mean and 95th percentile values of AUC of time-intensity curve were calculated at 60, 90, and 120 s of Enhancement-cycle-1 and -2. Optimal DCE parameters were identified by ROC analysis. Diagnostic performance of conventional MRI and DCE-MRI was compared using McNemar's test. On WE, residual tumor was present in 23 (71.9%) of 32 patients. On MRI, Lesion-type-1 was found in 16/32 (50%) patients and Lesion-type-2 in 16/32 (50%). The optimal DCE parameter was the 95th percentile value of AUC at 120s of Enhancement-cycle-2. The sensitivity, specificity, and AUC were as follows: 65.2% (95% CI, 45.8-85.7%), 88.9% (CI, 68.4-100%), and 0.77 (CI, 0.62-0.92) for conventional MRI, and 100%, 55.6% (CI, 23.1-88.0%), and 0.78 (CI, 0.61-0.95) for combined conventional and DCE-MRI. Additional DCE-MRI aided in detecting residual STS after UPE, particularly in cases without definite soft tissue nodular enhancement. Close follow up may be suggested for patients showing abnormality in DCE-MRI, with more suspicion of residual tumor.

The value of dynamic contrast-enhanced MRI in the differentiation between benign and malignant lacrimal epithelial tumors

Objective: To investigate the diagnostic performance of multiparametric dynamic contrast-enhanced MRI(DCE-MRI) for the differentiation between benign and malignant larcrimal gland epithelial tumors. Methods: The clinical and imaging data of 104 patients with epithelial tumors of the lacrimal gland who underwent orbital MRI scan and met the inclusion criteria in Beijing Tongren Hospital from January 2011 to December 2017 were retrospectively collected, including 48 males and 56 females, aged from 12 to 77 (43±7) years. Sixty-three cases of benign epithelial tumors and 41 cases of malignant epithelial tumors were examined by DCE-MRI. The parameters of semiquantitative analysis including: time to peak enhancement (Tpeak), maximum enhancement ratio (ERmax), Slope, washout ratio (WR) and time-signal intensity curve (TIC) types. The parameters of quantitative analysis including: volume transfer constant (Ktrans), the extravascular extracellular volume fraction (Ve) and rate constant (Kep). Receiver operating characteristic (ROC) curve analysis was performed for DCE-MRI parameters with statistically significant differences, the area under the curve (AUC) was calculated, the diagnostic threshold was determined, and the diagnostic performance was evaluated. Logistic regression analysis was used to determine the best parameters for differential diagnosis of benign and malignant epithelial tumors of the lacrimal gland. Results: For the semiquantitative analysis of DCE-MRI, malignant lacrimal gland epithelial tumor had a significantly shorter Tpeak than benign masses [(103.77±57.87) s vs (187.80±77.01) s,P<0.001)], while had a higher value in ERmax, Slope [M(Q1,Q3)] and WR in malignant masses compared with benign one [1.55±0.39 vs 1.36±0.33; 1.76 (0.97,2.27) vs 0.62 (0.50,0.93); 7.70%(1.40%, 21.60%)% vs 0(0, 0),all P<0.05)].The TICs of benign lacrimal tumors mainly showed a persistent type (49/63),while most malignant lacrimal tumors mainly showed a plateau type (25/41). For the quantitative analysis of DCE-MRI, the values of Ktrans and Kep[M(Q1,Q3)] in malignant tumors were significantly greater than those of benign tumors (0.99±0.52/min vs 0.43±0.23/min, P<0.001; 1.33(0.83, 1.55)/min vs 0.55(0.46, 0.68)/min, P<0.001). No significant difference in Ve was found between the groups (0.76±0.20 vs 0.73±0.22,P=0.467). Through the statistical analysis, TIC types (OR=3.887,95%CI: 1.409-10.725) and Ktrans(OR=50.979,95%CI: 6.046-429.830) can provide superior diagnostic performance for predicting malignant lacrimal gland epithelial tumors, with a sensitivity of 78.05%, specificity of 77.78%,and sensitivity of 70.73%, specificity of 95.24%, respevtively. Furthermore, the comprehensive diagnostic performance of Ktrans in AUC was proven to be significantly better than that of TIC [0.875 (0.796-0.932) vs 0.798 (0.708-0.870),P=0.049]. Conclusions: Multiparametric DCE-MRI is helpful for the differential diagnosis of benign and malignant epithelial tumors of lacrimal gland. TIC type and Ktrans have higher diagnostic value, and the diagnostic performance of Ktrans is better than that of TIC.

Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas.

Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging. To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas. This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas. Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp-90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05). Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.

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

BackgroundTriple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC.MethodsThis retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived.ResultsThe binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group.ConclusionHistogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.

Open source code for the generation of digital reference objects for dynamic contrast-enhanced MRI analysis software validation.

Dynamic contrast-enhanced MR images can be analyzed through the application of a wide range of kinetic models. This process is prone to variability and a lack of standardization that can affect the measured metrics. There is a need for customized digital reference objects (DROs) for the validation of DCE-MRI software packages that undertake kinetic model analysis. DROs are currently available only for a small subset of the kinetic models commonly applied to DCE-MRI data. This work aimed to address this gap. Code was written in the MATLAB programming environment to generate customizable DROs. This modular code allows the insertion of a plug-in to describe the kinetic model to be tested. We input our generated DROs into three commercial and open-source analysis packages and assessed the agreement of kinetic model parameter values output with the 'ground-truth' values used in the DRO generation. For the five kinetic models tested, the concordance correlation coefficient values were >98%, indicating excellent agreement of the results with 'ground-truth'. Testing our DROs on three independent software packages produced concordant results, strongly suggesting our DRO generation code is correct. This implies that our DROs can be used to validate other third party software for the kinetic model analysis of DCE-MRI data. This work extends published work of others to allow customized generation of test objects for any applied kinetic model and allows the incorporation of B1 mapping into the DRO for application at higher field strengths.

Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.

The aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. This retrospective study included 95 women (mean age, 48.1 years; range, 29-77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. The overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort. The combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.

Corrigendum for: Evaluation of Blood-Brain Barrier Integrity by the Analysis of Dynamic Contrast-Enhanced MRI - a Comparison of Quantitative and Semi-Quantitative Methods.

List of changes: On the basis of author's request the publisher of Physiological Research decided to change the license of the article to CC BY license.

Evaluation of blood-brain barrier integrity by the analysis of dynamic contrast-enhanced MRI - a comparison of quantitative and semi-quantitative methods.

Disruption of the blood-brain barrier (BBB) is a key feature of various brain disorders. To assess its integrity a parametrization of dynamic magnetic resonance imaging (DCE MRI) with a contrast agent (CA) is broadly used. Parametrization can be done quantitatively or semi-quantitatively. Quantitative methods directly describe BBB permeability but exhibit several drawbacks such as high computation demands, reproducibility issues, or low robustness. Semi-quantitative methods are fast to compute, simply mathematically described, and robust, however, they do not describe the status of BBB directly but only as a variation of CA concentration in measured tissue. Our goal was to elucidate differences between five semi-quantitative parameters: maximal intensity (Imax), normalized permeability index (NPI), and difference in DCE values between three timepoints: baseline, 5 min, and 15 min (delta5-0, delta15-0, delta15-5) and two quantitative parameters: transfer constant (Ktrans) and an extravascular fraction (Ve). For the purpose of comparison, we analyzed DCE data of four patients 12-15 days after the stroke with visible CA enhancement. Calculated parameters showed abnormalities spatially corresponding with the ischemic lesion, however, findings in individual parameters morphometrically differed. Ktrans and Ve were highly correlated. Delta5-0 and delta15-0 were prominent in regions with rapid CA enhancement and highly correlated with Ktrans. Abnormalities in delta15-5 and NPI were more homogenous with less variable values, smoother borders, and less detail than Ktrans. Moreover, only delta15-5 and NPI were able to distinguish vessels from extravascular space. Our comparison provides important knowledge for understanding and interpreting parameters derived from DCE MRI by both quantitative and semi-quantitative methods.

Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma.

Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI. The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome. Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases. The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.

