Pretreatment Dynamic Contrast-enhanced Magnetic Resonance Imaging 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.

Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study.

This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.

Development and validation of a four-dimensional registration technique for DCE breast MRI

BackgroundPatient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.ResultsAfter applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman’s rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient Ktrans as surrogate parameter for motion artifacts. Mean Ktrans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001).ConclusionsFour-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.

Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma.

To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (Ktrans ) and extracellular extravascular volume (Ve ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan-Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from Ktrans , Ve, and Ktrans +Ve images, respectively). The combined Radscore k trans +Ve (C-index, 0.703, 95% confidence interval [CI]: 0.571-0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore k trans (C-index, 0.693; 95% CI, 0.560-0.826) and Radscore Ve (C-index, 0.614; 95% CI, 0.481-0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore k trans +Ve and risk factors (C-index, 0.732; 95% CI: 0.599-0.864) was better than Radscore k trans +Ve in predicting PFS in patients with advanced NPC. A lower Radscore k trans +Ve (HR 3.5584, 95% CI 2.1341-5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262-4.854), lower T stage (HR 1.4365, 95% CI 0.6745-3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899-1.267) were associated with longer PFS. Kaplan-Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI

PurposeTo investigate if the pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics machine learning predicts the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. MethodsSeventy-eight breast cancer patients who underwent DCE-MRI before NAC and confirmed as pCR or non-pCR were enrolled. Early enhancement mapping images of pretreatment DCE-MRI were created using subtraction formula as follows: Early enhancement mapping = (Signal 1 min – Signal pre)/Signal pre. Images of the whole tumors were manually segmented and radiomics features extracted. Five prediction models were built using five scenarios that included clinical information, subjective radiological findings, first order texture features, second order texture features, and their combinations. In texture analysis workflow, the corresponding variables were identified by mutual information for feature selection and random forest was used for model prediction. In five models, the area under the receiver operating characteristic curves (AUC) to predict the pCR and several metrics for model evaluation were analyzed. ResultsThe best diagnostic performance based on F-score was achieved when both first and second order texture features with clinical information and subjective radiological findings were used (AUC = 0.77). The second best diagnostic performance was achieved with an AUC of 0.76 for first order texture features followed by an AUC of 0.76 for first and second order texture features. ConclusionsPretreatment DCE-MRI can improve the prediction of pCR in breast cancer patients when all texture features with clinical information and subjective radiological findings are input to build the prediction model.

Neurofibromatosis type 2 versus sporadic vestibular schwannoma: The utility of MR diffusion and dynamic contrast‐enhanced imaging

The goal of this study was to assess the utility of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish sporadic vestibular schwannomas (VSs) from those related to neurofibromatosis type 2 (NF2). We retrospectively reviewed 265 patients pathologically diagnosed with VSs between January 2015 and October 2020 in a single institution. There were 28 patients (male: 19, female: 9; age 11-67 years) including 23 sporadic and five NF2-related VSs, who had pretreatment DWI and DCE-MRI. Normalized mean apparent diffusion coefficient (nADCmean) and DCE-MRI parameters along with tumor characteristics were compared between sporadic and NF2-related VSs as appropriate. The diagnostic performances were calculated based on the receiver operating characteristic curve analysis for the values that showed significant differences. To identify significant modalities, multivariate logistic regression analysis was performed using nADCmean and the combination of statistically significant DCE-MRI parameters. NADCmean, fractional volume of extracellular space (Ve), and forward volume transfer constant (Ktrans) were significantly different between sporadic and NF2-related VSs (nADCmean: median 1.62 vs. 1.16, P=.002; Ve: median 0.40 vs. 0.66, P=.007; Ktrans: median 0.17 vs. 0.33, P=.007), whereas fractional plasma volume (Vp), reversereflux rateconstant (Kep), and tumor characteristics were not. The diagnostic performances of nADCmean, Ve, and Ktrans were 0.93, 0.90, and 0.90 area under the curves with cutoffs of 1.46, 0.51, and 0.29, respectively. nADCmean and the combination of Ve and Ktrans were both chosen as significant differentiators by multivariate logistic regression analysis (P=.027). DWI and DCE-MRI are both promising modalities to distinguish sporadic and NF2-related VSs.

Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC). A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model. Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively. Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment. • Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

Prediction of distant metastases in patients with squamous cell carcinoma of head and neck using DWI and DCE-MRI.

The primary purpose was to evaluate the prognostic potential of diffusion imaging (DWI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in predicting distant metastases in squamous cell carcinoma of head and neck (HNSCC) patients. The secondary aim was to examine differences in DWI and DCE-MRI-derived parameters on the basis of human papilloma virus (HPV) status, differentiation grade, and nodal stage of HNSCC. Fifty-six patients underwent pretreatment DWI and DCE-MRI. Patients were divided into groups who subsequently did (n = 12) or did not develop distant metastases (n = 44). Median values of apparent diffusion coefficient (ADC), volume transfer constant (Ktrans ), and mean intracellular water-lifetime (τi ) and volume were computed from metastatic lymph nodes and were compared between two groups. Prognostic utility of HPV status, differentiation grading, and nodal staging was also evaluated both in isolation or in combination with MRI parameters in distinguishing patients with and without distant metastases. Additionally, MRI parameters were compared between two groups based on dichotomous HPV status, differentiation grade, and nodal stage. Lower but not significantly different Ktrans (0.51 ± 0.15 minute-1 vs 0.60 ± 0.05 minute-1 ) and not significantly different τi (0.13 ± 0.03 second vs 0.19 ± 0.02 second) were observed in patients who developed distant metastases than those who did not. Additionally, no significant differences in ADC or volume were found. τi, was the best parameter in discriminating two groups with moderate sensitivity (67%) and specificity (61.4%). Multivariate logistic regression analyses did not improve the overall prognostic performance for combination of all variables. A trend toward higher τi was observed in HPV-positive patients than those with HPV-negative patients. Also, a trend toward higher Ktrans was observed in poorly differentiated HNSCCs than those with moderately differentiated HNSCCs. Pretreatment DCE-MRI may be useful in predicting distant metastases in HNSCC.

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.

Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.

The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78±0.29 vs. 0.34±0.18min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.

Histogram analysis of dynamic contrast enhancement MRI in predicting long-term treatment effect of cervical cancer after chemoradiotherapy

Objective To investigate the potential value of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting long-term survival of cervical cancer treated by concurrent chemoradiotherapy. Methods Uterine cervical cancer patients who received chemo-radiation therapy during 2008 to 2014 in a single institute were retrospectively collected and followed up. Pre-treatment DCE-MRI images were retrieved to generate four hemodynamic related parameters including positive enhancement integral (PEI), maximum slop of increase (MSI), maximum slope of decrease (MSD) and signal enhancement ratio (SER). Region of interests were drawn manually on T2WI along the rim of tumors on each slice, and were then registered to DCE-MRI parametric maps. Histogram analysis software was used to calculate the mean, median, maximum, minimum,10th percentile, 90th percentile, kurtosis and skewness values for each DCE-MRI parameters. Median follow-up time was 54.7 months (range 2.1 to 94.6 months).Uni-and multivariable Cox regression analyses were used to evaluate correlation between the above values and the disease free survival (DFS). Kaplan-Meier curve was used to evaluate survival time. Results Of the 75 patients, 16 of them died from cervical cancer, 3 patients had metastasis, and 1 patient continued to progress. Median, mean, 10th and 90th percentile from MSI, and minimum, kurtosis, skewness were the influencing factors of disease-free survival of cervical cancer with concurrent chemoradiotherapy(P<0.1) Pearson relationship analysis and multivariable Cox regression analysis was performed which indicated PEI kurtosis value of cervical cancer was an independent influencing factor for cervical cancer without disease survival (hazard ratio 1.658, P=0.001). Conclusion PEI kurtosis was an independent factor for DFS of locally advanced cervical cancer treated with concurrent chemoradiotherapy. Key words: Uterine cervical neoplasms; Long-time survival; Magnetic resonance imaging; Histogram analysis

Differentiation between orbital malignant and benign tumors using intravoxel incoherent motion diffusion-weighted imaging: Correlation with dynamic contrast-enhanced magnetic resonance imaging.

