Magnetic resonance imaging measurements of the response of murine and human tumors to the vascular-targeting agent ZD6126

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

OBJECTIVES

To evaluate diagnostic utility of additional DCE-MRI for detecting residual soft tissue sarcomas (STS) after unplanned excision (UPE).

METHODS

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.

RESULTS

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.

CONCLUSIONS

Additional DCE-MRI aided in detecting residual STS after UPE, particularly in cases without definite soft tissue nodular enhancement.

ADVANCES IN KNOWLEDGE

Close follow up may be suggested for patients showing abnormality in DCE-MRI, with more suspicion of residual tumor.

Assessment of quantitative dynamic contrast-enhanced MRI in distinguishing different histologic grades of breast phyllode tumor

OBJECTIVES

To investigate whether quantitative DCE-MRI (qDCE-MRI) could help distinguish breast phyllodes tumor (PT) grades.

MATERIALS AND METHODS

This retrospective study included 67 breast PTs (26 benign lesions, 25 borderline lesions, and 16 malignant lesions) from April 2016 to July 2020. MRI was performed with a 1.5-T MR system. Perfusion parameters (K^trans, k_ep, v_e, iAUC60) derived from qDCE-MRI, tumor size, and the mean ADC value were correlated with histologic grades using Spearman's rank correlation coefficient. K^trans, k_ep, v_e, and iAUC60 of three histologic grades were also calculated and compared.

RESULTS

The Spearman correlation coefficient with histologic grade of the tumor size was 0.578 (p < 0.001); the ADC value was not correlated with histologic grades of breast PT (p = 0.059). The K^trans, k_ep, v_e, and iAUC60 of benign breast PTs were significantly lower than those of borderline breast PTs (p < 0.001) and lower than those of malignant breast PTs (p < 0.001). In comparison, the K^trans, v_e, and iAUC60 of borderline breast PTs were significantly lower than those of malignant breast PTs (p < 0.001, p < 0.001, p = 0.007, respectively). For ROC analysis, AUCs of K^trans, v_e, and iAUC60 were higher than tumor size and ADC value for differentiating three PT grades.

CONCLUSION

Quantitative and semi-quantitative perfusion parameters (K^trans, v_e, and iAUC60, especially K^trans) derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs. Therefore, qDCE-MRI may be helpful for preoperative differentiating breast PT grades.

KEY POINTS

• Quantitative dynamic contrast-enhanced MRI can be used as a complementary noninvasive method to improve the differential diagnosis of breast PT. • K^trans, v_e, and iAUC60 derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs.

Non-Invasive Evaluation of Acute Effects of Tubulin Binding Agents: A Review of Imaging Vascular Disruption in Tumors

Tumor vasculature proliferates rapidly, generally lacks pericyte coverage, and is uniquely fragile making it an attractive therapeutic target. A subset of small-molecule tubulin binding agents cause disaggregation of the endothelial cytoskeleton leading to enhanced vascular permeability generating increased interstitial pressure. The resulting vascular collapse and ischemia cause downstream hypoxia, ultimately leading to cell death and necrosis. Thus, local damage generates massive amplification and tumor destruction. The tumor vasculature is readily accessed and potentially a common target irrespective of disease site in the body. Development of a therapeutic approach and particularly next generation agents benefits from effective non-invasive assays. Imaging technologies offer varying degrees of sophistication and ease of implementation. This review considers technological strengths and weaknesses with examples from our own laboratory. Methods reveal vascular extent and patency, as well as insights into tissue viability, proliferation and necrosis. Spatiotemporal resolution ranges from cellular microscopy to single slice tomography and full three-dimensional views of whole tumors and measurements can be sufficiently rapid to reveal acute changes or long-term outcomes. Since imaging is non-invasive, each tumor may serve as its own control making investigations particularly efficient and rigorous. The concept of tumor vascular disruption was proposed over 30 years ago and it remains an active area of research.

Light-assisted gadofullerene nanoparticles disrupt tumor vasculatures for potent melanoma treatment

The traditional photodynamic therapy (PDT) using a photosensitizer and oxygen under light generates reactive oxygen species (ROS) to kill tumor cells. However, its treatment efficiency is limited by insufficient oxygen in tumor cells. Herein, β-alanine modified gadofullerene nanoparticles (GFNPs) were explored to disrupt tumor vasculatures assisted by light for potent melanoma treatment. As tumor vasculatures are oxygen-rich, the yields of photo-induced singlet oxygen (1O2) by GFNPs are not subjected to the hypoxemia of tumor tissues. Different from the small molecule photosensitizer Chlorin e6 (Ce6), GFNPs realize high-efficiency tumor vascular disruption under light observed by using the mice tumor vascular dorsal skin fold chamber (DSFC) model. The tumor vascular disruption efficiency of GFNPs is size-dependent, and the smallest one (hydration diameter of ca. 126 nm) is more efficient. Mechanistically, the high yields of photo-induced 1O2 by GFNPs can lead to the destruction of the tumor vascular endothelial adherent junction protein-VE cadherin and the decrease of tumor vascular endothelial cells-CD31 proteins, inducing rapid tumor necrosis. In conclusion, our work provides an insight into the design of well-sized nanoparticles to powerfully treat melanoma assisted by light, as well as greatly extending the applications of PDT for robust tumor therapy.

Intravoxel Incoherent Motion Magnetic Resonance Imaging for Prediction of Induction Chemotherapy Response in Locally Advanced Hypopharyngeal Carcinoma: Comparison With Model-Free Dynamic Contrast-Enhanced Magnetic Resonance Imaging

BACKGROUND

Multiparametric intravoxel incoherent motion (IVIM) provides diffusion and perfusion information for the treatment prediction of cancer. However, the superiority of IVIM over dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in locally advanced hypopharyngeal carcinoma (LAHC) remains unclear.

PURPOSE

To compare the diagnostic performance of IVIM and model-free DCE in assessing induction chemotherapy (IC) response in patients with LAHC.

STUDY TYPE

Prospective.

POPULATION

Forty-two patients with LAHC.

FIELD STRENGTH/SEQUENCE

3.0 T MRI, including IVIM (12 b values, 0-800 seconds/mm² ) with a single-shot echo planar imaging sequence and DCE-MRI with a volumetric interpolated breath-hold examination sequence. IVIM MRI is a commercially available sequence and software for calculation and analysis from vendor.

ASSESSMENT

The IVIM-derived parameters (diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) and DCE-derived model-free parameters (Wash-in, time to maximum enhancement [Tmax], maximum enhancement [Emax], area under enhancement curve [AUC] over 60 seconds [AUC₆₀ ], and whole area under enhancement curve [AUC_w ]) were measured. At the end of IC, patients with complete or partial response were classified as responders according to the Response Evaluation Criteria in Solid Tumors.

STATISTICAL TESTS

The differences of parameters between responders and nonresponders were assessed using Mann-Whitney U tests. The performance of parameters for predicting IC response was evaluated by the receiver operating characteristic curves.

RESULTS

Twenty-three (54.8%) patients were classified as responders. Compared with nonresponders, the perfusion parameters D*, f, f × D*, and AUC_w were significantly higher whereas Wash-in was lower in responders (all P-values <0.05). The f × D* outperformed other parameters, with an AUC of 0.84 (95% confidence interval [CI]: 0.69-0.93), sensitivity of 79.0% (95% CI: 54.4-93.9), and specificity of 82.6% (95% CI: 61.2-95.0).

DATA CONCLUSION

The IVIM MRI technique may noninvasively help predict the IC response before treatment in patients with LAHC.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Comparison of Dynamic Contrast-Enhancement Parameters between Gadobutrol and Gadoterate Meglumine in Posttreatment Glioma: A Prospective Intraindividual Study

BACKGROUND AND PURPOSE

Differences in molecular properties between one-molar and half-molar gadolinium-based contrast agents are thought to affect parameters obtained from dynamic contrast-enhanced imaging. The aim of our study was to investigate differences in dynamic contrast-enhanced parameters between one-molar nonionic gadobutrol and half-molar ionic gadoterate meglumine in patients with posttreatment glioma.

MATERIALS AND METHODS

This prospective study enrolled 32 patients who underwent 2 20-minute dynamic contrast-enhanced examinations, one with gadobutrol and one with gadoterate meglumine. The model-free parameter of area under the signal intensity curve from 30 to 1100 seconds and the Tofts model-based pharmacokinetic parameters were calculated and compared intraindividually using paired t tests. Patients were further divided into progression (n = 12) and stable (n = 20) groups, which were compared using Student t tests.

