A novel approach to tracer-kinetic modeling for (macromolecular) dynamic contrast-enhanced MRI.

PURPOSE: To develop a novel tracer-kinetic modeling approach for multi-agent dynamic contrast-enhanced MRI (DCE-MRI) that facilitates separate estimation of parameters characterizing blood flow and microvascular permeability within one individual. METHODS: Monte Carlo simulations were performed to investigate the performance of the constrained multi-agent model. Subsequently, multi-agent DCE-MRI was performed on tumor-bearing mice (n = 5) on a 7T Bruker scanner on three measurement days, in which two dendrimer-based contrast agents having high and intermediate molecular weight, respectively, along with gadoterate meglumine, were sequentially injected within one imaging session. Multi-agent data were simultaneously fit with the gamma capillary transit time model. Blood flow, mean capillary transit time, and bolus arrival time were constrained to be identical between the boluses, while extraction fractions and washout rate constants were separately determined for each agent. RESULTS: Simulations showed that constrained multi-agent model regressions led to less uncertainty and bias in estimated tracer-kinetic parameters compared with single-bolus modeling. The approach was successfully applied in vivo, and significant differences in the extraction fraction and washout rate constant between the agents, dependent on their molecular weight, were consistently observed. CONCLUSION: A novel multi-agent tracer-kinetic modeling approach that enforces self-consistency of model parameters and can robustly characterize tumor vascular status was demonstrated.

MRI methods for the evaluation of high intensity focused ultrasound tumor treatment: Current status and future needs.

Thermal ablation with high intensity focused ultrasound (HIFU) is an emerging noninvasive technique for the treatment of solid tumors. HIFU treatment of malignant tumors requires accurate treatment planning, monitoring and evaluation, which can be facilitated by performing the procedure in an MR-guided HIFU system. The MR-based evaluation of HIFU treatment is most often restricted to contrast-enhanced T1 -weighted imaging, while it has been shown that the non-perfused volume may not reflect the extent of nonviable tumor tissue after HIFU treatment. There are multiple studies in which more advanced MRI methods were assessed for their suitability for the evaluation of HIFU treatment. While several of these methods seem promising regarding their sensitivity to HIFU-induced tissue changes, there is still ample room for improvement of MRI protocols for HIFU treatment evaluation. In this review article, we describe the major acute and delayed effects of HIFU treatment. For each effect, the MRI methods that have been-or could be-used to detect the associated tissue changes are described. In addition, the potential value of multiparametric MRI for the evaluation of HIFU treatment is discussed. The review ends with a discussion on future directions for the MRI-based evaluation of HIFU treatment.

Cluster analysis of DCE-MRI data identifies regional tracer-kinetic changes after tumor treatment with high intensity focused ultrasound.

Evaluation of high intensity focused ultrasound (HIFU) treatment with MRI is generally based on assessment of the non-perfused volume from contrast-enhanced T1-weighted images. However, the vascular status of tissue surrounding the non-perfused volume has not been extensively investigated with MRI. In this study, cluster analysis of the transfer constant K(trans) and extravascular extracellular volume fraction ve , derived from dynamic contrast-enhanced MRI (DCE-MRI) data, was performed in tumor tissue surrounding the non-perfused volume to identify tumor subregions with distinct contrast agent uptake kinetics. DCE-MRI was performed in CT26.WT colon carcinoma-bearing BALB/c mice before (n = 12), directly after (n = 12) and 3 days after (n = 6) partial tumor treatment with HIFU. In addition, a non-treated control group (n = 6) was included. The non-perfused volume was identified based on the level of contrast enhancement. Quantitative comparison between non-perfused tumor fractions and non-viable tumor fractions derived from NADH-diaphorase histology showed a stronger agreement between these fractions 3 days after treatment (R(2) to line of identity = 0.91) compared with directly after treatment (R(2) = 0.74). Next, k-means clustering with four clusters was applied to K(trans) and ve parameter values of all significantly enhanced pixels. The fraction of pixels within two clusters, characterized by a low K(trans) and either a low or high ve , significantly increased after HIFU. Changes in composition of these clusters were considered to be HIFU induced. Qualitative H&E histology showed that HIFU-induced alterations in these clusters may be associated with hemorrhage and structural tissue disruption. Combined microvasculature and hypoxia staining suggested that these tissue changes may affect blood vessel functionality and thereby tumor oxygenation. In conclusion, it was demonstrated that, in addition to assessment of the non-perfused tumor volume, the presented methodology gives further insight into HIFU-induced effects on tumor vascular status. This method may aid in assessment of the consequences of vascular alterations for the fate of the tissue.

