Current Medical Imaging - Current Issue

Predicting the recurrence risk of NMIBC after TURBT is crucial for individualized clinical treatment.

The objective of this study is to evaluate the ability of radiomic feature analysis of intratumoral and peritumoral regions based on computed tomography (CT) imaging to predict recurrence in non-muscle-invasive bladder cancer (NMIBC) patients who underwent transurethral resection of bladder tumor (TURBT).

A total of 233 patients with NMIBC who underwent TURBT were retrospectively analyzed. Within the intratumoral and peritumoral regions of the venous phase images, 1316 radiomics features were extracted. Feature selection was used to identify a set of top recurrence-associated features within the training cohort. Three models were constructed to predict recurrence for a given patient using Random Forest (RF): Model 1 was based on the radiomics features set from the intratumoral region, Model 2 was based on a combination of intratumoral and peritumoral regions, and Model 3 combined the radiomics features from Model 2 and clinical factors. The three models were then independently tested on internal and external cohorts, and their performance was evaluated. We also employed the bootstrap method on the internal cohort to further validate the performance of the model.

Combining intratumoral and peritumoral regions, Model 2 yielded a higher area under the receiver operator characteristic curves (AUC) than Model 1, with 0.826 AUCs of the training cohort. After adding clinical factors, the predictive performance of Model 3 for postoperative recurrence of NMIBC was further improved, and the AUCs of the training, internal, and external validation cohorts of Model 3 were 0.860 (95% CI: 0.829-0.954), 0.829 (0.812-0.863), and 0.805 (0.652-0.840), respectively (all p>0.05). The bootstrap value of Model 3 on the internal cohort was 0.852. Model 3 stratified patients into high- and low-risk groups with significantly different recurrence-free survival (RFS) (p<0.001).

Radiomic features derived from intratumoral regions can predict the 2-year recurrence risk following TURBT in patients with NMIBC. The predictive performance is further enhanced when combined with radiomic features from peritumoral regions and clinical risk factors.

Image enhancement of the Hypoxic-Ischemic Encephalopathy (HIE) lesion region in neonatal brain MR images is a challenging task due to the diffuse (i.e., multi-focal) nature, small size, and low contrast of the lesions. Classifying the stages of HIE is also difficult because of the unclear boundaries and edges of the lesions, which are dispersed throughout the brain. Moreover, unclear boundaries and edges are due to chemical shifts, partial volume artifacts, and motion artifacts. Further, voxels may reflect signals from adjacent tissues. Existing algorithms perform poorly in HIE lesion enhancement due to artifacts, voxels, and the diffuse nature of the lesion.

In this paper, we propose a Rough Neural Network and Anti-Homomorphic Filter (RNN-AHF) framework for the enhancement of the HIE lesion region.

The RNN-AHF framework reduces the pixel dimensionality of the feature space, eliminates unnecessary pixels, and preserves essential pixels for lesion enhancement.

The RNN efficiently learns and identifies pixel patterns and facilitates adaptive enhancement based on different weights in the neural network. The proposed RNN-AHF framework operates using optimized neural weights and an optimized training function. The hybridization of optimized weights and the training function enhances the lesion region with high contrast while preserving the boundaries and edges.

The proposed RNN-AHF framework achieves a lesion image enhancement and classification accuracy of approximately 93.5%, which is better than traditional algorithms.

This study aims to evaluate the potential of Dual-Energy Computed Tomography (DECT)-based radiomics in preoperative risk stratification for the prediction of initial recurrence in Papillary Thyroid Carcinoma (PTC).

The retrospective analysis included 236 PTC cases (165 in the training cohort, 71 in the validation cohort) collected between July 2020 and June 2021. Tumor segmentation was carried out in both intratumoral and peritumoral areas (1 mm inner and outer to the tumor boundary). Three region-specific rad-scores were developed (rad-score [VOI^whole], rad-score [VOI^{outer layer}], and rad-score [VOI^{inner layer}]), respectively. Three radiomics models incorporating these rad-scores and additional risk factors were compared to a clinical model alone. The optimal radiomics model was presented as a nomogram.

