Diagnostic cerebral angiography (DCA) remains the gold standard for evaluating cerebrovascular pathology despite advances in non-invasive imaging. This guideline provides evidence-based recommendations for the contemporary practice of DCA from a patient-centered perspective. A structured literature review was performed searching MEDLINE from January 2019 to July 2024 with regard to the concept of DCA. The strength and quality of evidence were graded according to established criteria. Recommendations were developed by consensus of the writing committee with input from the SNIS Standards and Guidelines Committee and Board of Directors. The management of DCA continues to evolve with advances in technology and technique. The expert panel agreed on the following recommendations:Recommendation 1: DCA should be employed as the reference standard imaging modality for problem-solving ambiguous findings from non-invasive imaging and for guiding endovascular interventions (Class 1, Level B-NR).Recommendation 2: We recommend consultation of the American College of Radiology Manual on Contrast Media for guidelines on the management of contrast reactions.Recommendation 3: A biplane angiographic system should be used for the acquisition of diagnostic cerebral angiograms in order to minimize patient contrast dose (Class 1, Level C-LD).Recommendation 4: Physicians trained and credentialed in performing and interpreting cerebral angiography, including complication avoidance and management, should perform DCA following established safety protocols (Class 1, Level C-EO).Recommendation 5: For conscious sedation during DCA we support the Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists developed by the American Society of Anesthesiologists Task Force.Recommendation 6: The choice of access site should consider anatomic factors, comorbidities, propensity for access site bleeding, patient preference, and individual operator experience (Class 1, Level C-EO).Recommendation 7: Continuous catheter flushing or double flushing and meticulous injection techniques should be employed to minimize the risk of embolic complications during DCA (Class 2a, Level B-NR).Recommendation 8: Normative exposure data should be collected by practitioners using x-ray fluoroscopy in order to adhere to neuroangiography practice guidelines and minimize potential harm to patients (Class 1, Level C-LD).Recommendation 9: It is incumbent on the practitioner to tailor the examination to the clinical question being answered (Class 1, Level C-EO).Recommendation 10: Prompt identification and management of intraoperative complications, including but not limited to the use of emergent thrombectomy for large emboli and treatment for flow-limiting dissections, are crucial to patient safety (Class 1, Level B-NR).Recommendation 11: It is reasonable to use a standardized reporting framework to ensure completeness of reporting with common elements of history, indication, technique, findings, and impression (Class 1, Level C-EO).Recommendation 12: It is reasonable to use arterial closure devices in patients who are at high risk for access site bleeding or who would benefit from a shorter recumbency duration (Class 2a, Level B-NR).Recommendation 13: It is recommended to discuss the study findings with the patient and/or family in a setting that enables processing and retention of the information presented (Class 1, Level C-EO).Recommendation 14: We affirm the practice recommendations of the SNIS Pediatric Committee pertinent to pediatric DCA. DCA continues to evolve as both a diagnostic and therapeutic guidance tool. These guidelines provide evidence-based recommendations for the safe and effective performance of DCA in the contemporary era.
Quantitative assessment of myocardial perfusion using 13N-ammonia PET with compartmental modeling enables evaluation of myocardial flow reserve (MFR) and prediction of patient prognosis. However, the reliability of these assessments can depend on the analytical methods used for quantitation. The present study aimed to evaluate the variability and agreement of values obtained using three quantitative software tools and to assess the impact of kinetic model selection on myocardial blood flow (MBF) and MFR estimates in a clinical setting. We analyzed 100 patients who underwent 13N-ammonia PET/CT, including 60 with normal perfusion and 20, 10, and 10 with single-, two-, and three-vessel disease, respectively. We derived MBF and MFR at global (entire left ventricle) and regional (coronary territories) levels and evaluated five analytical pipelines: SyngoMBF, QPET, and three implementations of PMOD tools (1-tissue compartment, Hutchins, and UCLA models). MBF and MFR showed high correlations among the software tools, although stress MBF statistically differed between PMOD and QPET. Correlation coefficients between software tools ranged from 0.81 to 0.91 at the global level, and Bland-Altman analysis demonstrated overall agreement with residual variability. In contrast, MBF and MFR values varied depending on the compartment model. The UCLA model yielded the highest stress MBF and MFR, and correlation coefficients between models ranged from 0.43 to 0.99 at the global level. Although Bland-Altman analysis showed overall agreement, noticeable scatter persisted and the UCLA model exhibited a positive bias. Quantitative MBF and MFR estimates from 13N-ammonia PET show good overall agreement across commonly used software tools but remain strongly dependent on kinetic model selection. These findings indicate that quantitative results are not directly interchangeable across different software and modeling approaches, underscoring the importance of methodological consistency when interpreting myocardial perfusion PET in clinical practice.
