Sensitive and specific detection of breast cancer lymph node metastasis through dual-modality magnetic particle imaging and fluorescence molecular imaging: a preclinical evaluation

Dual-channel end-to-end network with prior knowledge embedding for improving spatial resolution of magnetic particle imaging

Magnetic particle imaging (MPI) is an emerging non-invasive medical imaging tomography technology based on magnetic particles, with excellent imaging depth penetration, high sensitivity and contrast. Spatial resolution and signal-to-noise ratio (SNR) are key performance metrics for evaluating MPI, which are directly influenced by the gradient of the selection field (SF). Increasing the SF gradient can improve the spatial resolution of MPI, but will lead to a decrease in SNR. Deep learning (DL) methods may enable obtaining high-resolution images from low-resolution images to improve the MPI resolution under low gradient conditions. However, existing DL methods overlook the physical procedures contributing to the blurring of MPI images, resulting in low interpretability and hindering breakthroughs in resolution. To address this issue, we propose a dual-channel end-to-end network with prior knowledge embedding for MPI (DENPK-MPI) to effectively establish a latent mapping between low-gradient and high-gradient images, thus improving MPI resolution without compromising SNR. By seamlessly integrating MPI PSF with DL paradigm, DENPK-MPI leads to a significant improvement in spatial resolution performance. Simulation, phantom, and in vivo MPI experiments have collectively confirmed that our method can improve the resolution of low-gradient MPI images without sacrificing SNR, resulting in a decrease in full width at half maximum by 14.8%-23.8 %, and the accuracy of image reconstruction is 18.2 %-27.3 % higher than other DL methods. In conclusion, we propose a DL method that incorporates MPI prior knowledge, which can improve the spatial resolution of MPI without compromising SNR and possess improved biomedical application.

Magnetic Particle Imaging: From Tracer Design to Biomedical Applications in Vasculature Abnormality

Magnetic particle imaging (MPI) is an emerging non-invasive tomographic technique based on the response of magnetic nanoparticles (MNPs) to oscillating drive fields at the center of a static magnetic gradient. In contrast to magnetic resonance imaging (MRI), which is driven by uniform magnetic fields and projects the anatomic information of the subjects, MPI directly tracks and quantifies MNPs in vivo without background signals. Moreover, it does not require radioactive tracers and has no limitations on imaging depth. This article first introduces the basic principles of MPI and important features of MNPs for imaging sensitivity, spatial resolution, and targeted biodistribution. The latest research aiming to optimize the performance of MPI tracers is reviewed based on their material composition, physical properties, and surface modifications. While the unique advantages of MPI have led to a series of promising biomedical applications, recent development of MPI in investigating vascular abnormalities in cardiovascular and cerebrovascular systems, and cancer are also discussed. Finally, recent progress and challenges in the clinical translation of MPI are discussed to provide possible directions for future research and development.

Adhesion molecule-targeted magnetic particle imaging nanoprobe for visualization of inflammation in acute lung injury

PURPOSE

Uncontrolled intra-alveolar inflammation is a central pathogenic feature, and its severity translates into a valid prognostic indicator of acute lung injury (ALI). Unfortunately, current clinical imaging approaches are unsuitable for visualizing and quantifying intra-alveolar inflammation. This study aimed to construct a small-sized vascular cell adhesion molecule-1 (VCAM-1)-targeted magnetic particle imaging (MPI) nanoprobe (ESPVPN) to visualize and accurately quantify intra-alveolar inflammation at the molecular level.

METHODS

ESPVPN was engineered by conjugating a peptide (VHPKQHRGGSK(Cy7)GC) onto a polydopamine-functionalized superparamagnetic iron oxide core. The MPI performance, targeting, and biosafety of the ESPVPN were characterized. VCAM-1 expression in HUVECs and mouse models was evaluated by western blot. The degree of inflammation and distribution of VCAM-1 in the lungs were assessed using histopathology. The expression of pro-inflammatory markers and VCAM-1 in lung tissue lysates was measured using ELISA. After intravenous administration of ESPVPN, MPI and CT imaging were used to analyze the distribution of ESPVPN in the lungs of the LPS-induced ALI models.

RESULTS

The small-sized (~10 nm) ESPVPN exhibited superior MPI performance compared to commercial MagImaging® and Vivotrax, and ESPVPN had effective targeting and biosafety. VCAM-1 was highly expressed in LPS-induced ALI mice. VCAM-1 expression was positively correlated with the LPS-induced dose (R = 0.9381). The in vivo MPI signal showed positive correlations with both VCAM-1 expression (R = 0.9186) and representative pro-inflammatory markers (MPO, TNF-α, IL-6, IL-8, and IL-1β, R > 0.7).