To evaluate the performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating orbital malignant from benign tumors, and to assess the correlation between IVIM-DWI parameters and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters.Twenty-seven patients (17 benign and 10 malignant) with orbital tumors underwent 3.0T MRI examination for pre-treatment evaluation, including IVIM-DWI and DCE-MRI. IVIM-DWI parameters (tissue diffusivity, D; pseudo-diffusion coefficient, D∗; and perfusion fraction, f) were quantified using bi-exponential fitting model. DCE-MRI parameters (Ktrans, the volume transfer constant between the plasma and the extracellular extravascular space [EES]; Ve, the volume fraction of the EES, and Kep, the rate constant from EES to blood plasma) were quantified using modified Tofts model. Independent-sample t test, receiver operating characteristic curve analyses and Spearman correlation test were used for statistical analyses.Malignant orbital tumors showed lower D (P <.001) and higher D∗ (P = .002) than benign tumors. Setting a D value of 0.966 × 10−3 mm2/s as the cut-off value, a diagnostic performance (AUC, 0.888; sensitivity, 100%; specificity, 82.35%) could be obtained for diagnosing malignant tumors. While setting a D∗ value of 42.371 × 10−3 mm2/s as cut-off value, a diagnostic performance could be achieved (AUC, 0.847; sensitivity, 90.00%; specificity, 70.59%). Poor or moderated correlations were found between IVIM-DWI and DCE-MRI parameters (D∗ and Kep, r = 0.427, P = .027; D and Ve, r = 0.626, P <.001).IVIM-DWI is potentially useful for differentiating orbital malignant from benign tumors. Poor or moderate correlations exist between IVIM-DWI and DCE-MRI parameters. IVIM-DWI may be a useful adjunctive perfusion technique for the differential diagnosis of orbital tumors.

Value of intravoxel incoherent motion and dynamic contrast-enhanced MRI for predicting the early and short-term responses to chemoradiotherapy in nasopharyngeal carcinoma.

The aim of the study was to investigate the value of intravoxel incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the early and short-term responses to chemoradiotherapy (CRT) in patients with nasopharyngeal carcinoma (NPC).Forty-three NPC patients underwent IVIM-DWI and DCE-MRI at baseline (pretreatment) and after the first cycle of induction chemotherapy (posttreatment). Based on whether locoregional lesions were identified, patients were divided into the residual and nonresidual groups at the end of CRT and into the good-responder and poor-responder groups 6 months after the end of CRT. The pretreatment and posttreatment IVIM-DWI parameters (ADC, D, D∗, and f) and DCE-MRI parameters (Ktrans, Kep, and Ve) values and their percentage changes (Δ%) were compared between the residual and nonresidual groups and between the good-responder and poor-responder groups.None of perfusion-related parametric values derived from either DCE-MRI or IVIM-DWI showed significant differences either between the residual and nonresidual groups or between the good-responder and poor-responder groups. The nonresidual group exhibited lower pre-ADC, lower pre-D, and higher Δ%D values than did the residual group (all P <0.05). The good-responder group had lower pre-D and pre-ADC values than did the poor-responder group (both P <0.05). Based on receiver operating characteristic (ROC) curve analysis, pre-D had the highest area under the curve in predicting both the early and short-term responses to CRT for NPC patients (0.817 and 0.854, respectively).IVIM-DWI is more valuable than DCE-MRI in predicting the early and short-term response to CRT for NPC, and furthermore diffusion-related IVIM-DWI parameters (pre-ADC, pre-D, and Δ%D) are more powerful than perfusion-related parameters derived from both IVIM-DWI and DCE-MRI.

