Current Medical Imaging - Volume 21, Issue 1, 2025

The use of magnetic resonance imaging (MRI), which has greater soft tissue contrast than other imaging modalities, has increased over the last 30 years. Studies have shown that MRI is frequently used for diagnosing neurodegenerative diseases. The incidence of Alzheimer's disease, a neurodegenerative condition, is increasing due to population aging and has a detrimental impact on quality of life. Volumetric changes in many important anatomical structures have been detected in magnetic resonance (MR) images of Alzheimer's disease patients. Various software programs, such as OsiriX, Horos, and VolBrain, are currently used to perform area and volume measurements in various brain structures. In this study, we compared the VolBrain and Horos applications for volume measurements of the cerebellum, whose relationship with Alzheimer's disease is not yet fully understood. We aimed to assess the consistency between the applications using various statistical methods and to highlight their respective advantages and disadvantages for researchers.

This was a retrospective study. The patient group comprised 50 individuals with Alzheimer's disease aged 30–65 years. T1 MR images of 50 Alzheimer's disease patients were first acquired via the VolBrain program and then via the Horos program.

The applications used yielded almost identical measurement results, and no significant differences were observed.

Both applications have been found to produce consistent results. This indicates that the methods are reliable and that either application can be effectively used for the intended purpose.

In conclusion, the choice between the two applications depends largely on the user’s data requirements, software preferences, and hardware capabilities. These factors play a decisive role in the selection process.

Accurate early diagnosis and assessment of liver fibrosis are important for patient treatment and prognosis. This study explored the value of Gd-BOPTA-enhanced T1 mapping via the B1 inhomogeneity-corrected Variable Flip Angle (VFA) method for staging liver fibrosis in rats.

Sprague‒Dawley rats were divided into one control group (n = 6) and four carbon tetrachloride-induced liver fibrosis groups (n = 6 each group). T1 mapping via B1 inhomogeneity-corrected VFA was performed before and 90 minutes after Gd-BOPTA administration. Precontrast T1 values (T1pre), postcontrast T1 values (T1post), and the reduction rate of T1 values (ΔT1%) were quantified on T1 mapping images. The diagnostic performance was evaluated by the Area Under the Receiver Operating Characteristic Curve (AUC). The correlations between T1pre, T1post, ΔT1% values, and the expression levels of hepatocyte transporters (Oatp1a1 and Mrp2) were evaluated.

T1post and ΔT1% were significantly correlated with liver fibrosis stage (r = 0.832, p 0.001; r = −0.798, p 0.001, respectively), whereas T1pre was not significantly correlated with fibrosis stage (r = 0.357, p = 0.062). The AUCs of T1post and ΔT1% were greater than those of postcontrast signal intensity for diagnosing stages F2–F4 (0.936, 0.941 vs. 0.791; p = 0.043, 0.038, respectively), F3–F4 (0.928, 0.861 vs. 0.660; p = 0.003, 0.028, respectively) and F4 (0.965, 0.896 vs. 0.761; p = 0.021, 0.049, respectively). Oatp1a1 and Mrp2 expression levels correlated significantly with T1post (r = −0.859, p = 0.001; r = −0.697, p = 0.017) and ΔT1% (r = 0.891, p 0.001; r = 0.685, p = 0.020), respectively.

T1post and ΔT1% were significantly correlated with liver fibrosis stages, and have good diagnostic performance for staging liver fibrosis. The protein expression levels of Oatp1a1 and Mrp2 correlated significantly with T1post and ΔT1%.

Gd-BOPTA-enhanced T1 mapping via the B1 inhomogeneity-corrected VFA shows promise as a potentially accurate and reliable tool for quantifying liver fibrosis stages.

This study aimed to investigate the correlation between magnetic resonance imaging (MRI) characteristics and molecular subtypes of breast carcinoma.

A retrospective analysis was carried out on 194 breast cancer patients who underwent preoperative MRI. Pathological confirmation and molecular subtyping were performed on postoperative specimens. Preoperative MRI features of the lesions were evaluated. Univariate and multivariate logistic regression analyses were employed to identify MRI features associated with each molecular subtype.