Ovarian ectopic pregnancy (OEP) is a rare and life-threatening condition that is typically diagnosed post-rupture. Its diagnosis and management become more complex when it is concurrent with ovarian endometrioma, as the latter may mask the clinical and radiological features of OEP. We report the case of a 28-year-old woman (gravida 2, para 1) who presented with 44 days of amenorrhoea and lower abdominal pain. Transvaginal ultrasound (TVS) revealed an empty uterus, a complex right adnexal mass containing a yolk sac (with synchronous movement with the ovary and a negative "sliding organ sign", raising a strong suspicion of ovarian ectopic pregnancy), and a separate "ground-glass" cystic lesion (consistent with an endometrioma). Corpus luteum blood flow signals were detected in the left ovary. The patient's preoperative haemoglobin concentration was 128 g/L. Diagnostic laparoscopy confirmed a right ovarian pregnancy co-existing with an ipsilateral endometrioma. Both lesions were excised laparoscopically while preserving the ovary. Haemostasis was achieved by primary suturing supplemented with minimal bipolar coagulation to preserve ovarian function. The patient recovered well; her postoperative haemoglobin concentration was 122 g/L, and her menses resumed at 6 weeks post-operatively, which confirmed preserved ovarian function. This case reaffirms a fundamental clinical principle: any reproductive-age woman with a positive pregnancy test, an empty uterus, and an adnexal mass should be presumed to have an ectopic pregnancy, prompting immediate surgical evaluation. In our patient, this principle alone mandated surgery. The transvaginal ultrasound findings (a yolk sac and a negative "sliding organ sign") did not change the need for surgery, but they provided critical preoperative localization of the gestational sac to the ovary. This allowed us to anticipate an ovarian pregnancy, obtain specific consent for ovary-conserving surgery, and plan a suture-dominant haemostatic strategy. To our knowledge, this is the first reported case of pre-rupture diagnosis of an ovarian ectopic pregnancy masked by an endometrioma using these sonographic signs. Clinicians must prioritize the clinical triad; when available, meticulous ultrasound adds precision for fertility preservation.
Cardiac magnetic resonance imaging (MRI) is a gold standard to assess functional and anatomical properties of the living heart. Inflammation changes the myocardial tissue and, furthermore, MR relaxation properties. Continuous-wave (CW) longitudinal rotating frame relaxation time (T1ρ) mapping has been used to assess myocardial fibrosis and inflammation. Conventional T2 relaxation time is a known marker of edema in the myocardium. In this study, we assessed myocardial inflammation after viral infection in a mouse heart using CW-T1ρ and T2 relaxation times. Adenoviral human vascular endothelial growth factor-A165 (AdVEGF-A165) and empty control adenoviral vector with cytomegalovirus promoter (AdCMV) gene transfers were used to induce inflammation in the mouse myocardium. In vivo CW-T1ρ and T2 relaxation time measurements were performed in both groups (AdVEGF-A165 and AdCMV) after -1-, 1-, 3-, 7-, 14-, 21-, and 28-day post-injection. The inflammation associated with gene transfer was verified by hematoxylin and eosin staining after 14-day post-injection. One day after AdVEGF-A165 and AdCMV injections and inflammatory reactions, CW-T1ρ showed a significant increase, which stayed increased as a function of time. T2 also increased significantly after both injections and inflammatory reactions as compared to before injections. Contrast difference between inflammation and remote areas was visually observed in both groups in CW-T1ρ and T2 maps. Hematoxylin and eosin staining revealed the area of inflammation after Ad injection in both groups after 14-day post-injection. This study showed that both acute and chronic phases of the inflammatory reaction in mouse myocardium caused by myocardial adenoviral injections were associated with increased CW-T1ρ and T2 relaxation time constants. Furthermore, the inflammatory reaction can be followed up with rotating frame and conventional relaxation time mappings.
Intestinal-type and pancreatobiliary-type periampullary carcinomas exhibit distinct biological behaviours and prognostic outcomes, yet accurate preoperative subtyping remains a major clinical challenge. This study aimed to evaluate the value of clinical variables and computed tomography (CT) imaging features in the differential diagnosis of intestinal-type and pancreatobiliary-type periampullary carcinoma, and to develop a subtype prediction model. This retrospective study included 83 patients with pathologically confirmed periampullary carcinoma, comprising 21 intestinal-type and 62 pancreatobiliary-type periampullary carcinomas. Clinical variables and conventional CT imaging features were evaluated using univariable and multivariable logistic regression analyses to identify predictors associated with PAC subtype. Based on these predictors, clinical, radiologic, and combined prediction models were developed, and a nomogram was constructed from the combined model. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). The DeLong test was employed to compare the diagnostic performance among different models. The AUCs of the clinical model and radiologic model were 0.82 (95%CI, 0.70-0.92) and 0.85 (95%CI, 0.79-0.93), respectively. The combined model showed significantly better diagnostic performance than either model alone, with an AUC of 0.92 (95%CI, 0.84-0.97), a sensitivity of 87.1%, and a specificity of 90.5%. Serum carbohydrate antigen 19 - 9, total bilirubin, tumor location, and enhancement degree of lesion were identified as the most important predictors in the combined model. In addition, the nomogram derived from the combined model demonstrated good discriminative ability for predicting histologic subtype. The combined model integrating clinical and CT imaging features enables more accurate preoperative differentiation between intestinal-type and pancreatobiliary-type periampullary carcinoma and yields higher sensitivity and specificity than models based on clinical or imaging features alone.