Quantitative texture analysis based on dynamic contrast enhanced MRI for differential diagnosis between primary thymic lymphoma from thymic carcinoma

To evaluate the value of texture analysis based on dynamic contrast enhanced MRI (DCE-MRI) in the differential diagnosis of thymic carcinoma and thymic lymphoma. Sixty-nine patients with pathologically confirmed (thymic carcinoma, n = 32; thymic lymphoma, n = 37) were enrolled in this retrospective study. Ktrans, Kep and Ve maps were automatically generated, and texture features were extracted, including mean, median, 5th/95th percentile, skewness, kurtosis, diff-variance, diff-entropy, contrast and entropy. The differences in parameters between the two groups were compared and the diagnostic efficacy was calculated. The Ktrans-related significant features yielded an area under the curve (AUC) of 0.769 (sensitivity 90.6%, specificity 51.4%) for the differentiation between thymic carcinoma and thymic lymphoma. The Kep-related significant features yielded an AUC of 0.780 (sensitivity 87.5%, specificity 62.2%). The Ve-related significant features yielded an AUC of 0.807 (sensitivity 75.0%, specificity 78.4%). The combination of DCE-MRI textural features yielded an AUC of 0.962 (sensitivity 93.8%, specificity 89.2%). Five parameters were screened out, including age, Ktrans-entropy, Kep-entropy, Ve-entropy, and Ve-P95. The combination of these five parameters yielded the best discrimination efficiency (AUC of 0.943, 93.7% sensitivity, 81.1% specificity). Texture analysis of DCE-MRI may be helpful to distinguish thymic carcinoma from thymic lymphoma.

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

Pharmacokinetic (PK) models are used to extract physiological information from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) sequences. Some of the most common models employed in clinical practice, such as the standard Tofts model (STM) or the extended Tofts model (ETM), do not account for passive delivery of contrast agent (CA) through diffusion. In this work, we introduce a diffusive term based on the concept of effective diffusivity into a finite element (FE) implementation of the ETM formulation, obtaining a new formulation for the diffusion-corrected ETM (D-ETM). A gradient-based optimization algorithm is developed to characterize the vascular properties of the tumour from the CA concentration curves obtained from imaging clinical data. To test the potential of our approach, several theoretical distributions of CA concentration are generated on a benchmark problem and a real tumour geometry. The vascular properties used to generate these distributions are estimated from an inverse analysis based on both the ETM and the D-ETM approaches. The outcome of these analyses shows the limitations of the ETM to retrieve accurate parameters in the presence of diffusion. The ETM returns smoothed distributions of vascular properties, reaching unphysical values in some of them, while the D-ETM accurately depicted the heterogeneity of KTrans, v_{e} and v_{p} distributions (mean absolute relative difference (ARD) of 16%, 15% and 9%, respectively, for the real geometry case) keeping all their values within their physiological ranges, outperforming the ETM.

Sigmoid model analysis of breast dynamic contrast-enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction.

Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype.A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis.The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05).The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.

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

AimThis study aimed to use multiparametric magnetic resonance imaging (MRI) techniques, namely, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate bone tumors. MethodsThirty-three patients with primary untreated bone tumors were assessed utilizing DWI and DCE-MRI. Various parameters like ADC values from DWI and percentage peak signal intensity (%PSI), the maximum slope of increase (MSI), and time to peak signal intensity (TTP) values were assessed in different cases, and the final correlation was drawn with histopathological findings. ResultParameters of semi-quantitative DCE-MRI, i.e., %PSI, MSI and, TTP, correlated significantly with the histopathological characteristics of the tumor (p values < 0.001). Minimum ADC value in the tumor also showed a strong correlation with the tumor characteristic (p values < 0.001). Also, the correlation between parameters of DWI and DCI-MRI is well correlated with each other. ConclusionThe results of this study provide grounds for the integration of multiparametric pre-treatment evaluation of bone tumors. In our study, we not only tried to utilize different parameters of functional MRI in bone tumors as well as re-explored the semi-quantitative analysis of DCE-MRI.

Feasibility of Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Differentiation of Benign Parotid Gland Tumors.