A total of 194 breast cancer patients who underwent preoperative MRI and surgical treatment were included, with a mean age of 52.31 ± 12.08 years. Invasive ductal carcinoma was the predominant diagnosis (94.84%), and the expression rates of ER, PR, and HER2 were 58.76%, 55.67%, and 35.05%, respectively. The Ki-67 index was >20% in 70.62% of patients. Luminal B (HER2−) was the most common molecular subtype (33.51%). Significant differences were observed in lesion morphology, T2-weighted signal intensity, enhancement pattern, and type across the five molecular subtypes, though delayed-phase enhancement kinetics showed no significant variation. Logistic regression indicated that low T2WI signal and restricted diffusion were associated with Luminal A, while mass-like morphology and delayed-phase washout were predictors of Luminal B. Non-Mass Enhancement (NME) and rapid early enhancement were linked to HER2-enriched tumors, and unifocal, high T2WI signal, delayed-phase washout, and irregular margins were characteristic of triple-negative breast cancer.

Distinct MRI features were found to be associated with specific molecular subtypes of breast cancer, providing valuable insights for subtype-specific diagnosis and therapeutic strategy formulation.

This study aimed to develop and validate a radiomics fusion model based on CT and MRI for distinguishing between spinal osteosarcoma and chondrosarcoma, and to compare the performance of models derived from different imaging modalities.

A retrospective analysis was conducted on 63 patients with histologically confirmed spinal osteosarcoma (n=20) and chondrosarcoma (n=43). Radiomics features were extracted from CT and MRI (T1-weighted, T2-weighted, and T2-weighted fat-suppressed) sequences, followed by feature selection using univariate logistic regression and LASSO. Eight machine learning models were utilized to construct radiomics models, based on CT, MR, both CT and MR, and clinical information combined with CT and MR. Models were evaluated via five-fold cross-validation and compared against radiologists’ interpretations using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, and Matthews correlation coefficient.

The MRI-based radiomics model using linear discriminant analysis achieved the highest diagnostic performance (AUC=0.963, sensitivity=95.3%, specificity=80.0%), significantly outperforming both CT-based models (AUC=0.700) and radiologists' diagnosis (p<0.001). The CTMR and clinico-CTMR models did not show significant improvement over the MR model. The MR model demonstrated excellent calibration and clinical utility, with substantial net benefit across threshold probabilities.

The superior performance of the MRI-based model highlighted the value of MRI radiomics in tumor differentiation. This clinically practical tool may support preoperative diagnosis using routine MRI, potentially facilitating more timely treatment decisions.

In conclusion, the MRI-based radiomics model enabled accurate preoperative discrimination between spinal osteosarcoma and chondrosarcoma.

Glymphatic dysfunction is proposed as a final common pathway to dementia. Cognitive impairment following ischemic stroke can gradually worsen, potentially leading to post-stroke dementia. This study aimed to examine the changes in glymphatic function in post-stroke patients and explore its relationship with cognition.

A total of thirty-two post-stroke patients and twenty-seven healthy controls (HCs) matched for age, sex, and educational level were enrolled in this study. All participants underwent neurological MRI scans and comprehensive cognitive assessments six months following the onset of the stroke. Three glymphatic markers derived from MRI were quantified, including diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, choroid plexus volume (CPV), and enlarged perivascular spaces (PVS) volume. The changes in glymphatic markers and their correlations with cognitive scores were analyzed.

Post-stroke patients exhibited a significantly decreased DTI-ALPS index (p < 0.001) and an increased CPV (p < 0.001) compared to HCs, while no significant difference was observed in PVS volume. Correlation analysis revealed that the DTI-ALPS index was positively correlated with Digit Span Test (r = 0.426, p = 0.015) and Digit Symbol Substitution Test (rs = 0.363, p = 0.041) scores, and PVS volume showed a positive correlation with Trail Making Test-B scores (rs = 0.391, p = 0.027). After adjusting for confounding factors, multiple linear regression analyses indicated that enlarged PVS volume was independently associated with worse performance in Trail Making Test-B (β = 0.428, p = 0.010).

The findings demonstrated that glymphatic dysfunction, as indicated by a reduced DTI-ALPS index and increased CPV volume, was evident in post-stroke patients and significantly linked to impairments in specific cognitive domains, including working memory, processing speed, and executive function. These observations supported the hypothesis that glymphatic impairment may represent a key mechanistic pathway underlying post-stroke cognitive impairment (PSCI). To further elucidate the causal relationships and identify potential therapeutic targets, future studies incorporating larger cohorts, longitudinal designs, and region-specific PVS analyses are warranted.

