Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

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.

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.

Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis

BACKGROUND The liver,as the main target organ for hematogenous metastasis of colorectal cancer,early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients.Herein,this study aims to investigate the application value of a combined machine learning(ML)based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis(MLM).AIM To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.METHODS We retrospectively analyzed 301 patients with rectal cancer confirmed by surgical pathology at Jingzhou Central Hospital from January 2017 to December 2023.All participants were randomly assigned to the training or validation queue in a 7:3 ratio.We first apply generalized linear regression model(GLRM)and random forest model(RFM)algorithm to construct an MLM prediction model in the training queue,and evaluate the discriminative power of the MLM prediction model using area under curve(AUC)and decision curve analysis(DCA).Then,the robustness and generalizability of the MLM prediction model were evaluated based on the internal validation set between the validation queue groups.RESULTS Among the 301 patients included in the study,16.28%were ultimately diagnosed with MLM through pathological examination.Multivariate analysis showed that carcinoembryonic antigen,and magnetic resonance imaging radiomics were independent predictors of MLM.Then,the GLRM prediction model was developed with a comprehensive nomogram to achieve satisfactory differentiation.The prediction performance of GLRM in the training and validation queue was 0.765[95%confidence interval(CI):0.710-0.820]and 0.767(95%CI:0.712-0.822),respectively.Compared with GLRM,RFM achieved superior performance with AUC of 0.919(95%CI:0.868-0.970)and 0.901(95%CI:0.850-0.952)in the training and validation queue,respectively.The DCA indicated that the predictive ability and net profit of clinical RFM were improved.CONCLUSION By combining multiparameter magnetic resonance imaging with the effectiveness and robustness of ML-based predictive models,the proposed clinical RFM can serve as an insight tool for preoperative assessment of MLM risk stratification and provide important information for individual diagnosis and treatment of rectal cancer patients.

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.

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.

Enhancing rectal cancer liver metastasis prediction:Magnetic resonance imaging-based radiomics,bias mitigation,and regulatory considerations

In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.

Hemispheric asymmetries and network dysfunctions in adolescent depression:A neuroimaging study using resting-state functional magnetic resonance imaging

BACKGROUND Currently,adolescent depression is one of the most significant public health concerns,markedly influencing emotional,cognitive,and social maturation.Despite advancements in distinguish the neurobiological substrates underlying depression,the intricate patterns of disrupted brain network connectivity in adolescents warrant further exploration.AIM To elucidate the neural correlates of adolescent depression by examining brain network connectivity using resting-state functional magnetic resonance imaging(rs-fMRI).METHODS The study cohort comprised 74 depressed adolescents and 59 healthy controls aged 12 to 17 years.Participants underwent rs-fMRI to evaluate functional connectivity within and across critical brain networks,including the visual,default mode network(DMN),dorsal attention,salience,somatomotor,and frontoparietal control networks.RESULTS Analyses revealed pronounced functional disparities within key neural circuits among adolescents with depression.The results demonstrated existence of hemispheric asymmetries characterized by enhanced activity in the left visual network,which contrasted the diminished activity in the right hemisphere.The DMN facilitated increased activity within the left prefrontal cortex and reduced engagement in the right hemisphere,implicating disrupted self-referential and emotional processing mechanisms.Additionally,an overactive right dorsal attention network and a hypoactive salience network were identified,underscoring significant abnormalities in attentional and emotional regulation in adolescent depression.CONCLUSION The findings from this study underscore distinct neural connectivity disruptions in adolescent depression,underscoring the critical role of specific neurobiological markers for precise early diagnosis of adolescent depression.The observed functional asymmetries and network-specific deviations elucidate the complex neurobiological architecture of adolescent depression,supporting the development of targeted therapeutic strategies.

Predictive value of magnetic resonance imaging parameters combined with tumor markers for rectal cancer recurrence risk after surgery

BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.

