Dynamic Contrast-enhanced Magnetic Resonance Imaging Scans Research Articles

Intravoxel incoherent motion diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting parametrial invasion in cervical cancer.

This study aimed to assess the predictive efficacy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in parametrial invasion (PMI) in cervical cancer patients. A total of 83 cervical cancer patients (32 PMI-positive and 51 PMI-negative) retrospectively underwent pretreatment IVIM-DWI and DCE-MRI scans. IVIM-DWI parameters included apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and perfusion fraction (f). DCE-MRI parameters included volume transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space volume (Ve). Logistic regression analyses were conducted to identify independent variables associated with PMI. Receiver operating characteristic curves were generated to assess the predictive performance of significant parameters. Multivariable analysis revealed that the MRI parameters D (odds ratio [OR]: 7.05; 95% CI 1.78-27.88; P = 0.005), D* (OR 6.58; 95% CI 1.49-29.10; P = 0.01), f (OR 5.12; 95% CI 1.23-21.37; P = 0.03), Ktrans (OR 4.60; 95% CI 1.19-17.81; P = 0.03), and Kep (OR 4.90; 95% CI 1.25-19.18; P = 0.02) were independent predictors of PMI in cervical cancer patients. The combined parameter incorporating these parameters demonstrated the highest performance in predicting PMI, yielding an area under the curve of 0.906, sensitivity of 84.4%, and specificity of 86.3%. The proposed combined parameter exhibited favorable performance in identifying PMI in cervical cancer patients.

DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is the sixth most common cancer, and the third leading cause of cancer death worldwide. Studies have shown that increased angiopoietin-2 (Ang-2) expression relative to Ang-1 expression in tumors is associated with a poor prognosis.The purpose of this study was to investigate the efficacy of predicting Ang-2 expression in HCC by preoperative dynamic contrast‐enhanced magnetic resonance imaging (DCE-MRI)-based radiomics.MethodsThe data of 52 patients with HCC who underwent surgical resection in our hospital were retrospectively analyzed. Ang-2 expression in HCC was analyzed by immunohistochemistry. All patients underwent preoperative upper abdominal DCE-MRI and intravoxel incoherent motion diffusion-weighted imaging scans. Radiomics features were extracted from the early and late arterial and portal phases of axial DCE-MRI. Univariate analysis and least absolute shrinkage and selection operator (LASSO) was performed to select the optimal radiomics features for analysis. A logistic regression analysis was performed to establish a DCE-MRI radiomics model, clinic-radiologic (CR) model and combined model integrating the radiomics score with CR factors. The stability of each model was verified by 10-fold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models.ResultsAmong the 52 HCC patients, high Ang-2 expression was found in 30, and low Ang-2 expression was found in 22. The areas under the ROC curve (AUCs) for the radiomics model, CR model and combined model for predicting Ang-2 expression were 0.800, 0.874, and 0.933, respectively. The DeLong test showed that there was no significant difference in the AUC between the radiomics model and the CR model (p > 0.05) but that the AUC for the combined model was significantly greater than those for the other 2 models (p < 0.05). The DCA results showed that the combined model outperformed the other 2 models and had the highest net benefit.ConclusionThe DCE-MRI-based radiomics model has the potential to predict Ang-2 expression in HCC patients; the combined model integrating the radiomics score with CR factors can further improve the prediction performance.Graphical abstract

Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods.

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has taken on a significant and increasing role in diagnostic procedures and treatments for patients who suffer from chronic kidney disease. Careful segmentation of kidneys from DCE-MRI scans is an essential early step towards the evaluation of kidney function. Recently, deep convolutional neural networks have increased in popularity in medical image segmentation. To this end, in this paper, we propose a new and fully automated two-phase approach that integrates convolutional neural networks and level set methods to delimit kidneys in DCE-MRI scans. We first develop two convolutional neural networks that rely on the U-Net structure (UNT) to predict a kidney probability map for DCE-MRI scans. Then, to leverage the segmentation performance, the pixel-wise kidney probability map predicted from the deep model is exploited with the shape prior information in a level set method to guide the contour evolution towards the target kidney. Real DCE-MRI datasets of 45 subjects are used for training, validating, and testing the proposed approach. The valuation results demonstrate the high performance of the two-phase approach, achieving a Dice similarity coefficient of 0.95 ± 0.02 and intersection over union of 0.91 ± 0.03, and 1.54 ± 1.6 considering a 95% Hausdorff distance. Our intensive experiments confirm the potential and effectiveness of that approach over both UNT models and numerous recent level set-based methods.

