Objective Idiopathic rolandic epilepsy syndrome （IRES） is the most common epilepsy syndrome in childhood， and its lesion site remains undetermined. This article aims to investigate the source of epileptiform discharges in IRES using magnetoencephalography （MEG）.Methods A total of 70 patients with IRES were enrolled in this prospective MEG-based study， among whom there were 53 children with benign epilepsy of childhood with centrotemporal spikes （BECTS）， 12 children with atypical benign partial epilepsy （ABPE）， 3 children with Landau-Kleffner syndrome （LKS）， and 2 children with epileptic encephalopathy with continuous spike-and-waves during slow-wave sleep （CSWS）. Epileptiform discharges were collected independently from each patient 10 times， and an MEG source analysis was performed. Standardized low-resolution brain electromagnetic tomography was used to perform source localization of the distributed source model. The spike source density was quantified into amplitude， and source location was determined according to the Desikan-Killiany atlas. The association between the distribution of spike source in brain and clinical manifestations was analyzed.Results In IRES， there were significant differences in the source locations of epilepsy discharge between BECTS， ABPE， LKS， and CSWS. The current source density of CSWS was stronger in the frontal lobe， the temporal lobe， and the anterior cingulate gyrus， while that of ABPE was stronger in the frontal lobe， and that of BECTS and LKS were stronger in the temporal lobe. The more severe phenotype of epilepsy， such as generalized tonic-clonic seizure， was associated with a stronger current source density in the brain， which was consistent with electroencephalography manifestations.Conclusion This study identifies different sources of epileptiform discharges in IRES. The density distribution of these spike sources may help to explain the discharge， cognitive， and neuropsychological characteristics in different subtypes of IRES.
Magnetoencephalography

OBJECTIVES: We propose a sparse-view cone-beam CT reconstruction algorithm based on bidirectional flow field guided projection completion (BBC-Recon) to solve the ill-posed inverse problem in sparse-view cone-beam CT imaging. METHODS: The BBC-Recon method consists of two main modules: the projection completion module and the image restoration module. Based on flow field estimation, the projection completion module, through the designed bidirectional and multi-scale correlators, fully calculates the correlation information and redundant information among projections to precisely guide the generation of bidirectional flow fields and missing frames, thus achieving high-precision completion of missing projections and obtaining pseudo complete projections. The image restoration module reconstructs the obtained pseudo complete projections and then refines the image to remove the residual artifacts and further improve the image quality. RESULTS: The experimental results on the public datasets of Mayo Clinic and Guilin Medical University showed that in the case of a 4-fold sparse angle, compared with the suboptimal method, the BBC-Recon method increased the PSNR index by 1.80% and the SSIM index by 0.29%, and reduced the RMSE index by 4.12%; In the case of an 8-fold sparse angle, the BBC-Recon method increased the PSNR index by 1.43% and the SSIM index by 1.49%, and reduced the RMSE index by 0.77%. CONCLUSIONS The BBC-Recon algorithm fully exploits the correlation information between projections to allow effective removal of streak artifacts while preserving image structure information, and demonstrates significant advantages in maintaining inter-slice consistency.
Algorithms ; Cone-Beam Computed Tomography/methods* ; Image Processing, Computer-Assisted/methods* ; Humans

OBJECTIVES: We propose a segmented backprojection tensor degradation feature encoding (SBP-MAC) model for motion artifact correction in dental cone beam computed tomography (CBCT) to improve the quality of the reconstructed images. METHODS: The proposed motion artifact correction model consists of a generator and a degradation encoder. The segmented limited-angle reconstructed sub-images are stacked into the tensors and used as the model input. A degradation encoder is used to extract spatially varying motion information in the tensor, and the generator's skip connection features are adaptively modulated to guide the model for correcting artifacts caused by different motion waveforms. The artifact consistency loss function was designed to simplify the learning task of the generator. RESULTS: The proposed model could effectively remove motion artifacts and improve the quality of the reconstructed images. For simulated data, the proposed model increased the peak signal-to-noise ratio by 8.28%, increased the structural similarity index measurement by 2.29%, and decreased the root mean square error by 23.84%. For real clinical data, the proposed model achieved the highest expert score of 4.4221 (against a 5-point scale), which was significantly higher than those of all the other comparison methods. CONCLUSIONS The SBP-MAC model can effectively extract spatially varying motion information in the tensors and achieve adaptive artifact correction from the tensor domain to the image domain to improve the quality of reconstructed dental CBCT images.
Cone-Beam Computed Tomography/methods* ; Artifacts ; Humans ; Motion ; Image Processing, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