Dynamic contrast-enhanced MRI serves as a predictor of HIFU treatment outcome for uterine fibroids with hyperintensity in T2-weighted images.

The aim of the present study was to investigate the efficacy of dynamic contrast-enhanced magnetic resonance imaging (MRI) in predicting the outcome of using ultrasound-guided high-intensity focused ultrasound (USgHIFU) ablation for the treatment of uterine fibroids with T2 hyperintensity under MRI. A total of 131 uterine fibroids from 131 patients that appeared hyperintense under T2-weighted MRI were analyzed. The uterine fibroids were subjectively categorized into slight, irregular or regular enhancement groups, according to pretreatment dynamic contrast-enhanced MRI in the arterial phase within 60 sec after the injection of gadolinium. The non-perfused volume (NPV), which is indicative of successful ablation, was represented as the non-perfused area inside the uterine fibroids on enhanced MRI scans following treatment. Additionally, the treatment duration, treatment efficiency, sonication duration, energy efficiency ratio and any adverse events were recorded. The results indicated that the average NPV ratio for all the treated fibroids was 68.5%, while the average NPV ratios for fibroids with slight, irregular or regular enhancement were 84.7, 70.6 and 57.1%, respectively. Fibroids with regular enhancement were associated with the lowest NPV ratio and the lowest treatment efficiency, but exhibited the highest energy effect ratio and an elevated risk of severe adverse effects. The results of the present study indicate that hyperintense uterine fibroids with slight and irregular enhancement in the arterial phase of dynamic contrast-enhanced MRI are suitable for USgHIFU treatment. By contrast, uterine fibroids with regular enhancement were associated with the lowest treatment efficacy and safety.

Dynamic Contrast-Enhanced MRI Kinetic Parameters as Prognostic Biomarkers for Prediction of Survival of Patient with Advanced Hepatocellular Carcinoma: A Pilot Comparative Study

Tracer kinetic model selection for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data analysis influences its use as a prognostic biomarker. Our aim was to find DCE-MRI parameters that predict 1-year survival (1YS) and overall survival (OS) among patients with advanced hepatocellular carcinoma (HCC) treated with antiangiogenic monotherapy by conducting a proof-of-concept comparative study of five different kinetic models. Twenty patients with advanced HCC underwent DCE-MRI and subsequently received sunitinib. Pretreatment DCE-MRI data were analyzed retrospectively by using the Tofts-Kety (TK), extended TK, two compartment exchange, adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter (DP) models. Arterial flow fraction (γ), arterial blood flow (BFA), permeability-surface area product (PS), fractional interstitial volume (vI), and other five parameters were calculated for each model. Individual parameters were evaluated for 1YS prediction using cross-validated Kaplan-Meier analysis, and for association with OS using univariate Cox regression analysis, with additional permutation testing. For 1YS prediction, the TK model-derived γ (P = .007) and vI (P = .029) and the AATH model-derived PS (P = .005) were significant; all these parameters were lower in the high-risk group. Increase in the AATH model-derived PS and the DP model-derived BFA was associated with significant increase in OS with hazard ratios of 0.766 (P = .023) and 0.809 (P = .025), respectively. The AATH model-derived PS was an effective prognostic biomarker for both 1YS and OS.