Multiparametric MRI analysis for the evaluation of MR-guided high intensity focused ultrasound tumor treatment.

For the clinical application of high intensity focused ultrasound (HIFU) for thermal ablation of malignant tumors, accurate treatment evaluation is of key importance. In this study, we have employed a multiparametric MRI protocol, consisting of quantitative T1, T2, ADC, amide proton transfer (APT), T1ρ and DCE-MRI measurements, to evaluate MR-guided HIFU treatment of subcutaneous tumors in rats. K-means clustering using all different combinations of the endogenous contrast MRI parameters (feature vectors) was performed to segment the multiparametric data into tissue populations with similar MR parameter values. The optimal feature vector for identification of the extent of non-viable tumor tissue after HIFU treatment was determined by quantitative comparison between clustering-derived and histology-derived non-viable tumor fractions. The highest one-to-one correspondence between these clustering-based and histology-based non-viable tumor fractions was observed for the feature vector {ADC, APT-weighted signal} (R(2) to line of identity (R(2)y=x) = 0.92) and the strongest agreement was seen 3 days after HIFU (R(2)y=x = 0.97). To compare the multiparametric MRI analysis results with conventional HIFU monitoring and evaluation methods, the histology-derived non-viable tumor fractions were also quantitatively compared with non-perfused tumor fractions (derived from the level of contrast enhancement in the DCE-MRI measurements) and 240 CEM tumor fractions (i.e. thermal dose > 240 cumulative equivalent minutes at 43 °C). The correlation between histology-derived non-viable tumor fractions directly after HIFU and the 240 CEM fractions was high, but not significant. The non-perfused fractions overestimated the extent of non-viable tumor tissue directly after HIFU, whereas an underestimation was observed 3 days after HIFU. In conclusion, we have shown that a multiparametric MR analysis, especially based on the ADC and the APT-weighted signal, can potentially be used to determine the extent of non-viable tumor tissue 3 days after HIFU treatment. We expect that this method can be incorporated in the current clinical workflow of MR-HIFU ablation therapies.

Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast.

OBJECT: Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only. MATERIALS AND METHODS: Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2-7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes. RESULTS: The highest similarity index between manual and automatic segmentation (SI manual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R (2) = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R (2) = 0.96). CONCLUSION: Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.

Amide proton transfer imaging of high intensity focused ultrasound-treated tumor tissue.

PURPOSE: In this study, the suitability of amide proton transfer (APT) imaging as a biomarker for the characterization of high intensity focused ultrasound (HIFU)-treated tumor tissue was assessed. METHODS: APT imaging was performed on tumor-bearing mice before (n = 15), directly after (n = 15) and at 3 days (n = 8) after HIFU treatment. A control group (n = 7) of nontreated animals was scanned at the same time points. Histogram analysis of the tumor APT-weighted signal distributions was performed to assess HIFU-induced changes in the tumor APT contrast. RESULTS: Distinct regions of decreased APT-weighted signal were observed at both time points after HIFU treatment. Analysis of the tumor APT-weighted signal distribution showed a pronounced shift toward lower APT-weighted signal values after HIFU treatment. A significantly increased fraction of pixels with an APT-weighted signal value between -10 and -2% was observed both directly (0.37 ± 0.16) and at 3 days (0.49 ± 0.16) after HIFU treatment as compared to baseline (0.22 ± 0.16). CONCLUSION: The presented results show that APT imaging is sensitive to HIFU-induced changes in tumor tissue and may thus serve as a new biomarker for monitoring the response of tumor tissue to HIFU treatment.

The Impact of Expectation Management and Model Transparency on Radiologists' Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study.