Rad-scores from peritumoral regions (VOI^{outer layer} and VOI^{inner layer}) outperformed the intratumoral rad-score (VOI^whole). All radiomics models surpassed the clinical model, with peritumoral-based models (radiomics models 2 and 3) outperforming the intratumoral-based model (radiomics model 1). The top-performing nomogram, which included tumor size, tumor site, and rad-score (VOI^{inner layer}), achieved an Area Under the Curve (AUC) of 0.877 in the training cohort and 0.876 in the validation cohort. The nomogram demonstrated good calibration, clinical utility, and stability.

DECT-based intratumoral and peritumoral radiomics advance PTC initial recurrence risk prediction, providing clinical radiology with precise predictive tools. Further work is needed to refine the model and enhance its clinical application.

Radiomics analysis of DECT, particularly in peritumoral regions, offers valuable predictive information for assessing the risk of initial recurrence in PTC.

Early-stage Brain tumor detection is critical for timely diagnosis and effective treatment. We propose a hybrid deep learning method, Convolutional Neural Network (CNN) integrated with YOLO (You Only Look once) and SAM (Segment Anything Model) for diagnosing tumors.

A novel hybrid deep learning framework combining a CNN with YOLOv11 for real-time object detection and the SAM for precise segmentation. Enhancing the CNN backbone with deeper convolutional layers to enable robust feature extraction, while YOLOv11 localizes tumor regions, SAM is used to refine the tumor boundaries through detailed mask generation.

A dataset of 896 MRI brain images is used for training, testing, and validating the model, including images of both tumors and healthy brains. Additionally, CNN-based YOLO+SAM methods were utilized successfully to segment and diagnose brain tumors.

Our suggested model achieves good performance of Precision as 94.2%, Recall as 95.6% and mAP50(B) score as 96.5% demonstrating and highlighting the effectiveness of the proposed approach for early-stage brain tumor diagnosis

The validation is demonstrated through a comprehensive ablation study. The robustness of the system makes it more suitable for clinical deployment.

This study aims to propose and evaluate a two-stage semi-supervised segmentation framework with dual multiscale uncertainty estimation and graph reasoning, addressing the challenges of obtaining high-precision pixel-level labels and effectively utilizing unlabeled data for accurate pneumonia lesion segmentation.

First, we design a guided supervised training strategy for modeling aleatoric uncertainty (AU) at dual scales, reducing the impact on segmentation performance caused by aleatoric uncertainties introduced by blurred lesions and their boundaries in the image. Second, we design a training strategy for multi-scale noisy pseudo-label correction to reduce the cognitive bias problem caused by unreliable predictions in the model. Finally, we design a new combination of fused feature interaction graph reasoning (FIGR) and attention modules, which enables the network model to better capture image features in small infected regions.

Our study was validated using the MosMedData public dataset. The proposed algorithm improves the performance by 1.25%, 1.03%, 2.98%, and 0.59% on Dice, Jaccard, normalized surface dice (NSD), and average distance of boundaries (ADB), respectively, compared to the baseline model.

Our semi-supervised pneumonia segmentation framework, through two-stage multi-scale uncertainty estimation and modeling, significantly improves segmentation performance by leveraging unlabeled data and addressing uncertainties, offering clinical benefits in pneumonia diagnosis while facing challenges in generalization and computational efficiency that future work will target with GAN-based data synthesis and architecture optimization.

It can be convincingly concluded that the proposed algorithm is of profound importance and value in the domain of clinical practice.

This meta-analysis aimed to evaluate the diagnostic performance of Machine Learning (ML) models for early prediction of bronchopulmonary dysplasia (BPD) in preterm infants, addressing the need for timely risk stratification.

Systematic searches of PubMed, Embase, and other databases identified 9 eligible studies (12,755 infants). Data were extracted and pooled using bivariate generalized linear mixed models. Study quality was assessed via QUADAS-2.

ML models demonstrated high accuracy (pooled sensitivity: 0.81, specificity: 0.85, AUC: 0.90). Multimodal models and ensemble algorithms (e.g., Random Forest) outperformed single-modality approaches. Models using data from the first 7 postnatal days achieved superior performance compared to those using data from day 28.

ML enables ultra-early BPD prediction, preceding conventional diagnosis by weeks. Heterogeneity in data modalities and validation strategies highlights the need for standardized reporting.

ML-based BPD prediction shows promise for clinical translation but requires prospective validation and cost-effectiveness analysis.

The noise emergence in the digital image can occur throughout image acquisition, transmission, and processing steps. Consequently, eliminating the noise from the digital image is required before further processing. This study aims to denoise noisy images (including Magnetic Resonance Images (MRIs)) by employing our proposed image denoising approach.