RESULTS

Gadobutrol and gadoterate meglumine did not show any significant differences in the area under the signal intensity curve or pharmacokinetic parameters of K ^trans, V_e, V_p, or K_ep (all P > .05). Gadobutrol showed a significantly higher mean wash-in rate (0.83 ± 0.64 versus 0.29 ± 0.63, P = .013) and a significantly lower mean washout rate (0.001 ± 0.0001 versus 0.002 ± 0.002, P = .02) than gadoterate meglumine. Trends toward higher area under the curve, K ^trans, V_e, V_p, wash-in, and washout rates and lower K_ep were observed in the progression group in comparison with the treatment-related-change group, regardless of the contrast agent used.

CONCLUSIONS

Model-free and pharmacokinetic parameters did not show any significant differences between the 2 gadolinium-based contrast agents, except for a higher wash-in rate with gadobutrol and a higher washout rate with gadoterate meglumine, supporting the interchangeable use of gadolinium-based contrast agents for dynamic contrast-enhanced imaging in patients with posttreatment glioma.

Dose-response assessment by quantitative MRI in a phase 1 clinical study of the anti-cancer vascular disrupting agent crolibulin

The vascular disrupting agent crolibulin binds to the colchicine binding site and produces anti-vascular and apoptotic effects. In a multisite phase 1 clinical study of crolibulin (NCT00423410), we measured treatment-induced changes in tumor perfusion and water diffusivity (ADC) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI), and computed correlates of crolibulin pharmacokinetics. 11 subjects with advanced solid tumors were imaged by MRI at baseline and 2–3 days post-crolibulin (13–24 mg/m²). ADC maps were computed from DW-MRI. Pre-contrast T₁ maps were computed, co-registered with the DCE-MRI series, and maps of area-under-the-gadolinium-concentration-curve-at-90 s (AUC_90s) and the Extended Tofts Model parameters k^trans, v_e, and v_p were calculated. There was a strong correlation between higher plasma drug \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${C}^{max}$$\end{document}Cmax and a linear combination of (1) reduction in tumor fraction with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${AUC}_{90s}>15.8$$\end{document}AUC90s>15.8 mM s, and, (2) increase in tumor fraction with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{e}<0.3$$\end{document}ve<0.3. A higher plasma drug AUC was correlated with a linear combination of (1) increase in tumor fraction with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADC}} < 1.1 \times 10^{ - 3} \;{\text{mm}}^{2} /{\text{s}}$$\end{document}ADC<1.1×10-3mm2/s, and, (2) increase in tumor fraction with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v_{e}<0.3$$\end{document}ve<0.3. These findings are suggestive of cell swelling and decreased tumor perfusion 2–3 days post-treatment with crolibulin. The multivariable linear regression models reported here can inform crolibulin dosing in future clinical studies of crolibulin combined with cytotoxic or immune-oncology agents.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study

Purpose

We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.

Materials and Methods

Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results

In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370–1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962–1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778–1.0000)/100%/90%/83.33%/100%, respectively.

Conclusion

Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

Detection of Local Recurrence in Patients with Head and Neck Squamous Cell Carcinoma Using Voxel-Based Color Maps of Initial and Final Area under the Curve Values Derived from DCE-MRI

BACKGROUND AND PURPOSE

Early detection of local recurrence is important to increase the chance of cure because local recurrence is the main cause of treatment failure in head and neck squamous cell carcinoma. We evaluated the added value of voxel-based color maps of dynamic contrast-enhanced MR imaging compared with conventional MR imaging alone for detecting local recurrence of head and neck squamous cell carcinoma.

MATERIALS AND METHODS

We retrospectively enrolled 63 consecutive patients with head and neck squamous cell carcinoma after definitive treatment and posttreatment surveillance MR imaging studies that demonstrated focal enhancement at the primary site. Three independent readers assessed conventional MR imaging and a pair of color maps of initial and final 90-second time-signal intensity areas under the curve from dynamic contrast-enhanced MR imaging. The sensitivities, specificities, and accuracies of both conventional MR imaging alone and combined interpretation of conventional and dynamic contrast-enhanced MR imaging were assessed using the clinicopathologic diagnosis as the criterion standard. κ statistics were used to evaluate interreader agreement.

RESULTS

There were 28 patients with subsequently documented local recurrence and 35 with posttreatment change. Adding dynamic contrast-enhanced MR imaging to conventional MR imaging significantly increased the diagnostic accuracies for detecting local recurrence (48%-54% versus 87%-91%; P < .05), with excellent interreader agreement (κ = 0.8; 95% CI, 0.67-0.92 to κ = 0.81; 95% CI, 0.69-0.93). By all 3 readers, the specificities were also significantly improved by adding dynamic contrast-enhanced MR imaging to conventional MR imaging (22%-43% versus 87%-91%; P < .001) without sacrificing the sensitivities (68%-82% versus 86%-89%; P > .05).

CONCLUSIONS

Adding voxel-based color maps of initial and final 90-second time-signal intensity areas under the curve from dynamic contrast-enhanced MR imaging to conventional MR imaging increases the diagnostic accuracy to detect local recurrence in head and neck squamous cell carcinoma by improving the specificity without sacrificing the sensitivity.

Repeatability of dynamic contrast enhanced vp parameter in healthy subjects and patients with brain tumors

PURPOSE

To study the repeatability of plasma volume (v_p) extracted from dynamic-contrast-enhanced (DCE) MRI in order to define threshold values for significant longitudinal changes, and to assess changes in patients with high-grade-glioma (HGG).

METHODS

Twenty eight healthy subjects, of which eleven scanned twice, were used to assess the repeatability of v_p within the normal-appearing brain tissue and to define threshold values for significant changes based on least-detected-differences (LDD) of mean v_p values and histogram comparisons using earth-mover's-distance (EMD). Sixteen patients with HGG were scanned longitudinally with eight patients scanned before and following bevacizumab therapy. Longitudinal changes were assessed based on defined threshold values in comparison to RANO criteria.

RESULTS

The threshold values for significant changes were: LDD = 0.0024 (ml/100 ml, 21%) for mean v_p and EMD = 4.14. In patients, in 20/24 comparisons, no significant longitudinal changes were detected for v_p within the normal-appearing brain tissue. Concurring results were obtained between changes in lesion volume (RANO criteria) and LDD or EMD values in cases diagnosed with progressive-disease, yet in about 50% of cases diagnosed with partial-response preliminary results demonstrated significant increase in v_p despite significant reductions in lesion volume. In two patients, these changes preceded progression detected at follow-up scans. In general, a good concordance was obtained between LDD and EMD.

CONCLUSION

This study shows high repeatability of v_p and provides threshold values for significant changes in longitudinal assessment of patients with brain tumors. Preliminary results suggest the use of v_p-DCE parameter to improve assessment of therapy response in patients with high-grade-glioma.

Preoperative image-guided identification of response to neoadjuvant chemoradiotherapy in esophageal cancer (PRIDE): a multicenter observational study

BACKGROUND

Nearly one third of patients undergoing neoadjuvant chemoradiotherapy (nCRT) for locally advanced esophageal cancer have a pathologic complete response (pCR) of the primary tumor upon histopathological evaluation of the resection specimen. The primary aim of this study is to develop a model that predicts the probability of pCR to nCRT in esophageal cancer, based on diffusion-weighted magnetic resonance imaging (DW-MRI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ¹⁸F-fluorodeoxyglucose positron emission tomography with computed tomography (¹⁸F-FDG PET-CT). Accurate response prediction could lead to a patient-tailored approach with omission of surgery in the future in case of predicted pCR or additional neoadjuvant treatment in case of non-pCR.

METHODS

The PRIDE study is a prospective, single arm, observational multicenter study designed to develop a multimodal prediction model for histopathological response to nCRT for esophageal cancer. A total of 200 patients with locally advanced esophageal cancer - of which at least 130 patients with adenocarcinoma and at least 61 patients with squamous cell carcinoma - scheduled to receive nCRT followed by esophagectomy will be included. The primary modalities to be incorporated in the prediction model are quantitative parameters derived from MRI and ¹⁸F-FDG PET-CT scans, which will be acquired at fixed intervals before, during and after nCRT. Secondary modalities include blood samples for analysis of the presence of circulating tumor DNA (ctDNA) at 3 time-points (before, during and after nCRT), and an endoscopy with (random) bite-on-bite biopsies of the primary tumor site and other suspected lesions in the esophagus as well as an endoscopic ultrasonography (EUS) with fine needle aspiration of suspected lymph nodes after finishing nCRT. The main study endpoint is the performance of the model for pCR prediction. Secondary endpoints include progression-free and overall survival.

