Advancement in utilization of magnetic resonance imaging and biomarkers in the understanding of schizophrenia

Historically,psychiatric diagnoses have been made based on patient’s reported symptoms applying the criteria from diagnostic and statistical manual of mental disorders.The utilization of neuroimaging or biomarkers to make the diagnosis and manage psychiatric disorders remains a distant goal.There have been several studies that examine brain imaging in psychiatric disorders,but more work is needed to elucidate the complexities of the human brain.In this editorial,we examine two articles by Xu et al and Stoyanov et al,that show developments in the direction of using neuroimaging to examine the brains of people with schizo-phrenia and depression.Xu et al used magnetic resonance imaging to examine the brain structure of patients with schizophrenia,in addition to examining neurotransmitter levels as biomarkers.Stoyanov et al used functional magnetic resonance imaging to look at modulation of different neural circuits by diagnostic-specific scales in patients with schizophrenia and depression.These two studies provide crucial evidence in advancing our understanding of the brain in prevalent psychiatric disorders.

Temporal dynamics of neonatal hypoxic-ischemic encephalopathy injuries on magnetic resonance imaging

Moderate to severe perinatal hypoxic-ischemic encephalopathy occurs in~1 to 3/1000 live births in high-income countries and is associated with a significant risk of death or neurodevelopmental disability.Detailed assessment is important to help identify highrisk infants,to help families,and to support appropriate interventions.A wide range of monitoring tools is available to assess changes over time,including urine and blood biomarkers,neurological examination,and electroencephalography.At present,magnetic resonance imaging is unique as although it is expensive and not suited to monitoring the early evolution of hypoxic-ischemic encephalopathy by a week of life it can provide direct insight into the anatomical changes in the brain after hypoxic-ischemic encephalopathy and so offers strong prognostic information on the long-term outcome after hypoxic-ischemic encephalopathy.This review investigated the temporal dynamics of neonatal hypoxic-ischemic encephalopathy injuries,with a particular emphasis on exploring the correlation between the prognostic implications of magnetic resonance imaging scans in the first week of life and their relationship to long-term outcome prediction,particularly for infants treated with therapeutic hypothermia.A comprehensive literature search,from 2016 to 2024,identified 20 pertinent articles.This review highlights that while the optimal timing of magnetic resonance imaging scans is not clear,overall,it suggests that magnetic resonance imaging within the first week of life provides strong prognostic accuracy.Many challenges limit the timing consistency,particularly the need for intensive care and clinical monitoring.Conversely,although most reports examined the prognostic value of scans taken between 4 and 10 days after birth,there is evidence from small numbers of cases that,at times,brain injury may continue to evolve for weeks after birth.This suggests that in the future it will be important to explore a wider range of times after hypoxic-ischemic encephalopathy to fully understand the optimal timing for predicting long-term outcomes.

Magnetic resonance imaging evaluation and nuclear receptor binding SET domain protein 1 mutation in the Sotos syndrome with attention-deficit/hyperactivity disorder

Sotos syndrome is characterized by overgrowth features and is caused by alterations in the nuclear receptor binding SET domain protein 1 gene.Attentiondeficit/hyperactivity disorder(ADHD)is considered a neurodevelopment and psychiatric disorder in childhood.Genetic characteristics and clinical presentation could play an important role in the diagnosis of Sotos syndrome and ADHD.Magnetic resonance imaging(MRI)has been used to assess medical images in Sotos syndrome and ADHD.The images process is considered to display in MRI while wavelet fusion has been used to integrate distinct images for achieving more complete information in single image in this editorial.In the future,genetic mechanisms and artificial intelligence related to medical images could be used in the clinical diagnosis of Sotos syndrome and ADHD.

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

Functional Liver Imaging Score Derived from Gadoxetic Acid-enhanced MRI Predicts Cachexia and Prognosis in Hepatocellular Carcinoma Patients