The therapeutic utility of combining dynamic contrast-enhanced magnetic resonance imaging with arterial spin labeling in the staging of nasopharyngeal carcinoma

BackgroundTo research the pathological and clinical staging uses of arterial spin labeling (ASL) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Materials and methods64 newly diagnosed nasopharyngeal carcinoma (NPC) patients were enrolled from December 2020 to January 2022, and 3.0 T MRI (Discovery 750W, GE Healthcare, USA) were used for ASL and DCE-MRI scans. The DCE-MRI and ASL raw data were processed post-acquisition on the GE image processing workstation (GE Healthcare, ADW 4.7, USA). The volume transfer constant (Ktrans), blood flow (BF), and accompanying pseudo-color images were generated automatically. Draw the region of interest (ROIs), and the Ktrans and BF values for each ROI were recorded separately. Based on pathological information and the most recent AJCC staging criteria, patients were divided into low T stage groups = T1–2 and high T stage groups = T3–4, low N stage groups = N0–1 and high N stage groups = N2–3, and low AJCC stage group = stage I–II and high AJCC stage group = stage III–IV. The association between the Ktranst and BF parameters and the T, N, and AJCC stages was compared using an independent sample t-test. Using a receiver operating characteristic (ROC) curve, the sensitivity, specificity, and AUC of Ktranst, BFt, and their combined use in T and AJCC staging of NPC were investigated and assessed.ResultThe tumor-BF (BFt) (t = − 4.905, P < 0.001) and tumor-Ktrans (Ktranst) (t = − 3.113, P = 0.003) in the high T stage group were significantly higher than those in the low T stage group. The Ktranst in the high N stage group was significantly higher than that in the low N stage group (t = − 2.071, P = 0.042). The BFt (t = − 3.949, P < 0.001) and Ktranst (t = − 4.467, P < 0.001) in the high AJCC stage group were significantly higher than those in the low AJCC stage group. BFt was moderately positively correlated with the T stage (r = 0.529, P < 0.001) and AJCC stage (r = 0.445, P < 0.001). Ktranst was moderately positively correlated with T staging (r = 0.368), N staging (r = 0.254), and AJCC staging (r = 0.411). There was also a positive correlation between BF and Ktrans in gross tumor volume (GTV) (r = 0.540, P < 0.001), parotid (r = 0.323, P < 0.009) and lateral pterygoid muscle (r = 0.445, P < 0.001). The sensitivity of the combined application of Ktranst and BFt for AJCC staging increased from 76.5 and 78.4 to 86.3%, and the AUC value increased from 0.795 and 0.819 to 0.843, respectively.ConclusionCombining Ktrans and BF measures may make it possible to identify the clinical stages in NPC patients.

Intra- and peri-tumoral radiomics for predicting the sentinel lymph node metastasis in breast cancer based on preoperative mammography and MRI.

This study aims to investigate values of intra- and peri-tumoral regions in the mammography and magnetic resonance imaging (MRI) image for prediction of sentinel lymph node metastasis (SLNM) in invasive breast cancer (BC). This study included 208 patients with invasive BC between Spe. 2017 and Apr. 2021. All patients underwent preoperative digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI) scans. Radiomics features were extracted from manually outlined intratumoral regions, and automatically dilated peritumoral tumor regions in each modality. The least absolute shrinkage and selection operator (LASSO) regression was used to select key features from each region to develop radiomics signatures (RSs). Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity and negative predictive value (NPV) were calculated to evaluate performance of the RSs. Intra- and peri-tumoral regions of BC can provide complementary information on the SLN status. In each modality, the Com-RSs derived from combined intra- and peri-tumoral regions always yielded higher AUCs than the Intra-RSs or Peri-RSs. A total of 10 and 11 features were identified as the most important predictors from mammography (DM plus DBT) and MRI (DCE-MRI plus DWI), respectively. The DCE-MRI plus DWI generated higher AUCs compared with DM plus DBT in the training (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.897 vs. 0.846) and validation (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.826 vs. 0.786) cohort. Radiomics features from intra- and peri-tumoral regions can provide complementary information to identify the SLNM in both mammography and MRI. The DCE-MRI plus DWI generated lower specificity, but higher AUC, accuracy, sensitivity and negative predictive value compared with DM plus DBT.

