World journal of radiology最新文献

Calvarial lesions are usually incidental and asymptomatic, rarely detected. However, these lesions can also present with pain, a palpable mass or a bone defect. Clinical information such as the patient's age and medical history are helpful in making the correct diagnosis. Calvarial lesions may occur due to congenital and anatomical variants, traumatic and iatrogenic, idiopathic, infectious and inflammatory, metabolic, benign and malignant neoplastic causes. Calvarial lesions may be solitary, multiple or diffuse, and may be lytic, sclerotic or mixed. Although most calvarial lesions are benign, radiologic imaging features can help to determine whether the lesion is benign or malignant. Methods that can guide treatment and are currently in use include plain radiography, ultrasonography, computed tomography, magnetic resonance imaging, angiographic studies, and nuclear scintigraphy studies such as 18F-fluorodeoxyglucose positron emission tomography and whole-body bone scintigraphy. Defects, lysis and sclerosis in the bone structure are assessed by plain radiography and computed tomography, and the soft tissue components of the lesions and their relationship to the surrounding soft tissue are assessed by magnetic resonance imaging. This article reviews the imaging findings of benign and malignant calvarial lesions and normal variants that may be confused with systemic diseases and pathologies affecting the calvarium.

Background: Acute kidney injury (AKI) is a frequent complication after liver transplantation (LT). How to realize the early diagnosis of AKI, perform active intervention, and reduce the mortality of post-LT patients is an urgent problem to be solved.

Aim: To investigate the accuracy of hepatorenal index (HRI) and renal resistive index (RRI) in monitoring of early AKI after LT.

Methods: This observational study included adult deceased-donor LT recipients at our center between February 2022 and February 2023 with no preoperative renal dysfunction. The HRI and RRI were recorded once per day in the postoperative period through to postoperative day (POD) 7. We followed up with the patients at 1 month after LT. The patients were divided into the AKI and non-AKI groups according to the Kidney Disease Improving Global Outcomes criteria.

Results: Of 121 patients were included in the study (mean age: 50.18 ± 8.88years; female: 17.36%). AKI developed in 53 patients (43.80%). The AKI and non-AKI groups were similar in terms of their baseline characteristics. An HRI of ≤ 1.12 on POD 1 detected AKI with a sensitivity of 62.30% and a specificity of 87.80% [area under the receiver operating characteristic curve (AUC) = 0.801, P < 0.01]. An RRI of ≥ 0.65 on POD 1 detected AKI with a sensitivity of 87.80% and a specificity of 67.60% (AUC = 0.825, P < 0.01). The HRI combined with the RRI was more effective at detecting AKI than either the HRI or RRI alone (AUC = 0.890, P < 0.01). The HRI increased as AKI resolved while the RRI decreased as AKI resolved.

Conclusion: The HRI and RRI are non-invasive bedside indices that can identify the occurrence and recovery of early AKI after LT.

Acromegaly, characterized by persistent hypersecretion of growth hormone (GH), is most often caused by a pituitary neuroendocrine tumor (PitNET), though, less often, ectopic GH or GH-releasing hormone secretion from various neoplasms outside the pituitary gland could cause it. Nearly 70% of somatotroph PitNETs are macroadenomas at diagnosis. Transsphenoidal surgery, the most effective treatment modality for acromegaly, could achieve remission in 73%. However, the remission rates could reach 87% if surgery is followed by medical therapy. Due to variable therapeutic responses to surgical and medical therapy, pre-treatment awareness regarding the best therapeutic modality based on clinical, biochemical, radiological, histopathological and genetic parameters would help in accurate pretreatment decision-making. Earlier studies have identified poor prognosis markers like tumor size, tumor invasion, T2-weighted hyperintensity, granulation, and pretreatment GH and/or insulin-like growth factor 1 levels. In a recent study, published by Alvarez et al identified that preoperative PitNET volume is a good predictor of control of acromegaly following surgical treatment and the likelihood of requiring more aggressive additional therapies after surgery. They found that PitNET volume exceeding 3697 mm³ was associated with poorer disease control in patients with somatotroph PitNETs.

Background: Back pain and sciatica are common complaints that often require imaging for accurate diagnosis and management. Conventional lumbar magnetic resonance imaging (MRI) protocols typically include sagittal and axial T1 and T2 sequences; however, these may miss certain pathologies. The addition of coronal short tau inversion recovery (STIR) sequences offers the potential to enhance the detection of both spinal and extra-spinal abnormalities, thereby improving clinical decision-making and patient outcomes.

Aim: To evaluate the impact of adding coronal STIR sequences to routine lumbar MRI in diagnosing back pain and sciatica.

