Contrast agents in dynamic contrast-enhanced magnetic resonance imaging

Iron oxide based magnetic nanoparticles for hyperthermia, MRI and drug delivery applications: a review

Iron-oxide nanoparticles (IONPs) have garnered substantial attention in both research and technological domains due to their exceptional chemical and physical properties. These nanoparticles have mitigated the adverse effects of conventional treatment procedures by facilitating advanced theranostic approaches in integration with biomedicine. These IONPs have been extensively utilized in MRI (as contrast agents in diagnosis), drug delivery (as drug carriers), and hyperthermia (treatment), demonstrating promising results with potential for further enhancement. This study elucidates the operational principles of these NPs during diagnosis, drug delivery, and treatment, and emphasizes their precision and efficacy in transporting therapeutic agents to targeted sites without drug loss. It also analyses various challenges associated with the application of these IONPs in this field, such as biocompatibility, agglomeration, and toxicity. Furthermore, diverse strategies have been delineated to address these challenges. Overall, this review provides a comprehensive overview of the applications of IONPs in the field of biomedicine and treatment, along with the associated challenges. It offers significant assistance to researchers, professionals, and clinicians in the field of biomedicine.

Quantitative pre-clinical imaging of hypoxia and vascularity using MRI and PET

During hypoxia, tissues are subjected to an inadequate oxygen supply, disrupting the balance needed to maintain normal function. This deficiency can occur due to reduced oxygen delivery caused by impaired blood flow or a decline in the blood's ability to carry oxygen. In tumors, hypoxia and vascularization play crucial roles, shaping their microenvironments and influencing cancer progression, response to treatment and metastatic potential. This chapter provides guidance on the use of non-invasive imaging methods including Positron Emission Tomography and Magnetic Resonance Imaging to study tumor oxygenation in pre-clinical settings. These imaging techniques offer valuable insights into tumor vascularity and oxygen levels, aiding in understanding tumor behavior and treatment effects. For example, PET imaging uses tracers such as [18F]-fluoromisonidazole (FMISO) to visualize hypoxic areas within tumors, while MRI complements this with anatomical and functional images. Although directly assessing tumor hypoxia with MRI remains challenging, techniques like Blood Oxygen Level Dependent (BOLD) and Dynamic Contrast-Enhanced MRI (DCE-MRI) provide valuable information. BOLD can track changes in oxygen levels during oxygen challenges, while DCE-MRI offers real-time access to perfusion and vessel permeability data. Integrating data from these imaging modalities can help assess oxygen supply, refine treatment strategies, enhance therapeutic effectiveness, and ultimately improve patient outcomes.

Current trends in the characterization and monitoring of vascular response to cancer therapy

Tumor vascular physiology is an important determinant of disease progression as well as the therapeutic outcome of cancer treatment. Angiogenesis or the lack of it provides crucial information about the tumor's blood supply and therefore can be used as an index for cancer growth and progression. While standalone anti-angiogenic therapy demonstrated limited therapeutic benefits, its combination with chemotherapeutic agents improved the overall survival of cancer patients. This could be attributed to the effect of vascular normalization, a dynamic process that temporarily reverts abnormal vasculature to the normal phenotype maximizing the delivery and intratumor distribution of chemotherapeutic agents. Longitudinal monitoring of vascular changes following antiangiogenic therapy can indicate an optimal window for drug administration and estimate the potential outcome of treatment. This review primarily focuses on the status of various imaging modalities used for the longitudinal characterization of vascular changes before and after anti-angiogenic therapies and their clinical prospects.

Shine and darkle the blood vessels: Multiparameter hypersensitive MR angiography for diagnosis of panvascular diseases

Magnetic resonance angiography (MRA) is pivotal for diagnosing panvascular diseases. However, single-modality MRA falls short in diagnosing diverse vascular abnormalities. Thus, contrast agents combining T1 and T2 effects are sought for multiparameter MRA with clinical promise, yet achieving a balance in T1 and T2 contrast enhancement effects remains a scientific challenge. Herein, we developed a hypersensitive multiparameter MRA strategy using dual-modality NaGdF4 nanoparticles. Because of the longer tumbling time (τR), NaGdF4 nanoparticles can improve the longitudinal relaxivity (r1), brightening vessels in T1-weighted sequences. Simultaneously, the regular arrangement of Gd3+ in the crystal induces magnetic anisotropy, creating local static magnetic field heterogeneity and generating negative signals in T2-weighted sequences. Consequently, the efficacy of NaGdF4-enhanced high-resolution multiparameter MRA has been validated in diagnosing ischemic stroke and Alzheimer's disease in rodent models. In addition, the dual-contrast imaging has been realized on swine with a clinical 3.0-T magnetic resonance imaging scanner, highly emphasizing the clinical translation prospect.

Updated criteria for the approval of subsequent pregnancy after cesarean section with a transverse uterine fundal incision based on 17 years of experience

In the case of placenta previa-accreta when the placenta covers the entire anterior uterine wall, it is difficult to avoid transecting the placenta by traditional low-transverse cesarean section (CS), resulting in catastrophic hemorrhage and fetal anemia. To prevent this critical risk, we developed the CS with transverse uterine fundal incision (TUFI) and this technique has been widely used as a beneficial surgical method in clinical practice owing to its safety advantages for the mother and neonate since our first report. However, the risk of uterine rupture during a subsequent pregnancy remains unclear. Based on our 17 years of experience, patients who require TUFI do not need to avoid this beneficial operative method simply because of their desire to conceive again, as long as certain conditions can be met. To approve a post-TUFI pregnancy, an appropriate suture method, delay in conception for at least 12 months with evaluation of the TUFI scar, and cautious postoperative management are at a minimum essential. In this article, we showed our recommendation for operative procedure and discuss the current status of the management of post-TUFI pregnancies based on the evaluation of the TUFI wound scar and experience with postoperative pregnancies.