BACKGROUND: Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems. OBJECTIVE: We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists' trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans. METHODS: In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists' trust in their assessments had changed based on the AI recommendations. RESULTS: Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists' confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists' confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs. CONCLUSIONS: Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists' trust in the AI-CAD system can be impaired. Radiologists' confidence in their assessments was improved by using the AI recommendations.

The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review.

To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists' performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists' workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.

Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions.

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

Targeting of ICAM-1 on vascular endothelium under static and shear stress conditions using a liposomal Gd-based MRI contrast agent.

BACKGROUND: The upregulation of intercellular adhesion molecule-1 (ICAM-1) on the endothelium of blood vessels in response to pro-inflammatory stimuli is of major importance for the regulation of local inflammation in cardiovascular diseases such as atherosclerosis, myocardial infarction and stroke. In vivo molecular imaging of ICAM-1 will improve diagnosis and follow-up of patients by non-invasive monitoring of the progression of inflammation. RESULTS: A paramagnetic liposomal contrast agent functionalized with anti-ICAM-1 antibodies for multimodal magnetic resonance imaging (MRI) and fluorescence imaging of endothelial ICAM-1 expression is presented. The ICAM-1-targeted liposomes were extensively characterized in terms of size, morphology, relaxivity and the ability for binding to ICAM-1-expressing endothelial cells in vitro. ICAM-1-targeted liposomes exhibited strong binding to endothelial cells that depended on both the ICAM-1 expression level and the concentration of liposomes. The liposomes had a high longitudinal and transversal relaxivity, which enabled differentiation between basal and upregulated levels of ICAM-1 expression by MRI. The liposome affinity for ICAM-1 was preserved in the competing presence of leukocytes and under physiological flow conditions. CONCLUSION: This liposomal contrast agent displays great potential for in vivo MRI of inflammation-related ICAM-1 expression.

Looking through the eyes of the multidisciplinary team: the design and clinical evaluation of a decision support system for lung cancer care.

BACKGROUND: Decision-making in lung cancer is complex due to a rapidly increasing amount of diagnostic data and treatment options. The need for timely and accurate diagnosis and delivery of care demands high-quality multidisciplinary team (MDT) collaboration and coordination. Clinical decision support systems (CDSSs) can potentially support MDTs in constructing a shared mental model of a patient case. This enables the team to assess the strength and completeness of collected diagnostic data, stratification for the right personalized therapy driven by clinical stage and other treatment-influencing factors, and adapt care management strategies when needed. Current CDSSs often have a suboptimal fit into the decision-making workflow, which hampers their impact in clinical practice. METHODS: A CDSS for multidisciplinary decision-making in lung cancer was designed to support the abovementioned goals through presentation of relevant clinical data in line with existing mental model structures of the MDT members. The CDSS was tested in a simulated multidisciplinary tumor board meeting for primary diagnosis and treatment selection, based on de-identified primary lung cancer cases (n=8). Decision course analysis, eye-tracking data and questionnaires were used to assess the impact of the CDSS on constructing shared mental models to improve the decision-making process and outcome. RESULTS: The CDSS supported the team in their self-correcting capacity for accurate diagnosis and TNM classification. It enabled cross-validation of diagnostic findings, surfaced discordance between diagnostic tests and facilitated cancer staging according the diagnostic evidence, as well as spotting contra-indications for personalized treatment selection. CONCLUSIONS: This study shows the potential of CDSS on clinical decision making, when these systems are properly designed in line with clinical thinking. The presented setup enables assessment of the impact of CDSS design on clinical decision making and optimization of CDSSs to maximize their effect on decision quality and confidence.

Rapid stromal remodeling by short-term VEGFR2 inhibition increases chemotherapy delivery in esophagogastric adenocarcinoma.