High-resolution direct magnetic resonance imaging fistulography with hydrogen peroxide for diagnosing anorectal fistula: A preliminary retrospective study

BACKGROUND Fistula-in-ano is an abnormal tunnel formation linking the anal canal with the perineum and perianal skin.Multiple imagining methods are available to evaluate it,among which magnetic resonance imaging(MRI)is the most advanced nonin-vasive preoperative method.However,it is limited in its visualization function.AIM To investigate the use of intraluminal MRI for perianal fistulas via a novel direct MRI fistulography method.METHODS We mixed 3%hydrogen peroxide(HP)with gadolinium for HPMRI fistulogra-phy,retrospectively analyzing 60 cases of complex/recurrent fistula-in-ano using physical examination,trans-perineal ultrasonography(TPUS),low-spatial-reso-lution MRI,and high-resolution direct HPMRI fistulography.We assessed detec-tion rates of fistula tracks,internal openings,their relationship with anal sphinc-ters,and perianal abscesses using statistical analyses,including interobserver agreement(Kappa statistic),and compared results with intraoperative findings.RESULTS Surgical confirmation in 60 cases showed that high-resolution direct HPMRI fis-tulography provided superior detection rates for internal openings(153)and fistula tracks(162)compared to physical exams,TPUS,and low-spatial-resolution MRI(Z>5.7,P<0.05).The effectiveness of physical examination and TPUS was also inferior to that of our method for detecting perianal abscesses(54)(Z=6.773,3.694,P<0.05),whereas that of low-spatial-resolution MRI was not significantly different(Z=1.851,P=0.06).High-resolution direct HPMRI fistulography also achieved the highest interobserver agreement(Kappa:0.89,0.85,and 0.80),while low-spatial-resolution MRI showed moderate agreement(Kappa:0.78,0.74,and 0.69).TPUS and physical examination had lower agreement(Kappa range:0.33-0.63).CONCLUSION High-resolution direct HPMRI fistulography enhances the visualization of recurrent and complex fistula-in-ano,including branched fistulas,allowing for precise planning and improved surgical outcomes.

Whole-body magnetic resonance imaging provides accurate staging of diffuse large B-cell lymphoma, but is less preferred by patients

BACKGROUND Whole-body magnetic resonance imaging(wbMRI)allows general assessment of systemic cancers including lymphomas without radiation burden.AIM To evaluate the diagnostic performance of wbMRI in the staging of diffuse large B-cell lymphoma(DLBCL),determine the value of individual MRI sequences,and assess patients’concerns with wbMRI.METHODS In this single-center prospective study,adult patients newly diagnosed with systemic DLBCL underwent wbMRI on a 3T scanner[diffusion weighted images with background suppression(DWIBS),T2,short tau inversion recovery(STIR),contrast-enhanced T1]and fluorodeoxyglucose(18F-FDG)positron emission tomo-graphy/computed tomography(PET/CT)(reference standard).The involvement of 12 nodal regions and extranodal sites was evaluated on wbMRI and PET/CT.The utility of wbMRI sequences was rated on a five-point scale(0=not useful,4=very useful).Patients received a questionnaire regarding wbMRI.RESULTS Of 60 eligible patients,14(23%)were enrolled and completed the study.The sensitivity of wbMRI in the nodal involvement(182 nodal sites)was 0.84,with 0.99 specificity,positive predictive value of 0.96,negative predictive value of 0.97,and 0.97 accuracy.PET/CT and wbMRI were concordant both in extranodal involvement(13 instances)and staging(κ=1.0).The mean scores of the utility of MRI sequences were 3.71±0.73 for DWIBS,2.64±0.84 for T1,2.14±0.77 for STIR,and 1.29±0.73 for T2(P<0.0001).Patients were mostly concerned about the enclosed environment and duration of the MRI examination(27%of patients).CONCLUSION The wbMRI exhibited excellent sensitivity and specificity in staging DLBCL.DWIBS and contrast-enhanced T1 were rated as the most useful sequences.Patients were less willing to undergo wbMRI as a second examination parallel to PET/CT,especially owing to the long duration and the enclosed environment.