CONCLUSION

ESPVPN effectively targeted inflammatory lungs and combined the advantages of MPI quantitative imaging to visualize and evaluate the degree of ALI inflammation.

Nanoparticle drug delivery systems responsive to tumor microenvironment: Promising alternatives in the treatment of triple-negative breast cancer

The conventional therapeutic treatment of triple-negative breast cancer (TNBC) is negatively influenced by the development of tumor cell drug resistant, and systemic toxicity of therapeutic agents due to off-target activity. In accordance with research findings, nanoparticles (NPs) responsive to the tumor microenvironment (TME) have been discovered for providing opportunities to selectively target tumor cells via active targeting or Enhanced Permeability and Retention (EPR) effect. The combination of the TME control and therapeutic NPs offers promising solutions for improving the prognosis of the TNBC because the TME actively participates in tumor growth, metastasis, and drug resistance. The NP-based systems leverage stimulus-responsive mechanisms, such as low pH value, hypoxic, excessive secretion enzyme, concentration of glutathione (GSH)/reactive oxygen species (ROS), and high concentration of Adenosine triphosphate (ATP) to combat TNBC progression. Concurrently, NP-based stimulus-responsive introduces a novel approach for drug dosage design, administration, and modification of the pharmacokinetics of conventional chemotherapy and immunotherapy drugs. This review provides a comprehensive examination of the strengths, limitations, applications, perspectives, and future expectations of both novel and traditional stimulus-responsive NP-based drug delivery systems for improving outcomes in the medical practice of TNBC. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

First magnetic particle imaging to assess pulmonary vascular leakage in vivo in the acutely injured and fibrotic lung

Increased pulmonary vascular permeability is a characteristic feature of lung injury. However, there are no established methods that allow the three-dimensional visualization and quantification of pulmonary vascular permeability in vivo. Evans blue extravasation test and total protein test of bronchoalveolar lavage fluid (BALF) are permeability assays commonly used in research settings. However, they lack the ability to identify the spatial and temporal heterogeneity of endothelial barrier disruption, which is typical in lung injuries. Magnetic resonance (MR) and near-infrared (NIR) imaging have been proposed to image pulmonary permeability, but suffer from limited sensitivity and penetration depth, respectively. In this study, we report the first use of magnetic particle imaging (MPI) to assess pulmonary vascular leakage noninvasively in vivo in mice. A dextran-coated superparamagnetic iron oxide (SPIO), synomag®, was employed as the imaging tracer, and pulmonary SPIO extravasation was imaged and quantified to evaluate the vascular leakage. Animal models of acute lung injury and pulmonary fibrosis (PF) were used to validate the proposed method. MPI sensitively detected the SPIO extravasation in both acutely injured and fibrotic lungs in vivo, which was confirmed by ex vivo imaging and Prussian blue staining. Moreover, 3D MPI illustrated the spatial heterogeneity of vascular leakage, which correlated well with CT findings. Based on the in vivo 3D MPI images, we defined the SPIO extravasation index (SEI) to quantify the vascular leakage. A significant increase in SEI was observed in the injured lungs, in consistent with the results obtained via ex vivo permeability assays. Overall, our results demonstrate that 3D quantitative MPI serves as a useful tool to examine pulmonary vascular integrity in vivo, which shows promise for future clinical translation.

Combination of time domain-system matrix and x-space methods to reconstruct magnetic particle images with isotropic resolution

Objective. Imaging of superparamagnetic iron oxide nanoparticles based on their non-linear response to alternating magnetic fields shows promise for imaging cells and vasculature in healthy and diseased tissue. Such imaging can be achieved through x-space reconstruction typically along a unidirectional Cartesian trajectory, which rapidly convolutes the particle distribution with a 'anisotropic blurring' point spread function (PSF), leading to images with anisotropic resolution.Approach. Here we propose combining the time domine-system matrix and x-space reconstruction methods into a forward model, where the output of the forward model is the PSF-blurred x-space reconstructed image. We then treat the blur as an inverse problem solved by Kaczmarz iteration.Main results. After we have proposed the method optimization, the normal resolution of simulation and device images has been increased from 3.5 mm and 5.25 mm to 1.5 mm and 3.25 mm, which has reached the level in the tangential resolution. Quantitative indicators of image quality such as PSNR and SSIM have also been greatly improved.Significance. Simulation and imaging of real phantoms indicate that our approach provides better isotropic resolution and image quality than the x-space method alone or other methods for removing PSF blur. Using our proposed method to optimize the image quality of x-space reconstructed images using unidirectional Cartesian trajectories, it will promote the clinical application of MPI in the future.

