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

Multispectral fluorescence imaging of EGFR and PD-L1 for precision detection of oral squamous cell carcinoma: a preclinical and clinical study

BACKGROUND

Early detection and treatment are effective methods for the management of oral squamous cell carcinoma (OSCC), which can be facilitated by the detection of tumor-specific OSCC biomarkers. The epidermal growth factor receptor (EGFR) and programmed death-ligand 1 (PD-L1) are important therapeutic targets for OSCC. Multispectral fluorescence molecular imaging (FMI) can facilitate the detection of tumor multitarget expression with high sensitivity and safety. Hence, we developed Nimotuzumab-ICG and Atezolizumab-Cy5.5 imaging probes, in combination with multispectral FMI, to sensitively and noninvasively identify EGFR and PD-L1 expression for the detection and comprehensive treatment of OSCC.

METHODS

The expression of EGFR and PD-L1 was analyzed using bioinformatics data sources and specimens. Nimotuzumab-ICG and Atezolizumab-Cy5.5 imaging probes were developed and tested on preclinical OSCC cell line and orthotopic OSCC mouse model, fresh OSCC patients' biopsied samples, and further clinical mouthwash trials were conducted in OSCC patients.

RESULTS

EGFR and PD-L1 were specifically expressed in human OSCC cell lines and tumor xenografts. Nimotuzumab-ICG and Atezolizumab-Cy5.5 imaging probes can specifically target to the tumor sites in an in situ human OSCC mouse model with good safety. The detection sensitivity and specificity of Nimotuzumab-ICG in patients were 96.4% and 100%, and 95.2% and 88.9% for Atezolizumab-Cy5.5.

CONCLUSIONS

EGFR and PD-L1 are highly expressed in OSCC, the combination of which is important for a precise prognosis of OSCC. EGFR and PD-L1 expression can be sensitively detected using the newly synthesized multispectral fluorescence imaging probes Nimotuzumab-ICG and Atezolizumab-Cy5.5, which can facilitate the sensitive and specific detection of OSCC and improve treatment outcomes.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2100045738. Registered 23 April 2021, https://www.chictr.org.cn/bin/project/edit?pid=125220.

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.

Accurate Concentration Recovery for Quantitative Magnetic Particle Imaging Reconstruction via Nonconvex Regularization

Magnetic particle imaging (MPI) uses nonlinear response signals to noninvasively detect magnetic nanoparticles in space, and its quantitative properties hold promise for future precise quantitative treatments. In reconstruction, the system matrix based method necessitates suitable regularization terms, such as Tikhonov or non-negative fused lasso (NFL) regularization, to stabilize the solution. While NFL regularization offers clearer edge information than Tikhonov regularization, it carries a biased estimate of the l1 penalty, leading to an underestimation of the reconstructed concentration and adversely affecting the quantitative properties. In this paper, a new nonconvex regularization method including min-max concave (MC) and total variation (TV) regularization is proposed. This method utilized MC penalty to provide nearly unbiased sparse constraints and adds the TV penalty to provide a uniform intensity distribution of images. By combining the alternating direction multiplication method (ADMM) and the two-step parameter selection method, a more accurate quantitative MPI reconstruction was realized. The performance of the proposed method was verified on the simulation data, the Open-MPI dataset, and measured data from a homemade MPI scanner. The results indicate that the proposed method achieves better image quality while maintaining the quantitative properties, thus overcoming the drawback of intensity underestimation by the NFL method while providing edge information. In particular, for the measured data, the proposed method reduced the relative error in the intensity of the reconstruction results from 28% to 8%.

SPFS: SNR peak-based frequency selection method to alleviate resolution degradation in MPI real-time imaging

Objective. The primary objective of this study is to address the reconstruction time challenge in magnetic particle imaging (MPI) by introducing a novel approach named SNR-peak-based frequency selection (SPFS). The focus is on improving spatial resolution without compromising reconstruction speed, thereby enhancing the clinical potential of MPI for real-time imaging.Approach. To overcome the trade-off between reconstruction time and spatial resolution in MPI, the researchers propose SPFS as an innovative frequency selection method. Unlike conventional SNR-based selection, SPFS prioritizes frequencies with signal-to-noise ratio (SNR) peaks that capture crucial system matrix information. This adaptability to varying quantities of selected frequencies enhances versatility in the reconstruction process. The study compares the spatial resolution of MPI reconstruction using both SNR-based and SPFS frequency selection methods, utilizing simulated and real device data.Main results.The research findings demonstrate that the SPFS approach substantially improves image resolution in MPI, especially when dealing with a limited number of frequency components. By focusing on SNR peaks associated with critical system matrix information, SPFS mitigates the spatial resolution degradation observed in conventional SNR-based selection methods. The study validates the effectiveness of SPFS through the assessment of MPI reconstruction spatial resolution using both simulated and real device data, highlighting its potential to address a critical limitation in the field.Significance.The introduction of SPFS represents a significant breakthrough in MPI technology. The method not only accelerates reconstruction time but also enhances spatial resolution, thus expanding the clinical potential of MPI for various applications. The improved real-time imaging capabilities of MPI, facilitated by SPFS, hold promise for advancements in drug delivery, plaque assessment, tumor treatment, cerebral perfusion evaluation, immunotherapy guidance, andin vivocell tracking.

Machine Learning and Deep Learning Applications in Magnetic Particle Imaging

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Development of an Immuno-SPECT/Fluorescent Bimodal Tracer Targeting Human or Murine PD-L1 on Preclinical Models

Detection of biomarkers to diagnose, treat, and predict the efficacy of cancer therapies is a major clinical challenge. Currently, biomarkers such as PD-L1 are commonly detected from biopsies, but this approach does not take into account the spatiotemporal heterogeneity of their expression in tumors. A solution consists in conjugating monoclonal antibodies (mAbs) targeting these biomarkers with multimodal imaging probes. In this study, a bimodal [111In]-DOTA-aza-BODIPY probe emitting in the near-infrared (NIR) was grafted onto mAbs targeting murine or human PD-L1 either in a site-specific or random manner. In vitro, these bimodal mAbs showed a good stability and affinity for PD-L1. In vivo, they targeted specifically PD-L1 and were detected by both fluorescence and SPECT imaging. A significant benefit of site-specific conjugation on glycans was observed compared to random conjugation on lysine. The potential of this bimodal agent was also highlighted, thanks to a proof of concept of fluorescence-guided surgery in a human PD-L1+ tumor model.

Nanoprobe-based molecular imaging for tumor stratification

The responses of patients to tumor therapies vary due to tumor heterogeneity. Tumor stratification has been attracting increasing attention for accurately distinguishing between responders to treatment and non-responders. Nanoprobes with unique physical and chemical properties have great potential for patient stratification. This review begins by describing the features and design principles of nanoprobes that can visualize specific cell types and biomarkers and release inflammatory factors during or before tumor treatment. Then, we focus on the recent advancements in using nanoprobes to stratify various therapeutic modalities, including chemotherapy, radiotherapy (RT), photothermal therapy (PTT), photodynamic therapy (PDT), chemodynamic therapy (CDT), ferroptosis, and immunotherapy. The main challenges and perspectives of nanoprobes in cancer stratification are also discussed to facilitate probe development and clinical applications.