Objective To develop a neural network-based deep learning model for predicting postoperative recurrence in thyroid tumor patients and validate the model with external datasets for providing clinicians with a reliable decision support tool.Methods An artificial neural network structure was adopted in the study,with thyroid tumor data from the SEER database serving as the training set.External validation was conducted with open-source data from the University of California,Irvine(UCIrvine),and the data from 100 patients at a general tertiary hospital in Hunan province.The model's accuracy and reliability in predicting recurrence were evaluated through multiple performance metrics.Results Experimental results showed that the model outperformed Logistic model in recurrence prediction,with accuracy,recall rate,precision and F1 score reaching 0.915 3,0.981 8,0.921 1 and 0.947 4 in internal validation.Moreover,the model achieved accuracies,recall rates,precisions,F1 scores and ROC_AUC values of 0.832 9,0.945 5,0.841 4,0.890 4 and 0.78 on the UCIrvine validation set,while 0.870 0,0.880 0,0.862 7,0.871 3 and 0.80 on the local validation set.Conclusion This neural network-based predictive model exhibits excellent performance in thyroid tumor recurrence prediction,providing clinicians with a valuable decision support tool that can help optimize postoperative treatment plans and improve patient prognosis management.

Objective:To analyze the APR triangle formed when the right hepatic vein (RHV) passed through the right anterior hepatic pedicle (RAHP) and the right posterior hepatic pedicle (RPHP) by 3D visualization technique.Methods:Clinical data of 100 subjects undergoing liver contrast-enhanced CT scan in the Second Affiliated Hospital of Harbin Medical University from September 2019 to September 2022 were retrospectively analyzed, including 57 males and 43 females, aged (53.2±13.0) years. The CT data were imported into a system for 3D image reconstruction of the liver. The intrahepatic vascular structure was observed, including the portal vein, APR triangle, the angle between the RAHP and RPHP, the distance between RHV and the vertex of angle when it crosses the angle plane of the RAHP and RPHP, the structure and branches of the right posterior branch of portal vein, and the number of branches into the APR triangle from the main trunk of the right anterior and right posterior portal vein.Results:The main type of portal vein was type A (the main portal vein was divided into left and right branches, and the right branch was further divided into right anterior branch and right posterior branch), in 86 cases (86.0%). The APR triangle was observed in 81 cases (81.0%), and the distance between the right hepatic vein and the vertex of the right anterior and right posterior hepatic pedicle was (1.70±0.36) cm, 1.00-2.00 cm was 82.7%(61/81). The angle between right anterior and right posterior hepatic pedicle was 45°-90° in 77 cases (77.0%, 77/100). In 100 subjects, the main branch of the right posterior portal vein accounted for 42.0% (42/100) of typeⅠ (bifurcation type) and 53.0% (arch type) of type Ⅱ (53/100). In 16 cases (19.8%, 16/81), the main right anterior portal vein branched into the APR triangle, with a total of 22 branches. In 9 cases (11.1%, 9/81), the main branch of the right posterior portal vein (branching type) branched into the triangle, with a total of 11 branches. There were 33 cases (40.7%, 33/81) of right posterior portal vein (arch type) main branch branching into the triangle, a total of 41 branches.Conclusion:The APR triangle, composed of the RAHP, RPHP and RHV, has an objective anatomical basis and is affected by the variants of portal vein and RHV. It is more common for the RHV to be within 1.00-2.00 cm of the vertex of the angle between the RAHP and RPHP, and the main trunk of the right anterior and right posterior branches of portal vein are less divided into the triangle, so it is relatively safe to dissect within this area.

