World journal of radiology最新文献

The combination of radiotherapy with bevacizumab represents a promising therapeutic strategy for advanced gastrointestinal cancers. While this combination leverages synergistic mechanisms to enhance antitumor efficacy, it also poses significant safety concerns, particularly regarding the risk of intestinal perforation. This letter discusses the current understanding of this dual effect and underscores the importance of careful patient selection, advanced radiotherapy techniques, and vigilant toxicity monitoring to optimize clinical outcomes.

Background: Spontaneous intracerebral hemorrhage (ICH) is a severe form of stroke with high early mortality, and hematoma enlargement (HE) occurs in roughly one-third of patients and strongly predicts poor outcomes. Quantitative image analysis using handcrafted radiomics and deep learning-derived features can capture hematoma and perihematomal edema (PHE) heterogeneity objectively that the combination of these approaches with clinical data may improve early prediction of HE and in-hospital mortality.

Aim: To evaluate and validate the predictive performance of hematoma- and PHE-derived features on non-contrast computed tomography via handcrafted radiomics and automatic deep learning analysis for prediction of early HE and hospital mortality in spontaneous ICH.

Methods: Of 322 patients with basal ganglia ICHs were included retrospectively between June 2018 and June 2020, and assigned into the training cohort (n = 225) and the testing cohort (n = 97). We extracted features on hematoma and PHE subregions via handcrafted radiomics analysis manually and deep learning analysis of pretrained convolutional neural networks via transfer learning automatically. Support vector machine was adopted as the classifier for prediction of HE and hospital mortality. The clinical-radiological integrated models for HE and hospital mortality were constructed on clinical data and radiological signatures generated from the radiological models with the optimal area under the receiver operating characteristics curve in the testing cohort.

Results: The clinical-radiological model combining clinical information and hematoma- and PHE-derived computed tomography features for prediction of HE implied an area under the receiver operating characteristics curve of 0.828 with 95% confidence interval of 0.714 to 0.942 with accuracy of 72.89%, sensitivity of 70.00%, and specificity of 74.52% in the testing cohort. The model integrating clinical and radiological features showed great identification performance for predicting hospital mortality, demonstrating significant classification and discrimination abilities after validation.

Conclusion: Quantitative radiomics features from hematoma and PHE regions on non-contrast computed tomography images showed good performance for predicting HE and hospital mortality in patients with ICH.

Background: Major trauma is the sixth leading cause of death worldwide and the leading cause of death and disability in the population aged 5 years to 45 years. The assessment is typically guided by strict protocols to quickly identify life-threatening conditions before conducting a comprehensive evaluation of other injuries. Whole-body computed tomography (WBCT) is often indiscriminately used in trauma cases.

Aim: To assess the effective use of WBCT in patients with trauma across radiology departments in State of Kuwait, evaluate the true incidence of critical injuries, and minimize unnecessary radiation exposure.

Methods: This multicenter, retrospective study across seven radiology departments included 1367 patients with trauma who underwent WBCT between 2022 and 2023, according to the American College of Radiology guidelines. Data on age, sex, injury mechanism, clinical indications, dose-length product, and WBCT findings were collected and analyzed using IBM SPSS version 25.

Results: Of 1367 referrals, 578 (42.3%) had no significant findings, while 789 (57.7%) showed positive trauma-related results. Among the positive findings, 530 patients (38.8%) had major injuries, including solid organ and vertebral column injuries. The most common causes of WBCT referrals were road traffic accidents (911 patients, 66.6%), falls from height (182 patients, 13%), falls of heavy objects (112 patients, 8%), head trauma (82 patients, 6%), buggy accidents (28 patients, 2%) and others. Negative WBCT findings had a mean effective dose of 19.98 ± 10.26 mSv.

Conclusion: This national audit demonstrates that a substantial proportion of WBCT scans in patients with trauma are negative (42.3%), underscoring the need to rationalize imaging practices. The findings highlight the importance of evidence-based stewardship to enhance trauma care delivery in State of Kuwait.

Background: Children with hepatoblastoma (HB) remain high heterogeneity with distinct survival outcomes among individuals after surgical resection. Therefore, it's essential to identify high-risk patients with poor outcomes before surgery in order to add appropriate neoadjuvant chemotherapy for improving prognosis.

Aim: To evaluate the performance of a deep learning (DL)-based radiomics (DLBR) score at predicting event-free survival (EFS) in patients with HB at the early stage who underwent surgical resection.

Methods: A total of 106 patients were included retrospectively at two hospitals who underwent magnetic resonance imaging scanning and surgical excision, and were assigned into the training cohort (n = 74) from one institution and the testing cohort (n = 32) from the other institution. The widely adopted clinicopathologic variables were collected, and the magnetic resonance imaging-derived DL-based features were extracted through automatic segmentation. We developed a DLBR score based on DL-based features and an integrated clinical-DL nomogram model, and validated them externally.

Results: The DLBR score was generated incorporating four DL-based features, including three TI-derived features and one T2-derived feature. The integrated clinical-DL nomogram was constructed based on the Pretreatment Extension of Disease stage, alpha-fetoprotein concentration, and the DLBR score. The integrated nomogram had relatively better prognostic and calibration abilities and less opportunity for prediction error compared with the clinicopathologic predictors alone and the DLBR score alone in both training and external validation. Additionally, the DLBR score could stratify the HB patients into two EFS-related risk subgroups accurately, and showed fine distinction abilities to identify patients with different survival outcomes within identical subgroups of clinical predictors.

Conclusion: The DLBR score acted as a noninvasive and reliable tool for predicting EFS in early-stage HB patients receiving survival resection, and might instruct therapeutic plans for improving prognosis.