Anti-angiogenic agents combined with chemotherapy is an important strategy for the treatment of solid tumors. However, survival benefit is limited, urging the improvement of combination therapies. We aimed to clarify the effects of vascular endothelial growth factor receptor 2 (VEGFR2) targeting on hemodynamic function and penetration of drugs in esophagogastric adenocarcinoma (EAC). Patient-derived xenograft (PDX) models of EAC were subjected to long-term and short-term treatment with anti-VEGFR2 therapy followed by chemotherapy injection or multi-agent dynamic contrast-enhanced (DCE-) MRI and vascular casting. Long-term anti-VEGFR2-treated tumors showed a relatively lower flow and vessel density resulting in reduced chemotherapy uptake. On the contrary, short-term VEGFR2 targeting resulted in relatively higher flow, rapid vasodilation, and improved chemotherapy delivery. Assessment of the extracellular matrix (ECM) revealed that short-term anti-angiogenic treatment drastically remodels the tumor stroma by inducing nitric oxide synthesis and hyaluronan degradation, thereby dilating the vasculature and improving intratumoral chemotherapy delivery. These previously unrecognized beneficial effects could not be maintained by long-term VEGFR2 inhibition. As the identified mechanisms are targetable, they offer direct options to enhance the treatment efficacy of anti-angiogenic therapy combined with chemotherapy in EAC patients.

Improved Evaluation of Antivascular Cancer Therapy Using Constrained Tracer-Kinetic Modeling for Multiagent Dynamic Contrast-Enhanced MRI.

Dynamic contrast-enhanced MRI (DCE-MRI) is a promising technique for assessing the response of tumor vasculature to antivascular therapies. Multiagent DCE-MRI employs a combination of low and high molecular weight contrast agents, which potentially improves the accuracy of estimation of tumor hemodynamic and vascular permeability parameters. In this study, we used multiagent DCE-MRI to assess changes in tumor hemodynamics and vascular permeability after vascular-disrupting therapy. Multiagent DCE-MRI (sequential injection of G5 dendrimer, G2 dendrimer, and Gd-DOTA) was performed in tumor-bearing mice before, 2 and 24 hours after treatment with vascular disrupting agent DMXAA or placebo. Constrained DCE-MRI gamma capillary transit time modeling was used to estimate flow F, blood volume fraction vb, mean capillary transit time tc, bolus arrival time td, extracellular extravascular fraction ve, vascular heterogeneity index α-1 (all identical between agents) and extraction fraction E (reflective of permeability), and transfer constant Ktrans (both agent-specific) in perfused pixels. F, vb, and α-1 decreased at both time points after DMXAA, whereas tc increased. E (G2 and G5) showed an initial increase, after which, both parameters restored. Ktrans (G2 and Gd-DOTA) decreased at both time points after treatment. In the control, placebo-treated animals, only F, tc, and Ktrans Gd-DOTA showed significant changes. Histologic perfused tumor fraction was significantly lower in DMXAA-treated versus control animals. Our results show how multiagent tracer-kinetic modeling can accurately determine the effects of vascular-disrupting therapy by separating simultaneous changes in tumor hemodynamics and vascular permeability.Significance: These findings describe a new approach to measure separately the effects of antivascular therapy on tumor hemodynamics and vascular permeability, which could help more rapidly and accurately assess the efficacy of experimental therapy of this class. Cancer Res; 78(6); 1561-70. ©2018 AACR.

Detection of Treatment Success after Photodynamic Therapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Early evaluation of response to therapy is crucial for selecting the optimal therapeutic follow-up strategy for cancer patients. PDT is a photochemistry-based treatment modality that induces tumor tissue damage by cytotoxic oxygen radicals, generated by a pre-injected photosensitive drug upon light irradiation of tumor tissue. Vascular shutdown is an important mechanism of tumor destruction for most PDT protocols. In this study, we assessed the suitability of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to evaluate treatment efficacy within a day after photodynamic therapy (PDT), using the tumor vascular response as a biomarker for treatment success. Methods: DCE-MRI at 7 T was used to measure the micro-vascular status of subcutaneous colon carcinoma tumors before, right after, and 24 h after PDT in mice. Maps of the area under the curve (AUC) of the contrast agent concentration were calculated from the DCE-MRI data. Besides, tracer kinetic parameters including Ktrans were calculated using the standard Tofts-Kermode model. Viability of tumor tissue at 24 h after PDT was assessed by histological analysis. Results: PDT led to drastic decreases in AUC and Ktrans or complete loss of enhancement immediately after treatment, indicating a vascular shutdown in treated tumor regions. Histological analysis demonstrated that the treatment induced extensive necrosis in the tumors. For PDT-treated tumors, the viable tumor fraction showed a strong correlation (ρ ≥ 0.85) with the tumor fraction with Ktrans > 0.05 min-1 right after PDT. The viable tumor fraction also correlated strongly with the enhanced fraction, the average Ktrans , and the fraction with Ktrans > 0.05 min-1 at 24 h after PDT. Images of the viability stained tumor sections were registered to the DCE-MRI data, demonstrating a good spatial agreement between regions with Ktrans > 0.05 min-1 and viable tissue regions. Finally, 3D post-treatment viability detection maps were constructed for the tumors of three mice by applying a threshold (0.05 min-1) to Ktrans at 24 h after PDT. As a proof of principle, these maps were compared to actual tumor progression after one week. Complete tumor response was correctly assessed in one animal, while residual viable tumor tissue was detected in the other two at the locations where residual tumor tissue was observed after one week. Conclusion: This study demonstrates that DCE-MRI is an effective tool for early evaluation of PDT tumor treatment.