Glucose-Regulated Protein 78 Targeting ICG and DOX Loaded Hollow Fe3O4 Nanoparticles for Hepatocellular Carcinoma Diagnosis and Therapy

Purpose

Liver cancer is considered as the third leading cause of cancer-related deaths, with hepatocellular carcinoma (HCC) accounting for approximately 90% of liver cancers. Improving the treatment of HCC is a serious challenge today. The primary objective of this study was to construct SP94-Fe3O4@ICG&DOX nanoparticles and investigate their potential diagnosis and treatment effect benefits on HCC.

Methods

Firstly, we synthesized and characterized SP94-Fe3O4@ICG&DOX nanoparticles and confirmed their in vitro release behavior, photothermal and photodynamic performance. Moreover, the in vivo imaging capability was also observed. Finally, the inhibitory effects on Hepa1-6 in vitro and in vivo were observed as well as biosafety.

Results

SP94-Fe3O4@ICG&DOX nanoparticles have a size of ~22.1 nm, with an encapsulation efficiency of 45.2% for ICG and 42.7% for DOX, showing excellent in vivo MPI and fluorescence imaging capabilities for precise tumor localization, and synergistic photo-chemotherapy (pH- and thermal-sensitive drug release) against tumors under irradiation. With the assistance of a fluorescence molecular imaging system or MPI scanner, the location and contours of the tumor were clearly visible. Under a constant laser irradiation (808 nm, 0.6 W/cm2) and a set concentration (50 µg/mL), the temperature of the solution could rapidly increase to ~45 °C, which could effectively kill the tumor cells. It could be effectively uptaken by HCC cells and significantly inhibit their proliferation under the laser irradiation (100% inhibition rate for HCC tumors). And most importantly, our nanoparticles exhibited favorable biocompatibility with normal tissues and cells.

Conclusion

This versatile agent can serve as an intelligent and promising nanoplatform that integrates multiple accurate diagnoses, precise positioning of cancer tissue, and effective coordination with synergistic tumor photodynamic therapy.

Nano-tracers for sentinel lymph node detection: current trends in technique and application

Sentinel lymph node (SLN) detection and biopsy is a critical staging component for several cancers. Apart from established methods using dyes or radiolabeled colloids, newer techniques are emerging, like near-infrared fluorescent compounds, targeted molecular radiopharmaceuticals and magnetic nano-tracers. In the overview section of this review, we categorize SLN detection tracers based on their principle of use. We discuss the merits of existing tracers and provide a glimpse of in-development formulations. A subsequent clinical section explores the expanded role of SLN detection in management of various cancers, citing current medical guidelines and the leading conclusions of long-term clinical trials. The concluding section tries to provide a perspective of promising developments and the work required to bring them to clinical fruition.

DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging

Objective. Magnetic particle imaging (MPI) shows potential for contributing to biomedical research and clinical practice. However, MPI images are effectively affected by noise in the signal as its reconstruction is an ill-posed inverse problem. Thus, effective reconstruction method is required to reduce the impact of the noise while mapping signals to MPI images. Traditional methods rely on the hand-crafted data-consistency (DC) term and regularization term based on spatial priors to achieve noise-reducing and reconstruction. While these methods alleviate the ill-posedness and reduce noise effects, they may be difficult to fully capture spatial features.Approach. In this study, we propose a deep neural network for end-to-end reconstruction (DERnet) in MPI that emulates the DC term and regularization term using the feature mapping subnetwork and post-processing subnetwork, respectively, but in a data-driven manner. By doing so, DERnet can better capture signal and spatial features without relying on hand-crafted priors and strategies, thereby effectively reducing noise interference and achieving superior reconstruction quality.Main results. Our data-driven method outperforms the state-of-the-art algorithms with an improvement of 0.9-8.8 dB in terms of peak signal-to-noise ratio under various noise levels. The result demonstrates the advantages of our approach in suppressing noise interference. Furthermore, DERnet can be employed for measured data reconstruction with improved fidelity and reduced noise. In conclusion, our proposed method offers performance benefits in reducing noise interference and enhancing reconstruction quality by effectively capturing signal and spatial features.Significance. DERnet is a promising candidate method to improve MPI reconstruction performance and facilitate its more in-depth biomedical application.