Methods: We prospectively analyzed data from patients aged 6 and older presenting with back pain or sciatica who underwent lumbar spine MRI at our institution. The standardized MRI protocol utilized included sagittal and axial T1 and T2 sequences, complemented by a coronal STIR sequence. Data on structural abnormalities were collected, reviewed, and analyzed using counts, percentages, and Fisher's exact test for categorical variables.

Results: Our cohort comprised 274 patients (115 males, 159 females; mean age 44.91 years). Notably, 39 patients exhibited abnormalities across all sequences, while 72.63% showed normal findings on the coronal STIR sequence. Importantly, 30.29% of cases were diagnosed as normal without the coronal STIR, and 36 patients with normal T1 and T2 sequences presented abnormalities on the coronal STIR. The coronal STIR sequence successfully identified 26 spinal and 10 non-spinal pathologies, including 17 cases of sacroiliitis, with a significant association (P < 0.0001) between sacroiliitis diagnosis and abnormalities visible solely on this sequence.

Conclusion: Integrating coronal STIR into routine lumbar MRI enhances detection of hidden spinal and extra-spinal pathologies, improves patient management, and offers a cost-effective, practical upgrade with significant diagnostic and clinical value.

Pericoronary adipose tissue (PCAT) plays an important role in the pathogenesis and progression of cardiovascular diseases due to its bidirectional communication with the coronary artery wall. In recent years, PCAT parameters measured using coronary computed tomography have emerged as potential noninvasive imaging biomarkers for quantifying coronary artery inflammation, with significant clinical value in the early detection, disease progression assessment, treatment efficacy evaluation, and prognosis prediction of cardiovascular diseases. Furthermore, new technologies such as PCAT radiomics analysis have broadened its potential applications in evaluating coronary plaque vulnerability, predicting cardiovascular events, and improving risk stratification. This review discusses recent advances in PCAT research, focusing on its role in coronary artery disease risk identification and inflammation monitoring, and aims to offer imaging-based insights to support its future clinical use in cardiovascular disease management.

Background: Accurate preoperative differentiation of benign and malignant thyroid nodules is critical for optimal patient management. However, conventional imaging modalities present inherent diagnostic limitations.

Aim: To develop a non-contrast computed tomography-based machine learning model integrating radiomics and clinical features for preoperative thyroid nodule classification.

Methods: This multicenter retrospective study enrolled 272 patients with thyroid nodules (376 thyroid lobes) from center A (May 2021-April 2024), using histopathological findings as the reference standard. The dataset was stratified into a training cohort (264 lobes) and an internal validation cohort (112 lobes). Additional prospective temporal (97 lobes, May-August 2024, center A) and external multicenter (81 lobes, center B) test cohorts were incorporated to enhance generalizability. Thyroid lobes were segmented along the isthmus midline, with segmentation reliability confirmed by an intraclass correlation coefficient (≥ 0.80). Radiomics feature extraction was performed using Pearson correlation analysis followed by least absolute shrinkage and selection operator regression with 10-fold cross-validation. Seven machine learning algorithms were systematically evaluated, with model performance quantified through the area under the receiver operating characteristic curve (AUC), Brier score, decision curve analysis, and DeLong test for comparison with radiologists interpretations. Model interpretability was elucidated using SHapley Additive exPlanations (SHAP).

Results: The extreme gradient boosting model demonstrated robust diagnostic performance across all datasets, achieving AUCs of 0.899 [95% confidence interval (CI): 0.845-0.932] in the training cohort, 0.803 (95%CI: 0.715-0.890) in internal validation, 0.855 (95%CI: 0.775-0.935) in temporal testing, and 0.802 (95%CI: 0.664-0.939) in external testing. These results were significantly superior to radiologists assessments (AUCs: 0.596, 0.529, 0.558, and 0.538, respectively; P < 0.001 by DeLong test). SHAP analysis identified radiomic score, age, tumor size stratification, calcification status, and cystic components as key predictive features. The model exhibited excellent calibration (Brier scores: 0.125-0.144) and provided significant clinical net benefit at decision thresholds exceeding 20%, as evidenced by decision curve analysis.

Conclusion: The non-contrast computed tomography-based radiomics-clinical fusion model enables robust preoperative thyroid nodule classification, with SHAP-driven interpretability enhancing its clinical applicability for personalized decision-making.

Background: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) frequently metastasize to the liver, with heterogeneity in tumor grade impacting patient prognosis and treatment. The Ki-67 index, a key prognostic marker, often varies between primary and metastatic sites; however, routine liver biopsy remains controversial. Although percutaneous computed tomography-guided core needle biopsy (PCT-CNB) is safe and effective for focal lesions, its role in detecting intertumor grading discrepancies and survival implications in GEP-NETs is underexplored. Conflicting survival associations with grade shifts have been reported in previous studies. We hypothesized that PCT-CNB could identify clinically significant grading heterogeneity in liver metastases, correlating with survival outcomes, thereby refining risk stratification and therapeutic strategies.

