Background:Artificial intelligence (AI) algorithms synthesizing virtual standard-dose images from low-dose contrast-enhanced images of brain magnetic resonance imaging (MRI) have been repurposed to boost contrast from standard-dose input. This study aimed to prospectively evaluate the impact of a Food and Drug Administration (FDA)-cleared, deep learning-based software on contrast enhancement, lesion visualization, and diagnostic confidence for standard-dose contrast-enhanced images. Methods:This prospective study enrolled patients undergoing contrast-enhanced brain MRI between August 2025 and September 2025. Precontrast and standard-dose postcontrast three-dimensional T1-weighted (T1w) images were acquired. AI-amplified contrast-enhanced images were generated via an FDA-cleared deep learning software based on precontrast and standard postcontrast images. Two radiologists independently performed quantitative analyses, examining contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR), and contrast enhancement percentage (CEP). Qualitative assessments of lesion border delineation, internal morphology, and contrast enhancement, and diagnostic confidence were performed with a 4-point Likert scale. Comparisons between AI-amplified and standard-dose images were conducted via the Wilcoxon signed-rank test. Results:Forty-one patients (mean age 51.1±12.6 years) with enhancing brain lesions were included. For both readers, AI-amplified images, as compared with standard contrast-enhanced images, exhibited a significantly higher CNR (82.56±41.15 vs. 20.33±23.28; 87.91±50.30 vs. 20.76±12.00), LBR (2.86±0.64 vs. 1.78±0.48; 2.83±0.53 vs. 1.76±0.40), and CEP (Reader 1: 393.00%±188.40% vs. 201.20%±111.32%; Reader 2: 382.26%±176.71% vs. 197.57%±109.90%) (all P values <0.001). Compared with standard-dose images, AI-amplified images produced an approximately 400% higher CNR, a 60% higher LBR, and a 200% higher CEP. The qualitative evaluation of both readers indicated that the AI-amplified images provided significantly improved lesion border delineation (Reader 1: 3.38±0.61 vs. 3.13±0.59; Reader 2: 3.46±0.60 vs. 3.00±0.63) and contrast enhancement (Reader 1: 3.88±0.31 vs. 2.94±0.24; Reader 2: 3.85±0.42 vs. 2.90±0.49) (all P values <0.001). No significant differences were observed in lesion internal morphology scores between AI-amplified and standard-dose images (P>0.05 for both readers). AI-amplified images demonstrated significantly higher diagnostic confidence than did standard-dose images for both readers (Reader 1: 3.59±0.50 vs. 3.20±0.60; Reader 2: 3.48±0.64 vs. 3.05±0.74; both P values <0.001). Conclusions:AI-based contrast amplification significantly increases the quantitative contrast metrics, qualitative lesion visualization, and diagnostic confidence for standard-dose contrast-enhanced brain MRI without increasing the gadolinium dose. These findings support the use of AI-based contrast amplification as a complementary tool in routine clinical neuroimaging.

Conventional transarterial chemoembolization (TACE) regimens for hepatocellular carcinoma (HCC) are often compromised in efficacy due to hypoxia and acidosis within the tumor microenvironment (TME), frequently leading to unsatisfactory treatment outcomes and tumor recurrence. To overcome these limitations, this study introduces an innovative approach by incorporating a hydrogen generator (calcium hydride, CaH₂) into an epirubicin (EPI)-iodized oil embolization system. This design enables local hydrogen release to remodel the TME following TACE, thereby enhancing the combined chemo-immunotherapeutic antitumor response. Nano-CaH₂ particles, co-delivered locally via TACE, undergo hydrolysis to continuously release hydrogen gas (H₂) and calcium ions (Ca2+). This reaction disrupts mitochondrial function in cancer cells, reduces oxygen consumption, alleviates tumor hypoxia, and consequently counteracts chemoresistance. Simultaneously, EPI induces immunogenic cell death (ICD) in moribund tumor cells, activating the host's antitumor immune response. Additionally, the hydroxide ions generated from CaH₂ hydrolysis neutralize the acidic TME, alleviating immunosuppression and further amplifying the chemo-immunotherapeutic synergy mediated by TACE. This strategy presents a novel method to improve TACE efficacy and facilitate its integration with immunotherapy, demonstrating considerable potential for clinical translation.