Background: Nocardia pneumonia is an infection that occurs in patients with underlying diseases. Previously, due to limited detection methods, its detection rate and typing posed significant challenges. However, with advancements in detection techniques, the detection rate has significantly increased, and different Nocardia species exhibit distinct imaging characteristics.

Aim: To retrospectively analyze the etiological and imaging features of pulmonary Nocardia pneumonia and to examine the differences in chest imaging manifestations among different Nocardia species.

Methods: The medical records of 102 patients with pulmonary nocardiosis who were admitted to Beijing Chaoyang Hospital from January 2017 to December 2024 were collected. Data including name, gender, underlying comorbidities, etiological characteristics, diagnostic methods, chest computed tomography features, and therapeutic agents were recorded.

Results: Among the 102 patients, 55 were male and 47 were female, with a median age of 61 years. Bronchiectasis was the most common comorbidity, observed in 54 patients (52.9%). Sixty percent were diagnosed using metagenomic next-generation sequencing. Nocardia gelsenkin was the most prevalent Nocardia specie, while Aspergillus and Pseudomonas aeruginosa were identified as the predominant co-pathogens in these pulmonary nocardiosis cases. Pneumonia caused by Nocardia wallacei primarily presented with bronchopneumonia as the main imaging feature, while other Nocardia species more commonly manifested as consolidation, often accompanied by nodules, cavities, and pleural effusion. The imaging features in immunosuppressed patients were more diverse, with frequent coexistence of multiple patterns.

Conclusion: Nocardia pneumonia commonly coexists with bronchiectasis. While metagenomic next-generation sequencing has greatly enhanced its detection rate, Nocardia wallacei pneumonia is distinguished on chest computed tomography by its primary presentation of bronchopneumonia, unlike other types.

Forensic dentistry is one of the important branches of forensic science and it is a branch which offers helpful information for the legal processes including identification of human remains along with age and sex determination. Dental forensic examination can involve the identification of a single individual or multiple individuals depending on the specific situation. Dentomaxillofacial radiology and radiological examination is a valuable tool for personal identification in forensic dentistry. Utilization of X-ray recordings are essential for many parameters related to identification since they shed light on many issues both in determining current conditions and in comparisons with the past. Comparing antemortem and postmortem radiographs taken from areas such as the skull or teeth is a reliable and objective method for identifying individuals. Radiographs and their proper preservation are crucial for present day assessments, historical comparisons and legal issues when necessary. While intraoral and extra oral radiographs were initially used in forensic dentistry, cone beam computed tomography application gained popularity in recent years. The use of radiology in forensic dentistry is not only necessary for identification, but also for age determination in mass casualties and disasters. The purpose of this mini-review is to provide information on the use of dental radiology in forensic dentistry.

Owing to their swift, precise, and tireless capabilities, artificial intelligence (AI) applications in emergency radiology are becoming powerful tools for radiologists. These applications, which are useful for improving diagnostic efficiency, are also a core engine driving the entire field of emergency medicine toward higher levels of precision, personalization, and efficiency. The integration of AI into emergency radiology thus represents a transformative advancement in precision medicine. We explore herein the expanding applications of AI in emergency radiology, focusing on their potential to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. By analyzing its current utilization and future directions, we demonstrate how AI is revolutionizing emergency care through intelligent image analysis and decision support systems. Although certain challenges remain, including data security, model interpretability, and clinical implementation standards, the immense potential of AI to reshape emergency workflows, promote precision medicine, and improve patient outcomes is unmistakable.

Background: Artificial intelligence (AI) is increasingly being explored in radiology, including its potential role in emergency imaging settings. However, global perspectives on AI adoption, usefulness, and limitations among emergency radiologists remain underexplored.

Aim: To assess awareness, usage, perceived benefits, and limitations of AI tools among radiologists practicing emergency radiology worldwide.

Methods: A 16-question survey was distributed globally between October 24, 2024, and August 4, 2025, targeting radiologists working in academic, community, and private settings who practice emergency radiology as a primary or secondary subspecialty. The survey was disseminated via direct emails extracted using automated and manual methods from recent publications in major radiology journals. A total of 57 responses were collected.

Results: AI awareness was high (93%), but frequent clinical use was reported by only 28%. Daily use of AI in emergent imaging was limited to 23% of respondents. The majority anticipated AI becoming essential within five years (68%), and 51% believed AI would replace certain radiological tasks. Image interpretation and acquisition were the most common AI applications. Key perceived benefits included improved diagnostic accuracy and increased efficiency, while concerns included limited accuracy, integration difficulties, and cost. Trust in AI varied by experience, with less experienced radiologists viewed as more trusting.

Conclusion: While emergency radiologists globally recognize AI's potential, significant barriers to its routine adoption remain. Addressing issues of trust, cost, accuracy, and workflow integration is essential to unlock AI's full utility in emergency radiology.

Developmental venous anomalies (DVAs) are benign congenital veins that collect normal brain drainage into a single outlet. Cerebral cavernous malformations (CMs) are clusters of thin-walled capillary cavities prone to bleeding. When both lesions coexist, the DVA's altered venous pressure and flow can promote CM formation or rupture. Detecting a DVA abutting an otherwise unexplained intracerebral hemorrhage can therefore raise suspicion of an occult CM as a likely cause, a clue which may be invaluable for daily clinical practice. The main focus of this review is to acknowledge the hallmark imaging appearances of DVAs and CMs, as well as their coexistence, explore the clinical consequences of mixed lesions, and emphasize that recognizing their partnership is vital for an accurate, timely diagnosis and appropriately targeted management.