Correlations of tumour permeability parameters with apparent diffusion coefficient in nasopharyngeal carcinoma.

Functional imaging has an established role in therapeutic monitoring of cancer treatments. This study evaluated the correlations of tumour permeability parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and tumour cellularity derived from apparent diffusion coefficient (ADC) in nasopharyngeal carcinoma (NPC). Twenty NPC patients were examined with DCE-MRI and RESOLVE diffusion-weighted MRI (DW-MRI). Tumour permeability parameters were quantitatively measured with Tofts compartment model. Volume transfer constant (Ktrans), volume of extravascular extracellular space (EES) per unit volume of tissue (Ve), and the flux rate constant between EES and plasma (Kep) from DCE-MRI scan were measured. The time-intensity curve was plotted from the 60 dynamic phases of DCE-MRI. The initial area under the curve for the first 60s of the contrast agent arrival (iAUC60) was also calculated. They were compared with the ADC value derived from DW-MRI with Pearson correlation analyses. Among the DCE-MRI permeability parameters, Kep had higher linearity in inverse correlation with ADC value (r=-0.69, p=<0.05). Ktrans (r=-0.60, p=<0.05) and iAUC60 (r=-0.64, p=<0.05) also had significant inverse correlations with ADC. Ve showed a significant positive correlation with ADC (r=0.63, p=<0.05). Nasopharyngeal tumour vascular permeability parameters derived from DCE-MRI scan were correlated linearly with tumour cellularity measured by free water diffusability with ADC. The clinical implementations of these linear correlations in the quantitative assessments of therapeutic response for NPC patients may be worth to further explore.

Radiomic Signature Based on Dynamic Contrast-Enhanced MRI for Evaluation of Axillary Lymph Node Metastasis in Breast Cancer.

Background To construct and validate a radiomic-based model for estimating axillary lymph node (ALN) metastasis in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods In this retrospective study, a radiomic-based model was established in a training cohort of 236 patients with breast cancer. Radiomic features were extracted from breast DCE-MRI scans. A method named the least absolute shrinkage and selection operator (LASSO) was applied to select radiomic features based on highly reproducible features. A radiomic signature was built by a support vector machine (SVM). Multivariate logistic regression analysis was adopted to establish a clinical characteristic-based model. The performance of models was analysed through discrimination ability and clinical benefits. Results The radiomic signature comprised 6 features related to ALN metastasis and showed significant differences between the patients with ALN metastasis and without ALN metastasis (P < 0.001). The area under the curve (AUC) of the radiomic model was 0.990 and 0.858, respectively, in the training and validation sets. The clinical feature-based model, including MRI-reported status and palpability, performed slightly worse, with an AUC of 0.784 in the training cohort and 0.789 in the validation cohort. The radiomic signature was confirmed to provide more clinical benefits by decision curve analysis. Conclusions The radiomic-based model developed in this study can successfully diagnose the status of lymph nodes in patients with breast cancer, which may reduce unnecessary invasive clinical operations.

Artificial Intelligence-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Quantitative Evaluation of the Efficacy of Targeted Therapy for Liver Metastasis from Colon Cancer and Nursing

