BMC Medical Imaging最新文献

Background: Chronic kidney disease induces alterations in the heterogeneity of iron deposition within the basal ganglia. Quantitative analysis of the heterogeneity of iron deposition within the basal ganglia may be valuable for diagnosing chronic kidney disease-related cognitive impairment.

Methods: In this prospective observational cohort study, quantitative susceptibility mapping (QSM) was performed in chronic kidney disease patients. Susceptibility values of each nucleus within the basal ganglia were measured. Radiomic features were extracted from habitats of the basal ganglia on QSM images. Habitat-based models for diagnosing cognitive impairment were constructed using the random forest algorithm. Logistic regression was employed to build the clinical model and the combined model. The performance of each model was evaluated by the receiver operating characteristic (ROC) analysis.

Results: A total of 146 patients (mean age, 51 ± 13 years; 92 male) were included, of which 79 had cognitive impairment. The two habitats-based model achieved an area under the curve of 0.926 (95% CI 0.842-1.000) on the test set, the highest among all prediction models. The two-habitat maps indicated that chronic kidney disease had two distinct patterns of impact on iron deposition in the basal ganglia region. The capability of the two habitats-based model to identify chronic kidney disease-related cognitive impairment was significantly superior to that of the susceptibility values measured in various nuclei (all p < 0.05).

Conclusions: This study innovatively applied a habitat-based quantitative analysis technique to QSM, successfully constructing a model that accurately diagnoses chronic kidney disease-related cognitive impairment.

Trial registration: This study was approved by the Beijing Friendship Hospital Ethics Board (ClinicalTrials.gov Identifier: NCTO5137470) and conducted in accordance with the Declaration of Helsinki ethical standards.

Introduction: Interstitial lung disease (ILD) is an important pulmonary complication of connective tissue disease (CTD). This study aimed to analyze high-resolution computed tomography (HRCT) manifestations of different connective tissue-associated interstitial lung diseases (CTD-ILDs) to improve diagnostic accuracy.

Method: This study retrospectively included 99 patients diagnosed with CTD-ILD between September 2017 and July 2024. Visual assessment and quantitative CT analysis were used to evaluate HRCT manifestations.

Results: The age of the rheumatoid arthritis (RA) group was significantly greater than that of the polymyositis/dermatomyositis (PM/DM) and systemic sclerosis (SSc) groups (p = 0.025 and p = 0.02), with a mean age of 64.4 ± 10 years. The most common HRCT pattern of CTD-ILD was nonspecific interstitial pneumonia (NSIP) (p = 0.008); the adjusted residual > 1.96, usual interstitial pneumonia (UIP) was most frequently observed in the RA group, organizing pneumonia (OP) was most commonly observed in the PM/DM group, and lymphocytic interstitial pneumonia (LIP) was observed only in the primary Sjögren's syndrome (pSjS) group. The CTD-ILD groups exhibited significant differences in bronchiectasis (χ2 = 11.256, p = 0.0022), esophageal dilatation (χ2 = 33.923, p < 0.001), mediastinal lymph node enlargement (χ2 = 10.103, p = 0.041), and thin-walled cysts (χ2 = 14.081, p = 0.006). Adjusted residual > 1.96, esophageal dilatation was commonly observed in the SSc group; bronchiectasis was more common in the RA group; mediastinal lymph node was more common in the pSjS group. Statistically significant differences in the predominance of different CTD-ILDs (χ2 = 20.814, p = 0.0046). The PM/DM group exhibited significant consolidation and reticulation. The extensive honeycombing was present in the RA-ILD group (p = 0.044). Based on logistic binary regression analysis, bronchial dilatation (odds ratio: 4.506, p = 0.005) and extensive honeycombing (odds ratio: 1.282, p = 0.021) were significant predictors of RA-ILD, while lymph node enlargement (odds ratio: 3.314, p = 0.039) and thin-walled cysts (odds ratio: 6.278, p = 0.001) were predictors of pSjS-ILD.

Conclusion: Different types of CTD-ILD have characteristic HRCT manifestations.

Clinical trial number: As this study involved standard clinical procedures and assessments without an experimental treatment protocol, it did not require registration with a public clinical trials registry.

Background: This study aimed to identify cerebral radiomic features related to migraine diagnosis and subtyping into migraine with aura (MwA) and migraine without aura (MwoA) and to develop predictive models based on these markers.