DISCUSSION

If the multimodal PRIDE concept provides high predictive performance for pCR, the results of this study will play an important role in accurate identification of esophageal cancer patients with a pCR to nCRT. These patients might benefit from a patient-tailored approach with omission of surgery in the future. Vice versa, patients with non-pCR might benefit from additional neoadjuvant treatment, or ineffective therapy could be stopped.

TRIAL REGISTRATION

The article reports on a health care intervention on human participants and was prospectively registered on March 22, 2018 under ClinicalTrials.gov Identifier: NCT03474341 .

In vivo MR in the drug pipeline

The application of in vivo magnetic resonance (MR) in drug development has several requirements that differ significantly from most applications primarily because the biopharmaceutical industry must develop new safe and effective drugs more quickly and at lower cost in a highly regulated environment. In vivo MR is recognized as a useful method to provide biomarkers for target engagement, treatment response, safety and mechanism of action that can be translated between animal and clinical studies. Thus, it has the potential to help identify drugs that are more likely to be safe and effective earlier in the process of drug development, which may help reduce the time and money required to get new drugs to patients with an unmet medical need. A brief introduction of how novel drugs are discovered and developed, what drives the biopharmaceutical industry's interest in using biomarkers and what it takes to use MR as a biomarker to support drug development, including regulatory concerns, provides context for understanding what makes the application of in vivo MR in drug development different from most others. Exploration of three programs (trebananib, ZD6126 and axitinib) using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in the development of antivascular agents provides insight into how in vivo MR biomarkers impact drug discovery and development and the limitations of biomarkers.

Correlation Between Tumor Metabolism and Semiquantitative Perfusion Magnetic Resonance Imaging Metrics in Non-Small Cell Lung Cancer

PURPOSE

To correlate semiquantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) for non-small cell lung cancer (NSCLC).

METHODS AND MATERIALS

Twenty-four NSCLC patients who underwent pretreatment ¹⁸F-FDG-PET and DCE-MRI were analyzed. The maximum standardized uptake value (SUVmax) was measured from 18F-FDG-PET. Dynamic contrast-enhanced MRI was obtained on a 3T MRI scanner using 4-dimensional T1-weighted high-resolution imaging with a volume excitation sequence. The DCE-MRI parameters, consisting of mean, median, standard deviation (SD), and median absolute deviation (MAD) of peak enhancement, time to peak (TTP), time to half peak (TTHP), wash-in slope (WIS), wash-out slope (WOS), initial gradient, wash-out gradient, signal enhancement ratio, and initial area under the relative signal enhancement curve taken up to 30, 60, 90, 120, 150, and 180 seconds, TTP, and TTHP (IAUCtthp), were calculated for each lesion. Univariate analysis (UVA) was performed using Spearman correlation. A linear regression model to predict SUVmax from DCE-MRI parameters was developed by multivariate analysis (MVA) using least absolute shrinkage selection operator in combination with leave-one-out cross-validation (LOOCV).

RESULTS

In UVA, mean(WOS) (ρ = -0.456, P = .025), mean(IAUCtthp) (ρ = -0.439, P = .032), median(IAUCtthp) (ρ = -0.543, P = .006), and MAD(IAUCtthp) (ρ = -0.557, P = .005) were statistically significant; all these parameters were negatively correlated with SUVmax. In MVA, a linear combination of SD(WIS), SD(TTP), MAD(TTHP), and MAD(IAUCtthp) was statistically significant for predicting SUVmax (LOOCV-based adjusted R² = 0.298, P = .0006). A decrease in SD(WIS), MAD(TTHP), and MAD(IAUCtthp) and an increase in SD(TTP) were associated with a significant increase in SUVmax.

CONCLUSION

An association was found between SUVmax, the SD, and MAD of DCE-MRI metrics derived during contrast uptake in NSCLC, reflecting that intratumoral heterogeneity in wash-in contrast kinetics is associated with tumor metabolism. Although MAD(IAUCtthp) was a significant feature in both UVA and MVA, the LASSO-based multivariate regression model yielded better predictability of SUVmax than a univariate regression model using MAD(IAUCtthp). This study will facilitate understanding of the complex relationship between tumor vascularization and metabolism and eventually help in guiding targeted therapy.

Intravoxel Incoherent Motion MR Imaging in the Head and Neck: Correlation with Dynamic Contrast-Enhanced MR Imaging and Diffusion-Weighted Imaging

Objective

To investigate the correlation between perfusion- and diffusion-related parameters from intravoxel incoherent motion (IVIM) and those from dynamic contrast-enhanced MR imaging (DCE-MRI) and diffusion-weighted imaging in tumors and normal muscles of the head and neck.

Materials and Methods

We retrospectively enrolled 20 consecutive patients with head and neck tumors with MR imaging performed using a 3T MR scanner. Tissue diffusivity (D), pseudo-diffusion coefficient (D^*), and perfusion fraction (f) were derived from bi-exponential fitting of IVIM data obtained with 14 different b-values in three orthogonal directions. We investigated the correlation between D, f, and D^* and model-free parameters from the DCE-MRI (wash-in, T_max, E_max, initial AUC₆₀, whole AUC) and the apparent diffusion coefficient (ADC) value in the tumor and normal masseter muscle using a whole volume-of-interest approach. Pearson's correlation test was used for statistical analysis.

Results

No correlation was found between f or D^* and any of the parameters from the DCE-MRI in all patients or in patients with squamous cell carcinoma (p > 0.05). The ADC was significantly correlated with D values in the tumors (p < 0.001, r = 0.980) and muscles (p = 0.013, r = 0.542), despite its significantly higher value than D. The difference between ADC and D showed significant correlation with f values in the tumors (p = 0.017, r = 0.528) and muscles (p = 0.003, r = 0.630), but no correlation with D^* (p > 0.05, respectively).

Conclusion

Intravoxel incoherent motion shows no significant correlation with model-free perfusion parameters derived from the DCE-MRI but is feasible for the analysis of diffusivity in both tumors and normal muscles of the head and neck.

Dynamic contrast-enhanced MRI for treatment response assessment in patients with oesophageal cancer receiving neoadjuvant chemoradiotherapy

PURPOSE

To explore and evaluate the potential value of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for the prediction of pathologic response to neoadjuvant chemoradiotherapy (nCRT) in oesophageal cancer.

MATERIAL AND METHODS

Twenty-six patients underwent DCE-MRI before, during (week 2-3) and after nCRT, but before surgery (pre/per/post, respectively). Histopathologic tumour regression grade (TRG) was assessed after oesophagectomy. Tumour area-under-the-concentration time curve (AUC), time-to-peak (TTP) and slope were calculated. The ability of these DCE-parameters to distinguish good responders (GR, TRG 1-2) from poor responders (noGR, TRG⩾3), and pathologic complete responders (pCR) from no-pCR was assessed.

RESULTS

Twelve patients (48%) showed GR of which 8 patients (32%) pCR. Analysis of AUC change throughout treatment, AUCper-pre, was most predictive for GR, at a threshold of 22.7% resulting in a sensitivity of 92%, specificity of 77%, PPV of 79%, and a NPV of 91%. AUCpost-pre was most predictive for pCR, at a threshold of -24.6% resulting in a sensitivity of 83%, specificity of 88%, PPV of 71%, and a NPV of 93%. TTP and slope were not associated with pathologic response.

CONCLUSIONS

This study demonstrates that changes in AUC throughout treatment are promising for prediction of histopathologic response to nCRT for oesophageal cancer.

Value of Dynamic Contrast-Enhanced MRI to Detect Local Tumor Recurrence in Primary Head and Neck Cancer Patients

Treatment failures in head and neck cancer patients are mainly related to locoregional tumor recurrence. The objective of the present study was to evaluate the diagnostic accuracy of model-free dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to detect local recurrence during the surveillance of head and neck cancer patients.Our retrospective study enrolled 24 patients with primary head and neck cancer who had undergone definitive treatment. Patients were grouped into local recurrence (n = 12) or posttreatment change (n = 12) groups according to the results of biopsy or clinicoradiologic follow-up. The types of time-signal intensity (TSI) curves were classified as follows: "progressive increment" as type I, "plateau" as type II, and "washout" as type III. TSI curve types and their parameters (i.e., wash-in, Emax, Tmax, area under the curve [AUC]60, AUC90, and AUC120) were compared between the 2 study groups.The distributions of TSI curve types for local recurrence versus posttreatment change were statistically significant (P < 0.001) (i.e., 0% vs 83.3% for type I, 58.3% vs 16.7% for type II, and 41.7% vs 0% for type III). There were statistically significant differences in Emax, Tmax, and all of the AUC parameters between 2 groups (P < 0.0083 [0.05/6]). Receiver operating characteristic (ROC) curve analyses indicated that the TSI curve type was the best predictor of local recurrence with a sensitivity of 100% (95% CI, 73.5-100.0) and a specificity of 83.3% (95% CI, 51.6-97.9) (cutoff with type II).Model-free DCE-MRI using TSI curves and TSI curve-derived parameters detects local recurrence in head and neck cancer patients with a high diagnostic accuracy.