Enhancing the Understanding of Breast Vascularity Through Insights From Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Comprehensive Review

Breast vascularity plays a crucial role in both physiological and pathological processes, particularly in the development and progression of breast cancer. Understanding vascular changes within breast tissue is essential for accurate diagnosis, treatment planning, and monitoring therapeutic response. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has emerged as a valuable tool for evaluating breast vascularity due to its ability to provide detailed functional and morphological insights. DCE-MRI utilizes contrast agents to highlight blood flow and vessel permeability, making it especially useful in differentiating between benign and malignant lesions. This review explores the significance of DCE-MRI in breast vascularity assessment, highlighting its principles, clinical applications, and role in detecting malignancy through vascular changes. We also examine its utility in monitoring treatment response and quantitative analysis of perfusion metrics such as Ktrans and extracellular-extravascular volume (Ve). While DCE-MRI offers remarkable diagnostic accuracy, challenges remain regarding its cost, accessibility, and potential overlap of enhancement patterns between benign and malignant conditions. The review further discusses emerging technologies and future directions for DCE-MRI, including advanced imaging techniques and machine learning-based quantification. Overall, DCE-MRI stands out as a powerful tool in the comprehensive evaluation of breast vascularity, with significant potential to improve patient outcomes in breast cancer management.

Greybox: A hybrid algorithm for direct estimation of tracer kinetic parameters from undersampled DCE-MRI data

BACKGROUND

A variety of deep learning-based and iterative approaches are available to predict Tracer Kinetic (TK) parameters from fully sampled or undersampled dynamic contrast-enhanced (DCE) MRI data. However, both the methods offer distinct benefits and drawbacks.

PURPOSE

To propose a hybrid algorithm (named as 'Greybox'), using both model- as well as DL-based, for solving a multi-parametric non-linear inverse problem of directly estimating TK parameters from undersampled DCE MRI data, which is invariant to undersampling rate.

METHODS

The proposed algorithm was inspired by plug-and-play algorithms used for solving linear inverse imaging problems. This technique was tested for its effectiveness in solving the nonlinear ill-posed inverse problem of generating 3D TK parameter maps from four-dimensional (4D; Spatial + Temporal) retrospectively undersampled k-space data. The algorithm learns a deep learning-based prior using UNET to estimate theK trans $\mathbf {K_{trans}}$ andV p $\mathbf {V_{p}}$ parameters based on the Patlak pharmacokinetic model, and this trained prior was utilized to estimate the TK parameter maps using an iterative gradient-based optimization scheme. Unlike the existing DL models, this network is invariant to the undersampling rate of the input data. The proposed method was compared with the total variation-based direct reconstruction technique on brain, breast, and prostate DCE-MRI datasets for various undersampling rates using the Radial Golden Angle (RGA) scheme. For the breast dataset, an indirect estimation using the Fast Composite Splitting algorithm was utilized for comparison. Undersampling rates of 8 × $\times$ , 12 × $\times$ and 20 × $\times$ were used for the experiments, and the results were compared using the PSNR and SSIM as metrics. For the breast dataset of 10 patients, data from four patients were utilized for training (1032 samples), two for validation (752 samples), and the entire volume of four patients for testing. Similarly, for the prostate dataset of 18 patients, 10 patients were utilized for training (720 samples), five for validation (216 samples), and the whole volume of three patients for testing. For the brain dataset of nineteen patients, ten patients were used for training (3152 samples), five for validation (1168 samples), and the whole volume of four patients for testing. Statistical tests were also conducted to assess the significance of the improvement in performance.

RESULTS

The experiments showed that the proposed Greybox performs significantly better than other direct reconstruction methods. The proposed algorithm improved the estimatedK trans $\mathbf {K_{trans}}$ andV p $\mathbf {V_{p}}$ in terms of the peak signal-to-noise ratio by up to 3 dB compared to other standard reconstruction methods.

CONCLUSION

The proposed hybrid reconstruction algorithm, Greybox, can provide state-of-the-art performance in solving the nonlinear inverse problem of DCE-MRI. This is also the first of its kind to utilize convolutional neural network-based encodings as part of the plug-and-play priors to improve the performance of the reconstruction algorithm.

Evaluation of incision healing status after transverse uterine fundal incision for cesarean delivery and postoperative pregnancy: a ten-year single-center retrospective study

BACKGROUND

Transverse uterine fundal incision (TUFI) is a beneficial procedure for mothers and babies at risk due to placenta previa-accreta, and has been implemented worldwide. However, the risk of uterine rupture during a subsequent pregnancy remains unclear. We therefore evaluated the TUFI wound scar to determine the approval criteria for pregnancy after this surgery.

METHODS

Between April 2012 and August 2022, we performed TUFI on 150 women. Among 132 of the 150 women whose uteruses were preserved after TUFI, 84 women wished to conceive again. The wound healing status, scar thickness, and resumption of blood flow were evaluated in these women by magnetic resonance imaging (MRI) and sonohysterogram at 12 months postoperatively. Furthermore, TUFI scars were directly observed during the Cesarean sections in women who subsequently conceived.

RESULTS

Twelve women were lost to follow-up and one conceived before the evaluation, therefore 71 cases were analyzed. MRI scans revealed that the "scar thickness", the thinnest part of the scar compared with the normal surrounding area, was ≥ 50% in all cases. The TUFI scars were enhanced in dynamic contrast-enhanced MRI except for four women. However, the scar thickness in these four patients was greater than 80%. Twenty-three of the 71 women conceived after TUFI and delivered live babies without notable problems until August 2022. Their MRI scans before pregnancy revealed scar thicknesses of 50-69% in two cases and ≥ 70% in the remaining 21 cases. And resumption of blood flow was confirmed in all patients except two cases whose scar thickness ≥ 90%. No evidence of scar healing failure was detected at subsequent Cesarean sections, but partial thinning was found in two patients whose scar thicknesses were 50-69%. In one woman who conceived seven months after TUFI and before the evaluation, uterine rupture occurred at 26 weeks of gestation.