Objective Cachexia occurs in approximately half of hepatocellular carcinoma(HCC)patients as the disease progresses and is correlated with a poor prognosis.Therefore,early identification of HCC patients at risk of developing cachexia and their prognosis is crucial.This study investigated the functional liver imaging score(FLIS)derived from gadoxetic acid-enhanced magnetic resonance imaging(MRI)to identify cachexia in HCC patients and their prognosis.Methods Pretreatment clinical and MRI data from 339 HCC patients who underwent gadoxetic acid-enhanced MRI scans were retrospectively collected.Patient weights were recorded for 6 months following the MRI scan to diagnose cachexia.The FLIS was calculated as the sum of the enhancement quality score,the excretion quality score,and the portal vein sign quality score.A Cox proportional hazards model was used to determine the significant factors affecting overall survival(OS).Multivariable logistic regression was then conducted to identify variables predicting cachexia in HCC patients,which were subsequently used to predict OS.Results Cox regression analysis revealed a significant association between cachexia and worse OS.Both FLIS(0–4 vs.5–6 points)(OR,9.20;95%CI:4.68–18.10;P<0.001)andα-fetoprotein>100 ng/mL(OR,4.08;95%CI:2.13–7.83;P<0.001)emerged as significant predictors of cachexia in patients with HCC.Furthermore,FLIS(0–4 vs.5–6 points)(HR,1.73;95%CI:1.19–2.51;P=0.004)was significantly associated with OS.Patients in the FLIS 0–4 points group had shorter OS than those in the FLIS 5–6 points group[20 months(95%CI,14.7–25.3)vs.43 months(95%CI,27.7–58.3);P=0.001].Conclusion Cachexia was associated with worse OS.The functional liver imaging score emerged as a significant predictor of cachexia in HCC patients and their prognosis.

Application Research of PETD Combined with MRI Nerve Root Water Imaging in the Minimally Invasive Treatment of LDH

Objective: This study aims to evaluate the safety and efficacy of PETD combined with nerve root water imaging of MRI for the treatment of lumbar disc herniation. Methods: A retrospective review was performed on 62 patients with lumbar disc herniation from March 2019 to March 2021. The study included an experimental group of 30 patients and a control group of 32 patients. The experimental group underwent PETD combined with nerve root water imaging of MRI, while the control group received traditional PETD treatment. The visual analogue scoring method (VAS score), and JOA lumbar spine function score before and after surgery were compared between the two groups, and efficacy was assessed and compared using the MacNab score. Results: The mean operation time was significantly reduced in the experimental group (56.43 &#177;10.40 minutes) compared to the control group (65.69 &#177;14.12 minutes). The VAS score was compared between the two groups with preoperative (p = 0.624), one month after surgery (p = 0.325), three months after surgery (p = 0.676), one year after surgery (p = 0.341);The JOA score was compared between the two groups with preoperative (p = 0.961), one month after the surgery (p = 0.266), three months after surgery (p = 0.185), one year after surgery (p = 0.870), they were no significant statistical difference;The efficacy evaluation of the last follow-up Macnab showed that all the 30 patients in the experimental group were excellent, 31 of 32 patients in the control group were excellent, 1 case was good;There was no statistical difference in the comparison between the two groups (p > 0.05). Conclusion: The study concludes that the combined approach of PETD with nerve root water imaging of MRI is a safe, effective, and more efficient alternative to conventional PETD for treating lumbar disc herniation.

Microwave-induced thermoacoustic elastic imaging:A simulation study

Microwave-induced thermoacoustic imaging(MTI)has the advantages of high resolution,high contrast,non-ionization,and non-invasive.Recently,MTI was used in the¯eld of breast cancer screening.In this paper,based on the¯nite element method(FEM)and COMSOL Multiphysics software,a three-dimensional breast cancer model suitable for exploring the MTI process is proposed to investigate the in°uence of Young's modulus(YM)of breast cancer tissue on MTI.It is found that the process of electromagnetic heating and initial pressure generation of the entire breast tissue is earlier in time than the thermal expansion process.Besides,compared with normal breast tissue,tumor tissue has a greater temperature rise,displacement,and pressure rise.In particular,YM of the tumor is related to the speed of thermal expansion.In particular,the larger the YM of the tumor is,the higher the heating and contraction frequency is,and the greater the maximum pressure is.Di®erent Young's moduli correspond to di®erent thermoacoustic signal spectra.In MTI,this study can be used to judge di®erent degrees of breast cancer based on elastic imaging.In addition,this study is helpful in exploring the possibility of microwave-induced thermoacoustic elastic imaging(MTAE).

Data-driven polarimetric imaging: a review

This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest.Polarization information is increasingly incorporated into convolutional neural networks(CNN)as a supplemental feature of objects to improve performance in computer vision task applications.Polarimetric imaging and deep learning can extract abundant information to address various challenges.Therefore,this article briefly reviews recent developments in data-driven polarimetric imaging,including polarimetric descattering,3D imaging,reflection removal,target detection,and biomedical imaging.Furthermore,we synthetically analyze the input,datasets,and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages.We also highlight the significance of data-driven polarimetric imaging in future research and development.