This proposed approach is based on the Stationary Wavelet Transform (SWT 2-D) and the 2 - D Dual-Tree Discrete Wavelet Transform (DWT). The first step of this approach consists of applying the 2 - D Dual-Tree DWT to the noisy image to obtain noisy wavelet coefficients. The second step of this approach consists of denoising each of these coefficients by applying an SWT 2-D based denoising technique. The denoised image is finally obtained by applying the inverse of the 2-D Dual-Tree DWT to the denoised coefficients obtained in the second step. The proposed image denoising approach is evaluated by comparing it to four denoising techniques existing in literature. The latters are the image denoising technique based on thresholding in the SWT-2D domain, the image denoising technique based on deep neural network, the image denoising technique based on soft thresholding in the domain of 2-D Dual-Tree DWT, and Non-local Means Filter.

The proposed denoising approach, and the other four techniques previously mentioned, are applied to a number of noisy grey scale images and noisy Magnetic Resonance Images (MRIs) and the obtained results are in terms of PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity), NMSE (Normalized Mean Square Error) and Feature Similarity (FSIM). These results show that the proposed image denoising approach outperforms the other denoising techniques applied for our evaluation.

In comparison with the four denoising techniques applied for our evaluation, the proposed approach permits to obtain highest values of PSNR, SSIM and FSIM and the lowest values of NMSE. Moreover, in cases where the noise level σ = 10 or σ = 20, this approach permits the elimination of the noise from the noisy images and introduces slight distortions on the details of the original images. However, in case where σ = 30 or σ = 40, this approach eliminates a great part of the noise and introduces some distortions on the original images.

The performance of this approach is proven by comparing it to four image denoising techniques existing in literature. These techniques are the denoising technique based on thresholding in the SWT-2D domain, the image denoising technique based on a deep neural network, the image denoising technique based on soft thresholding in the domain of 2 - D Dual-Tree DWT and the Non-local Means Filter. All these denoising techniques, including our approach, are applied to a number of noisy grey scale images and noisy MRIs, and the obtained results are in terms of PSNR (Peak Signal to Noise Ratio), SSIM(Structural Similarity), NMSE (Normalized Mean Square Error) and FSIM (Feature Similarity). These results show that this proposed approach outperforms the four denoising techniques applied for our evaluation.

This study aimed to compare the consistency between smart pelvic floor ultrasound and biplanar transrectal ultrasound in detecting early stress urinary incontinence (SUI) by assessing urethral dilation and bladder structure.

We selected 40 multiparas who went through prenatal assessment after delivery and had standard pelvic floor ultrasounds at 6 weeks after childbirth, spanning from June 2022 to September 2022. The Bland-Altman method was employed to evaluate the consistency between biplanar transrectal ultrasound and transperineal pelvic floor ultrasound in assessing the mobility of the bladder neck and the posterior bladder wall in women.

Biplanar transrectal ultrasound and transperineal pelvic floor ultrasound demonstrated strong consistency in evaluating bladder neck and posterior bladder wall mobility in women (P>0.05). The analysis of each pelvic floor observation index using Bland-Altman plots indicated that approximately 97.5% of data points fell within the 95% consistency limit.

Our findings suggest that biplanar transrectal ultrasound is a reliable supplementary method to transperineal pelvic floor ultrasound for diagnosing SUI.

Driven by environmental pollution and the rise in infectious diseases, the increasing prevalence of lung conditions demands advancements in diagnostic techniques.

This study explores the use of various features, such as spectrograms, chromograms, and Mel Frequency Cepstral Coefficients (MFCC), to extract crucial information from auscultation recordings. It addresses challenges through filter-based audio enhancement methods. The primary goal is to improve disease detection accuracy by leveraging convolutional neural networks (CNNs) for feature extraction and dense neural networks for classification.

While deep learning models like CNNs and Recurrent Neural Network (RNN) outperform traditional machine learning models such as Sequence Vector Machine, K-Nearest Neighbours (KNN) and random forest with accuracies ranging from 70% to 85%. The combination of CNN, RNN, and long short-term memory achieved an accuracy of 88%. By integrating MFCC, Chroma Short-Term Fourier Transform (STFT), and spectrogram features with a CNN-based classifier, the proposed multi-feature deep learning model achieved the highest accuracy of 92%, surpassing all other methods.