Different diagnostic values of imaging parameters to predict pseudoprogression in glioblastoma subgroups stratified by MGMT promoter methylation

OBJECTIVES

The aim of this study was to determine whether diffusion and perfusion imaging parameters demonstrate different diagnostic values for predicting pseudoprogression between glioblastoma subgroups stratified by O⁶-mythylguanine-DNA methyltransferase (MGMT) promoter methylation status.

METHODS

We enrolled seventy-five glioblastoma patients that had presented with enlarged contrast-enhanced lesions on magnetic resonance imaging (MRI) one month after completing concurrent chemoradiotherapy and undergoing MGMT promoter methylation testing. The imaging parameters included 10 or 90 % histogram cutoffs of apparent diffusion coefficient (ADC10), normalized cerebral blood volume (nCBV90), and initial area under the time signal-intensity curve (IAUC90). The results of the areas under the receiver operating characteristic curve (AUCs) with cross-validation were compared between MGMT methylation and unmethylation groups.

RESULTS

MR imaging parameters demonstrated a trend toward higher accuracy in the MGMT promoter methylation group than in the unmethylation group (cross-validated AUCs = 0.70-0.95 and 0.56-0.87, respectively). The combination of MGMT methylation status with imaging parameters improved the AUCs from 0.70 to 0.75-0.90 for both readers in comparison with MGMT methylation status alone. The probability of pseudoprogression was highest (95.7 %) when nCBV90 was below 4.02 in the MGMT promoter methylation group.

CONCLUSIONS

MR imaging parameters could be stronger predictors of pseudoprogression in glioblastoma patients with the methylated MGMT promoter than in patients with the unmethylated MGMT promoter.

KEY POINTS

• The glioblastoma subgroup was stratified according to MGMT promoter methylation status. • Diagnostic values of diffusion and perfusion parameters for predicting pseudoprogression were compared. • Imaging parameters showed higher diagnostic accuracy in the MGMT promoter methylation group. • Imaging parameters were independent to MGMT promoter methylation status for predicting pseudoprogression. • Imaging biomarkers might demonstrate different diagnostic values according to MGMT promoter methylation.

Assessment of Tumor Angiogenesis: Dynamic Contrast-enhanced MR Imaging and Beyond

Dynamic contrast-enhanced (DCE) MR imaging is used increasingly often to evaluate tumor angiogenesis and the efficacy of antiangiogenic drugs. In clinical practice DCE-MR imaging applications are largely centered on lesion detection, characterization, and localization. In research, DCE-MR imaging helps inform decision making in early-phase clinical trials by showing efficacy and by selecting dose and schedule. However, the role of these techniques in patient selection is uncertain. Future research is required to optimize existing DCE-MR imaging methods and to fully validate these biomarkers for wider use in patient care and in drug development.

Detecting microvascular changes in the mouse spleen using optical computed tomography

Methods of monitoring drug toxicity in off-target organs are very important in the development of effective and safe drugs. Standard 2-D techniques, such as histology, are prone to sampling errors and can miss important information. We demonstrate a novel application of optical computed tomography (CT) imaging to quantitatively assess, in 3-D, the response of adult murine spleen to off-target drug toxicity induced by treatment with the vascular disrupting agent ZD6126. Reconstructed images from optical CT scans sensitive to haemoglobin absorption reveal detailed, high-contrast 3-D maps of splenic structure and microvasculature. A significant difference in total splenic volume was found between vehicle and ZD6126-treated cohorts, with mean volumes of 61±3mm(3) and 44±3mm(3) respectively (both n=3, p=0.05). Textural statistics for each sample were calculated using grey-level co-occurrence matrices (GLCMs). Standard 2-D GLCM analysis was found to be slice-dependent while 3-D GLCM contrast and homogeneity analysis resulted in separation of the vehicle and ZD6126-treated cohorts over a range of length scales.

Dependence of DCE-MRI biomarker values on analysis algorithm

Background

Dynamic contrast-enhanced MRI (DCE-MRI) biomarkers have proven utility in tumors in evaluating microvascular perfusion and permeability, but it is unclear whether measurements made in different centers are comparable due to methodological differences.

Purpose

To evaluate how commonly utilized analytical methods for DCE-MRI biomarkers affect both the absolute parameter values and repeatability.

Materials and Methods

DCE-MRI was performed on three consecutive days in twelve rats bearing C6 xenografts. Endothelial transfer constant (K^trans), extracellular extravascular space volume fraction (v_e), and contrast agent reflux rate constant (k_ep) measures were computed using: 2-parameter (“Tofts” or “standard Kety”) vs. 3-parameter (“General Kinetic” or “extended Kety”) compartmental models (including blood plasma volume fraction (v_p) with 3-parameter models); individual- vs. population-based vascular input functions (VIFs); and pixel-by-pixel vs. whole tumor-ROI. Variability was evaluated by within-subject coefficient of variation (wCV) and variance components analyses.

Results

DCE-MRI absolute parameter values and wCVs varied widely by analytical method. Absolute parameter values ranged, as follows, median K^trans, 0.09–0.18 min^-1; k_ep, 0.51–0.92 min^-1; v_e, 0.17–0.23; and v_p, 0.02–0.04. wCVs also varied widely by analytical method, as follows: mean K^trans, 32.9–61.9%; k_ep, 11.6–41.9%; v_e, 16.1–54.9%; and v_p, 53.9–77.2%. K^trans and k_ep values were lower with 3- than 2-parameter modeling (p<0.0001); k_ep and v_p were lower with pixel- than whole-ROI analyses (p<0.0006). wCVs were significantly smaller for v_e, and larger for k_ep, with individual- than population-based VIFs.

Conclusions

DCE-MRI parameter values and repeatability can vary widely by analytical methodology. Absolute values of DCE-MRI biomarkers are unlikely to be comparable between different studies unless analyses are carefully standardized.

Review of treatment assessment using DCE-MRI in breast cancer radiation therapy

As a noninvasive functional imaging technique, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is being used in oncology to measure properties of tumor microvascular structure and permeability. Studies have shown that parameters derived from certain pharmacokinetic models can be used as imaging biomarkers for tumor treatment response. The use of DCE-MRI for quantitative and objective assessment of radiation therapy has been explored in a variety of methods and tumor types. However, due to the complexity in imaging technology and divergent outcomes from different pharmacokinetic approaches, the method of using DCE-MRI in treatment assessment has yet to be standardized, especially for breast cancer. This article reviews the basic principles of breast DCE-MRI and recent studies using DCE-MRI in treatment assessment. Technical and clinical considerations are emphasized with specific attention to assessment of radiation treatment response.

Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility

PURPOSE

To compare the added value of dynamic contrast material-enhanced ( CE contrast enhanced ) ( DCE dynamic CE ) magnetic resonance (MR) imaging with that of dynamic susceptibility CE contrast enhanced ( DSC dynamic susceptibility CE ) MR imaging with the combination of CE contrast enhanced T1-weighted imaging and diffusion-weighted ( DW diffusion weighted ) imaging for predicting recurrent glioblastoma.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board, with the requirement for informed patient consent waived. CE contrast enhanced T1-weighted images, DW diffusion weighted images, DSC dynamic susceptibility CE MR images, and DCE dynamic CE MR images in 169 patients with pathologically or clinicoradiologically diagnosed recurrent glioblastoma (n = 87) or radiation necrosis (n = 82) were retrospectively reviewed. Histogram cutoffs of quantitative parametric values were calculated from DW diffusion weighted images, DSC dynamic susceptibility CE MR images, and DCE dynamic CE MR images. Area under the receiver operating characteristic curve ( Az area under the ROC curve ) and interreader agreement were assessed.