CONCLUSIONS

Certain criteria, including an appropriate suture method, delayed conception for at least 12 months, evaluation of the TUFI scar at 12 months postoperatively, and cautious postoperative management, must all be met in order to approve a post-TUFI pregnancy. Possible scar condition criteria for permitting a subsequent pregnancy could include the scar thickness being ≥ 70% of the surrounding area on MRI scans, at least partially resumed blood flow, and no abnormalities on the sonohysterogram.

TRIAL REGISTRATION

Retrospectively registered.

Decoding Tumor Angiogenesis for Therapeutic Advancements: Mechanistic Insights

Tumor angiogenesis, the formation of new blood vessels within the tumor microenvironment, is considered a hallmark of cancer progression and represents a crucial target for therapeutic intervention. The tumor microenvironment is characterized by a complex interplay between proangiogenic and antiangiogenic factors, regulating the vascularization necessary for tumor growth and metastasis. The study of angiogenesis involves a spectrum of techniques, spanning from biomarker assessment to advanced imaging modalities. This comprehensive review aims to provide insights into the molecular intricacies, regulatory dynamics, and clinical implications of tumor angiogenesis. By delving into these aspects, we gain a deeper understanding of the processes driving vascularization in tumors, paving the way for the development of novel and effective antiangiogenic therapies in the fight against cancer.

Functional Imaging of Hypoxia: PET and MRI

Molecular and functional imaging have critical roles in cancer care. Existing evidence suggests that noninvasive detection of hypoxia within a particular type of cancer can provide new information regarding the relationship between hypoxia, cancer aggressiveness and altered therapeutic responses. Following the identification of hypoxia inducible factor (HIF), significant progress in understanding the regulation of hypoxia-induced genes has been made. These advances have provided the ability to therapeutically target HIF and tumor-associated hypoxia. Therefore, by utilizing the molecular basis of hypoxia, hypoxia-based theranostic strategies are in the process of being developed which will further personalize care for cancer patients. The aim of this review is to provide an overview of the significance of tumor hypoxia and its relevance in cancer management as well as to lay out the role of imaging in detecting hypoxia within the context of cancer.

Preparation of PFPE-Based Polymeric Nanoparticles via Polymerization-Induced Self-Assembly as Contrast Agents for 19F MRI

Fluorine-19 magnetic resonance imaging (¹⁹F MRI) probes have received considerable research interest as imaging contrast agents (CAs), but they remain neglected and underutilized due to the limited fluorine content or poor performance of fluorinated tracers. Here, we present polymeric nanoparticles (NPs) as ¹⁹F MRI CAs with a simple synthesis method and promising imaging performance. First, hydrophilic random copolymers were synthesized from oligo(ethylene glycol) methyl ether acrylate and perfluoropolyether methacrylate by reversible addition-fragmentation chain transfer (RAFT) polymerization. The optimal fluorine content, polymer concentration, and cytotoxicity as ¹⁹F MRI CAs were investigated in detail. Then, the optimal copolymer was selected as the macromolecular chain transfer agent, and the chain extension was performed with 2-(perfluorooctyl ethyl methacrylate). Subsequently, the NPs with different morphologies, such as ellipsoidal, spherical nanoparticles and vesicles, were prepared in situ by the RAFT-mediated polymerization-induced self-assembly method. In addition, the ¹⁹F MRI signal and cytotoxicity studies further confirmed that these polymeric NPs are nontoxic and have great potential as promising ¹⁹F MRI CAs for biological applications.

VTDCE-Net: A time invariant deep neural network for direct estimation of pharmacokinetic parameters from undersampled DCE MRI data

PURPOSE

To propose a robust time and space invariant deep learning (DL) method to directly estimate the pharmacokinetic/tracer kinetic (PK/TK) parameters from undersampled dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data.

METHODS

DCE-MRI consists of 4D (3D-spatial + temporal) data and has been utilized to estimate 3D (spatial) tracer kinetic maps. Existing DL architecture for this task needs retraining for variation in temporal and/or spatial dimensions. This work proposes a DL algorithm that is invariant to training and testing in both temporal and spatial dimensions. The proposed network was based on a 2.5-dimensional Unet architecture, where the encoder consists of a 3D convolutional layer and the decoder consists of a 2D convolutional layer. The proposed VTDCE-Net was evaluated for solving the ill-posed inverse problem of directly estimating TK parameters from undersampled k - t $k-t$ space data of breast cancer patients, and the results were systematically compared with a total variation (TV) regularization based direct parameter estimation scheme. In the breast dataset, the training was performed on patients with 32 time samples, and testing was carried out on patients with 26 and 32 time samples. Translation of the proposed VTDCE-Net for brain dataset to show the generalizability was also carried out. Undersampling rates (R) of 8× , 12× , and 20× were utilized with PSNR and SSIM as the figures of merit in this evaluation. TK parameter maps estimated from fully sampled data were utilized as ground truth.

RESULTS

Experiments carried out in this work demonstrate that the proposed VTDCE-Net outperforms the TV scheme on both breast and brain datasets across all undersampling rates. For K trans $\mathbf {K_{trans}}$ and V p $\mathbf {V_{p}}$ maps, the improvement over TV is as high as 2 and 5 dB, respectively, using the proposed VTDCE-Net.

CONCLUSION

Temporal points invariant DL network that was proposed in this work to estimate the TK-parameters using DCE-MRI data has provided state-of-the-art performance compared to standard image reconstruction methods and is shown to work across all undersampling rates.

Predictive model based on DCE-MRI and clinical features for the evaluation of pain response after stereotactic body radiotherapy in patients with spinal metastases

OBJECTIVE

To investigate the correlation of conventional MRI, DCE-MRI and clinical features with pain response after stereotactic body radiotherapy (SBRT) in patients with spinal metastases and establish a pain response prediction model.

METHODS

Patients with spinal metastases who received SBRT in our hospital from July 2018 to April 2022 consecutively were enrolled. All patients underwent conventional MRI and DCE-MRI before treatment. Pain was assessed before treatment and in the third month after treatment, and the patients were divided into pain-response and no-pain-response groups. A multivariate logistic regression model was constructed to obtain the odds ratio and 95% confidence interval (CI) for each variable. C-index was used to evaluate the model's discrimination performance.