The study effectively addresses key issues, including the overrepresentation of Chronic Obstructive Pulmonary Disease (COPD) samples over Lower Respiratory Tract Infections (LRTI) and Upper Respiratory Tract Infections (URTI) which hampers generalization across test audio samples.

The proposed methodology caters common challenges like background noise in recordings, and the limited and imbalanced nature of datasets. These findings pave the way for enhanced clinical applications, showcasing the transformative potential of multi-feature deep learning methods in the classification of pulmonary diseases.

This cross-sectional study aimed to evaluate the relationship between presternal adipose tissue thickness and the pericardial adipose tissue volume in relation to coronary artery disease.

A total of 108 patients who underwent coronary computed tomography angiography (CCTA) for suspected coronary disease between 2019 and 2022 were evaluated. Patients whose epicardial adipose tissue could not be optimally measured due to imaging artifacts, those with a pre-existing coronary artery anomaly or known heart disease, individuals under 18 years of age, and those who had undergone sternotomy and bypass surgery were excluded from the study. Accordingly, 95 patients (61 males and 34 females) who met the inclusion criteria and did not meet any of the exclusion criteria were included in the study. CCTA images were analyzed retrospectively. Pericardial adipose tissue volume was measured automatically using Syngo Via software. Presternal fat thickness (PFTT) was measured at the level of the pulmonary artery bifurcation, from the anterior to the posterior surface.

The study sample comprised 64.2% males and 35.7% females. The median thickness of the presternal fat tissue was found to be 11.5 mm, with a range of 3 to 44 mm. The median PFTT was measured at 9 mm (3−23 mm) in the male patient group, while in the female patient group, it was 20 mm (10−44 mm). The median epicardial fat volume (EFV) for the full sample was 83.1 ml (22.3−171 ml), measuring 81.1 ml (37−171 ml) and 79.5 ml (22.3−167 ml) in males and females, respectively. A significant correlation was observed between PFTT and EFV in the full sample (Rho = 0.236, p = 0.02), as well as among male patients (Rho = 0.409, p = 0.001), but not in the female patient group (Rho = 0.264, p = 0.131). In the male cohort, there was no significant difference between EFV and PFTT, and the presence of coronary plaque.

This study examines the relationship between presternal adipose tissue thickness (PFTT) and coronary artery disease (CAD), building on previous evidence that links epicardial adipose tissue (EAT) to cardiovascular risk. We found a significant correlation between PFTT and epicardial fat volume (EFV) in male patients, but not in females, which is likely due to hormonal influences and variability in breast tissue. Importantly, measurement of PFTT provides a practical, non-invasive method for assessing CAD risk in clinical settings. Although our small sample size limits the generalisability of our findings, these results suggest that PFTT may serve as an indirect marker of CAD risk and highlight the need for further research with larger cohorts to validate its clinical relevance. Incorporating PFTT assessment into routine practice may improve the early identification of high-risk patients and enhance strategies for preventing ischemic heart disease.

The study reveals that increased presternal fat thickness correlates with elevated epicardial fat volume, indicating that presternal fat measurements could serve as a simple and effective tool for assessing the risk of coronary artery disease, particularly in male patients.

Hematemesis is a rare manifestation of a bronchial artery aneurysm (BAA), as bleeding from a ruptured BAA typically occurs into the bronchial tree, leading to hemoptysis rather than gastrointestinal bleeding.

A 71-year-old male presented to the emergency department with syncope and hematemesis. Computed tomography angiography (CTA) revealed a ruptured bronchial artery aneurysm in the posterior mediastinum, with contrast extravasation into the lower esophagus. The patient underwent transcatheter arterial embolization (TAE) using coils, a mixture of N-butyl cyanoacrylate and ethiodized oil. However, due to persistent bleeding signs and recanalization observed on follow-up CTA, a second TAE was performed the following day using the same technique. Hemostasis was achieved, and the patient recovered well, being discharged on the 16^th day without complications.

Ruptured BAA presenting as hematemesis is extremely rare, making it difficult to diagnose. Prompt diagnosis with CTA and timely intervention, such as TAE, can be important in achieving favorable outcomes and preventing life-threatening complications.