Method: We retrospectively analyzed MR imaging from 88 migraine patients (32 MwA and 56 MwoA) and 49 healthy control subjects (HCs). Features representing the gray matter morphometry and diffusion properties were extracted from participants via histogram analysis. These features were put through an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power for migraine diagnosis and subtyping. Based on the selected features, the predictive ability of the random forest models constructed from the previous sample was tested in an independent sample of 30 patients (10 MwA) and 17 HCs.

Result: No overall differences in total brain volume or gray matter volume were revealed between patients and HCs, or between MwA and MwoA (all P values > 0.05). Six features significantly differed between patients and HCs for migraine diagnosis, and four features distinguished MwA from MwoA for subtyping (all P values < 0.001). Four features were significantly correlated with headache severity score (all P values < 0.01). Based on these relevant features, the random forest models achieved accuracies of 80.9% in distinguishing patients from HCs and 76.7% in differentiating MwA from MwoA in the testing cohort.

Conclusion: Our findings suggest cerebral radiomic alterations in migraine patients may potentially serve as a biomarker to assist in migraine diagnosis and subtyping, contributing to personalized treatment strategy.

Clinical trial number: Not applicable.

Objectives: To explore the value of a T1 mapping-based radiomic model for evaluating liver function.

Methods: From September 2020 to October 2022, 163 patients were retrospectively recruited and categorized into normal liver function group, chronic liver disease group without cirrhosis, Child‒Pugh class A group, and Child‒Pugh class B and C group. Patients were randomly split into training and testing sets. Radiomic features were extracted from T1 mapping images taken both pre- and post-contrast injection, as well as during the hepatobiliary phase (HBP). Radiomic models were constructed to stratify chronic liver disease, cirrhosis and decompensated cirrhosis. Model performance was assessed with receiver operating characteristic curve analysis, and decision curve analysis.

Results: The K-Nearest Neighbors model demonstrated the best generalization across native T1 map, HBP T1 maps and HBP images. In the training set, based on native T1 maps, it achieved accuracies of 0.83, 0.86, and 0.86 in distinguishing chronic liver disease, cirrhosis, and decompensated cirrhosis, with corresponding AUCs of 0.92, 0.92, and 0.95. In the testing set, the accuracies were 0.75, 0.89, and 0.71, with AUCs of 0.79, 0.92, and 0.83, respectively. When using HBP images with T1 maps, the accuracies were 0.72, 0.90, and 0.72 in the testing set in identifying chronic liver disease, cirrhosis, and decompensated cirrhosis with AUCs of 0.82, 0.93, and 0.79, respectively.

Conclusion: Radiomic analysis based on native T1 map, and HBP with or without T1 map images shows promising potential for liver function assessment, particularly in distinguishing cirrhosis.

Background: Precision and operator expertise are critical for bone tumour biopsies. In this study, we investigated the impact of combining a soft guiding template with a laser device on the success rate of computed tomography (CT)-guided bone biopsies and the associated radiation dose.

Methods: A cohort of 114 patients with bone tumours requiring CT-guided biopsies were assigned to the auxiliary device group, utilising a soft guiding template and a laser device. Another 197 patients (control group) underwent biopsies with conventional guiding templates. The χ2 test compared biopsy success rates and concordance rates between biopsy findings and surgical outcomes. Biopsy success rates for limb bones, limb girdles, and axial bones were also compared. Independent sample t-tests analysed differences in age, volume CT dose index (CTDI_vol), dose-length product (DLP), and effective dose (ED) between groups, as well as for limb bones, limb girdles, and axial bones individually.

Results: The biopsy success rate in the auxiliary device group (85.09%) was significantly higher than in the control group (74.62%; P = 0.032). No significant differences were observed for limb girdles (P = 0.40) or axial bones (P = 0.19). However, the biopsy success rate for limb bones was significantly higher in the auxiliary device group (85.51%) than in the control group (70.87%; P = 0.028). The concordance rate between biopsy findings and surgical outcomes did not differ significantly (P = 1.00). CTDI_vol showed no significant differences for limb girdles (P = 0.66), limb bones (P = 0.23), or axial bones (P = 0.8). While DLP (P = 0.41)and ED (P = 0.42) showed no significant differences for limb girdles, they were significantly lower for limb bones (DLP: P = 0.012; ED: P = 0.012) and axial bones (DLP: P = 0.005; ED: P = 0.002) in the auxiliary device group.

Conclusion: The combination of a soft guiding template and laser device significantly improved the success rate of CT-guided bone biopsies, providing a solid histological foundation for early and accurate diagnosis. Furthermore, these devices reduced the associated radiation dose, lowering radiation-related risks for patients.