RESULTS

Overall, 112 independent spinal lesions in 89 patients were included. There were 73 (65.2%) and 39 (34.8%) lesions in the pain-response and no-pain-response groups, respectively. Multivariate analysis showed that the number of treated lesions, pretreatment pain score, Karnofsky performance status score, Bilsky grade, and the DCE-MRI quantitative parameter K^trans were independent predictors of post-SBRT pain response in patients with spinal metastases. The discrimination performance of the prediction model was good; the C index was 0.806 (95% CI: 0.721-0.891), and the corrected C-index was 0.754.

CONCLUSION

Some imaging and clinical features correlated with post-SBRT pain response in patients with spinal metastases. The model based on these characteristics has a good predictive value and can provide valuable information for clinical decision-making.

KEY POINTS

• SBRT can accurately irradiate spinal metastases with ablative doses. • Predicting the post-SBRT pain response has important clinical implications. • The prediction models established based on clinical and MRI features have good performance.

Diagnostic Value of Multimodal Magnetic Resonance Imaging in Discriminating Between Metastatic and Non-Metastatic Pelvic Lymph Nodes in Cervical Cancer

Background

The status of pelvic lymph node (PLN) metastasis affects treatment and prognosis plans in patients with cervical cancer. However, it is hard to be diagnosed in clinical practice.

Purpose

The present study aimed to evaluate the diagnostic value of multimodal magnetic resonance imaging (MRI) in discriminating between metastatic and non-metastatic pelvic lymph nodes (PLNs) in cervical cancer.

Methods

This retrospective study analyzed MRIs of 209 PLNs in 25 women with pathologically proven cervical cancer. All PLNs had been assessed by pre-treatment multimodal MRIs, and their status was finally confirmed by histopathology. In conventional MRI, lymph node characteristics were compared between metastatic and non-metastatic PLNs. Signal intensity, time–intensity curve (TIC) patterns minimal and mean apparent diffusion coefficients (ADC) were compared between them in DWI. In DCE-MRI, quantitative (K_trans, K_ep and V_e) analyses were performed on DCE-MRI sequences, and their predictive values were analyzed by ROC curves.

Results

Of 209 PLNs, 22 (10.53%) were metastases and 187 (89.47%) were non-metastases at histopathologic examination. Considering a comparison of lymph node characteristics, the short axis size, the long axis size, and the boundary differed significantly between the two groups (P<0.05).The differences in ADC_min, TIC types, K_trans and V_e between metastatic and non-metastatic PLNs were significant as well (P<0.05). The good diagnostic performance of multimodal MRI was shown in discriminating between metastatic and non-metastatic PLNs, with the sensitivity of 85.0% (17/20), specificity of 97.3% (184/189), and accuracy of 96.2% (201/209). ROC analyses showed that the diagnostic accuracy of ADC_min, K_trans and V_e for discriminating between metastatic and non-metastatic PLNs in cervical cancer was 83.7%, 91.4%, and 92.4% with the cut-off values of 0.72 × 10⁻³mm²/s, 0.52 min⁻¹, and 0.53 min⁻¹, respectively.

Conclusion

Multimodal MRI showed good diagnostic performance in determining PLN status in cervical cancer.

Predicting the Proliferation of Tongue Cancer With Artificial Intelligence in Contrast-Enhanced CT

Tongue squamous cell carcinoma (TSCC) is the most common oral malignancy. The proliferation status of tumor cells as indicated with the Ki-67 index has great impact on tumor microenvironment, therapeutic strategy making, and patients’ prognosis. However, the most commonly used method to obtain the proliferation status is through biopsy or surgical immunohistochemical staining. Noninvasive method before operation remains a challenge. Hence, in this study, we aimed to validate a novel method to predict the proliferation status of TSCC using contrast-enhanced CT (CECT) based on artificial intelligence (AI). CECT images of the lesion area from 179 TSCC patients were analyzed using a convolutional neural network (CNN). Patients were divided into a high proliferation status group and a low proliferation status group according to the Ki-67 index of patients with the median 20% as cutoff. The model was trained and then the test set was automatically classified. Results of the test set showed an accuracy of 65.38% and an AUC of 0.7172, suggesting that the majority of samples were classified correctly and the model was stable. Our study provided a possibility of predicting the proliferation status of TSCC using AI in CECT noninvasively before operation.

Tumor Microenvironment Modifications Recorded With IVIM Perfusion Analysis and DCE-MRI After Neoadjuvant Radiotherapy: A Preclinical Study

PURPOSE

Neoadjuvant radiotherapy (NeoRT) improves tumor local control and facilitates tumor resection in many cancers. Some clinical studies demonstrated that both timing of surgery and RT schedule influence tumor dissemination, and subsequently patient overall survival. Previously, we developed a pre-clinical model demonstrating the impact of NeoRT schedule and timing of surgery on metastatic spreading. We report on the impact of NeoRT on tumor microenvironment by MRI.

METHODS

According to our NeoRT model, MDA-MB 231 cells were implanted in the flank of SCID mice. Tumors were locally irradiated (PXI X-Rad SmART) with 2x5Gy and then surgically removed at different time points after RT. Diffusion-weighted (DW) and Dynamic contrast enhancement (DCE) MRI images were acquired before RT and every 2 days between RT and surgery. IntraVoxel Incoherent Motion (IVIM) analysis was used to obtain information on intravascular diffusion, related to perfusion (F: perfusion factor) and subsequently tumor vessels perfusion. For DCE-MRI, we performed semi-quantitative analyses.

RESULTS

With this experimental model, a significant and transient increase of the perfusion factor F [50% of the basal value (n=16, p<0.005)] was observed on day 6 after irradiation as well as a significant increase of the WashinSlope with DCE-MRI at day 6 (n=13, p<0.05). Using immunohistochemistry, a significant increase of perfused vessels was highlighted, corresponding to the increase of perfusion in MRI at this same time point. Moreover, Tumor surgical resection during this peak of vascularization results in an increase of metastasis burden (n=10, p<0.05).