Simple SummaryMultiparametric magnetic resonance imaging gains recognition in the diagnostic algorithm of salivary gland tumors, providing data from different diffusion-weighted imaging (DWI) models and dynamic contrast enhanced sequences (DCE-MRI). The aim of this prospective study is to identify quantitative intravoxel incoherent motion and DCE-MRI parameters of the most frequent benign parotid tumors, pleomorphic adenomas and Warthin tumors, to compare their utility and diagnostic accuracy. With a precise pre-treatment diagnostic tool, patients can potentially avoid unnecessary diagnostic procedures and be offered optimal treatment. Aim: The aim of this prospective study is to identify quantitative intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging parameters of the most frequent benign parotid tumors, compare their utility and diagnostic accuracy. Methods: The study group consisted of 52 patients with 64 histopathologically confirmed parotid focal lesions. Parametric maps representing apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (FP) and transfer constant (Ktrans), reflux constant (Kep), extra-vascular extra-cellular volume fraction (Ve), and initial area under curve in 60 s (iAUC) have been obtained from multiparametric MRI. Results: Statistically significant (p < 0.001) inter-group differences were found between pleomorphic adenomas (PA) and Warthin tumors (WT) in all tested parameters but iAUC. Receiver operating characteristic curves were constructed to determine the optimal cut-off levels of the most significant parameters allowing differentiation between WT and PA. The Area Under the Curve (AUC) values and thresholds were for ADC: 0.931 and 1.05, D: 0.896 and 0.9, Kep: 0.964 and 1.1 and Ve: 0.939 and 0.299, respectively. Lesions presenting with a combination of ADC, D, and Ve values superior to the cut-off and Kep values inferior to the cut-off are classified as pleomorphic adenomas. Lesions presenting with combination of ADC, D, and Ve values inferior to the cut-off and Kep values superior to the cut-off are classified as Warthin tumors. Conclusions: DWI, IVIM and quantitative analysis of DCE-MRI derived parameters demonstrated distinctive features of PAs and WT and as such they seem feasible in differentiation of benign parotid gland tumors.

Abstract P3-02-09: Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams

Abstract Background: Standard of care (SOC) breast MRI exams typically acquire 4-7 frames of dynamic contrast-enhanced MRI (DCE-MRI) for cancer screening and staging. Post-contrast images depict lesion spiculations and boundaries to identify and characterize tumors. Pharmacokinetic (PK) analysis of DCE-MRI involves modeling blood flow to the lesion and surrounding tissue and has shown promise in diagnosis and prediction of therapeutic response. Currently, SOC DCE-MRI requires ~60-90 seconds per volume for images with sufficient quality and spatial resolution. However, PK analysis of DCE-MRI requires faster time course sampling. For this reason, PK modeling is limited to research scans with lower spatial resolution and higher temporal resolution. PK modeling would improve feedback of treatment response, and implementation in the SOC exam would increase imaging trial participation. In this study, we tested the estimation of Ktrans, a mixed perfusion and permeability PK parameter, from three images at optimal time points after contrast agent (CA) injection, and compared it to the Ktrans estimation from analysis of the full-length time course.. Methods: Women (N=23) with newly diagnosed invasive breast cancers who were eligible for neoadjuvant therapy (NAT) were scanned with a research MRI protocol as part of a treatment-monitoring study. Images acquired prior to the start of NAT were used. MRI was performed on 3.0T Siemens Skyra scanners at two sites with bilateral breast coils. The research protocol included ten sagittal slices centered about the primary tumor. The DCE-MRI images came from a fast sequence with 1.3 × 1.3 × 5.0 mm resolution acquired at 7.3 seconds per frame (66 frames total,) with a gadolinium-based CA injected one minute into the scan. A population arterial input function was used to implement a mathematical graph-based search of possible tissue CA concentration curves from the expected range of PK parameters. The search results gave a set of three optimized sub-sampled timepoints, Topt, from the full set of sample times, Tfull, at which to best sample the CA concentration curves to optimally estimate PK values. The imaging data was analyzed to find one parameter map from image times Tfull, and another from the subset of images at times Topt. The difference in Ktrans was computed at each parameter map voxel, and the concordance correlation coefficient (CCC) was computed per patient to determine agreement. The median Ktrans values were also compared for each patient. Results: The graph-based search of CA concentration curves found optimal times Topt of 37, 66, and 153 seconds after injection. The averaged values over all patients for median and maximum Ktrans from the original Tfull image set were 0.07 and 0.5 (min)-1. The average difference in Ktrans values between the Topt and Tfull sets was 0.02 (min)-1. When the median Ktrans values for each patient were compared, the average difference in median Ktrans values was 15% +/- 9%. The concordance correlation coefficients comparing the Topt and Tfull -sampled parameter maps for each patient were 0.89 +/- 0.12, showing high agreement. Discussion: This retrospective analysis suggests that it is possible to estimate PK parameters from a few properly selected post-contrast images inserted into a SOC DCE-MRI exam. The combination of optimal timing with fast acquisition techniques for high-resolution imaging could be used to provide quantitative data while preserving post-contrast images with the necessary spatial resolution for clinical reading. Importantly, the test images were acquired in the community setting with widely available MRI hardware, further indicating the potential for integration with SOC exams. Funding: NCI U24 CA226110, NCI U01 CA174706, NCI U01 CA142565, CPRIT RR160005 Citation Format: Julie C DiCarlo, Angela M Jarrett, Anum S Kazerouni, John Virostko, Anna G Sorace, Kalina P Slavkova, Debra Patt, Boone W Goodgame, Sarah Avery, Thomas E Yankeelov. Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams [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 P3-02-09.