Rationale and Objectives This study aims to establish a nomogram predictive model capable of identifying high-grade tumors among IDH-mutant astrocytomas exhibiting positive T2-FLAIR mismatch sign (T2FM) before surgery. Materials and Methods We collected T2FM-positive IDH-mutant astrocytomas from three distinct centers. Using data from one center as the training set (154 cases) to establish predictive model. Data from the remaining two centers served as external validation set (29 cases) to evaluate the model’s performance. The assessment of the predictive model included the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis, area under the curve (AUC), sensitivity, and specificity. Results In the clinical features, we identified female sex (p=0.02) and the presence of enhancement within the tumor in contrast-enhanced T1-weighted imaging (p<0.001) as independent predictors of high-grade T2FM-positive IDH-mutant astrocytomas. We also found that radiomics features, such as LeastAxisLength, could aid in distinguishing WHO grades. Based on clinical and radiomics features, we developed an combined predictive model, which demonstrated superior performance compared to models relying solely on clinical or radiomics features. The combined predictive model achieved AUC of 0.819 and 0.858, sensitivity of 0.615 and 0.500, and specificity of 0.941 and 1.000, in the training and validation sets, respectively. Conclusion We developed a predictive model based on gender, contrast enhancement, and radiomics features to predict high-grade tumors among T2FM-positive IDH-mutant astrocytomas with high specificity but low sensitivity.

IntroductionThis study aimed to investigate the value of intratumoral and peritumoral radiomics in predicting the risk grade of gastrointestinal stromal tumors (GISTs) using contrast-enhanced computed tomography (CT) images. MethodsA total of 217 pathology-confirmed GISTs were retrospectively enrolled and divided into low-risk and high-risk groups. Significant predictors were selected from clinical and radiological characteristics to build a prediction model. Radiomics features were extracted from the intratumoral region, the 3-mm peritumoral region, and the 5-mm peritumoral region. After ANOVA and LASSO feature screening, logistic regression was applied to construct the radiomics model. The Rad-score of the optimal radiomics model was calculated and combined with the selected radiological characteristics to develop a combined model and a nomogram. ROC curves were used to assess the predictive performance of each model, while calibration curves and decision curve analysis were used to evaluate their clinical utility. The SHapley Additive Explanations (SHAP) method was applied to perform interpretability analysis of the optimal model. ResultsA radiological model (RM), five radiomics models, and a combined radiological characteristics plus Rad-score model (CRM) were constructed. In the validation set, the AUCs of the RM and CRM were 0.839 and 0.924, respectively. The intratumoral plus 3-mm peritumoral radiomics model (ITV+PTV3) achieved the best performance in the validation set, with an AUC of 0.934. DiscussionThe ITV+PTV3 model shows strong potential for objective GIST risk stratification but requires multi-center prospective validation to ensure generalizability beyond the limitations of this retrospective dataset. ConclusionRadiomics models based on intratumoral and peritumoral regions perform well in predicting the risk grade of GISTs and may effectively guide accurate preoperative diagnosis and treatment planning.

Transarterial chemoembolization (TACE) remains the standard of care for patients with unresectable hepatocellular carcinoma (HCC). However, clinical outcomes are frequently compromised by off-target toxicity, incomplete tumour necrosis, and the induction of a post-procedural immunosuppressive microenvironment. The integration of nanotechnology represents a paradigm shift designed to circumvent these biological and technical barriers. For instance, magnesium-enhanced TACE has demonstrated an objective response rate of similar to 93.3 %, substantially surpassing that of conventional TACE therapies. This review evaluates nanotechnology's impact on TACE through four domains: Smart nanocarriers for stimuli-responsive delivery; theranostic platforms for real-time imaging; immune remodeling to boost immunotherapy; and next-generation embolic materials such as biodegradable polymers and liquid metals. Finally, we address the preclinical-to-clinical translation gap and outline a roadmap for personalized, high-precision interventional oncology. (c) 2026 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.