CONCLUSION

Significant differences in perfusion-related parameters (F and WashinSlope) were observed on day 6 in a neoadjuvant radiotherapy model using SCID mice. These modifications are correlated with an increase of perfused vessels in histological analysis and also with an increase of metastasis spreading after the surgical procedure. This experimental observation could potentially result in a way to personalize treatment, by modulating the time of surgery guided on MRI functional data, especially tumor perfusion.

Multiparametric Magnetic Resonance Imaging for Immediate Target Hit Assessment of CD13-Targeted Tissue Factor tTF-NGR in Advanced Malignant Disease

Simple Summary

Since the knowledge of tumor biology has advanced, a variety of targeted therapies has been developed. These do not immediately affect the tumor size, so optimized oncological imaging is needed. In this phase I study of patients with advanced malignant disease, a multiparametric imaging approach was used to assess changes in tumor perfusion after vessel-occluding therapy with the CD13 targeted truncated tissue factor with a C-terminal NGR-peptide. It comprises different sequences and the use of two different contrast media, ferucarbotran and gadobutrol. This multiparametric MRI protocol enables assessing the therapy effectiveness as early as five hours after therapy initiation.

Abstract

Early assessment of target hit in anti-cancer therapies is a major task in oncologic imaging. In this study, immediate target hit and effectiveness of CD13-targeted tissue factor tTF-NGR in patients with advanced malignant disease enrolled in a phase I trial was assessed using a multiparametric MRI protocol. Seventeen patients with advanced solid malignancies were enrolled in the trial and received tTF-NGR for at least one cycle of five daily infusions. Tumor target lesions were imaged with multiparametric MRI before therapy initiation, five hours after the first infusion and after five days. The imaging protocol comprised ADC, calculated from DWI, and DCE imaging and vascular volume fraction (VVF) assessment. DCE and VVF values decreased within 5 h after therapy initiation, indicating early target hit with a subsequent decrease in tumor perfusion due to selective tumor vessel occlusion and thrombosis induced by tTF-NGR. Simultaneously, ADC values increased at five hours after tTF-NGR administration. In four patients, treatment had to be stopped due to an increase in troponin T hs, with subsequent anticoagulation. In these patients, a reversed effect, with DCE and VVF values increasing and ADC values decreasing, was observed after anticoagulation. Changes in imaging parameters were independent of the mean vessel density determined by immunohistochemistry. By using a multiparametric imaging approach, changes in tumor perfusion after initiation of a tumor vessel occluding therapy can be evaluated as early as five hours after therapy initiation, enabling early assessment of target hit.

Passive targeting of high-grade gliomas via the EPR effect: a closed path for metallic nanoparticles?

Passive tumor targeting via the enhanced permeability and retention (EPR) effect has long been considered the most effective mechanism for the accumulation of nanoparticles inside solid tumors. However, several studies have demonstrated that the EPR effect is largely dependent on the tumor type and location. Particularly complex is the situation in brain tumors, where the presence of the blood-brain tumor barrier (BBTB) adds an extra limiting factor in reaching the tumor interstitium. However, it remains unclear whether these restraints imposed by the BBTB prevent the EPR effect from acting as an efficient tumor targeting mechanism for metallic nanoparticles. In this work, we have studied the EPR effect of metallic magnetic nanoparticles (MMNPs) in a glioblastoma (GBM) model by parametric MRI. Our results showed that only MMNPs ≤50 nm could reach the tumor interstitium, whereas larger MMNPs were unable to cross the BBTB. Furthermore, even for MMNPs around 30-50 nm, the amount of them found within the tumor was scarce and restricted to the vicinity of large tumor vessels, indicating that the BBTB strongly limits the passive accumulation of metallic nanoparticles in brain tumors. Therefore, active targeting becomes the most reasonable strategy to target metallic nanoparticles to GBMs.

Use of dynamic contrast-enhanced MRI for the early assessment of outcome of CyberKnife stereotactic radiosurgery for patients with spinal metastases

AIM

To explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for evaluating early outcomes of CyberKnife radiosurgery for spinal metastases.

MATERIALS AND METHODS

Patients with spinal metastases who were treated with CyberKnife radiosurgery from July 2018 to December 2020 were enrolled. Conventional MRI and DCE-MRI were performed before treatment and at 3 months after treatment. Patients showing disease progression were defined as the progressive disease (PD) group and those showing complete response, partial response, and stable disease were defined as the non-PD group. The haemodynamic parameters (volume transfer constant [Ktrans], rate constant [Kep], and extravascular space [Ve]) before and after treatment between the groups were analysed. Area under the curve (AUC) values were calculated.

RESULTS

A total of 27 patients with 39 independent spinal lesions were included. The median follow-up time was 18.6 months (6.2-36.4 months). There were 27 lesions in the non-PD group and 12 lesions in the PD group. Post-treatment Kep, ΔKtrans and ΔKep in the non-PD group (0.959/min, - 32.6% and -41.1%, respectively) were significantly lower than the corresponding values in PD group (1.429/min, 20.4% and -6%; p<0.05). Post-treatment Ve and ΔVe (0.223 and 27.8%, respectively) in the non-PD group were significantly higher than that of the PD group (0.165 and -13.5%, p<0.05). ΔKtrans showed the highest diagnostic efficiency, with an AUC of 0.821.

CONCLUSIONS

DCE-MRI parameters change significantly at an early stage after CyberKnife stereotactic radiosurgery for spinal metastases. DCE-MRI may be of value in determining the early treatment response.