Post-stroke patients exhibited a reduced DTI-ALPS index and an increased CPV, potentially reflecting impaired glymphatic function. Furthermore, these metrics were associated with specific cognitive domains.

Inflammatory myofibroblastic tumor (IMT) is a neoplasm originating from mesenchymal tissue and can occur in multiple parts of the body, such as the lungs, abdomen, pelvis, and retroperitoneum. Although the lung is a relatively common site for IMT, airway involvement in adults is rare, and most reported cases involve the central airway. Reports of IMT arising within the bronchus are uncommon.

We, herein, report the case of a 72-year-old male patient with bronchial IMT who was admitted due to a recurrent cough that worsened over two weeks. Tumor markers showed no significant elevation, and imaging examinations suggested a tumor in the left upper lobe bronchus. Due to the suspicion of malignancy, the patient underwent thoracoscopic left upper lobectomy. Postoperative pathological examination revealed an inflammatory myxoid myofibroblastic tumor of the left upper lobe bronchus. During a 12-month postoperative follow-up, no significant signs of metastasis or recurrence were observed.

We have reported the case of endobronchial IMT in an adult, with a degree of contrast enhancement on CT lower than that previously reported for intratracheal IMT. The tumor lacks specific clinical symptoms and laboratory findings, which poses a challenge for accurate and timely preoperative diagnosis. Based on literature reports, in patients with recurrent cough, hemoptysis, or dyspnea, if CT shows a smoothly marginated endobronchial nodule with mild enhancement, the possibility of this disease should be considered.

Mediastinal cholesterol granuloma (MCG) is an exceedingly rare condition, with a limited number of cases reported worldwide. The clinical and imaging characteristics of MCG remain poorly understood and often lead to misdiagnosis. This case report of a young female patient contributes to the literature by summarizing the clinical features, imaging findings, and differential diagnosis of MCG in a demographic category rarely described in previous reports.

A 30-year-old female with a history of community-acquired pneumonia, pulmonary tuberculosis (cured), and syphilis was incidentally found to have an anterior mediastinal mass on imaging. This patient had no history of trauma or other risk factors related to the onset of MCG. Meanwhile, the gender and age characteristics were also different from those commonly seen in the literature. Surgical resection at our hospital confirmed the diagnosis of thymic cholesterol granuloma. Literature review identified 24 reported cases of MCG, predominantly in older males (94.74%; average age, 58.3 years), with a geographic distribution across Europe, East Asia, and North America (36.8%, 31.6%, and 26.3%, respectively). Notably, three of the cases involved young and middle-aged patients with a history of chest trauma. The imaging features varied, with magnetic resonance imaging (MRI) showing low signal (indicating cholesterol crystals) or high signal intensity (due to methemoglobin) on T1/T2-weighted images. Positron emission tomography (PET) scans typically revealed high uptake signals attributed to chronic granulomatous inflammation.

MCG is a rare anterior mediastinal lesion with nonspecific imaging features. A history of dyslipidemia or chest trauma combined with compatible imaging findings should prompt consideration of MCG in the differential diagnosis. The possibility of MCG should also be considered in young women with a history of tuberculosis or syphilis. This case highlights the importance of recognizing atypical presentations of MCG to reduce misdiagnoses and guide appropriate management.

The objective is to develop and compare risk prediction models for Iodine Contrast Media (ICM)-related Acute Adverse Reactions (AAR) in patients without a prior history of such reactions, and to construct a nomogram based on the superior model.

546 patients without a history of ICM-related AAR who underwent ICM administration during CT contrast-enhanced scan were retrospectively enrolled, and divided into training (n=234), test (n=101), and external validation (n=211) sets. Clinical, medication information, and environmental factors were collected. Features were selected by univariate logistical analysis and least absolute shrinkage and selection operator, and four Machine Learning (ML) models, including Logistic Regression (LR), decision tree, k-nearst neighbors and linear support vector classification were used to construct ICM-related AAR risk prediction models were developed and evaluated using AUC, accuracy and F1 score. A nomogram was constructed based on the superior model.

History of ICM exposure and allergy due to other factors, hypertension, type of ICMs, ICM dose, oral metformin, hyperglycaemia, and glomerular filtration rate were selected for modeling (all p < 0.05). The LR model demonstrated superior performance, with AUCs of 0.894 (test set) and 0.814 (external validation), and was used to construct a clinically applicable nomogram.