Moyamoya disease (MMD) is widely recognized as an important cause of ischemic stroke and hemorrhagic stroke. Cerebral revascularization is the standard treatment for preventing stroke recurrence in patients with MMD. However, cerebral hyperperfusion syndrome (CHS) is one of the main complications of cerebral revascularization in patients with MMD. This article reviews the predictive factors for postoperative CHS in patients with MMD undergoing cerebral revascularization.

Objective To compare the efficacy and safety of perioperative chemotherapy regimens based on docetaxel administered in different cycles for locally advanced gastric cancer(LAGC)and gastroesophageal junction cancer(GEJC)through a network Meta-analysis.Methods PubMed,Embase,Cochrane Library,and Web of Science databases were electronically searched to collect randomized controlled trials(RCTs)evaluating docetaxel-based perioperative regimens for the treatment of LAGC and GEJC from inception to February 28,2025.Two researchers independently screened the literature,extracted relevant data,and assessed the risk of bias in the included studies.Subsequently,a network Meta-analysis was performed using R 4.5.0 software.Results A total of 10 RCTs involving 2,064 patients were included.The results of the network Meta-analysis showed that,compared with three cycles of epirubicin,cyclophosphamide,and fluorouracil(ECF regimen)and four cycles of platinum and fluorouracil(PF regimen),both three-cycle and four-cycle docetaxel,platinum,and fluorouracil(TPF regimen)significantly increased the complete tumor resection rate(R0 resection rate).Compared with surgery alone,the three-cycle TPF regimen significantly improved the R0 resection rate.Additionally,the four-cycle TPF regimen significantly improved 3-year and 5-year progression-free survival(PFS)compared with the two-cycle PF regimen,and significantly improved overall survival(OS)at 3 and 5 years compared with the three-cycle ECF regimen.Compared with surgery alone,the three-cycle TPF regimen also significantly increased the pathological complete response rate(pCR rate).Furthermore,compared with the two-cycle TPF and three-cycle ECF regimens,the four-cycle TPF and PF regimens were associated with a lower risk of surgical complications.Conversely,the two-cycle TPF and three-cycle ECF regimens significantly increased the risk of surgical complications compared with surgery alone.No statistically significant differences were observed in the incidence of grade 3 to 4 adverse events among the treatment regimens(P＞0.05).Based on the surface under the cumulative ranking curve(SUCRA)plot and pairwise comparisons,the three-cycle PF regimen appeared to be the most effective in achieving the highest R0 resection rate,the three-cycle TPF regimen appeared to be the most effective in achieving the highest pCR rate,while the four-cycle TPF regimen was associated with the best outcomes in terms of 3-year and 5-year PFS and OS.Conclusion The four-cycle TPF regimen may be associated with the optimal PFS and OS among perioperative chemotherapy regimens.No statistically significant differences were observed between the four-cycle TPF and the three-cycle PF in terms of R0 resection rate,between the four-cycle TPF and the three-cycle TPF in terms of pCR rate,or between the four-cycle TPF and the other seven regimens included in the study in terms of safety assessment.The four-cycle TPF regimen may represent the optimal perioperative chemotherapy regimen for patients with LAGC and GEJC.