Finding a convenient, accurate, and non-invasive method to differentiate between benign and malignant breast masses is especially important for clinical practice, and this study aimed to explore the clinical value of Nomogram model based on multimodality ultrasound image characteristics and clinical baseline data for detecting benign and malignant breast masses.

A retrospective analysis of the clinical data and ultrasound imaging characteristics of 132 patients with breast masses. Data were randomly divided into a training set (92 cases) and a validation set (40 cases) in a ratio of 7:3. Logistic regression was applied to the training set data to analyze risk factors related to malignant breast masses and to construct a Nomogram model. Clinical applicability of the model was evaluated and validated.

In training set, ROC cure analysis results showed that AUC of Nomogram model constructed with CA15-3, CA125, Emax, Esd, Ratio of Elastic Moduli, Elasticity Scoring, blurry boundaries, irregular shape, penetrating vessels, and stiff rim sign was 1.00 (95%CI: 0.99-1.00), Hosmer-Lemeshow goodness-of-fit test result showed predicted curve closely aligns with ideal curve, and DCA showed that Nomogram model exhibited high net benefits across multiple thresholds. The clinical applicability of the Nomogram model was also confirmed with consistent results in the validation set.

In this study, we constructed a Nomogram model using risk factors associated with malignant breast masses, and the model showed good clinical applicability in distinguishing benign and malignant breast masses. However, this study is a single-center study, and the sample size of the dataset is relatively small, which, to some extent, limits the breadth and depth of validation.

The Nomogram model built on multimodal ultrasound imaging features and clinical data demonstrates a strong discriminative ability for malignant breast masses, allowing patients to achieve a significant net benefit.

Brachial plexopathy is a diagnostically challenging condition that requires a comprehensive evaluation, including physical examination, imaging, and Electrodiagnostic (EDx).testing. Ultrasound guidance may improve the efficiency and precision of nerve conduction studies by addressing the limitations of blind techniques, such as discomfort and inaccurate localization.

We prospectively enrolled 30 patients undergoing electrodiagnostic testing. The left upper limb was examined with ultrasound guidance (Group A), while the right upper limb underwent the blind method (Group B). The examined nerves included the median, ulnar, radial, medial and lateral antebrachial cutaneous, axillary, musculocutaneous, suprascapular, and long thoracic nerves. Stimulation duration, number of stimulation attempts, average current, and total examination time were recorded. The differences in data between the two groups were compared and analyzed.

Group A demonstrated significantly lower stimulation duration (156.70±50.13 vs. 260.17±53.19 s), fewer stimulation attempts (17.73±3.94 vs. 25.80±5.23), and lower average current [32.45 (30.28, 40.13) vs. 42.75 (37.78,50.68) mA] compared to Group B (all P 0.001). No significant difference was observed in total examination time (387.40 ± 33.72 vs. 372.00 ± 47.01 s; P = 0.150).

Ultrasound guidance improves procedural precision and reduces the need for repeated stimulations and higher electrical intensities. These benefits are achieved without extending the total examination time, making it a feasible and patient-friendly approach for routine use in clinical neurophysiology.

Ultrasound-guided nerve conduction studies of the brachial plexus enhance procedural efficiency and patient comfort compared to the blind method. Further large-scale studies are recommended to validate these findings and assess broader clinical applications.

An accurate staging diagnosis of rectal cancer holds crucial importance in determining the appropriate treatment plan for patients.

To evaluate the application of transrectal biplane ultrasonography combined with Sound Touch Elastography (STE) technology in preoperative uT stage of rectal cancer.

A retrospective analysis was conducted on the ultrasonographic data of 32 patients. The STE values within the tumor and the adjacent peritumoral fat tissue were recorded, and the ratio of STE values between adjacent and distant peritumoral fat tissues was defined as the Stiffness Ratio (SR).

The STE values were not statistically significantly different between the high and low pT stage groups within tumors (P > 0.05). However, there were statistically significant differences in the STE values of the adjacent peritumoral fat tissue and the SR between the two groups (P < 0.05). Binary logistic regression analysis showed that the SR was a relevant factor in distinguishing high and low pT stages of rectal cancer. The optimal cut-off value of the SR was 1.915, with a sensitivity of 95.7% and a specificity of 88.9% in predicting high pT stages of rectal cancer. The consistency observed between traditional TRUS and pathological staging in differentiating between high and low pT stages of rectal cancer was moderate. However, the incorporation of SR had enhanced this consistency to a favorable level.