双髋关节MRI预测股骨头坏死塌陷风险

背景:MRI是诊断股骨头坏死的金标准,以往基于MRI图像预测股骨头坏死塌陷的方法多需要冠状面、矢状面图像联合评估。然而由于股骨头坏死好发于双侧,临床上大多医院检查时多行双侧髋关节MRI扫描,但双髋扫描仅可查看冠状面及横断面图像,难以获取矢状面图像,从而影响塌陷风险的评估。因此,建立一种应用双髋MRI可获取的图像评估早期股骨头坏死塌陷风险的方法有一定的临床实用价值。目的:建立一种应用双髋关节MRI冠状面与横断面图像评估股骨头坏死塌陷风险的方法。方法:回顾性分析2017年10月至2019年10月在西安交通大学附属红会医院门诊确诊的早期股骨头坏死患者111例(181髋)的病历资料。根据末次随访时股骨头塌陷情况分为2组,其中塌陷组69髋,未塌陷组112髋。在MRI影像系统上测量正中冠状面、横断面或其上下各一个层面图像上的坏死范围角度,以冠状面、横断面上坏死角两角之和作为联合坏死角,并取每髋3种联合坏死角的平均值得到每髋的平均联合坏死角。最后,分析3种联合坏死角及平均联合坏死角与股骨头坏死塌陷的相关性,利用受试者工作特征曲线评价4种联合坏死角预测塌陷的特异性与敏感性。结果与结论:①末次随访69髋(38.1%)发生股骨头塌陷,纳入塌陷组;112髋(61.9%)未发生塌陷进展,纳入非塌陷组;②塌陷组与未塌陷组在ARCO分期方面比较差异有显著性意义(P<0.001);在年龄、体质量指数、随访时间、性别分布、发病侧别、致病因素等方面比较差异均无显著性意义(P>0.05);③独立样本t检验结果提示,4种联合坏死角均与塌陷显著相关(P<0.0001);且ARCOⅠ期、Ⅱ期中塌陷组与未塌陷组患者的联合坏死角相比差异均有显著性意义(P<0.0001);④在受试者工作特征分析中,平均联合坏死角曲线下面积大于正中下一层面、正中层面、正中上一层面上的联合坏死角;⑤平均联合坏死角塌陷预测准确度高于正中下一层面、正中层面、正中上一层面上的联合坏死角;⑥提示平均联合坏死角对股骨头坏死塌陷风险的预测精准度较高,临床实用性较强,可考虑使用该方法对股骨头坏死塌陷风险进行预测。

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model:A dual-center study

BACKGROUND Perineural invasion(PNI)has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer(RC).Preoperative prediction of PNI status is helpful for individualized treatment of RC.Recently,several radiomics studies have been used to predict the PNI status in RC,demonstrating a good predictive effect,but the results lacked generalizability.The preoperative prediction of PNI status is still challenging and needs further study.AIM To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers.The patients underwent preoperative high-resolution magnetic resonance imaging(MRI)between May 2019 and August 2022.Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging(T2WI)and contrast-enhanced T1WI(T1CE)sequences.The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared(T2WI,T1CE and T2WI+T1CE fusion sequences).A clinical-radiomics(CR)model was established by combining the radiomics features and clinical risk factors.The internal and external validation groups were used to validate the proposed models.The area under the receiver operating characteristic curve(AUC),DeLong test,net reclassification improvement(NRI),integrated discrimination improvement(IDI),calibration curve,and decision curve analysis(DCA)were used to evaluate the model performance.RESULTS Among the radiomics models,the T2WI+T1CE fusion sequences model showed the best predictive performance,in the training and internal validation groups,the AUCs of the fusion sequence model were 0.839[95%confidence interval(CI):0.757-0.921]and 0.787(95%CI:0.650-0.923),which were higher than those of the T2WI and T1CE sequence models.The CR model constructed by combining clinical risk factors had the best predictive performance.In the training and internal and external validation groups,the AUCs of the CR model were 0.889(95%CI:0.824-0.954),0.889(95%CI:0.803-0.976)and 0.894(95%CI:0.814-0.974).Delong test,NRI,and IDI showed that the CR model had significant differences from other models(P<0.05).Calibration curves demonstrated good agreement,and DCA revealed significant benefits of the CR model.CONCLUSION The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively,which facilitates individualized treatment of RC patients.