Early diagnosis and accurate staging of endometrial cancer (EC) are crucial for effective treatment planning. Distinguishing stage IA from stage IB EC is challenging due to large variations in tumor and uterine morphology, as well as the limited availability of annotated magnetic resonance imaging (MRI) data for training robust models. A genetic programming (GP)-based framework was developed for the classification of FIGO stage IA and IB EC using a small number of MRI images. The framework consists of three main components: (1) automatic detection of regions of interest (ROI) on MRI images, (2) GP-based feature extraction and construction, and (3) EC stage classification. A fast single-shot detector (SSD) was employed to automatically localize the uterus as the ROI. The detected ROI images were cropped and resized to construct training and test datasets. Four GP-based methods with different structures and primitives were employed for feature extraction and construction: GP with convolutional operators (COGP), GP with image descriptors (IDGP), GP with flexible program structures and image-related operators (FlexGP), and GP with automatic simultaneous learning of features and evolutionary ensembles (FELGP). The best-performing GP individuals were used to generate discriminative features, which were subsequently used to train classifiers for stage IA and IB EC. Experimental results from three MRI datasets revealed that GP-based methods achieved competitive performance relative to both neural and traditional non-neural machine learning approaches. The proposed methods achieved classification accuracies of up to 0.92 on cropped axial diffusion-weighted imaging (DWI), 0.87 on cropped axial T2-weighted imaging (T2WI), and 0.83 on cropped sagittal T2WI images of EC patients. GP-based methods effectively classify FIGO stage IA and IB endometrial cancer using limited MRI data. By automatically extracting discriminative and interpretable features from ROI within lesions, the proposed framework provides a reliable and transparent solution for EC staging, highlighting the potential of GP in medical image analysis and clinical decision support.
Osteoporosis is a chronic skeletal disorder characterized by reduced bone mineral density and disrupted bone microarchitecture, affecting over 200 million individuals worldwide. The lumbar spine, containing the largest volume of metabolically active trabecular bone, is particularly vulnerable to osteoporotic degeneration and compression fractures. This narrative review examines recent advances in imaging modalities for lumbar spine osteoporosis assessment, emphasizing the diagnostic utility and emerging clinical applications of ¹⁸F-sodium fluoride (NaF) positron emission tomography/computed tomography (PET/CT). A comprehensive narrative review was conducted, synthesizing findings from pivotal studies investigating conventional imaging methods and newer PET-based technologies for osteoporosis evaluation. Particular focus was given to studies utilizing quantitative and kinetic PET biomarkers for assessing bone metabolic activity with ¹⁸F-NaF. While dual-energy X-ray absorptiometry (DXA) remains the clinical standard for bone mineral density assessment, it has significant limitations including poor spatial resolution, lack of three-dimensional capability, and inability to differentiate cortical from trabecular bone. In contrast, ¹⁸F-NaF PET/CT demonstrates superior image quality, rapid tracer kinetics, and quantitative assessment of regional osteoblastic activity. Studies show strong correlations between ¹⁸F-NaF uptake and bone turnover markers, mineral density measurements, and therapeutic response. Kinetic modeling approaches provide detailed insights into bone remodeling dynamics, supporting personalized treatment planning and prognostic assessment. Diagnostic performance studies report area under the receiver operating characteristic curves as high as 0.96 for osteoporosis detection when evaluated against DXA-derived BMD, though no study has yet compared both modalities against an independent gold standard such as fracture outcomes. ¹⁸F-NaF PET/CT offers optimal clinical applications for early treatment response monitoring, evaluation of patients with discordant clinical risk and DXA findings, pre-surgical assessment in patients with borderline bone density, and investigation of complex metabolic bone disorders. Ideally, ¹⁸F-NaF PET/CT should be utilized in a complementary fashion to DXA. Primary barriers to clinical adoption include cost, limited accessibility, and absence of standardized kinetic modeling protocols. Future research should focus on establishing reference ranges across age and sex demographics, validating fracture prediction models, and determining cost-effectiveness thresholds for specific clinical scenarios such as high-risk patients with discordant DXA and fracture history.