The combination of TRUS and STE technology enhanced the accuracy of pT stage in rectal cancer, with SR serving as a critical indicator for predicting high pT stages and constituting a valuable supplement to traditional TRUS.

Detecting Pulmonary Embolism (PE) is critical for effective patient care, and Artificial Intelligence (AI) has shown promise in supporting radiologists in this task. Integrating AI into radiology workflows requires not only evaluation of its diagnostic accuracy but also assessment of its acceptance among clinical staff.

This study aims to evaluate the performance of an AI algorithm in detecting pulmonary embolisms (PEs) on contrast-enhanced computed tomography pulmonary angiograms (CTPAs) and to assess the level of acceptance of the algorithm among radiology department staff.

This retrospective study analyzed anonymized computed tomography pulmonary angiography (CTPA) data from a university clinic. Surveys were conducted at three and nine months after the implementation of a commercially available AI algorithm designed to flag CTPA scans with suspected PE. A thoracic radiologist and a cardiac radiologist served as the reference standard for evaluating the performance of the algorithm. The AI analyzed 59 CTPA cases during the initial evaluation and 46 cases in the follow-up assessment.

In the first evaluation, the AI algorithm demonstrated a sensitivity of 84.6% and a specificity of 94.3%. By the second evaluation, its performance had improved, achieving a sensitivity of 90.9% and a specificity of 96.7%. Radiologists’ acceptance of the AI tool increased over time. Nevertheless, despite this growing acceptance, many radiologists expressed a preference for hiring an additional physician over adopting the AI solution if the costs were comparable.

Our study demonstrated high sensitivity and specificity of the AI algorithm, with improved performance over time and a reduced rate of unanalyzed scans. These improvements likely reflect both algorithmic refinement and better data integration. Departmental feedback indicated growing user confidence and trust in the tool. However, many radiologists continued to prefer the addition of a resident over reliance on the algorithm. Overall, the AI showed promise as a supportive “second-look” tool in emergency radiology settings.

The AI algorithm demonstrated diagnostic performance comparable to that reported in similar studies for detecting PE on CTPA, with both sensitivity and specificity showing improvement over time. Radiologists’ acceptance of the algorithm increased throughout the study period, underscoring its potential as a complementary tool to physician expertise in clinical practice.

Ultrasound has been used in the field of clinical nutrition to measure body composition. However, the consistency of these measurements varies across studies, and the impact of examination posture remains largely unexplored, creating a critical methodological gap in clinical practice. The purpose of this study was to investigate the consistency of ultrasonic measurement of fat thickness (FT) and evaluate the impact of posture on these measurements.

FT was measured at 10 body sites in routine and special postures using ultrasound to determine intra-observer and inter-observer consistency and to assess the impact of different postures on FT measurements. Body fat mass (BFM) was measured by bioelectrical impedance analysis (BIA), and subcutaneous skinfold thickness was measured with calipers for correlation analysis.

Results revealed significant sex differences in BFM (P<0.05) and FT at most sites (P<0.001), with women exhibiting thicker fat measurements. High intra-observer and inter-observer consistency was demonstrated in special examination postures (intraclass correlation coefficients were both ≥0.925). Posterior upper arm FT measured in the sitting posture was greater than that measured in the prone posture (P<0.001) while there was no significant difference in subscapular FT between the two postures (P = 0.289).

There were significant differences in posterior lower leg FT among the four postures (P<0.001). Positive correlations were observed between FT and skinfold at site 5 (abdominal subcutaneous fat), site 7 (posterior upper arm), and site 8 (subscapular) (r = 0.921, 0.878, 0.882, P<0.01).

Ultrasound measurements of FT have proven reliable, offering advantages in cost, ease, accuracy, and scalability. The findings highlight the importance of posture in ultrasound measurement of FT, which may influence clinical practice and research protocols. The limitations of the study mainly lie in the narrow age and BMI ranges of the sample, which restrict the generalizability of the research findings.

This study provides a comprehensive evidence base for posture-specific ultrasound protocols in fat thickness measurement. Our results demonstrate that ultrasound is a reliable method for measuring fat thickness, exhibiting good to excellent inter-observer and intra-observer consistency. The impact of body posture on fat thickness measurements varies by anatomical location. Strong correlations were found between ultrasound measurements and skinfold thickness at subcutaneous sites, confirming the validity of ultrasound for fat thickness assessment.