Quantitative Multi-Parametric Magnetic Resonance Imaging of Tumor Response to Photodynamic Therapy.

OBJECTIVE: The aim of this study was to characterize response to photodynamic therapy (PDT) in a mouse cancer model using a multi-parametric quantitative MRI protocol and to identify MR parameters as potential biomarkers for early assessment of treatment outcome. METHODS: CT26.WT colon carcinoma tumors were grown subcutaneously in the hind limb of BALB/c mice. Therapy consisted of intravenous injection of the photosensitizer Bremachlorin, followed by 10 min laser illumination (200 mW/cm2) of the tumor 6 h post injection. MRI at 7 T was performed at baseline, directly after PDT, as well as at 24 h, and 72 h. Tumor relaxation time constants (T1 and T2) and apparent diffusion coefficient (ADC) were quantified at each time point. Additionally, Gd-DOTA dynamic contrast-enhanced (DCE) MRI was performed to estimate transfer constants (Ktrans) and volume fractions of the extravascular extracellular space (ve) using standard Tofts-Kermode tracer kinetic modeling. At the end of the experiment, tumor viability was characterized by histology using NADH-diaphorase staining. RESULTS: The therapy induced extensive cell death in the tumor and resulted in significant reduction in tumor growth, as compared to untreated controls. Tumor T1 and T2 relaxation times remained unchanged up to 24 h, but decreased at 72 h after treatment. Tumor ADC values significantly increased at 24 h and 72 h. DCE-MRI derived tracer kinetic parameters displayed an early response to the treatment. Directly after PDT complete vascular shutdown was observed in large parts of the tumors and reduced uptake (decreased Ktrans) in remaining tumor tissue. At 24 h, contrast uptake in most tumors was essentially absent. Out of 5 animals that were monitored for 2 weeks after treatment, 3 had tumor recurrence, in locations that showed strong contrast uptake at 72 h. CONCLUSION: DCE-MRI is an effective tool for visualization of vascular effects directly after PDT. Endogenous contrast parameters T1, T2, and ADC, measured at 24 to 72 h after PDT, are also potential biomarkers for evaluation of therapy outcome.

Multiparametric MRI analysis for the identification of high intensity focused ultrasound-treated tumor tissue.

PURPOSE: In this study endogenous magnetic resonance imaging (MRI) biomarkers for accurate segmentation of High Intensity Focused Ultrasound (HIFU)-treated tumor tissue and residual or recurring non-treated tumor tissue were identified. METHODS: Multiparametric MRI, consisting of quantitative T1, T2, Apparent Diffusion Coefficient (ADC) and Magnetization Transfer Ratio (MTR) mapping, was performed in tumor-bearing mice before (n = 14), 1 h after (n = 14) and 72 h (n = 7) after HIFU treatment. A non-treated control group was included (n = 7). Cluster analysis using the Iterative Self Organizing Data Analysis (ISODATA) technique was performed on subsets of MRI parameters (feature vectors). The clusters resulting from the ISODATA segmentation were divided into a viable and non-viable class based on the fraction of pixels assigned to the clusters at the different experimental time points. ISODATA-derived non-viable tumor fractions were quantitatively compared to histology-derived non-viable tumor volume fractions. RESULTS: The highest agreement between the ISODATA-derived and histology-derived non-viable tumor fractions was observed for feature vector {T1, T2, ADC}. R1 (1/T1), R2 (1/T2), ADC and MTR each were significantly increased in the ISODATA-defined non-viable tumor tissue at 1 h after HIFU treatment compared to viable, non-treated tumor tissue. R1, ADC and MTR were also significantly increased at 72 h after HIFU. CONCLUSIONS: This study demonstrates that non-viable, HIFU-treated tumor tissue can be distinguished from viable, non-treated tumor tissue using multiparametric MRI analysis. Clinical application of the presented methodology may allow for automated, accurate and objective evaluation of HIFU treatment.

