GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.

Interobserver and intraobserver reliability of skull base angles measured on magnetic resonance images

BACKGROUND Determination of platybasia and basilar kyphosis are significant parts of routine cranial magnetic resonance(MR)interpretations.These situations may explain a patient’s symptoms or may be associated with other anomalies.AIM To indicate the interobserver and intraobserver reliability of the skull base angles(SBA)(Koenigsberg standard)and modified SBA(mSBA)measurement techniques.METHODS In total,391 patients who had undergone cranial MR imaging were re-assessed regarding the SBA measurements.The SBA and mSBA techniques were used on MR images.Two reviewers independently measured the same angles twice within a 15-day interval,using different monitors.Intraclass correlation coefficient(ICC)was calculated to reveal the intraobserver and interobserver agreements.RESULTS There was an excellent agreement between reviewers regarding both angle measurements(ICC was 0.998 for SBA and mSBA).Excellent agreement levels were also observed for intraobserver measurements.ICC was 0.998 for SBA and 0.999 for mSBA for reviewer 1.ICC was 0.997 for SBA and 0.999 for mSBA according to the measurement results of reviewer 2.Higher SBA and mSBA values were observed for females compared to males.There was no correlation between SBA and age for SBA.However,a negative and low-level correlation was observed between mSBA values and age for both reviewers.CONCLUSION SBA and mSBA measurements indicated excellent agreement regarding interobserver and intraobserver differences.The study results showed that SBA angles were reliable measurement techniques to be used on MR images.

Detection Algorithm of Knee Osteoarthritis Based on Magnetic Resonance Images

Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee OA in the early stage.Since magnetic resonance(MR)imaging can observe the early features of knee OA,the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered.Firstly,the knee MR images are preprocessed before training,including a region of interest clipping,slice selection,and data augmentation.Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model.The method divides the training process into two stages.The first stage freezes all the base layers and only trains the weights of the embedding neural networks.The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network.In this step,we design a block-by-block fine-tuning strategy for training based on the dense blocks,which improves detection accuracy.We have conducted training experiments with different depth modules,and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer.We achieve an accuracy of 0.921,a sensitivity of 0.960,a precision of 0.885,a specificity of 0.891,an F1-Score of 0.912,and an MCC of 0.836.The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%.

The Magnetic Resonance Image and Pathology of Spinal Cord Cavernous Hemangioma

Objective To investigate the characteristics of magnetic resonance image (MRI) of spinal cord cavernous hemangioma. Methods Six cases of spinal cord cavernous heman-gioma diagnosed by MRI and confirmed by pathology were reviewed. The characteristics of MRI were analyzed and correlated with pathological characteristics of spinal cord cavernous hemangioma. Results In 4 cases, the tumors were located in thoracic segment of the spinal cord and 2 in cervical cord. All lesions were solitary and the spinal cords were normal or a little thicker. The MRI showed that the images of focus were ball-like popcorn or mulberry with mixed signal,with short T2 signal around the focus. Under microscope, the hemangioma was composed of highly expanded blood sinusoids and its wall was thin and consisted of flat epithelial cells. There were some red blood cells in the cavity of the sinusoid and a little fibrous tissue in the diazoma between blood sinusoids. And also some fresh and old hemorrhages could be seen in the specimen. Conclusion MRI has high sensitivity and specificity in the diagnosis of spinal cavernous hemangioma.

Multifunctional magnetic nanoparticles for magnetic resonance image-guided photothermal therapy for cancer

Key advances in multifunctional magnetic nanoparticles （MNPs） for magnetic resonance （MR） image-guided pho- tothermal therapy of cancer are reviewed. We briefly outline the design and fabrication of such multifunctional MNPs. Bimodal image-guided photothermal therapies （MR/fluorescence and MR/ultrasound） are also discussed.