Rationale and Objectives: To assess the value of radiomics models and clinical models (CM) based on diverse volumes of interest and clinical indicators in differentiating low Gleason grade group (GGG) from high-GGG in prostate cancer (PCa). Materials and Methods: This study included 312 PCa patients diagnosed pathologically from center 1 and center 2, divided into internal training (dataset A, n=144, center 1), internal validation (dataset B, n=63, center 1) and external test set (dataset C, n=105, center 2). The CM and radiomics models for intratumoral volume (ITV), 2 mm reduction from tumor border (CTV), 2 mm and 4 mm extensions beyond tumor border (PTV2, PTV4), and 4 mm peritumoral transition (PTT) were developed. Model performance was evaluated using area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: The combined model ITV_PTV4_CM1 demonstrated optimal performance (AUC 0.871 [95% confidence interval: 0.802-0.938] for dataset C), significantly outperformed ITV (0.778 [0.690-0.864]), and PTV4 (0.774 [0.681-0.866]), with all p < 0.05, and also better than CM1 (0.830 [0.753-0.905]). The introduction of ITV+PTV4, PSAD+PSA, and PSA all provide positive gains to model (NRI/IDI > 0, all p < 0). PTT with AUCs of 0.858 and 0.869 for datasets A and B, outperformed other individual radiomics models in center 1. ADC/ DWI_gldm emerged as the top-weighted feature. Conclusion: The ITV_PTV4_CM1 enhances predictive efficacy for preoperative PCa risk stratification. PTV4 and PTT highlight the importance of transition zone features at the peritumoral invasion margin, offering insights into optimal peritumoral extents. The similar efficacy of intratumoral and peritumoral radiomics suggests strategies for optimizing PCa diagnostic workflow.

Growing evidence implicates the hypothalamus as a key structure in migraine pathophysiology; however, our understanding of its precise role and of the specific nuclei involved remains limited. We combined MRI data from our laboratory with publicly available MRI datasets from OpenNeuro to examine hypothalamic subunit volumes in episodic migraine and to assess whether comparable alterations were observed in available chronic pain datasets. Structural MRI combined with an automated atlas-based segmentation algorithm was employed to investigate cross-sectional volumetric differences across 5 bilateral hypothalamic subunits in two independent migraine cohorts: DS1-MIG (DS1-MIG-base, n = 111 patients, n = 35 controls) and DS2-MIG (n = 28 patients, n = 31 controls). The adjusted volumes were compared between groups using MANOVA as an omnibus test, followed by Welch t-tests to test univariate follow-up. Longitudinal volumetric changes were additionally assessed in DS1-MIG participants with available follow-up scans using linear mixed models. To examine whether comparable hypothalamic alterations were observed in available clinical comparison datasets, the same cross-sectional pipeline was applied to two chronic pain datasets, one including patients with fibromyalgia (DS-FM, n = 33 patients, n = 33 controls) and the other including patients with trigeminal neuralgia (DS-TN, n = 119 patients, n = 55 controls). MANOVA revealed significant multivariate group differences in both independent migraine cohorts (DS1-MIG-base: p = .006 ; DS2-MIG: p = .008 ). Follow-up univariate analyses identified a consistent enlargement of the left anterior-superior subunit across both cohorts ( p_FDR = .023 in DS1-MIG-base and p_FDR = .046 in DS2-MIG), representing the only cross-cohort replication finding. Beyond this shared signature, DS2-MIG exhibited additional significant enlargements of the right anterior-inferior and right inferior tubular subunits. Longitudinal analyses in DS1-MIG showed no significant group-by-time interactions in hypothalamic subunit volumes over the available follow-up period. No significant volumetric alterations were detected in the fibromyalgia or trigeminal neuralgia cohorts, either in multivariate or univariate analyses. These findings provide evidence for subunit-specific hypothalamic structural alterations in migraine localized to the left anterior-superior hypothalamic subunit. The absence of detectable differential longitudinal change over the available follow-up period, together with the absence of comparable alterations in the available fibromyalgia and trigeminal neuralgia datasets, supports a migraine-related pattern of hypothalamic structural organization. However, this finding does not establish diagnostic specificity and should be confirmed in harmonized multi-diagnostic cohorts.