Thermal ablation offers a safer, less invasive, and more cost-effective curative-intent treatment for selected patients with primary and metastatic liver tumours than surgery; when done with appropriate technique, ablation can deliver similar oncological outcomes. However, effectiveness in routine practice varies because structured training, planning, and procedural governance remain scarce. These international multidisciplinary, multi-society guidelines-formally endorsed by the European Society of Surgical Oncology, the Cardiovascular and Interventional Radiological Society of Europe, and the Society of Interventional Oncology-define key domains contributing to procedural difficulty and practice variation in liver tumour thermal ablation. A Delphi consensus initiative held in Innsbruck, Austria, engaged 72 experts across three iterative rounds of scoring across 135 statements grouped into five domains: credentialing, indications, approach, procedural factors, and safety measures. Consensus was achieved for 94 (70%) of 135 statements. The least invasive route-typically percutaneous-should be prioritised, and margin adequacy was reaffirmed as the principal technical goal. Procedural difficulty was considered context-dependent, shaped by tumour factors, institutional infrastructure, and operator experience. Organ displacement techniques were endorsed to maintain safety and expand treatable indications. Complex ablations should be done by experienced operators (more than 100 previous cases), with programmes underpinned by structured training, multidisciplinary team participation, and routine audit. Future efforts should develop and validate practical tools such as difficulty scoring systems, standardised procedural reporting templates, and comprehensive training curricula to improve consistency, standardisation, and clinical outcomes globally.
Agenesis of the corpus callosum (ACC) presents with highly heterogeneous clinical features. Common methods rarely achieve accurate prenatal or early postnatal diagnosis and prognosis. We aimed to develop and test an interpretable deep neural network (DNN) that combines multimodal clinical data to improve diagnostic accuracy and neurodevelopmental outcome prediction. We collected data from 205 pediatric patients with ACC at Wuhan Children's Hospital between 2016 and 2024. A total of 27 clinical features were extracted, including neuroimaging findings, perinatal risk factors, and follow-up developmental quotients (Gesell Developmental Schedules and Gross Motor Function scores). Five-fold cross-validation was adopted. We built an eight-layer fully connected DNN with ReLU activation in the hidden layers. For categorical endpoints, a sigmoid output layer with binary cross-entropy loss was used. For continuous endpoints, a linear output layer with mean squared error loss was used. SHAP (Shapley Additive Explanations) values were used to quantify the contribution of individual features to model predictions. Performance was compared with a support vector machine (SVM) baseline and across hyperparameter settings. Area under the receiver-operating-characteristic curve (AUC), F1 score, precision, recall, mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) served as primary metrics. Across 12 neurodevelopmental disorders, the model reached an average AUC of 0.97. AUCs for intellectual disability, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), specific learning disorder and developmental coordination disorder ranged from 0.98 to 1.00. Prediction remained moderate for cerebral palsy (AUC = 0.74) and epilepsy (AUC = 0.67). MAE for both Gesell and Gross Motor Function scores was 0.10, with corresponding R2 values of 0.62 and 0.63. SHAP analysis identified extracranial malformation (clinical type III), facial dysmorphism and birth weight as the most influential features for developmental outcome. The DNN model outperformed the SVM baseline, with an AUC improvement of 0.16 for communication disorder and an R2 increase of 0.19 for Gesell score (p < 0.001). Ablation experiments confirmed eight layers, sixteen neurons per layer, a learning rate of 0.01 and ten training epochs as the optimal configuration. Additional layers or higher learning rates caused overfitting. The proposed interpretable DNN framework outperforms traditional classifiers in early ACC diagnosis and developmental outcome prediction. It provides a potential tool for clinical decision support. Larger samples and integration of raw imaging data are needed to enhance prediction of complex phenotypes such as cerebral palsy and epilepsy.
To further standardize the clinical use of integrated PET/MRI brain imaging for Alzheimer's disease (AD) in China, improve early identification and precision diagnosis and management of AD, promote standardized care, and provide evidence-based and consensus recommendations for clinicians and imaging professionals, experts in nuclear medicine, radiology, neurology, and related fields developed this guideline through a consensus process informed by the available research evidence and clinical practice. AD is one of the most common forms of dementia in older adults, with an insidious course; early diagnosis and timely intervention are crucial to improving patient outcomes. Integrated PET/MRI enables the simultaneous, one-stop acquisition of multidimensional information-including cerebral amyloid burden, brain metabolism, and high-resolution anatomical detail-and can be used for early diagnosis, clinical staging, assessment of disease progression, and clinical trial research, making it an important imaging tool for precision care in AD. As integrated PET/MRI systems become increasingly available in China, broader clinical implementation continues to face challenges, including appropriate patient selection and indications, standardization of scanning workflows, image quality control, consistency in image interpretation, and reporting standards. With a focus on clinical feasibility, this guideline provides recommendations across key domains-indications, examination and quality-control procedures, image interpretation, and standardized reporting-with the aim of promoting the standardized use of integrated PET/MRI in AD care and advancing research and translation of relevant imaging biomarkers. 为进一步规范我国阿尔茨海默病（AD）一体化PET/MRI脑成像的临床应用，提高AD的早期识别与精准诊疗水平、促进规范化管理并为影像相关专业人员与临床医师提供循证与共识建议，由中国医学影像技术研究会、中华医学会放射学分会、中华医学会核医学分会、北京医学会核医学分会、北京医学会放射学分会联合组织，集结核医学科、放射科、神经内科等多学科专家，经多轮共识会议制订了本指南。AD是最常见的老年痴呆类型之一，病程进展隐匿，早期诊断与干预对改善患者结局具有重要意义。一体化PET/MRI显像可在同一检查中同步获取病理蛋白、脑代谢以及精细解剖结构等多维信息，可用于AD的早期诊断、临床分期、疾病进展评估与临床试验研究，是AD精准诊疗的重要影像手段。随着一体化PET/MRI设备在我国逐步普及，其临床推广仍面临适应证选择、检查流程统一、图像质量控制、影像判读一致性及报告规范等问题。本指南以我国现有一体化PET/MRI设备与显像剂的可及性为前提，以国内外最新循证证据为依据，紧密结合中国临床实践现状，围绕适应证建议、检查与质控流程、图像判读要点及报告规范等关键环节提出推荐意见，旨在为AD相关影像与临床专业人员提供系统、实用的循证指导。.