Semi-automated segmentation of magnetic resonance images for thigh skeletal muscle and fat using threshold technique after spinal cord injury

Magnetic resonance imaging is considered the ＂gold standard＂ technique for quantifying thigh muscle and fat cross-sectional area. We have developed a semi-automated technique to segment seven thigh compartments in persons with spinal cord injury. Thigh magnetic resonance images from 18 men（18–50 years old） with traumatic motor-complete spinal cord injury were analyzed in a blinded fashion using the threshold technique. The cross-sectional area values acquired by thresholding were compared to the manual tracing technique. The percentage errors for thigh circumference were（threshold： 170.71 ± 38.67; manual： 169.45 ± 38.27 cm2） 0.74%, subcutaneous adipose tissue（threshold： 65.99±30.79; manual： 62.68 ± 30.22） 5.2%, whole muscle（threshold： 98.18 ± 20.19; manual： 98.20 ± 20.08 cm2） 0.13%, femoral bone（threshold： 6.53 ± 1.09; manual： 6.53 ± 1.09 cm2） 0.64%, bone marrow fat（threshold： 3.12 ± 1.12; manual： 3.1 ± 1.11 cm2） 0.36%, knee extensor（threshold： 43.98 ± 7.66; manual： 44.61 ± 7.81 cm2） 1.78% and % intramuscular fat（threshold： 10.45 ± 4.29; manual： 10.92 ± 8.35%） 0.47%. Collectively, these results suggest that the threshold technique provided a robust accuracy in measuring the seven main thigh compartments, while greatly reducing the analysis time.

Lesion localization of global aphasia without hemiparesis by overlapping of the brain magnetic resonance images

Global aphasia without hemiparesis is a striking stroke syndrome involving language impairment without the typically manifested contralateral hemiparesis, which is usually seen in patients with global aphasia following large left perisylvian lesions. The objective of this study is to elucidate the specific areas for lesion localization of global aphasia without hemiparesis by retrospectively studying the brain magnetic resonance images of six patients with global aphasia without hemiparesis to define global aphasia without hemiparesis-related stroke lesions before overlapping the images to visualize the most overlapped area. Talairach coordinates for the most overlapped areas were converted to corresponding anatomical regions. Lesions where the images of more than three patients overlapped were considered significant. The overlapped global aphasia without hemiparesis related stroke lesions of six patients revealed that the significantly involved anatomi- cal lesions were as follows： frontal lobe, sub-gyral, sub-lobar, extra-nuclear, corpus callosum, and inferior frontal gyrus, while caudate, claustrum, middle frontal gyrus, limbic lobe, temporal lobe, superior temporal gyrus, uncus, anterior cingulate, parahippocampal, amygdala, and subcallosal gyrus were seen less significantly involved. This study is the first to demonstrate the heterogeneous anatomical involvement in global aphasia without hemiparesis by overlapping of the brain magnetic resonance images.

Comparative Study of Images with Pathology:Superparamagnetic Iron Oxide-enhanced Magnetic Resonance Image(MRI)of Splenic VX2 Tumor in Rats

Objective： To establish a rodent model of VX2 tumor of the spleen, to analyze relationship between the change of the signal intensity on superparamagnetic iron oxide enhanced magnetic resonance image （MRI） and pathologic change to evaluate the ability of superparamagnetic iron oxide enhanced MRI for detection of splenic metastases. Methods： 8 rodent models of VX2 tumor of spleen were established successfully. The images were obtained before and after administration of superparamagnetic iron oxide. T1-weighted spin-echo （SE） pulse sequence with a repetition time （TR） of 450 msec, and echo time （TE） of 12 msec （TR/TE=450/12） was used. The imaging parameters of T2-weighted SE pulse sequence were as follows： TR/TE=4000/128. Results： On plain MR scanning T1-weighted splenic VX2 tumor showed hypointensity or isointensity which approximated to the SI of splenic parenchyma. Therefore all lesions were not displayed clearly. On superparamagnetic iron oxide enhancement T2WI sequence the SI of splenic parenchyma decreased obviously with percentage of signal intensity loss （PSIL） of 55.04%, But the SI of tumor was not evidently changed with PSIL of 0.87%. Nevertheless the SNR of normal splenic parenchyma around the lesions had obvious difference （P〈0.001） comparatively. Therefore the contrast between tumor and spleen increased, and tumor displayed more clearly. Moreover the contrast-to-noise （CNR） between VX2 tumor and splenic parenchyma had an evident difference before and after admininstration of superparamagnetic iron oxide （P〈0.001）. Conclusion： On superparamagnetic iron oxide enhancement T1WI sequence the contrast of tumor-to-spleen is poor. Therefore it is not sensitive to characterize the lesions in spleen. On superparamagnetic iron oxide enhanced T2WI the contrast degree of lesions increases obviously. Consequently, superparamagnetic iron oxide -enhanced T2WI MRI scanning can improve the rate of detection and characterization for lesions of spleen.

Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform

A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign (noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the radiologist is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologists reach their goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images. The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet Transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.3%.

MAGNETIC RESONANCE IMAGES OF THE HYPOTHALAMIC-PITUITARY AREA IN IDIOPATHIC GROWTH DEFICIENCY

In order to describe the magnetic resonance imaging (MRI) findings in hypothalamic-pituitary area and its clinical relevance in patients with idiopathic growth hormone deficiency (IGHD), the MR imagings of 26 patients with IGHD were analyzed. On MRI, 24 out of 26 cases (92. 3%) showed apparent pituitary upper margin depression; 8 out of 26 cases (30. 8%) showed definite pituitary stalk transection; 22 out of 26 cases (84. 6%) showed absence of the normal posterior pituitary bright spot. The bright lipidlike signal on T1W1 images at the median eminence distal to the breaking point (so-called ectopic posterior lobe) was found in 4 out of 26 cases (15. 4%). According to the MRI findings of the pituitary stalks, the 26 cases were divided into three groups; group A of 8 cases (31%) characterized by the definite transaction of stalk; group B of 13 cases (50%) defined by the possible stalk transection; and group C of 5 cases (19%) with no definite stalk transection.MRI findings were consistent with the clinical and endocrine tests. The stalk transection was statistically significantly difference in insulin test, L-dopa/p test, and height standard deviation score (P< 0.05). The MRI of hypothalamic-pituitary area may differentiate partial IGHD form stalk-transected, doubtful transection and without transection.

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.

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.

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.

Outcomes and efficacy of magnetic resonance imaging-compatible sacral nerve stimulator for management of fecal incontinence: A multi-institutional study

BACKGROUND Fecal incontinence(FI)is an involuntary passage of fecal matter which can have a significant impact on a patient’s quality of life.Many modalities of treatment exist for FI.Sacral nerve stimulation is a well-established treatment for FI.Given the increased need of magnetic resonance imaging(MRI)for diagnostics,the In-terStim which was previously used in sacral nerve stimulation was limited by MRI incompatibility.Medtronic MRI-compatible InterStim was approved by the United States Food and Drug Administration in August 2020 and has been widely used.AIM To evaluate the efficacy,outcomes and complications of the MRI-compatible InterStim.METHODS Data of patients who underwent MRI-compatible Medtronic InterStim placement at UPMC Williamsport,University of Minnesota,Advocate Lutheran General Hospital,and University of Wisconsin-Madison was pooled and analyzed.Patient demographics,clinical features,surgical techniques,complications,and outcomes were analyzed.Strengthening the Reporting of Observational studies in Epidemiology(STROBE)cross-sectional reporting guidelines were used.RESULTS Seventy-three patients had the InterStim implanted.The mean age was 63.29±12.2 years.Fifty-seven(78.1%)patients were females and forty-two(57.5%)patients had diabetes.In addition to incontinence,overlapping symptoms included diarrhea(23.3%),fecal urgency(58.9%),and urinary incontinence(28.8%).Fifteen(20.5%)patients underwent Peripheral Nerve Evaluation before proceeding to definite implant placement.Thirty-two(43.8%)patients underwent rechargeable InterStim placement.Three(4.1%)patients needed removal of the implant.Migration of the external lead connection was observed in 7(9.6%)patients after the stage I procedure.The explanation for one patient was due to infection.Seven(9.6%)patients had other complications like nerve pain,hematoma,infection,lead fracture,and bleeding.The mean follow-up was 6.62±3.5 mo.Sixty-eight(93.2%)patients reported significant improvement of symptoms on follow-up evaluation.CONCLUSION This study shows promising results with significant symptom improvement,good efficacy and good patient outcomes with low complication rates while using MRI compatible InterStim for FI.Further long-term follow-up and future studies with a larger patient population is recommended.