Dual-targeting of αvß3 and galectin-1 improves the specificity of paramagnetic/fluorescent liposomes to tumor endothelium in vivo.

Molecular imaging of angiogenesis requires a highly specific and efficient contrast agent for targeting activated endothelium. We have previously demonstrated that paramagnetic and fluorescent liposomes functionalized with two angiogenesis-specific ligands, the galectin-1-specific anginex (Anx) and the α(v)ß(3) integrin-specific RGD, produce synergistic targeting effect in vitro. In the current study, we applied Anx and RGD dual-conjugated liposomes (Anx/RGD-L) for angiogenesis-specific MRI in vivo, focusing on the specificity and efficacy of liposome association with tumor endothelium. The targeting properties, clearance kinetics and biodistribution of Anx/RGD-L were investigated in B16F10 melanoma-bearing mice, and compared to liposomes functionalized with either Anx (Anx-L) or RGD (RGD-L). The contrast enhancement produced by dual- and single-targeted nanoparticles in the tumor was measured using in vivo T(1)-weighted MRI, complemented by ex vivo immunohistochemical evaluation of tumor tissues. Blood clearance kinetics of Anx/RGD-L was three-fold more rapid than for RGD-L, but comparable to Anx-L. Both dual- and single-targeted liposomes produced similar changes in MRI contrast parameters in tumors with high inter-tumor variability (ΔR(1)=0.04±0.03s(-1), 24h post-contrast). Importantly, however, the specificity of Anx/RGD-L association with tumor endothelium of 53±6%, assessed by fluorescence microscopy, was significantly higher compared to 43±9% (P=0.043) and 28±8% (P=0.0001) of Anx-L and RGD-L, respectively. In contrast, long-circulating RGD-L were on average 16% more efficient in targeting tumor endothelium compared to Anx/RGD-L. Significant differences were also found in the biodistribution of investigated contrast agents. In conclusion, synergistic targeting of α(v)ß(3) and galectin-1 improved the specificity of the association of the liposomal contrast agent to tumor endothelium in vivo, providing therefore a more reliable MRI readout of the angiogenic activity.

Noninvasive visualization of in vivo release and intratumoral distribution of surrogate MR contrast agent using the dual MR contrast technique.

A direct evaluation of the in vivo release profile of drugs from carriers is a clinical demand in drug delivery systems, because drug release characterized in vitro correlates poorly with in vivo release. The purpose of this study is to demonstrate the in vivo applicability of the dual MR contrast technique as a useful tool for noninvasive monitoring of the stability and the release profile of drug carriers, by visualizing in vivo release of the encapsulated surrogate MR contrast agent from carriers and its subsequent intratumoral distribution profile. The important aspect of this technique is that it incorporates both positive and negative contrast agents within a single carrier. GdDTPA, superparamagnetic iron oxide nanoparticles, and 5-fluorouracil were encapsulated in nano- and microspheres composed of poly(D,L-lactide-co-glycolide), which was used as a model carrier. In vivo studies were performed with orthotopic xenograft of human breast cancer. The MR-based technique demonstrated here has enabled visualization of the delivery of carriers, and release and intratumoral distribution of the encapsulated positive contrast agent. This study demonstrated proof-of-principle results for the noninvasive monitoring of in vivo release and distribution profiles of MR contrast agents, and thus, this technique will make a great contribution to the field.