RESULTS

For predicting recurrent glioblastoma, adding DCE dynamic CE MR imaging to the combination of CE contrast enhanced T1-weighted imaging and DW diffusion weighted imaging significantly improved Az area under the ROC curve from 0.84 to 0.96 for reader 1 and from 0.81 to 0.97 for reader 2, respectively. Adding DSC dynamic susceptibility CE MR imaging also significantly improved Az area under the ROC curve (0.95 for reader 1 and 0.93 for reader 2). However, there was no significant difference in Az between the combination of CE contrast enhanced T1-weighted imaging, DW diffusion weighted imaging, and DSC dynamic susceptibility CE MR imaging and the combination of CE contrast enhanced T1-weighted imaging, DW diffusion weighted imaging, and DCE dynamic CE MR imaging for both readers. The interreader agreement was highest for the combination of CE contrast enhanced T1-weighted imaging, DW diffusion weighted imaging, and DCE dynamic CE MR imaging (κ = 0.78) and lowest for CE contrast enhanced T1-weighted imaging and DW diffusion weighted imaging (κ = 0.65).

CONCLUSION

Adding perfusion MR imaging to the combination of CE contrast enhanced T1-weighted imaging and DW diffusion weighted imaging significantly improves the prediction of recurrent glioblastoma; however, selection of perfusion MR method does not affect the diagnostic performance.

Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling

PURPOSE

To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers.

METHODS

A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create "variable signal," whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity.

RESULTS

Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images.

CONCLUSION

Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.

Prediction of pseudoprogression in patients with glioblastomas using the initial and final area under the curves ratio derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging

BACKGROUND AND PURPOSE

Dynamic contrast-enhanced T1-weighted perfusion MR imaging is much less susceptible to artifacts, and its high spatial resolution allows accurate characterization of the vascular microenvironment of the lesion. The purpose of this study was to test the predictive value of the initial and final area under the time signal-intensity curves ratio derived from dynamic contrast-enhanced perfusion MR imaging to differentiate pseudoprogression from early tumor progression in patients with glioblastomas.

MATERIALS AND METHODS

Seventy-nine consecutive patients who showed new or enlarged, contrast-enhancing lesions within the radiation field after concurrent chemoradiotherapy were assessed by use of conventional and dynamic contrast-enhanced perfusion MR imaging. The bimodal histogram parameters of the area under the time signal-intensity curves ratio, which included the mean area under the time signal-intensity curves ratio at a higher curve (mAUCRH), 3 cumulative histogram parameters (AUCR50, AUCR75, and AUCR90), and the area under the time signal-intensity curves ratio at mode (AUCRmode), were calculated and correlated with the final pathologic or clinical diagnosis. The best predictor for differentiation of pseudoprogression from early tumor progression was determined by receiver operating characteristic curve analyses.

RESULTS

Seventy-nine study patients were subsequently classified as having pseudoprogression (n=37, 46.8%) or early tumor progression (n=42, 53.2%). There were statistically significant differences of mAUCRH, AUCR50, AUCR75, AUCR90, and AUCRmode between the 2 groups (P < .0001, each). Receiver operating characteristic curve analyses showed the mAUCRH to be the best single predictor of pseudoprogression, with a sensitivity of 90.1% and a specificity of 82.9%. AUCR50 was found to be the most specific predictor of pseudoprogression, with a sensitivity of 87.2% and a specificity of 83.1%.

CONCLUSIONS

A bimodal histogram analysis of the area under the time signal-intensity curves ratio derived from dynamic contrast-enhanced perfusion MR imaging can be a potential, noninvasive imaging biomarker for monitoring early treatment response in patients with glioblastomas.

Integrin α(v)β₃-targeted radiotracer (99m)Tc-3P-RGD₂ useful for noninvasive monitoring of breast tumor response to antiangiogenic linifanib therapy but not anti-integrin α(v)β₃ RGD₂ therapy

Purpose: ^99mTc-3P-RGD₂ is a ^99mTc-labeled dimeric cyclic RGD peptide that binds to integrin α_vβ₃ with high affinity and specificity. The purpose of this study was to demonstrate the utility of ^99mTc-3P-RGD₂ SPECT/CT (single photon emission computed tomography/computed tomography) as a molecular imaging tool for noninvasive monitoring breast tumor early response to antiangiogenesis therapy with linifanib, and to illustrate its limitations in monitoring the efficacy of anti-α_vβ₃ treatment.

Methods: To support SPECT/CT imaging, biodistribution and therapy studies, the xenografted breast cancer model was established by subcutaneous injection of 5 × 10⁶ MDA-MB-435 cells into the fat pad of each athymic nude mouse. Linifanib (ABT-869) was used as antiangiogenesis agent. The tumor volume was 180 ± 90 mm³ on the day (-1 day) before baseline SPECT/CT. Each animal was treated twice daily with vehicle or 12.5 mg/kg linifanib. Longitudinal ^99mTc-3P-RGD₂ SPECT/CT imaging was performed on days -1, 1, 4 and 11. Tumors were harvested at each time point for pathological analysis of hematoxylin and eosin (H&E) and immunohistochemistry (IHC). Tumor uptake of ^99mTc-3P-RGD₂ was calculated from SPECT/CT quantification. When cyclic peptide E[c(RGDfK)]₂ (RGD₂) was used as the anti-α_vβ₃ agent, SPECT/CT images were obtained only at 7 and 21 days after last RGD₂ dose.

Results: The tumor uptake of ^99mTc-3P-RGD₂ from SPECT/CT quantification was almost identical to that from biodistribution. There was a dramatic reduction in both %ID and %ID/cm³ tumor uptake of ^99mTc-3P-RGD₂ during the first 24 hours of linifanib therapy. The therapeutic effect of linifanib was on both tumor cells and vasculature, as determined by IHC analysis of integrin α_vβ₃ and CD31. Changes in tumor vasculature were further confirmed by pathological H&E analysis of tumor tissues. While its %ID tumor uptake increased steadily in vehicle-treated group, the %ID tumor uptake of ^99mTc-3P-RGD₂ decreased in linifanib-treated group slowly over the 11-day study period. The degree of tumor response to linifanib therapy correlated well to the integrin α_vβ₃ expression levels before linifanib therapy.

Conclusion: ^99mTc-3P-RGD₂ is an excellent radiotracer for monitoring integrin α_vβ₃ expression during and after linifanib therapy. ^99mTc-3P-RGD₂ SPECT/CT is an useful molecular imaging tool for patient selection before antiangiogenic and anti-α_vβ₃ therapy; but it would be difficult to use ^99mTc-3P-RGD₂ for accurate and noninvasive monitoring of early tumor response to anti-α_vβ₃ therapy.

Recurrent glioblastoma: optimum area under the curve method derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging

PURPOSE

To determine whether the ratio of the initial area under the time-signal intensity curve (AUC) (IAUC) to the final AUC--or AUCR--derived from dynamic contrast material-enhanced magnetic resonance (MR) imaging can be an imaging biomarker for distinguishing recurrent glioblastoma multiforme (GBM) from radiation necrosis and to compare the diagnostic accuracy of the AUCR with commonly used model-free dynamic contrast-enhanced MR imaging parameters.

MATERIALS AND METHODS

The institutional review board approved this retrospective study and waived the informed consent requirement. Fifty-seven consecutive patients with pathologically confirmed recurrent GBM (n = 32) or radiation necrosis (n = 25) underwent dynamic contrast-enhanced MR imaging. Histogram parameters of the IAUC at 30, 60, and 120 seconds and the AUCR, which included the mean value at the higher curve of the bimodal histogram (mAUCR(H)), as well as 90th percentile cumulative histogram cutoffs, were calculated and were correlated with final pathologic findings. The best predictor for differentiating recurrent GBM from radiation necrosis was determined by means of receiver operating characteristic (ROC) curve analysis.

RESULTS

The demographic data were not significantly different between the two patient groups. There were statistically significant differences in all of the IAUC and AUCR parameters between the recurrent GBM and the radiation necrosis patient groups (P < .05 for each). ROC curve analyses showed mAUCR(H) to be the best single predictor of recurrent GBM (mAUCR(H) for recurrent GBM = 0.35 ± 0.11 [standard deviation], vs 0.19 ± 0.17 for radiation necrosis; P < .0001; optimum cutoff, 0.23), with a sensitivity of 93.8% and a specificity of 88.0%.

CONCLUSION

A bimodal histogram analysis of AUCR derived from dynamic contrast-enhanced MR imaging can be a potential noninvasive imaging biomarker for differentiating recurrent GBM from radiation necrosis.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13130016/-/DC1.