Short-term pretreatment DCE-MRI in prediction of outcome in locally advanced cervical cancer

Background and purposeSeveral investigators have indicated that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to provide biomarkers for personalized treatment of cervical carcinoma. However, some clinical studies have suggested that treatment failure is associated with low tumor signal enhancement, whereas others have reported associations between high signal enhancement and poor outcome. The purpose of this investigation was to clear up these conflicting reports and to provide a method for identifying biomarkers that easily can be implemented in routine DCE-MRI diagnostics. MethodsThe study involved 85 patients (FIGO stage IB through IVA) treated with concurrent chemoradiotherapy. Low-enhancing tumor volume (LETV) and low-enhancing tumor fraction (LETF), defined as the volume and fractional volume of low-enhancing voxels, respectively, were calculated from signal intensities recorded within 1min after contrast administration by using two methods reported to give conflicting conclusions. ResultsMultivariate analysis involving tumor volume, lymph node status, FIGO stage, and LETV or LETF revealed that LETV and LETF provided independent prognostic information on treatment outcome, independent of the method of calculation. ConclusionLow signal enhancement is associated with poor prognosis in cervical carcinoma, and biomarkers predicting poor outcome can be provided by short-term DCE-MRI without advanced image analysis.

Regional Chemotherapy for Unresectable Intrahepatic Cholangiocarcinoma: A Potential Role for Dynamic Magnetic Resonance Imaging as an Imaging Biomarker and a Survival Update from Two Prospective Clinical Trials

For patients with unresectable intrahepatic cholangiocarcinoma (ICC), treatment options are limited and survival is poor. This study summarizes the long-term outcome of two previously reported clinical trials using hepatic arterial infusion (HAI) with floxuridine and dexamethasone (with or without bevacizumab) in advanced ICC. Prospectively collected clinicopathologic and survival data were retrospectively reviewed. Response was based on Response Evaluation Criteria in Solid Tumors (RECIST). Pre-HAI dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images were reviewed, and tumor perfusion data correlated with outcome. Forty-four patients were analyzed (floxuridine, 26; floxuridine/bevacizumab, 18). At a median follow-up of 29.3months, 41 patients had died of disease. Partial response by RECIST was observed in 48%, and 50% had stable disease. Three patients underwent resection after response, and82% received additional HAI after removal from the trials. Median survival was similar in both trials (floxuridine 29.3months vs. floxuridine/bevacizumab 28.5months; p=0.96). Ten (23%) patients survived ≥3years, including 5 (11%) who survived ≥5years. Tumor perfusion measured on pre-treatment DCE-MRI [area under the gadolinium concentration curve at 90 and 180s (AUC90 and AUC180, respectively)] was significantly higher in ≥3-year survivors and was the only factor that distinguished this group from <3-year survivors (mean AUC90 22.6 vs. 15.9mMs, p=0.025, and mean AUC180 48.9 vs. 32.3mMs, p=0.003, respectively). Median hepatic progression-free survival was longer in ≥3-year survivors (12.9 vs. 9.3months, respectively; p=0.008). HAI chemotherapy can result in prolonged survival in unresectable ICC. Pre-HAI DCE-MRI may predict treatment outcome.

Prognostic value of pretreatment dynamic contrast-enhanced MR imaging in breast cancer patients receiving neoadjuvant chemotherapy: Overall survival predicted from combined time course and volume analysis

The prognostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in breast cancer has been explored, and the results are promising. To investigate the possible correlation between pretreatment DCE-MRI and overall survival 5 years after diagnosis in breast cancer patients receiving neoadjuvant chemotherapy (NAC) using combined time course analysis and volume measurement from DCE-MRI data acquired with 1 min temporal resolution. Pretreatment DCE-MR images of 32 female patients were examined. The total enhancing volume was calculated by including the voxels with >60% signal enhancing 1 min postcontrast. The signal intensity time course data were automatically classified on a voxel-by-voxel basis according to the enhancing characteristics: persistent (type I), plateau (type II) or washout (type III), and the resulting volumes of each enhancement type were calculated. A significant correlation between total enhancing volume and 5-year survival was found, P=0.05 (log-rank). The survival was 51 +/-15 months (mean +/-95% confidence intervals (CI)) and 73+/-12 months in patients with a total enhancing volume >41 cm(3) and < or =41 cm(3), respectively. A two-dimensional discriminator, taking both total enhancing volume and type III enhancing volume into account, improved the prediction of survival, resulting in a P value (log-rank) between survivors and non-survivors of <0.001. The survival was 44+/-16 months (mean +/-95% CI) and 74+/-11 months in patients with a total enhancing volume >58 cm(3) and/or a type III volume >8 cm(3), and < or =58 cm(3) and < or =8 cm(3), respectively. Pretreatment DCE-MRI might help in predicting prognosis in breast cancer patients receiving NAC.