Since its initial introduction to interventional cardiology over two decades ago, optical coherence tomography (OCT) has emerged as a powerful tool in neurovascular intervention. This intravascular imaging modality uses near-infrared light to provide cross-sectional visualization of the vessel wall with a resolution approaching 10 μm. The resolution of OCT far surpasses that of other imaging techniques. This higher resolution enables radiologists to directly assess arterial wall disease, including atherosclerotic plaque, aneurysm, and thrombus, as well as the interaction between therapeutic devices and the arterial wall in real time, providing actionable information during neurovascular interventions. The growing reliance on endovascular approaches to treat intracranial aneurysms and ischemia underscores the importance of precise vessel evaluation during treatment to provide accurate imaging guidance. However, digital subtraction angiography and cone beam computed tomography angiography often fail to reveal underlying arterial disease and other key features, such as the presence of thrombi, dissections, and malapposed stents, that could lead to incomplete treatment and acute and chronic complications. By enabling direct visualization of these microstructural details, OCT may overcome some of the most persistent challenges in neurovascular practice, ultimately improving diagnostic accuracy, procedural safety, and long-term patient outcomes. Nevertheless, integrating OCT into neurovascular settings remains challenging. There is still a lack of large-scale clinical validation, and existing coronary devices are not suitable for reliable use in tortuous intracranial vascular circulations. To overcome the technical limitations of current technologies, neuroOCT technology was designed specifically for neurovascular use and was evaluated in a first-in-human study. This technology will enable future clinical studies to investigate using neuroOCT to guide and optimize neurovascular treatments. This review article aims to provide a comprehensive perspective on the potential of neuroOCT in neurovascular practice. It highlights the technology's technical principles, current applications, limitations, and prospects for reshaping vascular imaging and therapy in the brain.
Functional motor disorders (FMDs) represent a frequent and disabling neurological condition. The lack of reliable diagnostic biomarkers and their heterogeneity might affect diagnosis. We identified multimodal biomarkers distinguishing FMDs from healthy controls (HCs) using machine-learning approaches. In this multicenter cross-sectional study, consecutive adults with a clinically established FMDs diagnosis (n = 75, 74.7% female; mean age 44.20 ± 12.92) and age- and sex-matched HCs (n = 75; 58.6% female; 48.42 ± 11.67) were recruited. All participants underwent standardized behavioral, neurophysiological, and brain MRI assessment exploring motor, exteroceptive, and interoceptive domains. A Random Forest (RF) classifier combined with repeated stratified k-fold cross-validation was trained on the collected features. Predictive performance was evaluated using accuracy, sensitivity, specificity, precision, F1-score, and AUC-ROC. SHapley Additive exPlanations interpreted feature importance. The strongest diagnostic biomarkers were lower dual-task effect scores for postural sway area under eyes-closed motor and cognitive conditions, and gait speed during the motor dual-task, followed by increased vDMN and basal ganglia networks functional connectivity, reduced baseline ipsilateral-contralateral R2 blink reflex area, and higher DNIC-to-baseline N2P2 amplitude ratios for the lower limb. The RF classifier achieved robust performance (accuracy 85.0%, sensitivity 83.9%, specificity 86.1%, F1-score 85.7%, AUC-ROC 0.921). Motor, functional neuroimaging, and neurophysiological markers demonstrated diagnostic value in distinguishing FMDs from healthy controls, addressing the current lack of objective tools and supporting more confident and accurate diagnosis of these heterogeneous conditions. Trial registration number NCT06328790. Registered on 26 March 2024.