Magnetic Particle Imaging-Guided Hyperthermia for Precise Treatment of Cancer: Review, Challenges, and Prospects

Magnetic particle imaging (MPI) is a novel quantitative imaging technique using the nonlinear magnetization behavior of magnetic nanoparticles (MNPs) to determine their local concentration. Magnetic fluid hyperthermia (MFH) is a promising non-invasive therapy using the heating effects of MNPs. MPI-MFH is expected to enable real-time MPI guidance, localized MFH, and non-invasive temperature monitoring, which shows great potential for precise treatment of cancer. In this review, we introduce the fundamentals of MPI and MFH and their applications in the treatment of cancer. Also, we discuss the challenges and prospects of MPI-MFH.

Lymph node metastases in breast cancer: Mechanisms and molecular imaging

Breast cancer is the most common malignant disease of women in the world. Breast cancer often metastasizes to axillary lymph nodes. Accurate assessment of the status of axillary lymph nodes is crucial to the staging and treatment of breast cancer. None of the methods used clinically for preoperative noninvasive examination of axillary lymph nodes can accurately identify cancer cells from a molecular level. In recent years, with the in-depth study of lymph node metastases, the mechanisms and molecular imaging of lymph node metastases in breast cancer have been reported. In this review, we highlight the new progress in the study of the main mechanisms of lymph node metastases in breast cancer. In addition, we analyze the advantages and disadvantages of traditional preoperative axillary lymph node imaging methods for breast cancer, and list molecular imaging methods that can accurately identify breast cancer cells in lymph nodes.

Development of a Rare Earth Nanoprobe Enables In Vivo Real-Time Detection of Sentinel Lymph Node Metastasis of Breast Cancer Using NIR-IIb Imaging

Sentinel lymph node (SLN) biopsy plays a critical role in axillary staging of breast cancer. However, traditional SLN mapping does not accurately discern the presence or absence of metastatic disease. Detection of SLN metastasis largely hinges on examination of frozen sections or paraffin-embedded tissues post-SLN biopsy. To improve detection of SLN metastasis, we developed a second near-infrared (NIR-II) in vivo fluorescence imaging system, pairing erbium-based rare-earth nanoparticles (ErNP) with bright down-conversion fluorescence at 1,556 nm. To visualize SLNs bearing breast cancer, ErNPs were modified by balixafortide (ErNPs@POL6326), a peptide antagonist of the chemokine receptor CXCR4. The ErNPs@POL6326 probes readily drained into SLNs when delivered subcutaneously, entering metastatic breast tumor cells specifically via CXCR4-mediated endocytosis. NIR fluorescence signals increased significantly in tumor-positive versus tumor-negative SLNs, enabling accurate determination of SLN breast cancer metastasis. In a syngeneic mouse mammary tumor model and a human breast cancer xenograft model, sensitivity for SLN metastasis detection was 92.86% and 93.33%, respectively, and specificity was 96.15% and 96.08%, respectively. Of note, the probes accurately detected both macrometastases and micrometastases in SLNs. These results overall underscore the potential of ErNPs@POL6326 for real-time visualization of SLNs and in vivo screening for SLN metastasis.

SIGNIFICANCE

NIR-IIb imaging of a rare-earth nanoprobe that is specifically taken up by breast cancer cells can accurately detect breast cancer macrometastases and micrometastases in sentinel lymph nodes.

Functional roles of magnetic nanoparticles for the identification of metastatic lymph nodes in cancer patients

Staging lymph nodes (LN) is crucial in diagnosing and treating cancer metastasis. Biotechnologies for the specific localization of metastatic lymph nodes (MLNs) have attracted significant attention to efficiently define tumor metastases. Bioimaging modalities, particularly magnetic nanoparticles (MNPs) such as iron oxide nanoparticles, have emerged as promising tools in cancer bioimaging, with great potential for use in the preoperative and intraoperative tracking of MLNs. As radiation-free magnetic resonance imaging (MRI) probes, MNPs can serve as alternative MRI contrast agents, offering improved accuracy and biological safety for nodal staging in cancer patients. Although MNPs' application is still in its initial stages, exploring their underlying mechanisms can enhance the sensitivity and multifunctionality of lymph node mapping. This review focuses on the feasibility and current application status of MNPs for imaging metastatic nodules in preclinical and clinical development. Furthermore, exploring novel and promising MNP-based strategies with controllable characteristics could lead to a more precise treatment of metastatic cancer patients.