The aim of this study was to evaluate the correlation between ultrasonography, magnetic resonance imaging, and pathology with Fibrolipomatous Hamartoma (FLH) of the peripheral nerve.

Sixteen patients who underwent surgical treatment for FLH of the peripheral nerve were included in the study. Ultrasound examination and Magnetic Resonance Imaging (MRI) were used to display the detailed anatomical structure of the region well enough to detect FLH. The features presented based on the results of ultrasound examination and magnetic resonance imaging were recorded.

The involved peripheral nerve showed expansive growth in ultrasonography and MRI. The hyperechoic fat tissue and hypoechoic nerve fibers alternated with one another. In ultrasonography and MRI, the peripheral nerve exhibited a lotus-like appearance in the transverse plane, and a cable-like appearance in the longitudinal plane, while there was no blood flow signal in the nerve.

The imaging features of FLH, including the characteristic lotus-like and cable-like appearances, align closely with pathological findings, underscoring the diagnostic value of ultrasonography and MRI. These non-invasive techniques facilitate differentiation from other peripheral nerve pathologies, such as carpal tunnel syndrome or hemangioma. Limitations include the retrospective design, small MRI subgroup, and lack of long-term follow-up. Future multicenter studies with larger cohorts are recommended to validate these findings.

Ultrasonography and MRI may be valuable in the diagnosis of FLH of the peripheral nerve.

This study aimed to explore the relationship between radiomics features and anaplastic lymphoma kinase (ALK) gene mutation status in lung adenocarcinoma and to develop a radiomics nomogram for preoperative prediction of ALK mutations.

A retrospective analysis was conducted on 210 patients with histologically confirmed lung adenocarcinoma (50 ALK mutation-positive, 160 mutation-negative), divided into training (n=147) and validation (n=63) cohorts (7:3 ratio). Preoperative enhanced CT images were analyzed using ITK-SNAP for region-of-interest delineation, and radiomics features were extracted via A.K. software. The least absolute shrinkage and selection operator algorithm selected features to generate a radiomics score. Multivariate logistic regression identified independent risk factors, and a radiomics nomogram combining clinical features and radiomics signatures was developed. Model performance was evaluated using AUC in both training and validation sets.

Nineteen radiomics features were selected to construct the radiomics signature. The signature achieved an AUC of 0.89 (95% CI: 0.84–0.95) in the training set and 0.79 (95% CI: 0.63–0.95) in the validation set. The radiomics nomogram demonstrated superior performance (AUC=0.80, 95% CI: 0.63–0.97) compared to the clinical model alone (AUC=0.66, 95% CI: 0.47–0.85) in the validation set. While the nomogram showed no statistically significant improvement over the radiomics signature alone (P>0.05), it outperformed the clinical model significantly (P<0.001 in training; P=0.0337 in validation).

The radiomics nomogram integrating clinical and radiomics data demonstrated robust predictive capability for ALK mutations, highlighting the potential of non-invasive CT-based radiomics in guiding personalized treatment. However, the lack of significant difference between the nomogram and radiomics signature alone suggests limited incremental value from clinical variables in this cohort. Limitations include the retrospective design, single-center data, and class imbalance (fewer ALK-positive cases), which may affect generalizability. External validation is warranted to confirm clinical utility.

The CT-derived radiomics signature and nomogram show promise for preoperative ALK mutation prediction in lung adenocarcinoma. These tools could enhance clinical decision-making by identifying candidates for targeted therapies, though further validation is needed to optimize their application in diverse populations.

This case study aimed to report the rare computed tomography (CT) features of advanced pericochlear otosclerosis, with an emphasis on a proposed new nomenclature to describe the imaging findings.

A 70-year-old woman with recurrent rhinosinusitis presented to our center for clinical management. The CT scan revealed the incidental rare findings of advanced retrofenestral otosclerosis in the form of extensive symmetrical pericochlear tubular lucencies in bilateral otic capsules. We coined the new term “C-hoop earring” sign for this CT appearance. She was asymptomatic and declined further audiological or imaging evaluation.

Herein, the CT features of advanced pericochlear otosclerosis are described and the imaging and clinical connotations of the presence of the C-hoop earring sign are reviewed. This new terminology provides a more intuitive description of the imaging findings in the temporal bones for clearer understanding and communication in clinical radiological practice and education.