Diffusion-weighted intravoxel incoherent motion imaging of renal tumors with histopathologic correlation

PURPOSE

The aim of this study was to use intravoxel incoherent motion diffusion-weighted imaging to discriminate subtypes of renal neoplasms and to assess agreement between intravoxel incoherent motion (perfusion fraction, fp) and dynamic contrast-enhanced magnetic resonance imaging (MRI) metrics of tumor vascularity.

SUBJECTS AND METHODS

In this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved prospective study, 26 patients were imaged at 1.5-T MRI using dynamic contrast-enhanced MRI with high temporal resolution and diffusion-weighted imaging using 8 b values (range, 0-800 s/mm). Perfusion fraction (fp), tissue diffusivity (Dt), and pseudodiffusivity (Dp) were calculated using biexponential fitting of the diffusion data. Apparent diffusion coefficient (ADC) was calculated with monoexponential fit using 3 b values of 0, 400, and 800 s/mm. Dynamic contrast-enhanced data were processed with a semiquantitative method to generate model-free parameter cumulative initial area under the curve of gadolinium concentration at 60 seconds (CIAUC60). Perfusion fraction, Dt, Dp, ADC, and CIAUC60 were compared between different subtypes of renal lesions. Perfusion fraction was correlated with CIAUC60.

RESULTS

We examined 14 clear cell, 4 papillary, 5 chromophobe, and 3 cystic renal cell carcinomas (RCCs). Although fp had higher accuracy (area under the curve, 0.74) for a diagnosis of clear cell RCC compared with Dt or ADC, the combination of fp and Dt had the highest accuracy (area under the curve, 0.78). The combination of fp and Dt diagnosed papillary RCC and cystic RCC with 100% accuracy, and clear cell RCC and chromophobe RCC, with 86.5% accuracy. There was significant strong correlation between fp and CIAUC60 (r = 0.82; P < 0.001).

CONCLUSION

Intravoxel incoherent motion parameters fp and Dt can discriminate renal tumor subtypes. Perfusion fraction demonstrates good correlation with CIAUC60 and can assess degree of tumor vascularity without the use of exogenous contrast agent.

An update on the clinical development of drugs to disable tumor vasculature

Traditional methods of improving cancer therapy have focused primarily on achieving increased tumor cell kill. However, more recent strategies involve impairing the nutritional support system of the tumor by targeting the tumor vasculature. Rapid developments in this field in recent years have resulted in the identification of a variety of potential targets and a large number of investigational drugs, many of which are now in clinical development. In the following paper the authors review vascular disrupting therapies.

Diverse responses to vascular disrupting agent combretastatin a4 phosphate: a comparative study in rats with hepatic and subcutaneous tumor allografts using MRI biomarkers, microangiography, and histopathology

OBJECTIVE

Differently located tumors of the same origin may exhibit diverse responses to the same therapeutics. To test this hypothesis, we compared the responses of rodent hepatic and subcutaneous engrafts of rhabdomyosarcoma-1 (R1) to a vascular disrupting agent Combretastatin A4 phosphate (CA4P).

METHODS

Twelve WAG/Rij rats, each bearing three R1 implanted in the right and left hepatic lobes and subcutaneously in the thoracic region, received CA4P intravenously at 5 mg/kg (n = 6) or solvent (n = 6). Therapeutic responses were compared interindividually and intraindividually among tumors of different sites till 48 hours after injection using in vivo MRI, postmortem digital microangiography, and histopathology.

RESULTS

MRI revealed that the subcutaneous tumors (STs) significantly increased in volume than hepatic tumors (HTs) 48 hours after CA4P (P < .05). Relative to vehicle controls and treated group at baseline, necrosis ratio, apparent diffusion coefficient, and enhancement ratio changed slightly with the STs but significantly with HTs (P < .05) after CA4P treatment. Vessel density derived from microangiography was significantly lower in STs compared to HTs without CA4P treatment. CA4P treatment resulted in decreased vessel density in HTs, while it did not affect vessel density in STs. MRI and microangiography outcomes were supported by histopathologic findings.

CONCLUSIONS

MRI and microangiography allowed quantitative comparison of therapeutic responses to CA4P in rats with multifocal tumors. The discovered diverse effects of the same drug on tumors of the same origin but different locations emphasize the presence of cancer heterogeneity and the importance of individualization of drug delivery.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Preclinical Studies of Antivascular Treatments

Antivascular treatments can either be antiangiogenic or targeting established tumour vasculature. These treatments affect the tumour microvasculature and microenvironment but may not change clinical measures like tumour volume and growth. In research on antivascular treatments, information on the tumour vasculature is therefore essential. Preclinical research is often used for optimization of antivascular drugs alone or in combined treatments. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an in vivo imaging method providing vascular information, which has become an important tool in both preclinical and clinical research. This review discusses common DCE-MRI imaging protocols and analysis methods and provides an overview of preclinical research on antivascular treatments utilizing DCE-MRI.

Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy

Reliable early assessment of breast cancer response to neoadjuvant therapy (NAT) would provide considerable benefit to patient care and ongoing research efforts, and demand for accurate and noninvasive early-response biomarkers is likely to increase. Response assessment techniques derived from quantitative magnetic resonance imaging (MRI) hold great potential for integration into treatment algorithms and clinical trials. Quantitative MRI techniques already available for assessing breast cancer response to neoadjuvant therapy include lesion size measurement, dynamic contrast-enhanced MRI, diffusion-weighted MRI, and proton magnetic resonance spectroscopy. Emerging yet promising techniques include magnetization transfer MRI, chemical exchange saturation transfer MRI, magnetic resonance elastography, and hyperpolarized MR. Translating and incorporating these techniques into the clinical setting will require close attention to statistical validation methods, standardization and reproducibility of technique, and scanning protocol design.

Impact of the arterial input function on microvascularization parameter measurements using dynamic contrast-enhanced ultrasonography

AIM: To evaluate the sources of variation influencing the microvascularization parameters measured by dynamic contrast-enhanced ultrasonography (DCE-US).

METHODS: Firstly, we evaluated, in vitro, the impact of the manual repositioning of the ultrasound probe and the variations in flow rates. Experiments were conducted using a custom-made phantom setup simulating a tumor and its associated arterial input. Secondly, we evaluated, in vivo, the impact of multiple contrast agent injections and of examination day, as well as the influence of the size of region of interest (ROI) associated with the arterial input function (AIF). Experiments were conducted on xenografted B16F10 female nude mice. For all of the experiments, an ultrasound scanner along with a linear transducer was used to perform pulse inversion imaging based on linear raw data throughout the experiments. Semi-quantitative and quantitative analyses were performed using two signal-processing methods.

RESULTS: In vitro, no microvascularization parameters, whether semi-quantitative or quantitative, were significantly correlated (P values from 0.059 to 0.860) with the repositioning of the probe. In addition, all semi-quantitative microvascularization parameters were correlated with the flow variation while only one quantitative parameter, the tumor blood flow, exhibited P value lower than 0.05 (P = 0.004). In vivo, multiple contrast agent injections had no significant impact (P values from 0.060 to 0.885) on microvascularization parameters. In addition, it was demonstrated that semi-quantitative microvascularization parameters were correlated with the tumor growth while among the quantitative parameters, only the tissue blood flow exhibited P value lower than 0.05 (P = 0.015). Based on these results, it was demonstrated that the ROI size of the AIF had significant influence on microvascularization parameters: in the context of larger arterial ROI (from 1.17 ± 0.6 mm³ to 3.65 ± 0.3 mm³), tumor blood flow and tumor blood volume were correlated with the tumor growth, exhibiting P values lower than 0.001.

CONCLUSION: AIF selection is an essential aspect of the deconvolution process to validate the quantitative DCE-US method.

Reproducibility and comparison of DCE-MRI and DCE-CT perfusion parameters in a rat tumor model

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and computed tomography (DCE-CT) provide independent measures of biomarkers related to tumor perfusion. We compared the reproducibilities and absolute values of DCE-MRI and DCE-CT biomarkers in the same tumors in an animal model, to investigate the physiologic validity of both approaches. DCE-MRI and DCE-CT were each performed sequentially on three consecutive days in each of twelve rats bearing C6 glioma xenografts. DCE-MRI yielded endothelial transfer constant (K(trans)), extracellular, extravascular space volume fraction (v(e)), and contrast agent reflux rate constant (k(ep)); and DCE-CT, blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability-surface area product (PS) using Tofts and deconvolution physiological models, with 6.6 and 0.4 seconds temporal resolutions, respectively. Variability in DCE-CT and DCE-MRI were evaluated by variance components analysis. Intra-rat coefficients of variation for DCE-CT parameters BF, BV, MTT and PS were 25%, 22%, 18% and 23%; and for DCE-MRI parameters K(trans), k(ep) and v(e) were 23%, 16% and 20%, respectively. Mean (±SD) BF, BV, MTT and PS were: 44.6 (±13.7) ml min(-1) 100 g(-1), 5.7 (±1.5) ml 100 g(-1), 10.8 (±2.3) seconds, and 14.6 (±4.7) ml min(-1) 100 g(-1), respectively. Mean (±SD) K(trans), k(ep) and v(e) were: 0.21 (±0.05) min(-1), 0.68 (±0.14) min(-1), and 0.29 (±0.06), respectively. Permeability estimates from DCE-MRI (K(trans)) were 44% higher than from DCE-CT (PS), despite application of appropriate corrections. DCE-MRI and DCE-CT biomarkers of tumor perfusion have similar reproducibilities suggesting that they may have comparable utility, but their derived parameter values are not equivalent.