SMART RHESINs-Superparamagnetic Magnetite Architecture Made of Phenolic Resin Hollow Spheres Coated with Eu(III) Containing Silica Nanoparticles for Future Quantitative Magnetic Particle Imaging Applications

Magnetic particle imaging (MPI) is a powerful and rapidly growing tomographic imaging technique that allows for the non-invasive visualization of superparamagnetic nanoparticles (NPs) in living matter. Despite its potential for a wide range of applications, the intrinsic quantitative nature of MPI has not been fully exploited in biological environments. In this study, a novel NP architecture that overcomes this limitation by maintaining a virtually unchanged effective relaxation (Brownian plus Néel) even when immobilized is presented. This superparamagnetic magnetite architecture made of phenolic resin hollow spheres coated with Eu(III) containing silica nanoparticles (SMART RHESINs) was synthesized and studied. Magnetic particle spectroscopy (MPS) measurements confirm their suitability for potential MPI applications. Photobleaching studies show an unexpected photodynamic due to the fluorescence emission peak of the europium ion in combination with the phenol formaldehyde resin (PFR). Cell metabolic activity and proliferation behavior are not affected. Colocalization experiments reveal the distinct accumulation of SMART RHESINs near the Golgi apparatus. Overall, SMART RHESINs show superparamagnetic behavior and special luminescent properties without acute cytotoxicity, making them suitable for bimodal imaging probes for medical use like cancer diagnosis and treatment. SMART RHESINs have the potential to enable quantitative MPS and MPI measurements both in mobile and immobilized environments.

Self-supervised Signal Denoising for Magnetic Particle Imaging

Magnetic particle imaging (MPI) is a medical imaging technology with high resolution and high sensitivity, which tracks the distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in the nonlinear response to dynamic excitation at a field-free region. However, various noises distort the signals resulting in a decline in imaging quality. Traditional threshold-based methods cannot remove dynamic noise in MPI signals. Therefore, a self-supervised denoising method is proposed to denoise MPI signals in this study. The approach adopted U-net as the backbone and modified the network for MPI signals. The network is trained using two periods of noisy signals and the shape prior knowledge of the MPI signals is introduced for promoting the convergence of the self-supervised net. The experiments show that the learning-based method can still denoising the MPI signal without labeling data and eventually improve image quality, and our approach can achieve the best performance compared with other self-supervised methods in MPI signal denoising.

Sensitive and quantitative in vivo analysis of PD-L1 using magnetic particle imaging and imaging-guided immunotherapy

PURPOSE

The programmed cell death protein-1 (PD-1) and programmed cell death ligand-1 (PD-L1) expression correlate with the immunotherapeutic response rate. The sensitive and non-invasive imaging of immune checkpoint biomarkers is favorable for the accurate detection and characterization, image-guided immunotherapy in cancer precision medicine. Magnetic particle imaging (MPI), as a novel and emerging imaging modality, possesses the advantages of high sensitivity, no image depth limitation, positive contrast, and absence of radiation. Hence, in this study, we performed the pioneer investigation of monitoring PD-L1 expression using MPI and the MPI-guided immunotherapy.

METHODS

We developed anti-PD-L1 antibody (aPDL1)-conjugated magnetic fluorescent hybrid nanoparticles (MFNPs-aPDL1) and utilized MPI in combination with fluorescence imaging (FMI) to dynamically monitor and quantify PD-L1 expression in various tumors with different PD-L1 expression levels. The ex vivo real-time polymerase chain reaction (qPCR), western blotting, and immunofluorescence staining analysis were further performed to validate the in vivo imaging observation. Moreover, the MPI was further performed for the guidance of immunotherapy.

RESULTS

Our data showed that PD-L1 expression can be specifically and sensitively monitored and quantified using MPI and FMI imaging methods, which were validated by ex vivo qPCR and western blotting analysis. In addition, MPI-guided PD-L1 immunotherapy can enhance the effectiveness of cancer immunotherapy.

CONCLUSION

To our best knowledge, this is the pioneer study to utilize MPI in combination with a newly developed MFNPs-aPDL1 imaging probe to dynamically visualize and quantify PD-L1 expression in tumor microenvironment. This imaging strategy may facilitate the clinical optimization of immunotherapy management.