Comparing the signal enhancement of a gadolinium based and an iron-oxide based contrast agent in low-field MRI

Recently, there has been a renewed interest in low-field MRI. Contrast agents (CA) in MRI have magnetic behavior dependent on magnetic field strength. Therefore, the optimal contrast agent for low-field MRI might be different from what is used at higher fields. Ultra-small superparamagnetic iron-oxides (USPIOs), commonly used as negative CA, might also be used for generating positive contrast in low-field MRI. The purpose of this study was to determine whether an USPIO or a gadolinium based contrast agent is more appropriate at low field strengths. Relaxivity values of ferumoxytol (USPIO) and gadoterate (gadolinium based) were used in this research to simulate normalized signal intensity (SI) curves within a concentration range of 0–15 mM. Simulations were experimentally validated on a 0.25T MRI scanner. Simulations and experiments were performed using spin echo (SE), spoiled gradient echo (SGE), and balanced steady-state free precession (bSSFP) sequences. Maximum achievable SIs were assessed for both CAs in a range of concentrations on all sequences. Simulations at 0.25T showed a peak in SIs at low concentrations ferumoxytol versus a wide top at higher concentrations for gadoterate in SE and SGE. Experiments agreed well with the simulations in SE and SGE, but less in the bSSFP sequence due to overestimated relaxivities in simulations. At low magnetic field strengths, ferumoxytol generates similar signal enhancement at lower concentrations than gadoterate.

nCP:Fe Nanocontrast Agent for Magnetic Resonance Imaging-Based Early Detection of Liver Cirrhosis and Hepatocellular Carcinoma

Early detection of liver tumors and cirrhotic lesions by magnetic resonance imaging (MRI) remains a great challenge. Here, we report a biomineral nanocontrast agent based on iron-doped nanocalcium phosphate (nCP:Fe-CA) for magnetic resonance imaging of early-stage liver cirrhotic and hepatocellular carcinoma nodules using rat models. We have optimized an intravenously injectable, aqueous suspension of nCP:Fe-CA having an average size of 137.6 nm, a spherical shape, magnetic relaxivity of 63 mM^-1S^-1, and colloidal stability for 48 h, post-resuspension in an aqueous phase. Compared to superparamagnetic iron oxide nanoparticles (SPIONs), the optimized nCP:Fe-CA could detect liver tumor lesions as small as ∼0.25 cm, whereas the current clinical detection limit is ∼1 cm. In addition, multiple cirrhotic nodules of size <0.2 cm could be detected by nCP:Fe-CA-assisted MRI. The number of nodules observed after injecting nCP:Fe-CA was ∼3 times higher than that without CA (5-10 nodules). A biocompatibility study on healthy rats injected with nCP:Fe-CA showed unaltered liver transaminases, blood urea nitrogen, serum creatinine, and insignificant hemolysis. Furthermore, hepatobiliary clearance of nCP:Fe-CA was observed in 72 h compared to prolonged retention of SPIONs for 30 days when tested under identical conditions. Overall, the nCP:Fe-CA nanoparticles showed promising results as a biocompatible, MR contrast (T2) agent for the early-stage imaging of liver cirrhosis and hepatocellular carcinoma.

Early response assessment after CyberKnife stereotactic radiosurgery for symptomatic vertebral hemangioma by quantitative parameters from dynamic contrast-enhanced MRI

PURPOSE

The present study aimed to explore the value of DCE-MRI to evaluate the early efficacy of CyberKnife stereotactic radiosurgery in patients with symptomatic vertebral hemangioma (SVH).

METHODS

A retrospective analysis of patients with spinal SVH who underwent CyberKnife stereotactic radiosurgery from January 2017 to August 2019 was performed. All patients underwent DCE-MRI before treatment and three months after treatment. The parameters included volume transfer constant (K^trans), transfer rate constant (K_ep), and extravascular extracellular space volume fraction (V_e).

RESULTS

A total of 11 patients (11 lesions) were included. After treatment, six patients (54.5%) had a partial response, five patients (45.4%) had stable disease, and three patients (27.3%) presented with reossification. K^trans and K_ep decreased significantly in the third month after treatment (p = 0.003 and p = 0.026, respectively). ΔK^trans was -46.23% (range, -87.37 to -23.78%), and ΔK_ep was -36.18% (range, -85.62 to 94.40%). The change in V_e was not statistically significant (p = 0.213), and ΔV_e was -28.01% (range, -58.24 to 54.76%).

CONCLUSION

DCE-MRI parameters K^trans and K_ep change significantly after CyberKnife stereotactic radiosurgery for SVH. Thus, DCE-MRI may be of value in determining the early efficacy of CyberKnife stereotactic radiosurgery.

Optical and magnetic resonance imaging approaches for investigating the tumour microenvironment: state-of-the-art review and future trends

The tumour microenvironment (TME) strongly influences tumorigenesis and metastasis. Two of the most characterized properties of the TME are acidosis and hypoxia, both of which are considered hallmarks of tumours as well as critical factors in response to anticancer treatments. Currently, various imaging approaches exist to measure acidosis and hypoxia in the TME, including magnetic resonance imaging (MRI), positron emission tomography and optical imaging. In this review, we will focus on the latest fluorescent-based methods for optical sensing of cell metabolism and MRI as diagnostic imaging tools applied both in vitro and in vivo. The primary emphasis will be on describing the current and future uses of systems that can measure intra- and extra-cellular pH and oxygen changes at high spatial and temporal resolution. In addition, the suitability of these approaches for mapping tumour heterogeneity, and assessing response or failure to therapeutics will also be covered.

Mapping of CSF transport using high spatiotemporal resolution dynamic contrast-enhanced MRI in mice: Effect of anesthesia

PURPOSE

Dynamic contrast-enhanced MRI (DCE-MRI) represents the only available approach for glymphatic cerebrospinal fluid (CSF) flow 3D mapping in the brain of living animals and humans. The purpose of this study was to develop a novel DCE-MRI protocol for mapping of the glymphatic system transport with improved spatiotemporal resolution, and to validate the new protocol by comparing the transport in mice anesthetized with either isoflurane or ketamine/xylazine.

METHODS

The contrast agent, gadobutrol, was administered into the CSF of the cisterna magna and its transport visualized continuously on a 9.4T preclinical scanner using 3D fast-imaging with a steady-state free-precession sequence (3D-FISP), which has a spatial resolution of 0.001 mm³ and a temporal resolution of 30 s. The MR signals were measured dynamically for 60 min in multiple volumes of interest covering the entire CSF space and brain parenchyma.