Background:Through the quantification of multiple microstructural parameters, time-dependent diffusion magnetic resonance imaging (Td-dMRI) offers a novel approach for establishing urgently needed imaging biomarkers of tumor heterogeneity. This study employed Td-dMRI-derived histogram parameters to predict Ki-67 expression in breast cancer. Methods:A total of 86 female patients with breast cancer were enrolled in this prospective study. Microstructural parameters, including mean cell diameter (d), intracellular volume fraction (fin), intracellular diffusion coefficient (Din), extracellular apparent diffusion coefficient (Dex), and intracellular water pre-exchange lifetime (Tauin), were estimated using the JOINT model based on Td-dMRI data acquired with pulsed (PGSE) and oscillating (OGSE) gradient spin-echo sequences. Two additional parameters, intracellular water exchange rate (Kin) and cellularity, were subsequently derived through calculation. The apparent diffusion coefficient (ADC) was calculated from the conventional diffusion-weighted imaging (DWI), PGSE (32 and 52 ms), and OGSE (17 and 33 Hz) sequences. Histogram features were extracted based on parameters derived from Td-dMRI. The independent t-test or Mann-Whitney U test was used to compare differences between Ki-67 groups. The dataset was divided into a training set and test set at a ratio of 7:3 for internal validation. Clinical and conventional imaging characteristics identified by univariate regression analysis (P<0.1), together with histogram features selected by least absolute shrinkage and selection operator (LASSO) regression, were used to further establish a logistic regression model in the training set. Model performance was assessed by receiver operating characteristic (ROC) curves in the training and test sets. The area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Results:Nine histogram parameters differed significantly between the Ki-67 expression groups; however, no significant difference in ADCDWI was observed (P=0.859). Axillary lymph node metastasis (ALNM) was identified as an independent predictor of high Ki-67 expression (odds ratio (OR) =2.991; 95% confidence interval (CI): 0.964-9.278, P=0.058). Among the eight histogram features ultimately selected by LASSO regression, Cellularity_P10 and ADC33Hz_Skewness contributed the most to the model. The combined model achieved an AUC of 0.890 (95% CI: 0.809-0.971), with a sensitivity, specificity, and accuracy of 89%, 65%, and 80%, respectively, in the training set, and an AUC of 0.870 (95% CI: 0.712-1), with a sensitivity, specificity, and accuracy of 80%, 50%, and 73%, respectively in the test set. Conclusions:Td-dMRI-derived histogram features show promise for predicting Ki-67 expression in breast cancer and may complement conventional imaging.

Noninvasive methods for liver fibrosis staging are urgently needed due to its significance in predicting significant morbidity and mortality. In this study, we developed an automated DL-based segmentation and classification model (Model-C). Test-time adaptation was used to address data distribution shifts. We then established a deep learning-radiologist complementarity decision system (DRCDS) via a decision model determining whether to adopt Model-C's diagnosis or defer to radiologists. Model-C (AUCs of 0.89-0.92) outperformed models based on liver (AUCs: 0.84-0.90) or spleen (AUCs: 0.69-0.70). With test-time adaptation, the Obuchowski index values of Model-C in three external sets improved from 0.81, 0.73, and 0.73 to 0.85, 0.85, and 0.81. DRCDS performed slightly better than Model-C or senior radiologists, with 73.7%-92.0% of cases adopting Model-C's diagnosis. In conclusion, DRCDS could diagnose liver fibrosis with high accuracy. Additionally, we provided solutions to model generalization and human-machine complementarity issues in multi-classification problems.

Background:With the increasing need for accurate liver disease diagnostics, non-invasive imaging techniques with rapid and precise quantitative measurements need to be established. This study introduced and validated the application of simultaneous multi-relaxation-time imaging (TXI) for the quantitative assessment of liver tissue by simultaneously acquiring proton density fat fraction (PDFF), lateral relaxation rate (R2*), and longitudinal relaxation time (T1) maps. It aimed to compare the accuracy and consistency of TXI with established quantitative magnetic resonance imaging (MRI) techniques, such as three-dimensional variable flip angle (VFA) T1 mapping, and multi-point quantitative Dixon (qDixon), in healthy volunteers and patients diagnosed with non-alcoholic fatty liver disease (NAFLD). Methods:A prospective cohort of 35 healthy volunteers (mean age: 52±13 years, 21 women) and nine NAFLD patients (mean age: 48±13 years, 6 women) underwent liver MRI using TXI, VFA T1 mapping, and qDixon sequences. Intraclass correlation coefficients (ICCs) and Bland-Altman plots were used to assess inter-observer agreement and measurement consistency. Paired T-tests and Pearson correlation coefficients were used to compare the TXI measurements with those from conventional MRI techniques. Differences between the healthy volunteers and NAFLD patients were evaluated using the independent sample T-test. Results:The ICCs for the TXI-derived T1, R2*, and PDFF in healthy volunteers were 0.985 [95% confidence interval (CI): 0.971-0.993], 0.999 (95% CI: 0.998-1.000), and 0.995 (95% CI: 0.990-0.997), respectively, indicating excellent agreement. The regression analysis revealed strong correlations between the TXI and reference MRI measurements for the T1 (R2=0.895), R2* (R2=0.984), and PDFF (R2=0.894) values with no significant differences (P=0.713, 0.090, and 0.072, respectively). Statistically significant differences were observed in the R2* (P=0.045) and PDFF (P<0.001) values between the NAFLD patients and healthy volunteers, but no significant difference was observed in the T1 values (P=0.965). Multiparametric imaging showed that TXI provides comprehensive liver tissue characterization, consistent with conventional MRI techniques. Conclusions:TXI offers a rapid and reliable method for the simultaneous acquisition of T1, R2*, and PDFF maps, and has high consistency with established quantitative MRI techniques. This approach has significant potential for non-invasive liver tissue characterization in clinical settings, particularly in the diagnosis and monitoring of conditions such as NAFLD.