The LR-based model effectively predicts ICM-related AAR risk using readily available clinical variables. It offers a practical tool for identifying high-risk patients prior to ICM administration, facilitating preventive measures.

LR can predict the risk of ICM-related AAR well in patients without a history of ICM-related AAR, and the corresponding nomogram is provided.

Construct and compare multiple machine learning models to predict lymph node (LN) metastasis in cervical cancer, utilizing radiomic features extracted from preoperative multi-parametric magnetic resonance imaging (MRI).

This study retrospectively enrolled 407 patients with cervical cancer who were randomly divided into a training cohort (n=284) and a validation cohort (n=123). A total of 4065 radiomic features were extracted from the tumor regions of interest on contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging for each patient. The Mann-Whitney U test, Spearman correlation analysis, and selection operator Cox regression analysis were employed for radiomic feature selection. The relationship between MRI radiomic features and LN status was analyzed using five machine-learning algorithms. Model performance was evaluated by measuring the area under the receiver-operating characteristic curve (AUC) and accuracy (ACC). Moreover, Kaplan–Meier analysis was used to validate the prognostic value of selected clinical and radiomic characteristics.

LN metastasis was pathologically detected in 24.3% (99/407) of patients. Following a three-step feature selection, 18 radiomic features were employed for model construction. The XGBoost model exhibited superior performance compared to other models, achieving an AUC, accuracy, sensitivity, specificity, and F1 score of 0.9268, 0.8969, 0.7419, 0.9891, and 0.8364, respectively, on the validation set. Additionally, Kaplan−Meier curves indicated a significant correlation between radiomic scores and progression-free survival in cervical cancer patients (p < 0.05).

Among the machine learning models, XGBoost demonstrated the best predictive ability for LN metastasis and showed prognostic value through its radiomic score, highlighting its clinical potential.

Machine learning-based multi-parametric MRI radiomic analysis demonstrated promising performance in the preoperative prediction of LN metastasis and clinical prognosis in cervical cancer.

Microsurgical treatment of posterior circulation aneurysms remains challenging due to their deep location, complex anatomical exposure, and close proximity to critical neurovascular structures. Ensuring adequate collateral circulation is paramount for preventing ischemic complications. Indocyanine Green (ICG) and Fluorescein Video Angiography (FL-VAG) have emerged as effective intraoperative tools for assessing cerebral perfusion and guiding surgical decision-making.

We report the case of a 29-year-old male presenting with a thunderclap headache, nausea, and vomiting, subsequently diagnosed with a fusiform aneurysm at the P2-P3 junction of the left posterior cerebral artery. The patient underwent a subtemporal approach with partial posterior petrosectomy for aneurysm clipping and remodeling. Initially, an STA-P3 and PITA-P3 bypass were considered; however, intraoperative ICG and FL-VAG confirmed sufficient retrograde collateral flow, allowing the bypass procedure to be avoided. Postoperative imaging demonstrated patent circulation in the occipitotemporal region without ischemic compromise.

This case highlights the crucial role of intraoperative fluorescence imaging in refining surgical strategies for complex aneurysm clipping. ICG and FL-VAG enhance surgical precision by providing real-time perfusion assessment, reducing the need for additional vascular interventions, and improving patient outcomes.

To address insufficient segmentation accuracy in athletes' soft tissue injury analysis, this study proposes an enhanced Swin-Unet model with multi-scale feature fusion via the FPN (Feature Pyramid Network) and an adaptive window selection mechanism for dynamic receptive field adjustment.

A weighted hybrid loss function integrating Dice Loss, Cross-Entropy Loss, and boundary auxiliary loss optimizes segmentation precision and boundary recognition.

Evaluated on the OAI-ZIB dataset using 10-fold cross-validation, the model achieves a DSC (Dice Similarity Coefficient) of 0.978, outperforming baseline Swin-Unet and mainstream architectures. Superior performance is demonstrated in IoU (Intersection over Union) (0.968) and boundary Hausdorff distance (3.21), alongside significantly reduced diagnosis time (6.0 minutes vs. 16.8 minutes manually).

This framework enhances real-time medical imaging analysis for athlete injuries, offering improved accuracy, efficiency, and clinical utility in soft tissue segmentation tasks.