The purpose of this study was to quantitatively evaluate the targeted treatment and nursing effect of patients with liver metastasis of colon cancer by enhanced magnetic resonance imaging. First, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) images were restored. 35 patients with liver metastases from colon cancer were selected as research subjects and treated by bevacizumab, and high-quality nursing methods were used to improve the psychophysiology of the patients. Enhanced CT examination and DCE-MRI scan were performed before and after the treatment. After the image was processed by the artificial intelligence algorithm, apparent diffusion coefficient (ADC), measurement transmission constant (Ktrans), reflux constant (Kep), extravascular extracellular volume fraction (Ve), and other data on the image were recorded. Two months after the treatment, 35 patients were divided into effective and ineffective groups. It was found that, after treatment, the Kep value and Ktrans value of the effective group were significantly reduced, showing statistically significant differences, and the P value was less than 0.05, and the Kep value and Ktrans value of the ineffective group showed no significant changes. The Ve value of the two groups gradually decreased with the progress of the treatment. In the effective group, the Ve values before the treatment, on the third day after the treatment, and on the seventh day after treatment were (0.235 ± 0.134), (0.338 ± 0.116), and (0.457 ± 0.097), respectively. Compared with the Ve values of the ineffective group, there was a statistical difference, F = 3.592, P = 0.0245 ; the ADC value was negatively correlated with the Ktrans and Kep values, and the results were statistically significant. It was concluded that the above indicators are effective in evaluating the efficacy of the targeted therapy of liver metastasis from colon cancer, and DCE-MRI is of great significance in predicting the efficacy of targeted therapy of liver metastasis from colon cancer.

The Role of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Predicting Treatment Response for Cervical Cancer Treated with Concurrent Chemoradiotherapy

PurposeTo evaluate the role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting early treatment response.Materials and MethodsPatients with locally advanced cervical cancer (LACC) treated with concurrent chemoradiotherapy (CCRT) were enrolled. Pelvic DCE-MRI scans were performed before RT (pre-RT), in the middle of RT (mid-RT), and at the end of RT (post-RT), separately. Parameters (ie, Ktrans, Kep, and Ve) were measured. Pre-, mid-, and post-RT Ktrans were denoted as Ktrans-preTx, Ktrans-midTx, and Ktrans-postTx, respectively. And the same denoting rule also went for Kep and Ve. Difference for the same parameter such as Ktrans measured between two consecutive time points was calculated as second Ktrans value minus first Ktrans value. The differences in Ktrans between pre-RT and post-RT, between pre-RT and mid-RT, and between mid-RT and post-RT were denoted as ΔKtrans-post-preTx, ΔKtrans-mid-preTx, and ΔKtrans-post-midTx, respectively, and the same denoting rule was also applied to Kep and Ve.ResultsA total of 57 patients were enrolled. After the treatment, 31 patients had complete response (CR group). The remaining 26 patients had partial response (NCR group). Significant differences were found in Ktrans-postTx, Kep-postTx, Ve-midTx, ΔKtrans-post-preTx, ΔKtrans-post-midTx, ΔKep-post-preTx, ΔKep-mid-preTx and ΔKep-post-midTx between the two groups. Receiver operating characteristic (ROC) analysis for their performances in predicting treatment response showed an area under curve (AUC) of 0.656–0.849, sensitivity of 61.3–93.5%, specificity of 46.1–73.1%, and maximal Youden Index of 36.5–66.6. Among those parameters, Kep-postTx was the best, and its AUC, sensitivity, specificity, maximal Youden Index, and cutoff value were 0.849, 87.1%, 73.1%, 60.2, and 0.341, respectively. These combined parameters showed an AUC of 0.952, with sensitivity of 87.1%, specificity of 96.1%, and maximal Youden Index of 83.2.ConclusionDCE-MRI parameters can predict early treatment outcome. Among those parameters, Kep-postTx is the best predictor. The combination of multi-parameters can increase the predictive potency.

The Assessment of Prostate Cancer Aggressiveness Using a Combination of Quantitative Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

