Recent developments of the reconstruction in magnetic particle imaging

Magnetic particle imaging(MPI)is an emerging molecular imaging technique with high sensitivity and temporal-spatial resolution.Image reconstruction is an important research topic in MPI,which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration distribution.MPI reconstruction primarily involves system matrix-and x-space-based methods.In this review,we provide a detailed overview of the research status and future research trends of these two methods.In addition,we review the application of deep learning methods in MPI reconstruction and the current open sources of MPI.Finally,research opinions on MPI reconstruction are presented.We hope this review promotes the use of MPI in clinical applications.

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma:A multicenter study

Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical approaches.This study aimed to investigate whether computed tomography(CT)-based radiomics analysis could help predict development of MaVI in HCC.Methods:A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups.CT-based radiomics signature was built via multi-strategy machine learning methods.Afterwards,MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model(CRIM,clinical-radiomics integrated model)via random forest modeling.Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development.Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development,progression-free survival(PFS),and overall survival(OS)based on the selected risk factors.Results:The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors(P<0.001).CRIM could predict MaVI with satisfactory areas under the curve(AUC)of 0.986 and 0.979 in the training(n=154)and external validation(n=72)datasets,respectively.CRIM presented with excellent generalization with AUC of 0.956,1.000,and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory.Peel9_fos_InterquartileRange[hazard ratio(HR)=1.98;P<0.001]was selected as the independent risk factor.The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development(P<0.001),PFS(P<0.001)and OS(P=0.002).Conclusions:The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Optical Imaging of Epigenetic Modifications in Cancer:A Systematic Review

Increasing evidence has demonstrated that abnormal epigenetic modifications are strongly related to cancer initiation.Thus,sensitive and specific detection of epigenetic modifications could markedly improve biological investigations and cancer precision medicine.A rapid development of molecular imaging approaches for the diagnosis and prognosis of cancer has been observed during the past few years.Various biomarkers unique to epigenetic modifications and targeted imaging probes have been characterized and used to discriminate cancer from healthy tissues,as well as evaluate therapeutic responses.In this study,we summarize the latest studies associated with optical molecular imaging of epigenetic modification targets,such as those involving DNA methylation,histone modification,noncoding RNA regulation,and chromosome remodeling,and further review their clinical application on cancer diagnosis and treatment.Lastly,we further propose the future direc-tions for precision imaging of epigenetic modification in cancer.Supported by promising clinical and preclinical studies associated with optical molecular imaging technology and epigenetic drugs,the central role of epigenetics in cancer should be increasingly recognized and accepted.

Multi-omics fusion analysis models with machine learning predict survival of HER2-negative metastatic breast cancer: a multicenter prospective observational study

To the Editor:Oncology precision medicine aims to identify patientsmostlikely torespondeffectivelytotherapies.Efforts to establish a survival prediction model using a single platform have not yet met the precision medicine goals.

Advances in magnetic particle imaging and perspectives on liver imaging

Magnetic particle imaging(MPI)is an emerging technique to visualize the spatial distribution of super-paramagnetic iron oxide with high temporal–spatial resolution,high sensitivity,unlimited image depth,and true quantitative information.MPI is based on the nonlinear response of superparamagnetic iron oxide in an alter-nating magnetic field without tissue background noise.It is a promising imaging modality for various applica-tions,including vascular imaging,cell tracking,tumor imaging,and catheter navigation.Many applications of liver imaging could be improved or created with MPI.In this review,we cover the principle and construction of MPI,we evaluate the features and advantages of MPI with relation to its own rationale and via comparison with other imaging modalities,and we review MPI liver imaging applications with a view toward assisting hepatic researchers in drawing inspiration.

Advances in artificial intelligence techniques drive the application of radiomics in the clinical research of hepatocellular carcinoma

Hepatocellular carcinoma(HCC)remains the most common malignancy to threaten public health globally.With advances in artificial intelligence techniques,radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings,and reveals pixel-level radiological information for medical imaging big data,correlating the radiological phenotype with targeted clinical issues.Conventional radiomics pipelines depend on handcrafted engineering features,and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning strategies.During the past decade,radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation,as well as in prognosis prediction.In this review,we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC.

Manganese therapy for dyslipidemia and plaque reversal in murine models

Precise control of circulating lipid levels is vital in both health and disease.We recently uncovered that bulk lipids,transported by lipoproteins,enter the circulation initially via the coat protein complex II(COPII)in a condensation-dependent manner.Divalent manganese,acting as a signaling messenger,selectively controls COPII condensation to regulate lipid homeostasis in vivo.Here,we present evidence for a manganese-based therapy in murine models of hypolipidemia and hyperlipidemia.

Artificial intelligence in gastric cancer:applications and challenges

Gastric cancer(GC)is one of the most common malignant tumors with high mortality.Accurate diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical images.However,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer discrepancies.Recently,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data automatically.Researchers are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including GC.Herein,we aim to provide a broad framework to summarize current research on AI in GC.In the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation.In the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype classification.For treatment decisions,AI can help with surgical margin determination and prognosis prediction.Meanwhile,current approaches are challenged by data scarcity and poor interpretability.To tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.