Assessment of quantitative perfusion parameters by dynamic contrast-enhanced sonography using a deconvolution method: an in vitro and in vivo study

OBJECTIVES

The purpose of this study was to investigate the impact of the arterial input on perfusion parameters measured using dynamic contrast-enhanced sonography combined with a deconvolution method after bolus injections of a contrast agent.

METHODS

The in vitro experiments were conducted using a custom-made setup consisting of pumping a fluid through a phantom made of 3 intertwined silicone pipes, mimicking a complex structure akin to that of vessels in a tumor, combined with their feeding pipe, mimicking the arterial input. In the in vivo experiments, B16F10 melanoma cells were xenografted to 5 nude mice. An ultrasound scanner combined with a linear transducer was used to perform pulse inversion imaging based on linear raw data throughout the experiments. A mathematical model developed by the Gustave Roussy Institute (patent WO/2008/053268) and based on the dye dilution theory was used to evaluate 7 semiquantitative perfusion parameters directly from time-intensity curves and 3 quantitative perfusion parameters from the residue function obtained after a deconvolution process developed in our laboratory based on the Tikhonov regularization method. We evaluated and compared the intraoperator variability values of perfusion parameters determined after these two signal-processing methods.

RESULTS

In vitro, semiquantitative perfusion parameters exhibited intraoperator variability values ranging from 3.39% to 13.60%. Quantitative parameters derived after the deconvolution process ranged from 4.46% to 11.82%. In vivo, tumors exhibited perfusion parameter intraoperator variability values ranging from 3.74% to 29.34%, whereas quantitative ones varied from 5.00% to 12.43%.

CONCLUSIONS

Taking into account the arterial input in evaluating perfusion parameters improves the intraoperator variability and may improve the dynamic contrast-enhanced sonographic technique.

Functional imaging: what evidence is there for its utility in clinical trials of targeted therapies?

Key issues in early clinical trials of targeted agents include the determination of target inhibition, rational patient selection based on pre-treatment tumour characteristics, and assessment of tumour response in the absence of actual shrinkage. There is accumulating evidence that functional imaging using advanced techniques such as dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI), DCE-computerised tomography (CT) and DCE-ultrasound, diffusion weighted-MRI, magnetic resonance spectroscopy and positron emission tomography-CT using various labelled radioactive tracers has the potential to address all three. This article reviews this evidence with examples from trials using targeted agents with established clinical efficacy and summarises the clinical utility of the various techniques. We therefore recommend that input from specialist radiologists is sought at the early stages of trial design, in order to ensure that functional imaging is incorporated appropriately for the agent under study. There is an urgent need to strengthen the evidence base for these techniques as they evolve, and to ensure standardisation of the methodology.

A novel multi-targeted tyrosine kinase inhibitor, linifanib (ABT-869), produces functional and structural changes in tumor vasculature in an orthotopic rat glioma model

Tyrosine kinase inhibitors represent a class of targeted therapy that has proven to be successful for cancer treatment. Linifanib is a novel, orally active multi-targeted receptor tyrosine kinase (RTK) inhibitor that exhibits potent antitumor and antiangiogenic activities against a broad spectrum of experimental tumors and malignancies in patients. The compound is currently being evaluated in phase 2 and 3 clinical trials. To investigate the effectiveness of linifinib against gliomas and the mechanism of drug action, we characterized treatment-induced antitumor and antiangiogenic responses to linifanib in an orthotopic rat glioma model. The effect of linifanib treatment on tumor growth was determined by tumor volume assessment using anatomical magnetic resonance imaging (MRI). Changes in tumor microvessel function were evaluated with dynamic contrast-enhanced MRI (DCE-MRI). Immunohistochemistry (IHC) was applied to excised tumor samples to examine underlying changes in vascular structures and target receptor expression. Linifanib (10 mg/kg) given twice daily inhibited tumor growth following treatment for 7 days with tumor volumes being 149 ± 30 and 66 ± 7 mm(3) for vehicle-and linifanib-treated groups, respectively. A significant reduction of 37 ± 13% in tumor perfusion and microvessel permeability (measured by K (trans)) was observed as early as 2 h after administration compared with vehicle treatment. Continuous linifanib administration further reduced K (trans) at later time points until the end of the study (7 days post-treatment). At day 7, K (trans) was reduced by 75 ± 32% for linifanib treatment compared with vehicle treatment. Significant reduction in total blood vessel density and improved vessel wall integrity were observed, and staining for target receptor expression confirmed inhibition of phospho VEGFR-2 and PDGFR-β by linifanib treatment. These results demonstrate significant antitumor and antiangiogenic activity against gliomas by linifanib, a property that may result from the inhibition of VEGFR-2 and PDGFR-β-mediated vascular changes. DCE-MRI measured K (trans) changes at early treatment stages may be a useful pharmacodynamic marker for linifanib activity in clinical trials, and basal K (trans) may provide predictive value for tumor progression.

Assessment of peripheral tissue perfusion disorder in streptozotocin-induced diabetic rats using dynamic contrast-enhanced MRI

PURPOSE

To assess peripheral tissue perfusion disorder in streptozotocin (STZ)-induced diabetic rats by using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

A rat diabetes model was produced by intravenous injection of STZ. Diabetic rats were sustainably treated with either saline or insulin using an Alzet osmotic pump. Hind paw tissue perfusion was measured by signal intensity (SI) enhancement after gadolinium diethylenetriaminepentaacetic acid injection in DCE-MRI study and quantified using the initial area under the SI-time curve (IAUC). Peripheral tissue uptake of [(14)C]iodoantipyrine (IAP) was also determined as a marker of tissue blood flow for comparison with the IAUC value indicating tissue perfusion.

RESULTS

STZ caused hyperglycemia at 1 and 2 weeks after injection. Treatment with insulin significantly alleviated hyperglycemia. At 2 weeks after STZ injection, peripheral tissue perfusion was clearly reduced in the diabetic rats and its reduction was significantly improved in the insulin-treated diabetic rats. Tissue perfusion evaluated by DCE-MRI was similar to the tissue blood flow measured by [(14)C]IAP uptake.

CONCLUSION

Our findings demonstrated that DCE-MRI can assess peripheral tissue perfusion disorder in diabetes. DCE-MRI could be suitable for noninvasive evaluation of peripheral tissue perfusion in both preclinical and clinical studies. It may also be useful for developing novel drugs to protect against diabetic vascular complications.

Non-invasive in vivo imaging of vessel calibre in orthotopic prostate tumour xenografts

Susceptibility contrast magnetic resonance imaging (MRI), utilising ultrasmall superparamagnetic iron oxide (USPIO) particles, was evaluated for the quantitation of vessel size index (Rv, μm), a weighted average measure of tumour blood vessel calibre, and fractional tumour blood volume (fBV, %), in orthotopically propagated murine PC3 prostate tumour xenografts. Tumour vascular architecture was assessed in vivo by MRI prior to and 24 hr after treatment with 200 mg/kg of the vascular disrupting agent ZD6126. A Bayesian hierarchical model (BHM) was used to reduce the uncertainty associated with quantitation of Rv and fBV. Quantitative histological analyses of the uptake of Hoechst 33342 for perfused vasculature, and haematoxylin and eosin staining for necrosis, were also performed to qualify the MRI data. A relatively large median Rv of 40.3 μm (90% confidence interval (CI90) = 37.4, 44.0 μm) and a high fBV of 5.4% (CI90 = 5.3, 5.5%) were determined in control tumours, which agreed with histologically determined vessel size index. Treatment with ZD6126 significantly (p < 0.01) reduced tumour Rv (34.2 μm, CI90 = 31.2, 38.0 μm) and fBV (3.9%, CI90 = 3.8, 4.1%), which were validated against histologically determined significant reductions in perfusion and vessel size, and increased necrosis. Together these data (i) highlight the use of a BHM to optimise the inferential power available from susceptibility contrast MRI data, (ii) provide strong evaluation and qualification of R(v) and fBV as non-invasive imaging biomarkers of tumour vascular morphology, (iii) reveal the presence of a different vascular phenotype and (iv) demonstrate that ZD6126 exhibits good anti-vascular activity against orthotopic prostate tumours.