ObjectiveTo explore the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters with apparent diffusion coefficient (ADC) values in the diagnosis of prostate cancer.MethodsThe clinical data of 146 patients with prostate lesions, including 87 patients with prostate cancer (PCa) and 59 with benign prostatic hyperplasia (BPH), were collected. After DCE-MRI and diffusion-weighted imaging (DWI) prostate scans, the magnitude of the DCE-MRI transfer constant (Ktrans), rate constant (kep), the volume of the extravascular extracellular space (ve), and the ADC between the groups were compared, and the correlations between the DCE-MRI parameters and Gleason scores were analyzed. The diagnostic efficacy of these quantitative parameters was assessed by the area under the receiver operating characteristic (ROC) curve.ResultsThe DCE-MRI parameters Ktrans and kep were significantly greater in the PCa group than in the BPH group (p < 0.05). The ROC curve showed the area under the Ktrans, kep, and ADC curves to be 0.665, 0.658, and 0.782, respectively. When all three quantitative indicators were combined, the area under the ROC curve was 0.904, with sensitivity and specificity rates of 83.6% and 93.7%, respectively. The Gleason scores were positively correlated with the Ktrans, kep, and ve (r = 0.39, 0.572, 0.30, respectively; p < 0.05) and negatively correlated with the ADC (r = –0.525; p < 0.05).ConclusionThe DCE-MRI quantitative parameters Ktrans and kep, as well as the ADC value, provided effective references for the differential diagnosis of PCa and BPH, as well as more precise and reliable quantitative parameters for grading the aggressiveness of PCa.

Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell Carcinoma.

BackgroundEsophageal squamous cell carcinoma (ESCC) is an aggressive type of cancer, associated with poor prognosis. The development of an accurate and non-invasive method to evaluate the pathologic response of patients with ESCC to chemoradiotherapy remains a critical issue. Therefore, the aim of this study was to assess the importance of vascular permeability and texture parameters in predicting the response to neoadjuvant chemoradiotherapy (NACRT) in patients with ESCC.MethodsThis prospective analysis included patients with T1–T2 stage of ESCC, without either lymphatic or metastasis, and distant metastasis. All patients underwent surgery having received two rounds of NACRT. All patients underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) twice, i.e., before the first NACRT and after the second NACRT. Patients were assessed for treatment response at 30 days after the second NACRT. Patients were divided into the complete response (CR) and partial response (PR) groups based on their responses to NACRT. Vascular permeability and texture parameters were extracted from the DCE-MRI scans. After assessing the diagnostic performance of individual parameters, a combined model with vascular permeability and texture parameters was generated to predict the response to NACRT.ResultsIn this study, the CR and PR groups included 16 patients each. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), and entropy values, as well as changes to each of these parameters, extracted from the second DCE-MRI scans, showed significant differences between the CR and PR groups. The area under the curve (AUC) of Ktrans, ve, and entropy values showed good diagnostic ability (0.813, 0.789, and 0.707, respectively). A logistic regression model combining Ktrans, ve, and entropy had significant diagnostic ability (AUC=0.977).ConclusionsThe use of a combined model with vascular permeability and texture parameters can improve post-NACRT prognostication in patients with ESCC.

Application of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with magnetic resonance spectroscopy (MRS) in prostate cancer diagnosis

Abstract Objective The aim of the study was to investigate the application of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with magnetic resonance spectroscopy (MRS) in prostate cancer diagnosis. Methods In the outpatient department of our hospital (Sichuan Cancer Hospital, Chengdu, China), 60 patients diagnosed with prostate disease were selected randomly and included in a prostate cancer group, 60 patients with benign prostatic hyperplasia were included in a proliferation group, and 60 healthy subjects were included in a control group, from January 2013 to January 2017. Using Siemens Avanto 1.5 T high-field superconducting MRI for DCE-MRI and MRS scans, after the MRS scan was completed, we used the workstation spectroscopy tab spectral analysis, and eventually obtained the crest lines of the prostate metabolites choline (Cho), creatine (Cr), citrate (Cit), and the values of Cho/Cit, and (Cho + Cr)/Cit. Results Participants who had undergone 21-s, 1-min, and 2-min dynamic contrast-enhanced MR revealed significant variations among the three groups. The spectral analysis of the three groups revealed a significant variation as well. DCE-MRI and MRS combined had a sensitivity of 89.67%, specificity of 95.78%, and accuracy of 94.34%. Conclusion DCE-MRI combined with MRS is of great value in the diagnosis of prostate cancer.