Melanoma recurrence risk is highest within the first 2 years after diagnosis and progressively declines thereafter. Current surveillance strategies remain largely guided by clinicopathologic risk stratification, with the comprehensive medical history, physical examination, and complete skin assessment forming the cornerstone of follow-up. Although cross-sectional imaging and lymph node ultrasound are used in selected higher-risk patients, routine intensive imaging has not consistently demonstrated survival benefit and may increase costs and false-positive findings. Emerging technologies are reshaping melanoma surveillance and clinical management. Circulating tumor DNA (ctDNA) has shown promise as a minimally invasive biomarker capable of detecting molecular residual disease and anticipating clinical recurrence. Persistent or newly positive ctDNA after surgery is consistently associated with inferior recurrence-free survival. However, ctDNA does not reliably detect all recurrence patterns and its sensitivity varies according to disease burden and metastatic site. Prospective validation and clarification of how ctDNA should guide adjuvant therapy or imaging strategies remain necessary. In parallel, CD8-targeted positron emission tomography (CD8 PET) has emerged as a novel functional imaging modality capable of noninvasively visualizing whole-body T-cell dynamics. By differentiating tumor burden from immune infiltration and capturing early T-cell recruitment, CD8 PET offers predictive insights into immunotherapy response. Nevertheless, limitations of this technique include dependence on optimal imaging timing, limited tracer availability, cost, and an inability to directly assess T-cell functionality. Together, ctDNA and immune-focused imaging approaches represent promising steps toward precision surveillance and management of melanoma. Further robust prospective studies are required to define their integration into clinical decision making and optimize patient outcomes.
To advance the precise diagnosis and treatment of Parkinson's disease (PD) and related movement disorders, standardize the clinical application of integrated PET/MRI, enhance diagnostic efficacy, and optimize patient management, the Clinical Practice Guideline for Integrated PET/MRI in Parkinson's Disease (2026 Edition) was developed jointly by multiple expert groups. This initiative was led by the Chinese Society of Medical Imaging Technology, the Radiological Society of Chinese Medical Association, the Chinese Society of Nuclear Medicine, the Beijing Society of Nuclear Medicine, and the Beijing Society of Radiology, in collaboration with a multidisciplinary panel of experts from Nuclear Medicine, Radiology, Neurology, and Functional Neurosurgery, following several rounds of consensus meetings. Compared to previous consensus statements or technical specifications, the key updates of this guideline include: in the clinical application pathway, it clarifies the advantages and appropriate scenarios for utilizing integrated PET/MRI over single-modality imaging when the clinical diagnosis of PD is uncertain; it emphasizes the value of simultaneous acquisition and fusion analysis in assessing both the function of the nigrostriatal dopaminergic pathway (e.g., via PET tracers) and structural alterations (e.g., via MRI neuromelanin imaging, diffusion tensor imaging, etc.). With the development and validation of novel tracers, the guideline provides a hierarchical recommendation for the differential diagnosis between PD and atypical parkinsonian syndromes (such as multiple system atrophy, progressive supranuclear palsy, and corticobasal degeneration), based on multimodal imaging biomarkers. Based on the premise of the accessibility of current integrated PET/MRI equipment and tracers in China, and grounded in the latest international and domestic evidence-based research, this guideline is meticulously aligned with the realities of Chinese clinical practice. It systematically delineates the technical specifications, clinical indications, and image interpretation criteria for the use of integrated PET/MRI in the diagnosis, differential diagnosis, disease severity assessment, and therapeutic decision support for PD. The aim is to provide systematic and practical evidence-based guidance for clinicians and imaging specialists involved in the care of patients with Parkinson's disease. 为推进帕金森病及相关运动障碍疾病的精准诊疗，规范一体化PET/MRI在临床中的应用，提升诊断效能并优化患者管理，由中国医学影像技术研究会、中华医学会放射学分会、中华医学会核医学分会、北京医学会核医学分会、北京医学会放射学分会联合组织，集结核医学科、放射科、神经内科、功能神经外科等多学科专家，经多轮共识会议制订了《帕金森病一体化PET/MRI临床应用指南（2026版）》。相较于既往相关共识或技术规范，本指南的主要更新包括：在临床应用路径部分，明确了帕金森病临床诊断不确定时，一体化PET/MRI相较于单一模态影像的优势与适用场景；强调了在评估黑质-纹状体多巴胺能通路功能（如通过PET示踪剂）与结构性改变（如MRI神经黑色素成像、弥散张量成像等）时，同步采集与融合分析的价值。随着新型示踪剂的研发与验证，指南对帕金森病与非典型帕金森综合征（如多系统萎缩、进行性核上性麻痹、皮质基底节变性）的鉴别诊断流程给出了基于多模态影像标志物的分层推荐。本指南以我国现有一体化PET/MRI设备与示踪剂的可及性为前提，以国内外最新循证证据为依据，紧密结合中国临床实践现状，系统阐述了一体化PET/MRI在帕金森病诊断、鉴别诊断、病情评估及治疗决策支持中的技术规范、临床应用指征与影像解读标准，旨在为帕金森病相关临床与影像专业人员提供系统、实用的循证指导。.