Background:Determining the molecular status of gliomas is crucial for evaluating treatment efficacy and prognosis. However, this process currently requires the invasive and cumbersome method of histological analysis. We aimed to develop and validate a non-invasive three-classification machine learning (ML) model to predict the three molecular subtypes of adult-type diffuse gliomas according to the 2021 World Health Organization classification of tumors of the central nervous system 5th edition (WHO CNS 5). Methods:This retrospective study included a total of 306 glioma patients, among whom 258 were from Center 1 (Huashan Hospital; 180 for the training and 78 for the internal validation set) and 48 were from Center 2 (The First Affiliated Hospital of Anhui Medical University; external validation set). Conventional magnetic resonance imaging (MRI) features of tumors were assessed, and the radiomics and Swin Transformer-based deep learning (RSTD) features were respectively extracted from tumor segmentation on axial three-dimensional contrast-enhanced T1-weighted (3D T1C) and T2-fluid-attenuated inversion recovery (T2-FLAIR) sequences. Three types of prediction models: conventional MRI (CM) model, RSTD model, and combined model were respectively trained using six ML classifiers [k-nearest neighbor (kNN), light gradient-boosting machine (LightGBM), random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and extreme gradient boosting (XGBoost)] to identify the three major molecular subtypes of adult-type diffuse gliomas. The performance of the models was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, precision, and F1-score. Results:XGBoost classifier was chosen as our algorithm for model construction due to its superior performance in the training and internal validation cohorts. The combined model, which incorporates CM features, RSTD features, as well as demographic features, achieved best performance in the internal [micro-AUC (0.905) and macro-AUC (0.878)] and external validation sets [micro-AUC (0.911) and macro-AUC (0.891)]. The SHapley Additive explanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) were used to explain the model. Conclusions:Our study constructed a three-classification ML model that combined CM features, RSTD features, and demographic characteristics, achieved promising performance in predicting molecular subtypes of diffuse glioma. The combined model provided a non-invasive, timely, and accurate diagnostic approach prior to patient treatment to assist clinical decision-making.

Myocardial strain, as a crucial quantitative indicator of myocardial deformation, can detect the changes of cardiac function earlier than parameters such as ejection fraction (EF). It has reported that cardiac magnetic resonance(CMR) and post-processing software possess the ability to obtain the stability and repeatability strain values. Recently, the normal strain values range of people are debatable, especially in the Chinese population. Therefore, we aim to explore the ventricular characteristics and the myocardial strain values of the Chinese people by using the cardiac magnetic resonance feature tracking (CMR-FT). Additionally, we attempted to use the myocardial and chordae tendineae contours to calculate the ventricular volumes by the CMR-FT. This study may provide valuable insights into the application of CMR-FT in tracking the ventricular characteristics and myocardial strain for Chinese population, especially in suggesting an referable myocardial strain parameters of the Chinese. A total of 109 healthy Chinese individuals (age range: 18 to 58 years; 52 males and 57 females) underwent 3.0T CMR to acquire the cardiac images. The commercial post-processing software was employed to analyse the image sequence by semi-automatic processing, then the biventricular morphology (End-Diastolic Volume, EDV; EDV/Body Surface Area, EDV/BSA), function(EF; Cardiac Output, CO; Cardiac Index, CI) and strain(Radial Strain, RS; Circumferential Strain, CS; Longitudinal Strain, LS) values were obtained.The biventricular myocardial strain values were stratified according to the age and gender. The Left Ventricular( LV base, mid, apex) and myocardial strain values of three coronary artery areas were calculated based on the the strain value of LV American Heart Association(AHA) 16 segments. It was shown that the females had larger LV globe strain values compared with the males (LVGPRS: 42.0 ± 8.5 versus 33.6 ± 6.2