DCE-MRI for Detection of Acute Myeloid Leukemia Derived Bone Marrow Vascular Dysfunction

Acute Myeloid Leukemia (AML) is the most common acute leukemia in adults. While the clinical presentation is quite uniform, it is a highly heterogeneous disease at the genetic level. Using intravital two-photon microscopy in AML xenograft models, we have previously showed that AML patient-derived samples induced a common pathologic bone marrow (BM) vascular phenotype. To understand the translational potential of our findings we have optimized dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for the assessment of BM vascular dysfunction. We quantified non-model based parameters from DCE-MRI scans: Contrast Enhancement (CE); Wash-in Rate (WiR); and Wash-out Rate (WoR). CE reflects tissue vascular density and is a measurement of the proportion of fully functional vessels per pixel area, WIR reflects blood flow and tissue perfusion, and WoR reflects vascular permeability and tissue clearance capacity. We first measured DCE parameters in control mice. We observed no gender-related differences in BM DCE parameters, while we found significant age-related changes. As mice age, BM vascular density decreases together with increased vascular permeability and reduced tissue clearance capacity. When compared to age- matched controls, mice injected with AML cell lines showed decreased vascular density, decreased blood inflow, increased vascular permeability and reduced tissue clearance capacity. There was a progressive decline in BM vascular function as the disease burden increased, particularly in WiR and WoR parameters, with even low levels of leukemia significantly altering BM vascular function. When analyzing mice injected with AML patient samples, changes in BM vascular function were more dependent on the AML sample than on the engraftment. Low disease burden did not significantly change DCE vascular parameters. At high disease burden, patient-derived xenografts show different degrees of vascular disruption, spanning from global altered DCE parameters to milder phenotypes. Upon AraC treatment, altered vascular parameters remained unchanged in most of the cases, suggesting inability to fully rescue BM vascular function. In conclusion, we showed that DCE-MRI is able to detect consistent changes associated with BM vasculature induced by aging and leukemia. Despite the heterogeneous nature of the disease, our method captures different degrees of vascular alterations, with WIR having the highest diagnostic potential. Our results further suggest that the detection of a pathologic vascular phenotype in the BM of AML patients could be of use in the clinical scenario. Disclosures No relevant conflicts of interest to declare.

Brain Glioma Localization Diagnosis Based on Magnetic Resonance Imaging

We used dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technology and its perfusion parameters to diagnose central glioma high-grade glioma (HGG), primary central nervous system glioma low-grade glioma (LGG), brain metastases, and meningioma and make differential diagnoses. Forty-one cases of brain tumors (8 cases of LGG, 17 cases of HGG, 5 cases of "primary central nervous system lymphoma" [PCNSL], 6 cases of brain metastases, and 5 cases of meningiomas) were subjected to routine and DCE-MRI scans. The DCE-MRI quantitative parameters of the tumor parenchymal area and peripheral enema area of each tumor were measured and recorded as t-Ktrans value, t-Ve value, t-Vp value, t-Kep value and p-Ktrans value, p-Ve value, p-Vp value, and p-Kep value. Compared with other tumor types, LGG showed lower t-Ktrans value (P < 0.01, sensitivity= 89%, specificity= 99%) and low t-Ve value (P < 0.01, sensitivity= 94%, specificity= 100%); PCNSL showed a high t-Ve value (P < 0.01, sensitivity= 100%, specificity= 88%), but other perfusion parameters overlap more obviously with other tumors. Compared with LGG, the difference between t-Ktrans value, t-Ve value, and t-Kep value is statistically significant. Among them, t-Ktrans value distinguishes the highest sensitivity and specificity (when t-Ktrans value= when 0.154 is the cutoff value, the area under the curve is 1.000, P= 0.000, specificity= 100%, sensitivity= 94.1%); compared with PCNSL, the difference of t-Ve value between HGG and PCNSL is statistically significant, t-Ve of PCNSL. The value is slightly higher, and its specificity and sensitivity are not high. DCE-MRI can distinguish HGG and LGG more accurately, of which t-Ktrans value has higher specificity and sensitivity, although the difference of t-Ve value between PCNSL and HGG is statistically significant but the sensitivity and specificity are not high; the p-Ktrans value and p-Kep value of metastatic tumors are lower than HGG and have higher specificity, but meningiomas and HGG and PCNSL, meningiomas and metastases are not accurate identification.