Background: The tricuspid annular plane systolic excursion/systolic pulmonary artery pressure ratio (TAPSE/sPAP) has limitations in evaluating right ventricle-to-pulmonary artery (RV-PA) coupling, particularly when pulmonary artery pressure cannot be accurately estimated by tricuspid regurgitation or when TAPSE cannot accurately reflect right ventricular systolic function in certain scenarios. Therefore, this study aimed to explore the value of three-dimensional echocardiography (3DE) coupling parameters in assessing RV-PA coupling in patients with pre-capillary pulmonary hypertension (PH).

Methods: Fifty-nine patients with pre-capillary PH were retrospectively recruited. The surrogate "gold standard" of RV-PA coupling was derived from right heart catheterization (RHC) and cardiac magnetic resonance imaging (CMR). The relationships between echocardiographic RV-PA coupling parameters and RHC-CMR coupling standard were analyzed by Pearson's test and Bland‒Altman test. Additionally, 24 chronic thromboembolic pulmonary hypertension (CTEPH) patients were enrolled to explore the changes in echocardiographic RV-PA coupling parameters before and after PEA. Multivariate ordinal regression analysis was performed to identify echocardiographic parameters associated with prognostic risk stratification in pre-capillary PH patients.

Results: 3DE coupling parameters demonstrated strong correlation and good agreement with the RHC-CMR coupling standard. In contrast, TAPSE/sPAP was moderately correlated to the RHC-CMR coupling standard, but showed poor consistency, with a significant bias of 0.44 (95% CI: 0.374, 0.511). Before and after PEA, stroke volume/end-systolic volume (SV/ESV) derived by 3DE remained moderately correlated with pulmonary vascular resistance (PVR) and mean pulmonary artery pressure (mPAP) (r =-0.614, -0.655, P < 0.001), whereas TAPSE/sPAP was only associated with PVR and mPAP in CTEPH patients before PEA (r=-0.605, -0.758, P < 0.001). Multivariate regression analysis revealed TAPSE/sPAP as the strongest predictor of prognostic risk.

Conclusions: 3DE-derived coupling parameters offer a noninvasive and reliable approach for assessing RV-PA coupling in patients with pre-capillary PH, especially for patients who cannot accurately estimate pulmonary artery pressure or have undergone cardiac surgery. 3DE SV/ESV is superior to TAPSE/sPAP for assessing postoperative RV-PA coupling in CTEPH patients, TAPSE/sPAP remains a valuable parameter for prognostic risk stratification in pre-capillary PH patients. Echocardiography can provide valuable information for assessing RV-PA coupling and prognosis in patients with pre-capillary PH. However, the application of echocardiographic coupling parameters should be determined based on the specific clinical context.

Background: Track sealing (TS) with gelatin sponge slurry (GSS) is efficient in reducing pneumothorax after CT-guided lung biopsy. Nodule appearance along the pulmonary track after TS with GSS is a potential issue that has not been previously evaluated.

Methods: A secondary analysis of two studies evaluating the efficacy of lung TS in 710 patients in reducing post-biopsy pneumothorax was performed. Among these patients, 377 had a follow-up CT within 2 months post-biopsy and were retrospectively included in this study (187 had TS with GSS, 83 with saline, and 107 no TS). Imaging findings of the pulmonary track were described. Binary logistic regression was used to determine factors associated with lung track nodules.

Results: Median time between biopsy and follow-up CT was 29 days (range, 1-61). A pulmonary track nodule was detected on follow-up CT in 65/377 (17.2%) patients. Sixty three out of these 65 nodules (97%) were observed in the GSS group. Factors significantly associated with nodules on multivariate analysis were GSS use (odds ratio: 47.4, 95%CI:11.8-189.5; p < .0001) and track length (odds ratio: 1.03, 95%CI:1.01-1.05; p = .009). Nodules were solid in 100%, ovoid in 83.1%, well-defined in 87.7%, and had smooth borders in 96.9%. Thirty-three nodules were still visible on imaging > 6 weeks after the biopsy.

Conclusion: A pulmonary nodule along the biopsy track was detected on follow-up CT in 34% of cases when TS with GSS was performed. Recognition of these nodules on chest imaging is essential to avoid misinterpretation.

Clinical trial number: Not applicable.

Background: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) images, we developed a novel radiomics model. It combined bi-regional features and two machine learning algorithms. The aim of this study was to validate its potential value for preoperative prediction of MVI.