RESULTS

The results confirm earlier findings that glymphatic CSF influx is higher under ketamine/xylazine than with isoflurane anesthesia. This was extended to account for new details about the distinct CSF efflux pathways under the two anesthetic regimens. Dynamic contrast MR shows that CSF clearance occurs mainly along the vagus nerve near the jugular vein under isoflurane and via the olfactory bulb under ketamine/xylazine.

CONCLUSION

The improved spatial and temporal sampling rates afforded by 3D-FISP shed new light on the pharmacological modulation of CSF efflux paths. The present observations may have the potential to set a new standard for future experimental DCE-MRI studies of the glymphatic system.

Saturation transfer properties of tumour xenografts derived from prostate cancer cell lines 22Rv1 and DU145

Histopathology is currently the most reliable tool in assessing the aggressiveness and prognosis of solid tumours. However, developing non-invasive modalities for tumour evaluation remains crucial due to the side effects and complications caused by biopsy procedures. In this study, saturation transfer MRI was used to investigate the microstructural and metabolic properties of tumour xenografts in mice derived from the prostate cancer cell lines 22Rv1 and DU145, which express different aggressiveness. The magnetization transfer (MT) and chemical exchange saturation transfer (CEST) effects, which are associated with the microstructural and metabolic properties in biological tissue, respectively, were analyzed quantitatively and compared amongst different tumour types and regions. Histopathological staining was performed as a reference. Higher cellular density and metabolism expressed in more aggressive tumours (22Rv1) were associated with larger MT and CEST effects. High collagen content in the necrotic regions might explain their higher MT effects compared to tumour regions.

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.

Combination of DCE-MRI and DWI in Predicting the Treatment Effect of Concurrent Chemoradiotherapy in Esophageal Carcinoma

Background

Concurrent chemoradiotherapy (CCRT) is the main treatment for esophageal cancer, but the response to treatment varies from individual to individual. MR imaging methods, such as diffusion-weighted (DW) MRI and the use of dynamic contrast-enhanced (DCE) MRI, have the potential to provide additional biomarkers that could evaluate the effect of CCRT in patients with esophageal carcinoma.

Materials and Methods

Fifty-six patients with esophageal carcinoma, verified by histopathology, underwent MRI examination before and at midtreatment (4^th week, radiotherapy 30-40 Gy) using the Siemens 3.0 T MR System. Parameter maps of apparent diffusion coefficient (ADC), and DCE maps of volume transfer constant (K ^rans), rate contrast (k _ep), and extracellular fluid space (v _e), were computed using a Siemens Company Multimodality Workplace (MMWP) model. Comparison of histogram parameters and their diagnostic performance was determined using the Mann-Whitney U test and receiver operating characteristic (ROC) analysis.

Results

56 patient MRI scans were available for analysis at baseline and at the third week, respectively. Pretreatment K ^rans, pretreatment k _ep, pretreatment ADC (P < 0.05), and during-treatment K ^rans (P < 0.05) and ΔK ^rans and ΔADC (P < 0.05) were significantly different after CCRT. Based on the binary logistic model, the ROC analysis demonstrated that the combined predictors demonstrated a high diagnostic performance with an AUC of 0.939. The sensitivity and specificity were 98.6% and 73.8%, respectively.

Conclusion

The combination of DCE and DWI can be used as an early biomarker in the prediction of the effect of CCRT three weeks after treatment in esophageal carcinoma.

Fluorescence Imaging of Breast Tumors and Gastrointestinal Cancer

Optical imaging offers a high potential for noninvasive detection and therapy of cancer in humans. Recent advances in instrumentation for diffuse optical imaging have led to new capabilities for the detection of cancer in highly scattering tissue such as the female breast. In particular, fluorescence imaging was made applicable as a sensitive technique to image molecular probes in vivo. We review recent developments in the detection of breast cancer and fluorescence-guided surgery of the breast by contrast agents available for application on humans. Detection of cancer has been investigated with the unspecific contrast agents "indocyanine green" and "omocianine" so far. Hereby, indocyanine green was found to offer high potential for the differentiation of malignant and benign lesions by exploiting vessel permeability for macromolecules as a cancer-specific feature. Tumor-specific molecular targeting and activatable probes have been investigated in clinical trials for fluorescence-guided tumor margin detection. In this application, high spatial resolution can be achieved, since tumor regions are visualized mainly at the tissue surface. As another example of superficial tumor tissue, imaging of lesions in the gastrointestinal tract is discussed. Promising results have been obtained on high-risk patients with Barrett´s esophagus and with ulcerative colitis by administering 5-aminolevulinic acid which induces accumulation of protoporphyrin IX serving as a tumor-specific fluorescent marker. Time-gated fluorescence imaging and spectroscopy are effective ways to suppress underlying background from tissue autofluorescence. Furthermore, recently developed tumor-specific molecular probes have been demonstrated to be superior to white-light endoscopy offering new ways for early detection of malignancies in the gastrointestinal tract.

Improving nanotherapy delivery and action through image-guided systems pharmacology

Despite recent advances in the translation of therapeutic nanoparticles (TNPs) into the clinic, the field continues to face challenges in predictably and selectively delivering nanomaterials for the treatment of solid cancers. The concept of enhanced permeability and retention (EPR) has been coined as a convenient but simplistic descriptor of high TNP accumulation in some tumors. However, in practice EPR represents a number of physiological variables rather than a single one (including dysfunctional vasculature, compromised lymphatics and recruited host cells, among other aspects of the tumor microenvironment) — each of which can be highly heterogenous within a given tumor, patient and across patients. Therefore, a clear need exists to dissect the specific biophysical factors underlying the EPR effect, to formulate better TNP designs, and to identify patients with high-EPR tumors who are likely to respond to TNP. The overall pharmacology of TNP is governed by an interconnected set of spatially defined and dynamic processes that benefit from a systems-level quantitative approach, and insights into the physiology have profited from the marriage between in vivo imaging and quantitative systems pharmacology (QSP) methodologies. In this article, we review recent developments pertinent to image-guided systems pharmacology of nanomedicines in oncology. We first discuss recent developments of quantitative imaging technologies that enable analysis of nanomaterial pharmacology at multiple spatiotemporal scales, and then examine reports that have adopted these imaging technologies to guide QSP approaches. In particular, we focus on studies that have integrated multi-scale imaging with computational modeling to derive insights about the EPR effect, as well as studies that have used modeling to guide the manipulation of the EPR effect and other aspects of the tumor microenvironment for improving TNP action. We anticipate that the synergistic combination of imaging with systems-level computational methods for effective clinical translation of TNPs will only grow in relevance as technologies increase in resolution, multiplexing capability, and in the ability to examine heterogeneous behaviors at the single-cell level.