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.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence(AI)methodology based on magnetic resonance(MR)images to aid in the management of prostate cancer(PCa).To this end,we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics,thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa.First,we found that,in the included studies of the present study,AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa,such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression.In particular,for the diagnosis of clinically significant PCa,the AI methods achieved a higher summary receiver operator characteristic curve(SROC-AUC)than that of the clinical assessment methods(0.87 vs.0.82).For the prediction of adverse pathology,the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods(0.86 vs.0.75).Second,as revealed by the radiomics quality score(RQS),the studies included in the present study presented a relatively high total average RQS of 15.2(11.0–20.0).Further,the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes,but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence,such as prospective studies and open-testing datasets.

Effect of magnetic resonance imaging in liver metastases

This letter to the editor is a commentary on a study titled"Liver metastases:The role of magnetic resonance imaging."Exploring a noninvasive imaging evaluation system for the biological behavior of hepatocellular carcinoma(HCC)is the key to achieving precise diagnosis and treatment and improving prognosis.This review summarizes the role of magnetic resonance imaging in the detection and evaluation of liver metastases,describes its main imaging features,and focuses on the added value of the latest imaging tools(such as T1 weighted in phase imaging,T1 weighted out of phase imaging;diffusion-weighted imaging,T2 weighted imaging).In this study,I investigated the necessity and benefits of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid for HCC diagnostic testing and prognostic evaluation.

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.

Brain mechanism of acupuncture for children with anisometropic amblyopia: a resting functional magnetic resonance imaging study based on voxel-mirrored homotopic connectivity

AIM:To explore the brain mechanism of acupuncture for children with anisometropic amblyopia using the voxelmirror homotopic connectivity(VMHC)analysis method of resting functional magnetic resonance imaging(rs-fMRI)technology based on clinical effectiveness.METHODS:Eighty children with anisometropic monocular amblyopia were randomly divided into two groups:control(40 cases,1 case of shedding)and acupuncture(40 cases,1 case of shedding)groups.The control group was treated with glasses,red flash,grating,and visual stimulations,with each procedure conducted for 5min per time.Based on routine treatment,the acupuncture group underwent acupuncture of“regulating qi and unblocking meridians to bright eyes”,Jingming(BL1),Cuanzhu(BL2),Guangming(GB37),Fengchi(GB20)acupoints were taken on both sides,with the needle kept for 30min each time.Both groups were treated once every other day,three times per week,for a total of 4wk.After the treatment,the overall curative effect of the two groups and the latency and amplitude changes of P100 wave of pattern visual-evoked potential were counted.At the same time,nine children with left eye amblyopia were randomly selected from the two groups and were scanned with rsfMRI before and after treatment.The differences in the brain regions between the two groups were compared and analyzed with VMHC.RESULTS:Chi-square test showed a notable difference in the total efficiency rate between the acupuncture(94.87%)and control groups(79.49%).Regarding the P100 wave latency and amplitude,the acupuncture group had significantly shorter latency and higher amplitude of P100 wave than the control group.Moreover,the VMHC values of the bilateral temporal lobe,superior temporal gyrus,and middle temporal gyrus were notably increased in the acupuncture group after treatment.CONCLUSION:Acupuncture combined with conventional treatment can significantly improve the corrected visual acuity and optic nerve conduction in children with anisometropic amblyopia.Compared with the conventional treatment,the regulation of acupuncture on the functional activities of the relevant brain areas in the anterior cerebellum may be an effective acupuncture mechanism for anisometropic amblyopia.

Magnetic resonance imaging techniques for lithium-ion batteries:Principles and applications

Operando monitoring of internal and local electrochemical processes within lithium-ion batteries(LIBs)is crucial,necessitating a range of non-invasive,real-time imaging characterization techniques including nuclear magnetic resonance(NMR)techniques.This review provides a comprehensive overview of the recent applications and advancements of non-invasive magnetic resonance imaging(MRI)techniques in LIBs.It initially introduces the principles and hardware of MRI,followed by a detailed summary and comparison of MRI techniques used for characterizing liquid/solid electrolytes,electrodes and commercial batteries.This encompasses the determination of electrolytes'transport properties,acquisition of ion distribution profile,and diagnosis of battery defects.By focusing on experimental parameters and optimization strategies,our goal is to explore MRI methods suitable to a variety of research subjects,aiming to enhance imaging quality across diverse scenarios and offer critical physical/chemical insights into the ongoing operation processes of LIBs.