Purpose: Isocitrate dehydrogenase (IDH) and cyclin-dependent kinase inhibitor (CDKN) 2A/B status holds important prognostic value in diffuse gliomas. We aimed to construct prediction models using clinically available and reproducible characteristics for predicting IDHmutant and CDKN2A/B homozygous deletion in adult-type diffuse glioma patients. Materials and Methods: This retrospective, two-center study analysed 272 patients with adult-type diffuse glioma (230 for primary cohort and 42 for external validation cohort). Two radiologists independently assessed the patients' images according to the Visually AcceSAble Rembrandt Images (VASARI) feature set. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimise variable selection. Multivariable logistic regression analysis was used to develop the prediction models. Calibration plots, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to validate the models. Nomograms were developed visually based on the prediction models. Results: The interobserver agreement between the two radiologists for VASARI features was excellent (kappa range, 0.813-1). For the IDHmutant prediction model, the area under the curves (AUCs) was 0.88-0.96 in the internal and external validation sets, For the CDKN2A/B homozygous deletion model, the AUCs were 0.80-0.86 in the internal and external validation sets. The decision curves show that both prediction models had good net benefits. Conclusion: The prediction models which basing on VASARI and clinical features provided a reliable and clinically meaningful preoperative prediction for IDH and CDKN2A/B status in diffuse glioma patients. These findings provide a foundation for precise preoperative non-invasive diagnosis and personalised treatment approaches for adult-type diffuse glioma patients.

Objective To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer. Methods One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS). Results Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233–5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247–5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710). Conclusion The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS. Critical relevance statement A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer. Key points • Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS. Graphical Abstract

Background:High signals on diffusion weighted imaging along the corticomedullary junction (CMJ) have demonstrated excellent diagnostic values for adult-onset neuronal intranuclear inclusion disease (NIID). However, the longitudinal course of diffusion weighted imaging high intensities in adult-onset NIID patients has rarely been investigated.Methods:We described four NIID cases that had been discovered using skin biopsy and NOTCH2NLC gene testing, after diffusion weighted imaging exhibiting the distinctive corticomedullary junction high signals. Then using complete MRI data from NIID patients, we analyzed the chronological diffusion weighted imaging alterations of those individuals that had been published in Pub Med.Results:We discussed 135 NIID cases with comprehensive MRI data, including our four cases, of whom 39 had follow-up outcomes. The following are the four primary diffusion weighted imaging dynamic change patterns: (1) high signal intensities in the corticomedullary junction were negative on diffusion weighted imaging even after an 11-year follow-up (7/39); (2) diffusion weighted imagings were initially negative but subsequently revealed typical findings (9/39); (3) high signal intensities vanished during follow-up (3/39); (4) diffusion weighted imagings were positive at first and developed in a step-by-step manner (20/39). We discovered that NIID lesions eventually damaged the deep white matter, which comprises the cerebral peduncles, brain stem, middle cerebellar peduncles, paravermal regions, and cerebellar white matter.Conclusion:The longitudinal dynamic changes in NIID of diffusion weighted imaging are highly complex. We find that there are four main patterns of dynamic changes on diffusion weighted imaging. Furthermore, as the disease progressed, NIID lesions eventually involved the deep white matter.