The prediction of Epidermal Growth Factor Receptor (EGFR) mutation status in advanced lung adenocarcinoma is crucial for targeted therapy. Since EGFR mutations manifest as both macroscopic imaging features on CT and microscopic morphological changes in tissue, integrating these multiscale signals is essential for a comprehensive diagnostic assessment. However, current related research faces two key limitations: on one hand, unimodal deep learning models suffer from limited representational power; on the other hand, existing multimodal methods fail to address the inherent data structural discrepancies between continuous CT and discrete WSI, often losing critical fine-grained details due to forced data compression or shared semantic bottlenecks. To address the above limitations and improve the reliability of EGFR mutation status prediction, this study aims to propose a novel multimodal fusion framework (MFCA) that can effectively capture cross-modal semantic interactions and align imaging features across different scales. A novel MFCA based on Cross-Attention (MFCA) is proposed, and its implementation steps are as follows: 1. First, a region-of-interest-guided approach is utilized to coarsely segment whole-slide histopathology images (WSI) into three constituent regions, namely cancerous, stromal, and other regions; 2. Then, a dual-branch encoder is employed to separately extract features from two types of imaging data-global features from Computed Tomography (CT) scans and region-specific features from the segmented WSI; 3. Critically, a bidirectional cross-attention module is introduced into the framework, which is designed to facilitate deep semantic interaction and alignment between the macroscopic context of CT imaging and the microscopic context of histopathology, thereby achieving highly efficient and discriminative feature fusion. On the external validation set, our MFCA framework achieved robust performance, with Area Under the Curve (AUC) values of 0.758(95% CI: 0.683-0.832) for cancerous regions, 0.805(95% CI: 0.716-0.900) for stromal regions, and 0.760(95% CI: 0.686-0.833) for other regions. The model's performance, particularly in the stromal component, was statistically superior to all baseline and competing models. The proposed MFCA framework predicts EGFR mutation status by innovatively integrating macroscopic CT imaging with region-specific microscopic WSI features. It serves as a valuable computational tool to support precision oncology for patients with advanced lung adenocarcinoma.
Early detection of pancreatic cancer is crucial for survival, but detecting smalllesions remains challenging. Intraductal Ultrasound (IDUS) using intracardiac echocardiography (ICE) catheters for B-mode and Shear-Wave Elastography (SWE) potentially offers improved visualization and characterization of small tumors. This study assesses the feasibility of IDUS using ICE catheters to detect and visualize periampullary tumors in surgically resected specimens. In this two-phase ex-vivo feasibility study, 25 pancreatic specimens were included, of which the first 10 were used to establish and standardize the imaging protocol, followed by technical feasibility evaluation in the remaining 15 specimens. Catheters were introduced into the pancreatic duct, common bile duct, or positioned extraductally to enable tumor visualization with B-mode imaging and shear-wave elastography (SWE). Tumor visualization rates, catheter insertion success, SWE measurements in normal and tumor tissue, and image quality were assessed. ICE catheter insertion was successful in 12 of 15 specimens; unsuccessful access was primarily related to large tumor size (>4 cm) or unidentifiable ductal anatomy following surgical resection. However, extraluminal imaging successfully visualized tumors in one of these cases. Median shear-wave speed and elastic modulus for normal pancreatic parenchyma were 1.58 m/s and 7.6 kPa, respectively. SWE measurements in tumor tissue were suboptimal, likely due to ex-vivo tissue variability and catheter strain during repeated use. IDUS with ICE is feasible for qualitative B-mode visualization of periampullary tumors and enables SWE assessment of pancreatic parenchyma in an ex-vivo setting. Reliable elastography of tumor tissue remained challenging, indicating the need for further technical refinement and in-vivo validation.
To develop and validate a CT-based radiomics model for differentiating primary gastric lymphoma (PGL) from Borrmann type IV gastric cancer (GC). A total of 136 patients with pathologically confirmed PGL (n = 56) and Borrmann type IV GC (n = 80) were retrospectively enrolled between January 2016 and May 2022. The cohort was randomly partitioned into a training set (n = 95) and a testing set (n = 41) at a 7:3 ratio. Radiomics features were extracted from unenhanced, arterial, venous, double-phase (arterial + venous), and three-phase (unenhanced + arterial + venous) CT images. After feature selection using the Least Absolute Shrinkage and Selection Operator, radiomics models were constructed via logistic regression. A clinical-radiomics model was developed through multivariate analysis. The models were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves with the Hosmer-Lemeshow test, and decision Curve Analysis (DCA) for clinical net benefit. Clinical model comprised of high-enhanced serosa sign, normalized CT value on venous phase, and perigastric fat infiltration showed good performance with AUCs of 0.902 (training set) and 0.878 (testing set). Among the radiomics models, the three-phase model outperformed others (AUC: 0.871 training, 0.865 testing). The clinical-radiomics combined model further improved discriminatory performance, achieving AUCs of 0.960 and 0.932 in the training and testing sets, respectively. DCA confirmed that the combined model provided the highest clinical net benefit. Clinical-radiomics model incorporating three-phase radiomics signatures and CT findings achieved satisfactory performance for differentiating PGL from Borrmann type IV GC, serving as a reliable non-invasive tool for clinical decision-making.