To evaluate the correlation between the effective cross-sectional area (eCSA) of the rotator cuff muscle measured using Dixon MRI and the outcomes of the Biodex Isokinetic Test.

The cross-sectional area (CSA) of the subscapularis (SSc), supraspinatus (SST), and infraspinatus+teres minor (ISTM) muscles of 87 patients who had undergone shoulder MRI and Biodex Isokinetic Test were measured in the oblique sagittal Y-view. The eCSA was calculated by multiplying the CSA by (1-fat fraction). Eight shoulder movements (FL60, EX60, FL180, EX180, ER60, IR60, ER180, and IR180) each assessed using four parameters (peak torque [PT], peak torque/body weight, torque at 30° [TQ30], and total work) were recorded on Biodex. Pearson correlation coefficients were calculated between eCSA and Biodex outcomes. Univariate regression analyses were conducted to identify the factors influencing the Biodex results. General linear models were applied to confirm the correlations between the eCSA and 32 Biodex parameters after adjusting for these factors.

The eCSA of the SSc, SST, and ISTM exhibited significant correlations with TQ30 at IR180 (r=0.549) and FL60 (r=0.522), PT at ER60 (r=0.656) and EX60 (r=0.575), and PT at ER60 (r=0.674) and FL180 (r=0.626), respectively. Age, sex, SST, and SSc tears were identified as factors influencing the Biodex results. FL60TQ30, EX60PT, and ER60PT exhibited significant associations with the eCSA of SSc, SST, and ISTM, respectively, after adjusting for these factors.

eCSA may be a useful quantitative imaging marker for assessing the function of the rotator cuff muscle. FL60TQ30, EX60PT, and ER60PT are useful Biodex indices for SSc, SST, and ISTM, respectively.

Liver fibrosis is a key pathological process that can progress to cirrhosis and liver failure. Although magnetic resonance elastography (MRE) is an established noninvasive method for fibrosis staging, its clinical application is limited by hardware dependence. The diagnostic utility of diffusion-weighted imaging-based virtual MRE (vMRE) and B1-corrected T1 mapping in liver fibrosis assessment remains to be further investigated.

Forty rabbits were included in the final analysis: CCl4-induced fibrosis (n=33) and control (n=7). Following Gd-EOB-DTPA administration, DWI and T1 mapping sequences were executed at 5 and 10 minutes. Diagnostic efficacy and correlations of vMRE and T1 mapping in a rabbit liver fibrosis model were evaluated.

Rabbits were classified into three groups: Control (n=7), Nonadvanced fibrosis (F1-F2, n=20), and Advanced fibrosis (F3-F4, n=13). The AUC values for T1post_5min, T1post_10min, rΔT1_10min, and μdiff in distinguishing controls from nonadvanced and advanced fibrosis groups were (0.78, 0.82, 0.71), (0.82, 0.85, 0.77), and (0.62, 0.69, 0.74), respectively, with μdiff showing (0.90, 0.93, 0.66). A significant positive correlation existed between μdiff and liver fibrosis grade (r=0.534, p<0.001).

μdiff correlated well with fibrosis severity and effectively identified fibrotic livers, but showed limited ability to distinguish fibrosis stages, likely due to overlapping tissue stiffness. B1-corrected T1 mapping offered complementary functional information, with the 10-minute post-contrast time point providing the best staging performance, thereby enhancing the overall diagnostic value.

Gd-EOB-DTPA-enhanced T1 mapping and DWI-based vMRE provide substantial noninvasive assessment of liver fibrosis.

Intracranial hemorrhage (IH) causes dementia and Alzheimer’s disease in the later stages. Until now, the accurate, early detection of IH, its prognosis, and therapeutic interventions have been a challenging task. Objective: A Multimodal Joint Fusion Sentiment Analysis (MJFSA) framework is proposed for the early detection and classification of IH, as well as sentiment analysis to support prognosis and therapeutic report generation.