Fibronectin-targeting and metalloproteinase-activatable smart imaging probe for fluorescence imaging and image-guided surgery of breast cancer

Residual lesions in the tumor bed have been a challenge for conventional white-light breast-conserving surgery. Meanwhile, lung micro-metastasis also requires improved detection methods. Intraoperative accurate identification and elimination of microscopic cancer can improve surgery prognosis. In this study, a smart fibronectin-targeting and metalloproteinase-activatable imaging probe CREKA-GK8-QC is developed. CREKA-GK8-QC possesses an average diameter of 21.7 ± 2.5 nm, excellent MMP-9 protein responsiveness and no obvious cytotoxicity. In vivo experiments demonstrate that NIR-I fluorescence imaging of CREKA-GK8-QC precisely detects orthotopic breast cancer and micro-metastatic lesions (nearly 1 mm) of lungs with excellent imaging contrast ratio and spatial resolution. More notably, fluorescence image-guided surgery facilitates complete resection and avoids residual lesions in the tumor bed, improving survival outcomes. We envision that our newly developed imaging probe shows superior capacity for specific and sensitive targeted imaging, as well as providing guidance for accurate surgical resection of breast cancer.

Improved Quantitative Analysis Method for Magnetic Particle Imaging Based on Deblurring and Region Scalable Fitting

PURPOSE

Magnetic particle imaging (MPI) is a technique for imaging magnetic particle concentration distribution. It has the advantages of high sensitivity, no signal attenuation with depth, and no ionizing radiation. Although MPI has been widely used in the biomedical field, accurate image analysis has been challenging due to its anisotropic point spread function (PSF). The purpose of this study is to propose an MPI image restoring and segmentation method to facilitate a more precise quantitative evaluation of the magnetic particle imaging in vivo.

PROCEDURES

We proposed a DeRSF method that combined deblurring and region scalable fitting (RSF) to determine the imaging tracer distribution. Then a uniform erosion and scaling criterion was established based on simulation experiments to correct the segmentation results, which was further validated on phantom imaging. Finally, we imaged the MPI tracer at gradient concentrations to establish the calibration curve between the MPI signal and iron mass for iron quantification in phantom and in vivo imaging.

RESULTS

The phantom imaging experiments showed that our method achieved improved segmentation performance. The mean value of the dice coefficients for segmentation was up to 0.86, demonstrating that our method can accurately map and quantify the distribution of the tracer. Moreover, the iron quantification on both phantom and in vivo mouse imaging was realized with the minimal error of 5.50%, by our established calibration curve.

CONCLUSIONS

Our proposed DeRSF method was successfully used for improved MPI quantitative analysis. More importantly, this method also showed accurate quantitative results on images with different shapes and tracer concentrations in both phantom and in vivo data, which laid the foundation for the biomedical study of MPI.

Non-invasive molecular imaging for precision diagnosis of metastatic lymph nodes: opportunities from preclinical to clinical applications

Lymph node metastasis is an indicator of the invasiveness and aggressiveness of cancer. It is a vital prognostic factor in clinical staging of the disease and therapeutic decision-making. Patients with positive metastatic lymph nodes are likely to develop recurrent disease, distant metastasis, and succumb to death in the coming few years. Lymph node dissection and histological analysis are needed to detect whether regional lymph nodes have been infiltrated by cancer cells and determine the likely outcome of treatment and the patient's chances of survival. However, these procedures are invasive, and tissue biopsies are prone to sampling error. In recent years, advanced molecular imaging with novel imaging probes has provided new technologies that are contributing to comprehensive management of cancer, including non-invasive investigation of lymphatic drainage from tumors, identifying metastatic lymph nodes, and guiding surgeons to operate efficiently in patients with complex lesions. In this review, first, we outline the current status of different molecular imaging modalities applied for lymph node metastasis management. Second, we summarize the multi-functional imaging probes applied with the different imaging modalities as well as applications of cancer lymph node metastasis from preclinical studies to clinical translations. Third, we describe the limitations that must be considered in the field of molecular imaging for improved detection of lymph node metastasis. Finally, we propose future directions for molecular imaging technology that will allow more personalized treatment plans for patients with lymph node metastasis.

Spatial normalization and quantification approaches of PET imaging for neurological disorders

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for ¹⁸F-FDG, ¹¹C-PIB, ¹⁸F-Florbetapir, ¹⁸F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.