Two-photon live imaging of direct glia-to-neuron conversion in the mouse cortex

Over the past decade,a growing number of studies have reported transcription factor-based in situ reprogramming that can directly conve rt endogenous glial cells into functional neurons as an alternative approach for n euro regeneration in the adult mammalian central ne rvous system.Howeve r,many questions remain regarding how a terminally differentiated glial cell can transform into a delicate neuron that forms part of the intricate brain circuitry.In addition,concerns have recently been raised around the absence of astrocyte-to-neuron conversion in astrocytic lineage-tra cing mice.In this study,we employed repetitive two-photon imaging to continuously capture the in situ astrocyte-to-neuron conversion process following ecto pic expression of the neural transcription factor NeuroD1 in both prolife rating reactive astrocytes and lineage-tra ced astrocytes in the mouse cortex.Time-lapse imaging over several wee ks revealed the ste p-by-step transition from a typical astrocyte with numero us short,tapered branches to a typical neuro n with a few long neurites and dynamic growth cones that actively explored the local environment.In addition,these lineage-converting cells were able to migrate ra dially or to ngentially to relocate to suitable positions.Furthermore,two-photon Ca2+imaging and patch-clamp recordings confirmed that the newly generated neuro ns exhibited synchronous calcium signals,repetitive action potentials,and spontaneous synaptic responses,suggesting that they had made functional synaptic connections within local neural circuits.In conclusion,we directly visualized the step-by-step lineage conversion process from astrocytes to functional neurons in vivo and unambiguously demonstrated that adult mammalian brains are highly plastic with respect to their potential for neuro regeneration and neural circuit reconstruction.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma:A quantitative review with Radiomics Quality Score

BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging(MRI)for different tasks related to the management of patients with hepatocellular carcinoma(HCC).However,its implementation in clinical practice is still far,with many issues related to the methodological quality of radiomic studies.AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score(RQS).METHODS A systematic literature search of PubMed,Google Scholar,and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023.The methodological quality of radiomic studies was assessed using the RQS tool.Spearman’s correlation(ρ)analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies.The level of statistical significance was set at P<0.05.RESULTS One hundred and twenty-seven articles were included,of which 43 focused on HCC prognosis,39 on prediction of pathological findings,16 on prediction of the expression of molecular markers outcomes,18 had a diagnostic purpose,and 11 had multiple purposes.The mean RQS was 8±6.22,and the corresponding percentage was 24.15%±15.25%(ranging from 0.0% to 58.33%).RQS was positively correlated with journal impact factor(IF;ρ=0.36,P=2.98×10^(-5)),5-years IF(ρ=0.33,P=1.56×10^(-4)),number of patients included in the study(ρ=0.51,P<9.37×10^(-10))and number of radiomics features extracted in the study(ρ=0.59,P<4.59×10^(-13)),and time of publication(ρ=-0.23,P<0.0072).CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients,our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

Laser spectroscopy imaging technique coupled withnanomaterials for cancer diagnosis: A review

Laser spectroscopic imaging techniques have received tremendous attention in the-eld of cancer diagnosis due to their high sensitivity,high temporal resolution,and short acquisition time.However,the limited tissue penetration of the laser is still a challenge for the in vivo diagnosis of deep-seated lesions.Nanomaterials have been universally integrated with spectroscopic imaging techniques for deeper cancer diagnosis in vivo.The components,morphology,and sizes of nanomaterials are delicately designed,which could realize cancer diagnosis in vivo or in situ.Considering the enhanced signal emitting from the nanomaterials,we emphasized their combination with spectroscopic imaging techniques for cancer diagnosis,like the surface-enhanced Raman scattering(SERS),photoacoustic,fluorescence,and laser-induced breakdown spectroscopy(LIBS).Applications ofthe above spectroscopic techniques offer new prospectsfor cancer diagnosis.