MJFSA integrates radiological images and the radiological clinical narrative reports (RCNRs). In the proposed MJFSA model, MRI brain images are enhanced using the modified Contrast Limited Adaptive Histogram Equalization (M-CLAHE) algorithm. Enhanced images are processed with the proposed Tuned Temporal-GAN (Tuned-T-GAN) algorithm to generate temporal images. RCNRs are generated for temporal images using the Microsoft-Phi2 language model. Temporal images are processed with the Tuned-Vision Image Transformer (T-ViT) model to extract image features. On the other hand, the Bio-Bidirectional Encoder Representation Transformer (Bio-BERT) processes the RCNR texts for text feature extraction. Temporal image and RCNR text features are used for IH classification, such as intracerebral hemorrhage (ICH), epidural hemorrhage (EDH), subdural hemorrhage (SDH), and intraventricular hemorrhage (IVH), resulting in sentiment analysis for prognosis and therapeutic reports.

The MJFSA model has achieved an accuracy of 96.5% in prognosis sentiment analysis and 94.5% in therapeutic sentiment analysis.

The Multimodal Joint Fusion Sentiment Analysis (MJFSA) framework detects IH and classifies it using sentiment analysis for prognosis and therapeutic report generation.

The MJFSA model’s prognosis and therapeutic sentiment analysis report aims to support the early identification and management of risk factors associated with dementia and Alzheimer’s disease.

Artificial intelligence has significantly enhanced disease diagnosis in healthcare, particularly through Deep Learning (DL) and Federated Learning (FL) approaches. These technologies have shown promise in detecting ocular diseases using medical imaging while addressing challenges related to data privacy and security. FL enables collaborative learning without sharing sensitive medical data, making it an attractive solution for healthcare applications. This systematic review aims to analyze the advancements in AI-driven ocular disease detection, with a particular focus on FL-based approaches. The article evaluates the evolution, methodologies, challenges, and effectiveness of FL in enhancing diagnostic accuracy while ensuring data confidentiality.

The systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure transparency and reliability. Research articles published between 2017 and 2024 were identified using academic databases, including Web of Science, Scopus, IEEE Xplore, and PubMed. Studies focusing on DL and FL models for detecting ocular diseases were selected based on predefined inclusion and exclusion criteria. A comparative analysis of the methodologies, architectures, datasets, and performance metrics of different FL models has been presented.

The findings indicated that FL preserves data privacy while achieving diagnostic performance comparable to traditional centralized AI models. Various FL models, including FedAvg and FedProx, have been implemented for ocular disease detection, with high accuracy and efficiency. However, challenges, such as data heterogeneity, communication efficiency, and model convergence, persist.

FL represents a promising approach for ocular disease detection, balancing diagnostic accuracy with data privacy. Future research may focus on optimizing FL frameworks for improving scalability, communication efficiency, and integrating advanced privacy-preserving techniques.

This article presents a case of a patient with a broad ligament perivascular epithelioid cell tumor (PEComa), focusing on the analysis of its imaging features in CT and MRI to enhance understanding and awareness of this rare tumor.

This article reports a case of a 27-year-old married woman who was found to have a pelvic mass two years ago. After detailed examination at our hospital, imaging studies, including enhanced CT and MRI, revealed a cystic-solid lesion in the left adnexal area, with preoperative considerations of ovarian cystadenoma or uterine leiomyoma. She was referred to a specialized obstetrics and gynecology hospital for surgery, and the postoperative pathology was diagnosed as PEComa. She has been undergoing regular follow-up at our hospital post-surgery. One year after the operation, her laboratory tests showed no significant abnormalities, and imaging studies did not reveal any signs of metastasis.

Uterine broad ligament PEComa is a rare tumor, and accurate imaging features and classification criteria can aid in improving preoperative diagnosis. A deeper understanding of the clinical and imaging characteristics of this rare disease is significant for enhancing diagnostic accuracy and treatment outcomes.

Ultrasound is routinely used for thyroid nodule diagnosis, yet distinguishing benign from malignant TI-RADS category 4 nodules remains challenging. This study has integrated two-dimensional ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) features via machine learning to improve diagnostic accuracy for these nodules.

A total of 117 TI-RADS 4 thyroid nodules from 108 patients were included and classified as benign or malignant based on pathological results. Two-dimensional ultrasound, CEUS, and SWE were compared. Predictive features were selected using LASSO regression. Feature importance was further validated using Random Forest, SVM, and XGBoost algorithms. A logistic regression model was constructed and visualized as a nomogram. Model performance was assessed using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).