Negative Predictive Value of Contrast-Enhanced Ultrasound of Liver and Kidney Thermal Ablation Sites for Local Tumour Progression During Long-term Follow-up: A Retrospective Consecutive Study

PURPOSE

To determine negative predictive value (NPV) of contrast-enhanced ultrasound (CEUS) to demonstrate local tumour progression (LTP) at thermal ablation (TA) sites.

METHODS

Our institutional review board approved this retrospective study; acquisition of consent was waived. Consecutive CEUS examinations performed between 2004-2014 for TA site evaluation on patients who could not undergo enhanced computed tomography (CT) or magnetic resonance imaging (MRI), or had inconclusive CT or MRI, were retrospectively reviewed. Those reported as no abnormal enhancement in or surrounding TA site were included. CEUS examination was considered true-negative based on stability or lack of enhancement/washout on follow-up imaging for at least 1 year, and false-negative (FN), if there was an arterially enhancing focus with wash-out at or surrounding TA site on subsequent follow-up imaging.

RESULTS

Study population included 56 tumours in 54 patients, 11 women, 43 men; mean age 71 years. Two patients had TA of two different hepatocellular carcinomas. Thirty-six examinations were for hepatic TA and twenty for renal TA. Lesion sizes ranged from 1 cm to 7 cm (mean 3.1 ± 1.2). Mean diameter of 7 recurrences was 13.8 mm. Overall FN rate was 12.5% (7/56). Corresponding numbers were 0% (0/20) for renal TA and 19.4% (7/36) for hepatic TA. Overall NPV of CEUS was 87.5% (49/56) (confidence interval [CI]: 78.8%-96.2%). NPV for renal TA was 100% (20/20) (CI: 100%-100%) and for hepatic TA 81.5% (29/36) (CI: 67.6 %-93.5%).

CONCLUSION

In this cohort, CEUS showed high NPV for exclusion of LTP at renal TA sites. NPV for hepatic TA sites was high but lower than renal TA.

Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration

Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images. This framework allows the robust registration of motion components over time without intensity variances. Existing sparse coding techniques recover a 3D image containing only contrast agents (named contrast enhancement component) from a manually labeled dictionary, whose column has the same size with the original 3D volume (3D-t mode). The high dimension of the recovery target (3D volume) and the indistinguishability between the unenhanced and enhanced images make accurate coding difficult. In this paper, we predefine an ideal time-intensity curve containing only contrast agents (named contrast agent curve) and recover it from the transpose dictionary (t-3D mode), whose column has been updated into the original time-intensity curves. The low dimension of the target (1D curve) and the significant intergroup difference between contrast agent curves and non-contrast agent curves can estimate a series of pure contrast agent curves. A "correlation-weighted" constraint is introduced for the selection of a coding subset with more contrast agent curves, leading to an efficient and accurate sparse recovery process. Then, the contrast enhancement component can be estimated by the solved sparse coefficients' map and the ideal curve and subtracted from the original DCE-MRI. Finally, we register the de-enhanced images and apply the obtained deformation fields for the original DCE-MRI to achieve the goal of motion correction. We conduct the experiments on both simulated and real liver DCE-MRI data. Compared with other state-of-the-art DCE-MRI registration methods, the experimental results show that our method achieves a better registration performance with less computational efficiency.

Update on imaging-based diagnosis of acute renal allograft rejection

Kidney transplantation is the preferred treatment for patients with end-stage renal disease. Despite effective immunosuppressants, acute allograft rejections pose a major threat to graft survival. In early stages, acute rejections are still potentially reversible, and early detection is crucial to initiate the necessary treatment options and to prevent further graft dysfunction or even loss of the complete graft. Currently, invasive core needle biopsy is the reference standard to diagnose acute rejection. However, biopsies carry the risk of graft injuries and cannot be immediately performed on patients receiving anticoagulation drugs. Therefore, non-invasive assessment of the whole organ for specific and rapid detection of acute allograft rejection is desirable. We herein provide a review summarizing current imaging-based approaches for non-invasive diagnosis of acute renal allograft rejection.

Comparison of Two Mathematical Models of Cellularity Calculation

OBJECT: Nowadays, there is increasing evidence that functional magnetic resonance imaging (MRI) modalities, namely, diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI (DCE MRI), can characterize tumor architecture like cellularity and vascularity. Previously, two formulas based on a logistic tumor growth model were proposed to predict tumor cellularity with DWI and DCE. The purpose of this study was to proof these formulas. METHODS: 16 patients with head and neck squamous cell carcinomas were included into the study. There were 2 women and 14 men with a mean age of 57.0 ± 7.5 years. In every case, tumor cellularity was calculated using the proposed formulas by Atuegwu et al. In every case, also tumor cell count was estimated on histopathological specimens as an average cell count per 2 to 5 high-power fields. RESULTS: There was no significant correlation between the calculated cellularity and histopathologically estimated cell count by using the formula based on apparent diffusion coefficient (ADC) values. A moderate positive correlation (r=0.515, P=.041) could be identified by using the formula including ADC and V_e values. CONCLUSIONS: The formula including ADC and V_e values is more sensitive to predict tumor cellularity than the formula including ADC values only.