NIR-II fluorescence imaging in liver tumor surgery: A narrative review

In liver tumor surgery,the recognition of tumor margin and radical resection of microcancer focis have always been the crucial points to reduce postoperative recurrence of tumor.However,naked-eye inspection and palpation have limited effectiveness in identifying tumor boundaries,and traditional imaging techniques cannot consistently locate tumors in real time.As an intraoperative real-time navigation imaging method,NIRfluorescence imaging has been extensively studied for its simplicity,reliable safety,and superior sensitivity,and is expected to improve the accuracy of liver tumor surgery.In recent years,the research focus of NIRfluorescence has gradually shifted from the-rst near-infrared window(NIR-I,700–900 nm)to the second near-infrared window(NIR-II,1000–1700 nm).Fluorescence imaging in NIR-II reduces the scattering effect of deep tissue,providing a preferable detection depth and spatial resolution while signi-cantly eliminating liver autofluorescence background to clarify tumor margin.Developingfluorophores combined with tumor antibodies will further improve the precision offluorescence-guided surgical navigation.With the development of a bunch offluorophores with phototherapy ability,NIR-II can integrate tumor detection and treatment to explore a new therapeutic strategy for liver cancer.Here,we review the recent progress of NIR-IIfluorescence technology in liver tumor surgery and discuss its challenges and potential development direction.

Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models

BACKGROUND Diffusion-weighted imaging(DWI)has been developed to stage liver fibrosis.However,its diagnostic performance is inconsistent among studies.Therefore,it is worth studying the diagnostic value of various diffusion models for liver fibrosis in one cohort.AIM To evaluate the clinical potential of six diffusion-weighted models in liver fibrosis staging and compare their diagnostic performances.METHODS This prospective study enrolled 59 patients suspected of liver disease and scheduled for liver biopsy and 17 healthy participants.All participants underwent multi-b value DWI.The main DWI-derived parameters included Mono-apparent diffusion coefficient(ADC)from mono-exponential DWI,intravoxel incoherent motion model-derived true diffusion coefficient(IVIM-D),diffusion kurtosis imaging-derived apparent diffusivity(DKI-MD),stretched exponential model-derived distributed diffusion coefficient(SEM-DDC),fractional order calculus(FROC)model-derived diffusion coefficient(FROC-D)and FROC model-derived microstructural quantity(FROC-μ),and continuous-time random-walk(CTRW)model-derived anomalous diffusion coefficient(CTRW-D)and CTRW model-derived temporal diffusion heterogeneity index(CTRW-α).The correlations between DWI-derived parameters and fibrosis stages and the parameters’diagnostic efficacy in detecting significant fibrosis(SF)were assessed and compared.RESULTS CTRW-D(r=-0.356),CTRW-α(r=-0.297),DKI-MD(r=-0.297),FROC-D(r=-0.350),FROC-μ(r=-0.321),IVIM-D(r=-0.251),Mono-ADC(r=-0.362),and SEM-DDC(r=-0.263)were significantly correlated with fibrosis stages.The areas under the ROC curves(AUCs)of the combined index of the six models for distinguishing SF(0.697-0.747)were higher than each of the parameters alone(0.524-0.719).The DWI models’ability to detect SF was similar.The combined index of CTRW model parameters had the highest AUC(0.747).CONCLUSION The DWI models were similarly valuable in distinguishing SF in patients with liver disease.The combined index of CTRW parameters had the highest AUC.

Diffusion-tensor imaging as an adjunct to dynamic contrastenhanced MRI for improved accuracy of differential diagnosis between breast ductal carcinoma in situ and invasive breast carcinoma

Objective： To determine the value of diffusion-tensor imaging （DTI） as an adjunct to dynamic contrastenhanced magnetic resonance imaging （DCE-MRI） for improved accuracy of differential diagnosis between breast ductal carcinoma in situ （DCIS） and invasive breast carcinoma （IBC）. Methods： The MRI data of 63 patients pathologically confirmed as breast cancer were analyzed. The conventional MRI analysis metrics included enhancement style, initial enhancement characteristic, maximum slope of increase, time to peak, time signal intensity curve （TIC） pattern, and signal intensity on FS- T2WI. The values of apparent diffusion coefficient （ADC）, directionally-averaged mean diffusivity （D^vg）, exponential attenuation （EA）, fractional anisotropy （FA）, volume ratio （VR） and relative anisotropy （RA） were calculated and compared between DCIS and IBC. Multivariate logistic regression was used to identify independent factors for distinguishing IBC and DCIS. The diagnostic performance of the diagnosis equation was evaluated using the receiver operating characteristic （ROC） curve. The diagnostic efficacies of DCE- MRI, DWI and DTI were compared independently or combined. Results： EA value, lesion enhancement style and TIC pattern were identified as independent factor for differential diagnosis of IBC and DCIS. The combination diagnosis showed higher diagnostic efficacy than a single use of DCE-MRI （P=0.02）, and the area of the curve was improved from 0.84 （95% CI, 0.67-0.99） to 0.94 （95% CI, 0.85-1.00）. Conclusions： Quantitative DTI measurement as an adjunct to DCE-MRI could improve the diagnostic performance of differential diagnosis between DCIS and IBC compared to a single use of DCE-MRI.