Malignant nodules exhibited significantly elevated serum FT3, FT4, FT3/FT4, TSH, and TI-RADS scores compared to benign lesions. Key imaging discriminators included unclear boundaries, aspect ratio ≥1, low internal echo, microcalcifications on ultrasound; enhancement degree, circumferential enhancement, and excretion on CEUS; and elevated SWE values (Emax, Emean, Esd, etc.) and altered CEUS quantitative parameters (PE, WiR, WoR, etc.) (all P< 0.05). A nomogram integrating four optimal predictors, including Emax, FT4, TI-RADS, and ∆PE, demonstrated robust predictive performance upon validation by ROC, calibration, and DCA curve analysis.

The nomogram incorporating Emax, FT4, TI-RADS, and ∆PE showed high predictive accuracy, particularly for papillary carcinoma in TI-RADS 4 nodules. Its applicability may, however, be constrained by the single-center retrospective design and limited pathological coverage.

The multimodal ultrasound-based machine learning model effectively predicted malignancy in TI-RADS category 4 thyroid nodules.

We developed and validated a novel CT-based radiomics nomogram aimed at improving the differentiation between checkpoint inhibitor-related pneumonitis (CIP) and COVID-19 pneumonia, addressing the persistent clinical uncertainty in pneumonia diagnosis.

A total of 97 patients were enrolled. CT image segmentation was performed, extracting 1,688 radiomics features. Feature selection was conducted using variance thresholding, the least absolute shrinkage and selection operator (LASSO) regression, and the Select K Best methods, resulting in the identification of 33 optimal features. Several classification models (K-Nearest Neighbors [KNN], Support Vector Machine [SVM], and Stochastic Gradient Descent [SGD]) were trained and validated using a 70:30 split and fivefold cross-validation. A radiomics nomogram was subsequently developed, incorporating the radiomics signature (Rad-score) alongside clinical factors. It was assessed based on area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA).

The SVM classifier exhibited the highest performance, achieving an AUC of 0.988 in the training cohort and 0.945 in the validation cohort. The constructed radiomics nomogram demonstrated a markedly improved predictive accuracy compared to the clinical model alone (AUC: 0.853 vs. 0.810 in training; 0.932 vs. 0.924 in validation). Calibration curves indicated a strong alignment of the model with observed outcomes, while DCA confirmed a greater net benefit across various threshold probabilities.

A radiomics nomogram integrated with radiomics signatures, demographics, and CT findings facilitates CIP differentiation from COVID-19, improving diagnostic efficacy.

Radiomics acts as a potential modality to supplement conventional medical examinations.

Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality. Accurate differentiation of tumor grade is crucial for prognosis and treatment planning. This study aimed to evaluate the diagnostic value of dual-source CT dual-energy technology parameters in distinguishing CRC differentiation grades.

A retrospective analysis was conducted on 87 surgically and pathologically confirmed CRC patients (64 with medium-high differentiation and 23 with low differentiation) who underwent dual-source CT dual-energy enhancement scanning. Normalized iodine concentration (NIC), spectral curve slope (K), and dual-energy index (DEI) of the tumor center were measured in arterial and venous phases. Differences in these parameters between differentiation groups were compared, and ROC curve analysis was performed to assess diagnostic efficacy.

The low-differentiation group exhibited significantly higher NIC, K, and DEI values in both arterial and venous phases compared to the medium-high differentiation group (P < 0.01). In the arterial phase, NIC, K, and DEI yielded AUC values of 0.920, 0.770, and 0.903, respectively, with sensitivities of 95.7%, 65.2%, and 91.3%, and specificities of 82.8%, 75.0%, and 75.0%, respectively. In the venous phase, AUC values were 0.874, 0.837, and 0.886, with sensitivities of 91.3%, 82.6%, and 91.3%, and specificities of 68.75%, 75.0%, and 73.4%. NIC in the arterial phase showed statistically superior diagnostic performance compared to K values (P < 0.05).

Dual-energy CT parameters, particularly NIC in the arterial phase, demonstrate high diagnostic accuracy in differentiating CRC grades. These findings suggest that quantitative dual-energy CT metrics can serve as valuable non-invasive tools for tumor characterization, aiding in clinical decision-making. Study limitations include its retrospective design and relatively small sample size.

NIC, K, and DEI values in dual-energy CT scans are highly effective in distinguishing CRC differentiation grades, with arterial-phase NIC showing the highest diagnostic performance. These parameters may enhance preoperative assessment and personalized treatment strategies for CRC patients.