A0471 - Is a quantitative analysis of dynamic contrast-enhanced MRI of added value for prostate cancer diagnosis?

A0471 - Is a quantitative analysis of dynamic contrast-enhanced MRI of added value for prostate cancer diagnosis?

EP006 - Is a quantitative analysis of dynamic contrast-enhanced MRI of added value for prostate cancer diagnosis?

EP006 - Is a quantitative analysis of dynamic contrast-enhanced MRI of added value for prostate cancer diagnosis?

Eye-specific quantitative dynamic contrast-enhanced MRI analysis for patients with intraocular masses

ObjectiveDynamic contrast enhanced (DCE)-MRI is currently not generally used for intraocular masses as lesions are small, have an inhomogeneous T1 and the eye is prone to motion. The aim of this paper is to address these eye-specific challenges, enabling accurate ocular DCE-MRI.Materials & methodsDCE-MRI of 19 uveal melanoma (UM) patients was acquired using a fat-suppressed 3D spoiled gradient echo sequence with TWIST (time-resolved angiography with stochastic trajectories sequence). The analysis consisted of a two-step registration method to correct for both head and eye motion. A T1 map was calculated to convert signal intensities to concentrations. Subsequently, the Tofts model was fitted voxel wise to obtain Ktrans and ve.ResultsRegistration significantly improved the concentration curve quality (p < 0.001). The T1 of melanotic lesions was significantly lower than amelanotic lesions (888 ms vs 1350 ms, p = 0.03). The average achieved B1+ in the lesions was 91%. The average Ktrans was 0.46 min−1 (range 0.13–1.0) and the average ve was 0.22 (range 0.10–0.51).ConclusionUsing this eye-specific analysis, DCE of intraocular masses is possible which might aid in the diagnosis, prognosis and follow-up of UM.

Consistency of Pituitary Adenoma: Prediction by Pharmacokinetic Dynamic Contrast-Enhanced MRI and Comparison with Histologic Collagen Content.

Simple SummaryTranssphenoidal resection of hard pituitary adenomas have a particularly high risk of residual tumor and complications. Therefore, prediction of tumor consistency is valuable for planning pituitary adenoma surgery. We prospectively examined whether quantitative pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful for predicting consistency of pituitary adenoma in 49 participants. We found that the measure of volume of extravascular extracellular space per unit volume of tissue derived from DCE-MRI could predict the consistency of pituitary adenomas. Furthermore, the volume of extravascular extracellular space per unit volume of tissue was significantly positively correlated with histopathologic collagen content of the adenoma. Our results suggest that volume of extravascular extracellular space per unit volume of tissue derived from quantitative pharmacokinetic analysis of DCE-MRI has a predictive value for consistency of pituitary adenomas.Prediction of tumor consistency is valuable for planning transsphenoidal surgery for pituitary adenoma. A prospective study was conducted involving 49 participants with pituitary adenoma to determine whether quantitative pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful for predicting consistency of adenomas. Pharmacokinetic parameters in the adenomas including volume of extravascular extracellular space (EES) per unit volume of tissue (ve), blood plasma volume per unit volume of tissue (vp), volume transfer constant between blood plasma and EES (Ktrans), and rate constant between EES and blood plasma (kep) were obtained. The pharmacokinetic parameters and the histologic percentage of collagen content (PCC) were compared between soft and hard adenomas using Mann–Whitney U test. Pearson’s correlation coefficient was used to correlate pharmacokinetic parameters with PCC. Hard adenomas showed significantly higher PCC (44.08 ± 15.14% vs. 6.62 ± 3.47%, p < 0.01), ve (0.332 ± 0.124% vs. 0.221 ± 0.104%, p < 0.01), and Ktrans (0.775 ± 0.401/min vs. 0.601 ± 0.612/min, p = 0.02) than soft adenomas. Moreover, a significant positive correlation was found between ve and PCC (r = 0.601, p < 0.01). The ve derived using DCE-MRI may have predictive value for consistency of pituitary adenoma.