Objective:To evaluate the sample preparation proficiency and storage proficiency of 11 clinical biobanks in Beijing through simulated experiments, and to establish an assessment method for the quality comparability of biological samples.Methods:An exploratory research design was adopted. In November 2023, artificial composite serum quality control materials containing six recombinant human protein markers—recombinant human alanine aminotransferase (rhALT), recombinant human aspartate aminotransferase (rhAST), recombinant human creatine kinase (rhCK), recombinant human creatine kinase-MB (rhCK-MB), recombinant human B-type natriuretic peptide (rhBNP), and recombinant human troponin I (rhTNI)—were distributed to 11 clinical biobanks in Beijing City. Sample preparation and storage followed the standardized operating procedures. Proficiency differences were assessed through statistical analysis.Results:Three-way repeated measures ANOVA revealed all six protein markers showed a declining trend over storage time in ultra-low-temperature environments ( F values 11.68-4 179.66, all P<0.01). However, neither long-term/temporary refrigerator types ( F values 0.01-1.23, all P>0.05)nor placement locations within refrigerators significantly affected the stability of these six proteins ( F valus 0.03-1.47, all P>0.05). The biases in detection results for rhALT, rhAST, rhTNI, and rhBNP at different storage time points were within the allowable bias limits for each item, supporting their use as markers for protein stability in biobank samples. All 11 institutions passed the storage proficiency assessment. In the preparation proficiency assessment, deviations were observed in post-preparation sample results, with a notably high out-of-control rate for rhCK (36.36%). Conclusion:Sample preparation proficiency can serve as a quality control metric for clinical biobanks. Future external quality assessment systems for biobanks should focus on sample preparation rather than storage processes.

Objective To investigate the value of parameters of MR diffusion kurtosis imaging(DKI)for predicting human epidermal growth factor receptor 2(HER2)-positive breast cancer(BC).Methods A total of 122 cases of BC diagnosed by pathology were retrospectively enrolled,including 37 cases of HER2-positive(HER2-positive group)and 85 cases of HER2-negative(HER2-negative group).Parameters obtained based on MR DKI(mean diffusivity[MD]and mean kurtosis[MK]values)and the results of immunohistochemical detection(expression of matrix metalloproteinase-2[MMP-2],vascular endothelial growth factor receptor 2[VEGFR-2])were compared between groups.Then maltivariable logistic regression was used to analyze the independent predictors of HER2-positive BC based on DKI parameters being statistically different between groups,and a combined model was constructed.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)were calculated to assess the efficacy of each single DKI parameter and their combined model for predicting HER2-positive BC,and the correlations of these DKI parameters with MMP-2 or VEGFR-2 expressions were analyzed.Results Compared with HER2-negative group,MD value,MK value,positive expression proportion of MMP-2 and VEGFR-2 increased in HER2-positive group(all P＜0.05).Both MD(OR=1.423)and MK(OR=1.624)values were independent predictors of HER2-positive BC(both P＜0.05),with AUC for predicting HER2-positive BC of 0.819 and 0.836,respectively,while the AUC of combined model of MD and MK values was 0.916.MD value of BC was positively correlated with MMP-2(rs=0.222)and VEGFR-2 expression(rs=0.232)(both P＜0.05),while MK value of BC was not significantly correlated with MMP-2 or VEGFR-2 expression(both P＞0.05).Conclusion As parameters of MR DKI,MD and MK values could be used to effectively predict HER2-positive BC.

Objective To develop a neural network-based deep learning model for predicting postoperative recurrence in thyroid tumor patients and validate the model with external datasets for providing clinicians with a reliable decision support tool.Methods An artificial neural network structure was adopted in the study,with thyroid tumor data from the SEER database serving as the training set.External validation was conducted with open-source data from the University of California,Irvine(UCIrvine),and the data from 100 patients at a general tertiary hospital in Hunan province.The model's accuracy and reliability in predicting recurrence were evaluated through multiple performance metrics.Results Experimental results showed that the model outperformed Logistic model in recurrence prediction,with accuracy,recall rate,precision and F1 score reaching 0.915 3,0.981 8,0.921 1 and 0.947 4 in internal validation.Moreover,the model achieved accuracies,recall rates,precisions,F1 scores and ROC_AUC values of 0.832 9,0.945 5,0.841 4,0.890 4 and 0.78 on the UCIrvine validation set,while 0.870 0,0.880 0,0.862 7,0.871 3 and 0.80 on the local validation set.Conclusion This neural network-based predictive model exhibits excellent performance in thyroid tumor recurrence prediction,providing clinicians with a valuable decision support tool that can help optimize postoperative treatment plans and improve patient prognosis management.