Methods: This retrospective study included 304 HCC patients (training cohort, 216 patients; testing cohort, 88 patients) from three hospitals. Intratumoral and peritumoral volumes of interest were delineated in arterial phase, portal venous phase, and hepatobiliary phase images. Conventional radiomics (CR) and deep learning radiomics (DLR) features were extracted based on FeAture Explorer software and the 3D ResNet-18 extractor, respectively. Clinical variables were selected using univariate and multivariate analyses. Clinical, CR, DLR, CR-DLR, and clinical-radiomics (Clin-R) models were built using support vector machines. The predictive capacity of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results: The bi-regional CR-DLR model showed more gains and gave better predictive performance than the single-regional models or single-machine learning models. Its AUC, accuracy, sensitivity, and specificity were 0.844, 76.9%, 87.8%, and 69.1% in the training cohort and 0.740, 73.9%, 50%, and 84.5% in the testing cohort. Alpha-fetoprotein (odds ratio was 0.32) and maximum tumor diameter (odds ratio was 1.270) were independent predictors. The AUCs of the clinical model and the Clin-R model were 0.655 and 0.672, respectively. There was no significant difference in the AUCs between all the models (P > 0.005).

Conclusion: Based on Gd-EOB-DTPA-enhanced MRI images, we focused on developing a radiomics model that combines bi-regional features and two machine learning algorithms (CR and DLR). The application of the new model will provide a more accurate and non-invasive diagnostic solution for medical imaging. It will provide valuable information for clinical personalized treatment, thereby improving patient prognosis.

Clinical trial number: Not applicable.

Objective: The purpose of this study was to examine the potential predictive impact of the T2-MRI radiomics model on the initial diagnosis of bone metastasis in patients with prostate cancer (PCa).

Methods: We retrospectively analyzed a total of 141 patients with confirmed PCa from clinical pathology records. Among them, 52 cases had bone metastasis and 89 cases did not. By employing a computer, the patients were randomly assigned to either a training group or a test group. Using ITK-SNAP software, we manually outlined T2WI images for all patients and performed radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. In the training group, a single-variable t-test was conducted to identify features strongly associated with PCa bone metastasis. Statistical significance was defined as P < 0.05. After dimensionality reduction, the Lasso model was employed to select the best subset, and a random forest model was established. To evaluate the performance of the radiomics model in predicting PCa bone metastasis in the test group, receiver operating characteristic (ROC) curves and confusion matrices were utilized.

Results: The selected imaging features exhibited a significant correlation with the differential diagnosis of prostate cancer presence or absence of metastasis. The radiomic model demonstrated high predictive efficiency for PCa bone metastasis, achieving accuracy rates of 0.81% and 0.85% in the training and test groups, respectively. The sensitivities were 92% and 93%, and the specificities were 85% and 81%. The area under the curve values were 0.88 and 0.80 for the training and test groups, respectively.

Conclusion: The MRI radiomics method based onT2WI images shows promise in accurately predicting PCa bone metastasis and can serve as a valuable tool for developing clinical treatment plans.

Background: Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. PD is diagnosed by a combination of motor symptoms including bradykinesia, resting tremors, rigidity and postural instability. Prodromal PD is the stage preceding the onset of classic motor symptoms of PD. The diagnosis of prodromal PD remains challenging despite many available diagnostic modalities.

Aim: This scoping review aims to investigate and explore the current diagnostic modalities used to detect prodromal PD, focusing particularly on multimodal imaging analysis and AI-based approaches.

Methods: We adhered to the PRISMA-SR guidelines for scoping reviews. We conducted a comprehensive literature search at multiple databases such as PubMed, Scopus, Web of Science, and the Cochrane Library from inception to July 2024, using keywords related to prodromal PD and diagnostic modalities. We included studies based on predefined inclusion and exclusion criteria and performed data extraction using a standardized form.

Results: The search included 9 studies involving 567 patients with prodromal PD and 35,643 control. Studies utilized various diagnostic approaches including neuroimaging techniques and AI-driven models. sensitivity ranging from 43 to 84% and specificity up to 96%. Neuroimaging and AI technologies showed promising results in identifying early pathological changes and predicting PD onset. The highest specificity was achieved by neuromelanin-sensitive imaging model, while highest sensitivity was achieved by standard 10-s electrocardiogram (ECG) + Machine learning model.

Conclusion: Advanced diagnostic modalities such as AI-driven models and multimodal neuroimaging revealed promising results in early detection of prodromal PD. However, their clinical application as screening tool for prodromal PD is limited because of the lack of validation. Future research should be directed towards using Multimodal imaging in diagnosing and screening for prodromal PD.

Clinical trial number: Not applicable.