Differentiating treatment-induced necrosis from recurrent/progressive brain tumor using nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion

Differentiating treatment-induced necrosis (TIN) from recurrent/progressive tumor (RPT) in brain tumor patients using conventional morphologic imaging features is a very challenging task. Functional imaging techniques also offer moderate success due to the complexity of the tissue microenvironment and the inherent limitation of the various modalities and techniques. The purpose of this retrospective study was to assess the utility of nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion (DCET1MRP) in differentiating TIN from RPT. Twenty-nine patients with previously treated brain tumors who showed recurrent or progressive enhancing lesion on follow-up MRI underwent DCET1MRP. Another 8 patients with treatment-naive high-grade gliomas who also underwent DCET1MRP were included as the control group. Semiquantitative indices derived from DCET1MRP included maximum slope of enhancement in initial vascular phase (MSIVP), normalized MSIVP (nMSIVP), normalized slope of delayed equilibrium phase (nSDEP), and initial area under the time-intensity curve (IAUC) at 60 and 120 s (IAUC(60) and IAUC(120)) obtained from the enhancement curve. There was a statistically significant difference between the 2 groups (P < .01), with the RPT group showing higher MSIVP (15.78 vs 8.06), nMSIVP (0.046 vs 0.028), nIAUC(60) (33.07 vs 6.44), and nIAUC(120) (80.14 vs 65.55) compared with the TIN group. nSDEP was significantly lower in the RPT group (7.20 × 10(-5) vs 15.35 × 10(-5)) compared with the TIN group. Analysis of the receiver-operating-characteristic curve showed nMSIVP to be the best single predictor of RPT, with very high (95%) sensitivity and high (78%) specificity. Thus, nonmodel-based semiquantitative indices derived from DCET1MRP that are relatively easy to derive and do not require a complex model-based approach may aid in differentiating RPT from TIN and can be used as robust noninvasive imaging biomarkers.

Vascular and pharmacokinetic effects of EndoTAG-1 in patients with advanced cancer and liver metastasis

BACKGROUND

EndoTAG-1 (ET), a novel formulation of cationic liposomes carrying embedded paclitaxel (Taxol), shows antitumoral activity, targeting tumor endothelial cells in solid tumors. Patients with advanced metastatic cancer were evaluated investigating effects on pharmacokinetics and tumor vasculature using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and contrast-enhanced ultrasound (CEUS).

PATIENTS AND METHODS

The pharmacokinetic (PK) profile of ET (22 mg/m(2) i.v.) was evaluated after single and repeated doses. DCE-MRI and CEUS explored hepatic metastases before, during and after the 4-week treatment cycle. Angiogenic biomarkers were assessed. Tumor response was evaluated by modified RECIST.

RESULTS

The PK profile demonstrated slight accumulation of paclitaxel after repeated doses. DCE-MRI parameters K(trans) and/or iAUC(60) showed a trend to decrease. Changes of blood flow-dependent parameters of DCE-MRI and CEUS were well correlated. Angiogenic biomarkers revealed no clear trend. ET was generally well tolerated; common toxic effects were fatigue and hypersensitivity reactions. Nine (9 of 18) patients had stable disease after the first treatment cycle. Four patients without disease progression continued treatment.

CONCLUSIONS

This study including multiple pretreated patients with different metastatic cancer revealed individually distinctive hemodynamic alterations by DCE-MRI. The PK profiles of ET were similar as observed previously.

Classification and toxicities of vascular disrupting agents

Vascular disrupting agents (VDAs) are an exciting new group of targeted therapies under active clinical research in many solid tumors, in particular, lung cancer. Small-molecule VDAs are the focus of current clinical research, and consist of the flavonoids and the tubulin-binding agents. Toxicities of single-agent VDAs are characterized by acute, transient, and generally noncumulative side effects including headaches, nausea and vomiting, tumor pain, hypertension, and tachycardia. Flavonoid agents can also cause infusion site pain, visual disturbances, electrocardiac abnormalities, and symptoms consistent with an acute release of serotonin. Tubulin-binding agents can result in cardiac ischemia, abdominal pain, neuromotor abnormalities and cerebellar ataxia, and acute hemodynamic changes. Clinical trials investigating VDAs in combination with traditional chemotherapy have also shown the potential for significant pharmacologic and adverse toxicity interactions. Further research will need to focus on pharmacokinetic and pharmacodynamic parameters to optimize dosing schedules, determine effective combinations with chemotherapy, and minimize toxicities associated with VDAs.

Synthesis and characterization of a theranostic vascular disrupting agent for in vivo MR imaging

Colchicine, a known tubulin binding agent and vascular disrupting agent, causes rapid vascular shut down and central necrosis in tumors. The binding of tubulin results in tubulin destabilization, with characteristic cell shape changes and inhibition of cell division, and results in cell death. A gadolinium(III) labeled derivative of colchicine (Gd·DOTA·Colchicinic acid) was synthesized and characterized as a theranostic agent (enabling simultaneous diagnostic/real time MRI contrast imaging). In vitro, Gd·DOTA·Colchicinic acid was shown to initiate cell changes characteristic of tubulin-destabilization in both OVCAR-3 and IGROV-1 ovarian carcinoma cell lines in vitro over a period of 24 h, while maintaining the qualities of the MR imaging tracer. In vivo, Gd·DOTA·Colchicinic acid (200 mg/kg) was shown to induce the formation of central necrosis, which was confirmed ex vivo by histology, in OVCAR-3 subcutaneous tumor xenografts, while simultaneously acting as an imaging agent to promote a significant reduction in the MR relaxation time T(1) (p < 0.05) of tumors 24 h post-administration. Morphological changes within the tumor which corresponded with areas derived from the formation of central necrosis were also present on MR images that were not observed for the same colchicine derivate that was not complexed with gadolinium that also presented with central necrosis ex vivo. However, Gd·DOTA·Colchicinic acid accumulation in the liver, as shown by changes in liver T(1) (p < 0.05), takes place within 2 h. The implication is that Gd·DOTA·Colchicinic acid distributes to tissues, including tumors, within 2 h, but enters tumor cells to lower T(1) times and promotes cell death over a period of up to 24 h. As the biodistribution/pharmacokinetic and pharmacodynamics data provided here is similar to that of conventional colchicines derivatives, such combined data are a potentially powerful way to rapidly characterize the complete behavior of drug candidates in vivo.

Estimation of intra-operator variability in perfusion parameter measurements using DCE-US

AIM: To investigate intra-operator variability of semi-quantitative perfusion parameters using dynamic contrast-enhanced ultrasonography (DCE-US), following bolus injections of SonoVue^®.

METHODS: The in vitro experiments were conducted using three in-house sets up based on pumping a fluid through a phantom placed in a water tank. In the in vivo experiments, B16F10 melanoma cells were xenografted to five nude mice. Both in vitro and in vivo, images were acquired following bolus injections of the ultrasound contrast agent SonoVue^® (Bracco, Milan, Italy) and using a Toshiba Aplio^® ultrasound scanner connected to a 2.9-5.8 MHz linear transducer (PZT, PLT 604AT probe) (Toshiba, Japan) allowing harmonic imaging (“Vascular Recognition Imaging”) involving linear raw data. A mathematical model based on the dye-dilution theory was developed by the Gustave Roussy Institute, Villejuif, France and used to evaluate seven perfusion parameters from time-intensity curves. Intra-operator variability analyses were based on determining perfusion parameter coefficients of variation (CV).

RESULTS: In vitro, different volumes of SonoVue^® were tested with the three phantoms: intra-operator variability was found to range from 2.33% to 23.72%. In vivo, experiments were performed on tumor tissues and perfusion parameters exhibited values ranging from 1.48% to 29.97%. In addition, the area under the curve (AUC) and the area under the wash-out (AUWO) were two of the parameters of great interest since throughout in vitro and in vivo experiments their variability was lower than 15.79%.

CONCLUSION: AUC and AUWO appear to be the most reliable parameters for assessing tumor perfusion using DCE-US as they exhibited the lowest CV values.