Magnetic resonance imaging of extraocular rectus muscles abnormalities in acute acquired concomitant esotropia

AIM:To investigate the difference of medial rectus(MR)and lateral rectus(LR)between acute acquired concomitant esotropia(AACE)and the healthy controls(HCs)detected by magnetic resonance imaging(MRI).METHODS:A case-control study.Eighteen subjects with AACE and eighteen HCs were enrolled.MRI scanning data were conducted in target-controlled central gaze with a 3-Tesla magnetic resonance scanner.Extraocular muscles(EOMs)were scanned in contiguous image planes 2-mm thick spanning the EOM origins to the globe equator.To form posterior partial volumes(PPVs),the LR and MR cross-sections in the image planes 8,10,12,and 14 mm posterior to the globe were summed and multiplied by the 2-mm slice thickness.The data were classified according to the right eye,left eye,dominant eye,and non-dominant eye,and the differences in mean cross-sectional area,maximum cross-sectional area,and PPVs of the MR and LR muscle in the AACE group and HCs group were compared under the above classifications respectively.RESULTS:There were no significant differences between the two groups of demographic characteristics.The mean cross-sectional area of the LR muscle was significantly greater in the AACE group than that in the HCs group in the non-dominant eyes(P=0.028).The maximum cross-sectional area of the LR muscle both in the dominant and non-dominant eye of the AACE group was significantly greater than the HCs group(P=0.009,P=0.016).For the dominant eye,the PPVs of the LR muscle were significantly greater in the AACE than that in the HCs group(P=0.013),but not in the MR muscle(P=0.698).CONCLUSION:The size and volume of muscles dominant eyes of AACE subjects change significantly to overcome binocular diplopia.The LR muscle become larger to compensate for the enhanced convergence in the AACE.

Potential applications of 7 Tesla magnetic resonance imaging in paediatric neuroimaging:Feasibility and challenges

The integration of 7 Tesla magnetic resonance imaging(7 T MRI)in adult patients has marked a revolutionary stride in radiology.In this article we explore the feasibility of 7 T MRI in paediatric practice,emphasizing its feasibility,applications,challenges,and safety considerations.The heightened resolution and tissue contrast of 7 T MRI offer unprecedented diagnostic accuracy,particularly in neuroimaging.Applications range from neuro-oncology to neonatal brain imaging,showcasing its efficacy in detecting subtle structural abnormalities and providing enhanced insights into neurological conditions.Despite the promise,challenges such as high cost,discomfort,and safety concerns necessitate careful consideration.Research suggests that,with precautions,7 T MRI is feasible in paediatrics,yet ongoing studies and safety assessments are imperative.

Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients

BACKGROUND About 10%-31% of colorectal liver metastases(CRLM)patients would concomitantly show hepatic lymph node metastases(LNM),which was considered as sign of poor biological behavior and a relative contraindication for liver resection.Up to now,there’s still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM,except for pathology examination of lymph node after resection.AIM To compare the ability of mono-exponential,bi-exponential,and stretchedexponential diffusion-weighted imaging(DWI)models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery.METHODS In this retrospective study,97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging,including DWI with ten b values before and after chemotherapy.Various parameters,such as the apparent diffusion coefficient from the mono-exponential model,and the true diffusion coefficient,the pseudo-diffusion coefficient,and the perfusion fraction derived from the intravoxel incoherent motion model,along with distributed diffusion coefficient(DDC)andαfrom the stretched-exponential model(SEM),were measured.The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups.A nomogram was constructed to predict the hepatic lymph node status.The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient.RESULTS Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes.A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients,with an area under the curve of 0.873.Furthermore,parameters from SEM showed substantial repeatability.CONCLUSION The developed nomogram,incorporating the pre-treatment DDC and the short axis of the largest lymph node,can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery.This nomogram was proven to be more valuable,exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI.The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.