India accounts for one-third of the global incidence and mortality of oral cancer. The national oral cancer screening program uses Conventional Oral Examination by Primary Healthcare (PHC) workers. This subjective assessment leads to unnecessary referrals to higher centers for biopsy and false negatives due to inappropriate biopsy site selection. Angiogenesis is one of the steps in early carcinogenesis, as solid tumors, such as oral cancers, cannot grow beyond 2-3 mm in diameter without inducing their own blood supply. The increased vascular supply and metabolic rate in malignant cells lead to a rise in temperature, which can be detected by sensitive digital infrared (IR) cameras. An Artificial Intelligence-enabled automatic analysis of intraoral IR images of oral lesions can be used for screening, early detection of Stage I and II oral cancers, and biopsy site selection as an objective Point-of-Care (POC) adjunct. A standardized protocol for passive and active IR imaging of intraoral lesions (index test) with a smartphone-based IR camera will be prepared to train a Medical Scientist and Technician. IR images of already diagnosed normal, Inflammatory, potentially malignant disorders, and malignant oral lesions (N = 100 each) will be used in Phase I to train an AI model to classify images as malignant or non-malignant. A second set of IR images of these oral lesions will be used in Phase II (N = 100 each) for evaluating the performance of the AI model. The reference test for malignancy will be histopathology (Gold standard). The intra/inter observer reliability will be assessed using the kappa statistics A clinical trial and validation of the proof of concept are proposed for IR imaging of intraoral lesions as a noninvasive POC adjunct for early detection and screening of oral cancer by PHC workers, and for the selection of biopsy sites by surgeons. © 2026 Wiley Periodicals LLC.
X-ray examinations are widely used in oral and maxillofacial practice, but the biological effects of diagnostic low-dose ionizing radiation (LDIR) remain incompletely understood. Growing evidence indicates that even low doses can induce molecular and cellular alterations, highlighting the need to clarify LDIR-induced biological responses in dental imaging. To investigate the effects of LDIR generated by oral and maxillofacial spiral CT on cell viability, cell cycle distribution, and apoptosis in human peripheral blood B lymphocytes. Human peripheral blood B lymphocyte (IM-9) cells were exposed to low-dose ionizing radiation using a spiral CT scanner commonly employed in oral imaging. Cells were collected at 0.25 h, 1 h, 24 h and 48 h after radiation for the assessment of cell viability, cell cycle distribution and apoptosis. Statistical analyses were performed using GraphPad Prism 9.0, with data presented as mean ± SD; group comparisons were conducted using an unpaired Student's t-test, with P < 0.05 considered statistically significant. Compared with the control group, LDIR exposure led to an increased proportion of cells in the G2/M phase at 1 h, a slight increase in early apoptotic cells at 0.25 h and 1 h, and enhanced cell viability at 48 h. These results indicate that LDIR induces transient cell cycle arrest and mild early apoptotic responses, followed by a promotion of cellular survival. LDIR from oral and maxillofacial spiral CT induces a transient and adaptive biological response in human peripheral blood B lymphocytes, characterized by G2/M phase arrest and enhanced cell viability. This response may represent an adaptive defense mechanism adopted by cells to maintain viability and homeostasis.
Chronic liver disease (CLD) and subsequent liver cirrhosis (LC) are common causes of death and healthcare-related socio-economical costs worldwide. Ultrasound (US) is the first-line imaging modality for assessing the liver and associated hepatocellular carcinomas. Poor quality liver US images caused by aging or inadequate management of US equipment, can pose significant challenges in both diagnosis and treatment. From this perspective, the aim of this study was to enhance and assess the image quality of liver US obtained from an older, lower-performing device using a deep learning approach. A neural network based on a switchable cycle generative adversarial network (CycleGAN) was trained in an unsupervised learning setting, with low-quality images as inputs and high-quality images as targets. The study included consecutively acquired grey-scale liver US examinations from both a 12-year-old and a 4-year-old US device. Images from the older device served as inputs, while images from the newer device were used as targets for the deep learning-based algorithm. Image quality was evaluated by two experienced reviewers. The algorithm significantly improved the brightness, contrast, and overall quality of the reconstructed liver US images (p < 0.001), as assessed by both reviewers. However, no significant differences in image resolution and reverberation artifacts were noted by one of the reviewers. The weighted kappa values for image quality and diagnostic performance ranged from 0.225 to 0.838, indicating fair to almost-perfect inter-reader agreement. The proposed algorithm effectively enhances low-quality liver US images to high diagnostic quality, thereby potentially supporting clinical assessment and intervention in patients with LC.