Semiquantitative and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging Measurements Predict Radiation Response in Cervix Cancer

To evaluate semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measurements in predicting the response to radiotherapy in cervix cancer. Patients with cervix cancer treated radically with chemoradiotherapy had DCE-MRI at three time points: before starting treatment, after 2 weeks of radiotherapy, and in the 5th week of radiotherapy. Semiquantitative measurements obtained from the signal intensity vs. time plots included arrival time of contrast, the slope and maximum slope of contrast uptake, time for peak enhancement, and the contrast enhancement ratio (CER). Pharmacokinetic modeling with a modeled vascular input function was used for the quantitative measurements volume transfer constant (K(trans)), rate constant (k(ep)), fraction plasma volume (fPV), and the initial area under gadolinium-time curve. The correlation of these measurements at each of the three time points with radiologic tumor response was investigated. Thirteen patients had a total of 38 scans. There was no correlation between the DCE-MRI measurements and the corresponding tumor volumes. A statistically significant correlation with percentage tumor regression was shown with the pretreatment DCE-MRI semiquantitative parameters of peak time (p = 0.046), slope (p = 0.025), maximum slope (p = 0.046), and CER (p = 0.025) and the quantitative parameters K(trans) (p = 0.043) and k(ep) (p = 0.022). Second and third scan measurements did not show any correlation. This is the first study to show that pretreatment DCE-MRI quantitative parameters predict the radiation response in cervix cancer. These measurements may allow a more meaningful comparison of DCE-MRI studies from different centers.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) as a predictor of response in head and neck squamous cell carcinoma (HNSCC)

6007 Background: We wish to assess on a preliminary basis whether pretreatment DCE-MRI predicts response to chemoradiation in HNSCC. Methods: Tumor perfusion was assessed in 14 patients (median age, 56y; 13 M, 1 F; squamous cell cancer 13, poorly differentiated carcinoma 1; base of tongue 6; tonsil 6; larynx 1; nasopharynx 1) with nodal metastases (size &gt;1 cc for DCE-MRI; N1, 3; N2a, 2; N2b, 4; N2c, 4; N3, 1; Stage III, 3; Stage IVa, 10; Stage IVb, 1) prior to platin-based concurrent chemoradiation therapy. Data were acquired on a 1.5 Tesla clinical scanner with a standard MRI protocol using a clinical contrast agent (Gd-DTPA). The two compartment model analysis measured the rate constants of the contrast agent transfer between the lesion and plasma compartments (kep) and elimination by the plasma (kel). Each patient was assigned a single MR imaging parameter of uptake slope and compartmental model (Akep) on the basis of the histogram analysis of all individually fit tumor voxels. Histogram analysis calculated the amplitude (a), width (s), and median (μ) of the distribution from the fitting procedure. Overall response was determined approximately 2–5 months post treatment. Results: DCE-MRI data showed a rapid rise in the time intensity curves for viable appearing tumor, whereas muscle showed minimal signal enhancement. The mean Akep value was higher for complete responders (CR/near CR, n = 7) than for incomplete responders (ICR, n = 7; 3 partial response, 4 no response) (12.29/min±4.09 vs 7.11/min±2.51 [P = 0.04]. The preliminary histogram analysis for the slope showed that the width and median were able to differentiate between CR and ICR, P=0.039 and P=0.013 respectively, while amplitude was not significant (P=0.150). Conclusions: These preliminary data suggest that DCE-MRI may prove to be a useful predictor of response in HNSCC patients, and thus may enhance prognostication, patient selection, and treatment outcomes. No significant financial relationships to disclose.