Aim: To investigate intertumor grading heterogeneity in GEP-NET liver metastases via PCT-CNB.

Methods: We retrospectively investigated 92 patients with liver metastases from GEP-NETs via PCT-CNB, 76 patient samples from the liver and primary sites, and 16 from the liver and secondary liver sites. Ki-67 immunohistochemistry was performed for tissue sampling, and grading classifications were determined. Intertumor grading classification heterogeneity and associated changes in patient survival outcomes were also evaluated.

Results: No procedure-related mortality was recorded during or after biopsy. In 37/92 patients (40.2%), the grading classifications changed: The grading increased from G1 to G2 in 13 patients, from G1 to G3 in 2, and from G2 to G3 in 14; the grading decreased from G2 to G1 in 5 patients, from G3 to G1 in 1, and from G3 to G2 in 2. Patients with G1 or G2 disease had better progression-free survival and overall survival (OS) outcomes than those with G3 disease did (P = 0.001 and P < 0.001, respectively). The 5-year and 10-year OS rates for stable G2 patients were 67.5% and 26.0%, respectively, decreasing to 46.4% and 23.2%, respectively, among G2 patients whose grade increased (P = 0.016).

Conclusion: The PCT-CNB of liver metastases from GEP-NETs differed in grade between the liver tumor and primary site/secondary liver metastases. Additionally, when grading increased from G2, the OS rate significantly decreased.

Background: Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. The accuracy of noninvasive biomarkers for detecting hepatic steatosis is still limited.

Aim: To assess the diagnostic performance of noninvasive steatosis biomarkers in diagnosing NAFLD using magnetic resonance imaging proton density fat fraction (MRI-PDFF) as the gold standard.

Methods: A total of 131 suspected NAFLD patients (60% male, median age 36 years) undergoing MRI-PDFF were consecutively recruited from a tertiary hospital. Steatosis grades determined by MRI-PDFF were classified as none (< 5%), mild (5%-11%), moderate (11%-17%), and severe (≥ 17%). Six steatosis biomarkers were calculated according to clinical parameters and laboratory tests, including fatty liver index, hepatic steatosis index, ZJU index, Framingham steatosis index, triglycerides and glucose index, and visceral adiposity index. The accuracy of these biomarkers in detecting hepatic steatosis was evaluated using the area under the receiver operating characteristic curves (AUCs). The Youden index was used to determine the optimal cut-off for each biomarker. The linear trend analysis of each biomarker across the steatosis grades was conducted by Mantel-Haenszel χ ² test. Spearman's rank correlation assessed the relationship between steatosis biomarkers and MRI-PDFF.

Results: Steatosis grades based on MRI-PDFF prevalence were: None 27%, mild 40%, moderate 15% and severe 18%. Six steatosis biomarkers showed a linear trend across the steatosis grades and a significant positive correlation with MRI-PDFF. The six steatosis biomarkers demonstrated AUCs near 0.90 (range: 0.857-0.912, all P < 0.001) for diagnosing NAFLD by MRI-PDFF ≥ 5%. The optimal cut-offs showed sensitivity between 84.4%-91.7% and specificity between 71.4%-85.7%. The diagnostic performance of these biomarkers in detecting moderate-to-severe and severe steatosis was relatively weaker.

Conclusion: These noninvasive steatosis biomarkers accurately diagnosed NAFLD and correlated well with MRI-PDFF for detecting NAFLD, but they did not effectively detect moderate or severe steatosis.

This narrative review examines the use of imaging biomarkers for diagnosing and monitoring hydrocephalus from birth through childhood. Early detection and longitudinal follow-up are essential for guiding timely interventions and assessing treatment outcomes. Cranial ultrasound and magnetic resonance imaging (MRI) are the primary imaging modalities, providing critical insights into ventricular size, cerebrospinal fluid dynamics, and neurodevelopmental implications. Key parameters, including Evans' index, Levene's index, and the Cella Media index, as well as volumetric and diffusion-based MRI techniques, have been explored for their diagnostic and prognostic value. Advances in automated image analysis and artificial intelligence have further improved measurement precision and reproducibility. Despite these developments, challenges remain in standardizing imaging protocols and establishing normative reference values across different pediatric populations. This review highlights the strengths and limitations of current imaging approaches, emphasizing the need for consistent methodologies to enhance diagnostic accuracy and optimize patient management in hydrocephalus.

Patent foramen ovale (PFO) is a common congenital heart disorder associated with stroke, decompression sickness and migraine. Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiography has important clinical significance and can improve the accuracy of detecting right-left shunts (RLSs) in patients with PFO. In this letter, regarding an original study presented by Yao et al, we present our insights and discuss how to better help clinicians evaluate changes in PFO-related RLS.