目的 探讨多层螺旋CT(MSCT)鉴别低度恶性和高度恶性阑尾原发性肿瘤的价值.方法 回顾性分析58例经病理证实的阑尾原发恶性肿瘤患者的影像特征,根据手术病理结果将其分为低度恶性与高度恶性组,比较两组病例MSCT特征的差异性.结果 58例阑尾原发恶性肿瘤中,低度恶性组42例(低级别黏液性肿瘤)、高度恶性组16例(神经内分泌瘤4例、腺癌6例、高级别黏液性肿瘤2例、低级别黏液性肿瘤伴腹膜假性黏液瘤4例).两组阑尾原发恶性肿瘤的MSCT特征包括肿瘤的边界、成分、强化以及周围淋巴结显示、是否伴发腹腔积液,组间比较差异均有统计学意义(P<0.05);MSCT评价阑尾原发恶性肿瘤侵袭性的准确率、灵敏度、特异度、阳性预测值、阴性预测值分别为0.793、0.881、0.563、0.841、0.643.结论 MSCT能在术前较准确显示阑尾原发恶性肿瘤的特征并评价其侵袭性,指导临床制定更精准的治疗方案.

Purpose: Surgical margin status in radical prostatectomy (RP) specimens is an established predictive indicator for determining biochemical prostate cancer recurrence and disease progression. Predicting positive surgical margins (PSMs) is of utmost importance. We sought to perform a meta-analysis evaluating the diagnostic utility of a high clinical tumor stage (≥3) on magnetic resonance imaging (MRI) for predicting PSMs. Method: A systematic search of the PubMed, Embase databases, and Cochrane Library was performed, covering the interval from 1 January 2000 to 31 December 2022, to identify relevant studies. The Quality Assessment of Diagnostic Accuracy Studies 2 method was used to evaluate the studies’ quality. A hierarchical summary receiver operating characteristic plot was created depicting sensitivity and specificity data. Analyses of subgroups and meta-regression were used to investigate heterogeneity. Results: This meta-analysis comprised 13 studies with 3924 individuals in total. The pooled sensitivity and specificity values were 0.40 (95% CI, 0.32–0.49) and 0.75 (95% CI, 0.69–0.80), respectively, with an area under the receiver operating characteristic curve of 0.63 (95% CI, 0.59–0.67). The Higgins I2 statistics indicated moderate heterogeneity in sensitivity (I2 = 75.59%) and substantial heterogeneity in specificity (I2 = 86.77%). Area, prevalence of high Gleason scores (≥7), laparoscopic or robot-assisted techniques, field strength, functional technology, endorectal coil usage, and number of radiologists were significant factors responsible for heterogeneity (p ≤ 0.01). Conclusions: T stage on MRI has moderate diagnostic accuracy for predicting PSMs. When determining the treatment modality, clinicians should consider the factors contributing to heterogeneity for this purpose.

磁共振成像是当前前列腺癌诊断与评价最主要的医学影像技术,其不仅派生了多参数、双参数、全身磁共振成像等常规成像技术,也衍生了扩散相关技术、磁共振弹性成像、磁共振指纹成像等新技术,这些技术在前列腺癌诊疗与管理过程中发挥着重要作用.同时,人工智能与医学影像结合又进一步推动了前列腺癌磁共振诊断水平的发展.本文通过复习文献,总结了前列腺癌磁共振成像诊断与相关技术的研究进展,并对其应用前景作一综述.

To build computed tomography enterography (CTE)-based multiregional radiomics model for distinguishing Crohn's disease (CD) from intestinal tuberculosis (ITB). A total of 105 patients with CD and ITB who underwent CTE were retrospectively enrolled. Volume of interest segmentation were performed on CTE and radiomic features were obtained separately from the intestinal wall of lesion, the largest lymph node (LN), and region surrounding the lesion in the ileocecal region. The most valuable radiomic features was selected by the selection operator and least absolute shrinkage. We established nomogram combining clinical factors, endoscopy results, CTE features, and radiomic score through multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to compare the performance of the models. The clinical–radiomic combined model comprised of four variables including one radiomic signature from intestinal wall, one radiomic signature from LN, involved bowel segments on CTE, and longitudinal ulcer on endoscopy. The combined model showed good diagnostic performance with an area under the ROC curve (AUC) of 0.975 (95

尽管国内肝细胞癌的发病率近年来有下降趋势,肝细胞癌仍然是国内疾病预防和控制的主要任务.微血管浸润与肝细胞癌的预后、疗效评估密切相关.随着精准医疗概念的提出,个体化治疗成为临床的追求.随着影像学技术的不断发展,利用影像学术前评估微血管浸润成为一大热点话题.本文就影像学在肝细胞癌微血管浸润评估中的研究现状及进展分析概述,以期提高肝细胞癌患者精准医疗水平.