Examining the Relationship between Semiquantitative Methods Analysing Concentration-Time and Enhancement-Time Curves from Dynamic-Contrast Enhanced Magnetic Resonance Imaging and Cerebrovascular Dysfunction in Small Vessel Disease.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to examine the distribution of an intravenous contrast agent within the brain. Computational methods have been devised to analyse the contrast uptake/washout over time as reflections of cerebrovascular dysfunction. However, there have been few direct comparisons of their relative strengths and weaknesses. In this paper, we compare five semiquantitative methods comprising the slope and area under the enhancement-time curve, the slope and area under the concentration-time curve ( and ), and changes in the power spectrum over time. We studied them in cerebrospinal fluid, normal tissues, stroke lesions, and white matter hyperintensities (WMH) using DCE-MRI scans from a cohort of patients with small vessel disease (SVD) who presented mild stroke. The total SVD score was associated with in WMH (), but not with the other four methods. In WMH, we found higher was associated with younger age () and fewer WMH (), whereas increased with younger age () and WMH burden (). Our results show the potential of different measures extracted from concentration-time curves extracted from the same DCE examination to demonstrate cerebrovascular dysfunction better than those extracted from enhancement-time curves.

DCE-MRI Evaluation of 10 patients with brain metastases treated with RRx-001, a Myc inhibitor and a CD47 and PD-L1 downregulator, in a phase I/II trial called BRAINSTORM.

e14509 Background: In a Phase 1/2 trial called BRAINSTORM (NCT02215512) for brain metastases from any histology, quantitative changes in perfusion MRI after administration of RRx-001, a mic inhibitor and CD47 and PD-L1 downregulator with vascular normalizing properties, were determined and correlated with response. Methods: Ten patients with 64 total lesions evaluable at baseline, 24 hours, and end of radiotherapy (RT) that participated in BRAINSTORM were subjected to a correlative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) examination four days prior to the start of whole brain radiotherapy (WBRT) that evaluated Ktrans (capillary permeability) and Vp (plasma volume). The treatment comprised RRx-001 on Day -4, pre-WBRT then twice weekly during WBRT. Four dose levels were administered (5 mg/m2, 8.4 mg/m2, 16.5 mg/m2, and 27.5 mg/m2. Results: 10 patients underwent DCE-MRI scans and eight patients with 44 total evaluable lesions had available imaging at 1 month, and 6 patients with 29 total evaluable lesions had imaging at 4 months. On univariate analysis, only a decrease in 24-hour Vp from baseline after a single dose of RRx-001 was marginally associated with absolute tumor volume response 1 month after treatment (p-0.07). In a multivariate model, only Vp prior to therapy and 24-hour change in Vp were retained in the model after stepwise selection. A reduction in Vp 24 hours after RRx-001 (prior to WBRT) was associated with reduced tumor volume at 1 month (Estimate 0.88, 95% CI 0.37-1.40, p = 0.001) and 4 months (Estimate 1.51, 95% CI 0.58-2.43, p = 0.003). Likewise, a lower Vp prior to therapy was associated with reduced tumor volume at 1 month (Estimate 0.73, 95% CI 0.29-1.17, p = 0.002) and 4 months (Estimate 1.8, 95% CI 0.95-2.65, p = 0.0002), suggesting anti-angiogenic activity and early potential vascular normalization after a single dose of RRx-001 predictive of longer-term tumor response. Conclusions: RRx-001 induced a reduction in blood plasma volume, which was associated with tumor response and which suggests a vascular normalizing effect that merits further investigation in future planned studies. Clinical trial information: NCT02215512.

Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer

To explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram metrics, relative to time intensity curve (TIC)-derived metrics, in patients with suspected lung cancer. This retrospective study enrolled 49 patients with suspected lung cancer on routine CT imaging who underwent DCE-MRI scans and had final histopathologic diagnosis. Three TIC-derived metrics (maximum enhancement ratio, peak time [Tmax] and slope) and eight intensity histogram metrics (volume, integral, maximum, minimum, median, coefficient of variation [CoV], skewness, and kurtosis) were extracted from DCE-MRI images. TIC-derived and intensity histogram metrics were compared between benignity versus malignancy using the Wilcoxon rank-sum test. Associations between imaging metrics and malignancy risk were assessed by univariate and multivariate logistic regression odds ratios (ORs). There were 33 malignant lesions and 16 benign lesions based on histopathology. Lower CoV (OR = 0.2 per 1-SD increase, p = 0.0006), lower Tmax (OR = 0.4 per 1-SD increase, p = 0.005), and steeper slope (OR = 2.4 per 1-SD increase, p = 0.010) were significantly associated with increased risk of malignancy. Under multivariate analysis, CoV was significantly independently associated with malignancy likelihood after accounting for either Tmax (OR = 0.3 per 1-SD increase, p = 0.007) or slope (OR = 0.3 per 1-SD increase, p = 0.011). This initial study found that DCE-MRI CoV was independently associated with malignancy in patients with suspected lung cancer. CoV has the potential to help diagnose indeterminate pulmonary lesions and may complement TIC-derived DCE-MRI metrics. Further studies are warranted to validate the diagnostic value of DCE-MRI intensity histogram analysis.

Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence.

Identifying imaging phenotypes and understanding their relationship with prognostic markers and patient outcomes can allow for a noninvasive assessment of cancer. The purpose of this study was to identify and validate intrinsic imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans and evaluate their prognostic performance in predicting 10 years recurrence. Pretreatment DCE-MRI scans of 95 women with primary invasive breast cancer with at least 10 years of follow-up from a clinical trial at our institution (2002-2006) were retrospectively analyzed. For each woman, a signal enhancement ratio (SER) map was generated for the entire segmented primary lesion volume from which 60 radiomic features of texture and morphology were extracted. Intrinsic phenotypes of tumor heterogeneity were identified via unsupervised hierarchical clustering of the extracted features. An independent sample of 163 women diagnosed with primary invasive breast cancer (2002-2006), publicly available via The Cancer Imaging Archive, was used to validate phenotype reproducibility. Three significant phenotypes of low, medium, and high heterogeneity were identified in the discovery cohort and reproduced in the validation cohort (P < 0.01). Kaplan-Meier curves showed statistically significant differences (P < 0.05) in recurrence-free survival (RFS) across phenotypes. Radiomic phenotypes demonstrated added prognostic value (c = 0.73) predicting RFS. Intrinsic imaging phenotypes of breast cancer tumor heterogeneity at primary diagnosis can predict 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a noninvasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

Effect of Radiation on DCE-MRI Pharmacokinetic Parameters in a Rabbit Model of Compromised Maxillofacial Wound Healing: A Pilot Study

Osteoradionecrosis (ORN), a potentially debilitating complication of maxillofacial radiation, continues to present a challenging clinical scenario, with limited treatment options that often fail. Translational animal models that can accurately mimic the human characteristics of the condition are lacking. In the present pilot study, we aimed to characterize the effects of radiation on the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic parameters in a rabbit model of compromised maxillofacial wound healing to determine its potential as a translational model of ORN. An experimental group underwent fractionated radiation of the mandible totaling 36Gy. At 4weeks after irradiation, the experimental and control groups (n=8 rabbits each) underwent a surgical procedure to create a critical size defect in the mandibular bone. DCE-MRI scans were acquired 1week after arrival (baseline; time point 1), 4weeks after completion of irradiation in the experimental group (just before surgery, time point 2), and 4weeks after surgery (time point 3). No differences in the analyzed DCE-MRI parameters were noted within the experimental or control group between the baseline values (time point 1) and those after irradiation (time point 2). The whole blood volume fraction (vb) in the experimental group was increased compared with that in the control group after irradiation (time point 2; P<.05). After surgery (time point 3), both the forward flux rate of contrast from blood plasma and the extracellular extravascular space and the vb were increased in the control group compared with the experimental group (P<.05). The results of the present study suggest that DCE-MRI of a rabbit model of compromised maxillofacial wound healing could reflect the DCE-MRI characteristics of human patients with ORN and those at risk of developing the condition. Future studies will focus on further characterization of this rabbit model as a translational preclinical model of ORN.

Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI.

Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers. To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC. We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann-Whitney U test, Pearson's Chi-squared test, or Fisher's exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis. All histogram parameters of Ktrans, kep, and ve were not significantly different between EOC patients with and without recurrence (P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of Ktrans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and kep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of Ktrans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively. Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.