BMC Medical Imaging最新文献

Objective: This study aimed to evaluate the diagnostic value of ColorViz fused images from multi-phase computed tomography angiography (mCTA) using GE Healthcare's FastStroke software for newly diagnosed cerebral infarctions in patients with acute ischemic stroke (AIS).

Methods: A total of 106 AIS patients with unilateral anterior circulation occlusion were prospectively enrolled. All patients underwent mCTA scans during the arterial peak phase, venous peak phase, and venous late phase. The vascular information from these mCTA phases was combined into a time-varying color-coded image using GE Healthcare's FastStroke software. All participants also underwent magnetic resonance diffusion-weighted imaging (MR-DWI) within three days. The diagnostic capability of the mCTA ColorViz fusion images for identifying newly diagnosed intracranial infarction was assessed using MR-DWI as the gold standard, focusing on the degree of delayed vascular perfusion and the number of visible blood vessels.

Results: The mCTA ColorViz fusion images revealed ischemic changes in brain tissue, demonstrating a sensitivity of 88.7% for superficial infarctions and 48.5% for deep infarctions. Additionally, the subjective vascular grading score of the mCTA ColorViz fusion images showed a strong negative correlation with the infarct area identified by MR-DWI (r = - 0.6, P < 0.001).

Conclusion: The mCTA ColorViz fusion images produced by FastStroke software provide valuable diagnostic insights for newly diagnosed cerebral infarction in AIS patients. The sensitivity of these images is notably higher for superficial infarctions compared to deep ones. This technique allows for relatively accurate detection of the ischemic extent and the likelihood of infarction in the superficial regions where lesions are located.

Background: PSMA PET/CT emerges as a pivotal technology in the diagnostic landscape of prostate cancer (PCa). It offers a suite of imaging interpretation criteria, notably the maximum standardized uptake value (SUVmax), the molecular imaging prostate-specific membrane antigen score (miPSMA score), and the PSMA reporting and data system (PSMA-RADS). Identifying the most valuable criteria for diagnosing PCa and standardizing imaging interpretation across various tracers is an unresolved question. Our study endeavors to pinpoint the most optimal criteria to enhance the precision of PCa diagnosis, encompassing clinically significant PCa (csPCa), by evaluating the consistency and diagnostic accuracy of these three criteria using two [¹⁸F]-labeled PSMA tracers.

Method: This retrospective analysis spans a five-year period, focusing on patients with clinically suspected or newly diagnosed, treatment-naïve PCa who underwent ¹⁸F-PSMA PET/CT. The study is bifurcated into two segments: 1.A direct comparison assessing the consistency in SUVmax, miPSMA scores, and PSMA-RADS among PSMA PET/CT tracers ([¹⁸F]DCFPyL and [¹⁸F]PSMA-1007) for prostate foci in 24 patients. 2. An analysis of the diagnostic accuracy of these three criteria for both PCa and csPCa across 55 [¹⁸F]DCFPyL and 65 [¹⁸F]PSMA-1007 PET/CT scans, respectively.

Results: 1.Our head-to-head study reveals that SUVmax and miPSMA score exhibit near-perfect consistency, with PSMA-RADS demonstrating substantial consistency. 2. The diagnostic accuracy ranking, considering both PCa and csPCa, stands as miPSMA score ≈ SUVmax > PSMA-RADS for [¹⁸F]DCFPyL PET/CT, contrasting with miPSMA score > SUVmax ≈ PSMA-RADS for [¹⁸F]PSMA-1007 PET/CT.

Conclusion: The miPSMA score outperforms SUVmax and PSMA-RADS in terms of inter-tracer consistency and diagnostic accuracy for the detection of PCa, including csPCa, when comparing [¹⁸F]DCFPyL and [¹⁸F]PSMA-1007 PET/CT scans. This underscores the miPSMA score's potential as a robust criterion for PCa and csPCa diagnosis, holding substantial promise for refining clinical decision-making and patient management strategies.

Clinical trial number: not applicable.

Background: The purpose of our study was to investigate the association between non-alcoholic fatty liver disease (NAFLD) and abdominal aortic aneurysms (AAA) progression using non-enhanced computed tomography (CT) and CT angiography (CTA).

Methods: Patients with AAA and age- and sex-matched healthy subjects who underwent abdominal CTA and non-enhanced CT examination between January 2015 and January 2023 from four hospitals were retrospectively analyzed. Patients with AAA were divided into progression (growth rate > 10 mL/year) and non-progression groups, as well as those with NAFLD and without NAFLD, based on abdominal CT results. The Kaplan-Meier and Cox regression were used to investigate the association between NAFLD and AAA progression.

Results: A total of 151 patients with AAA (mean age: 69.1 ± 10.5 years old, 133 men) were included, among which 66 patients (43.7%) had NAFLD. During a median of 10.7 months (6.0-76.0 months), 57 patients (37.7%) had AAA progression. The prevalence of NAFLD was significantly higher in the AAA group compared to the control group (43.7% vs. 31.1%, p = 0.024). Multivariable regression analysis revealed that the NAFLD was independently associated with AAA progression (HR, 4.28; 95% CI, 2.20-8.31; p < 0.001). The area under curve of combined NAFLD and AAA maximal diameter was 0.857 for predicting AAA progression.

Conclusions: NAFLD on non-enhanced CT is an independent predictor of AAA progression. It can improve the diagnostic efficacy of predicting the progression of abdominal aortic aneurysms.

Clinical trial number: Not applicable. This research is a retrospective analysis.

Objectives: The objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs).

Methods: Eighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively.

Results: One hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility.

Conclusions: The integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning.

Trial registration: ChiCTR2400091906.

Objective: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy.

Methods: Data of 2,591 pulmonary nodules from five medical centers (Zhengzhou People's Hospital, etc.) were collected. Applying exclusion criteria, 370 nodules (78 benign, 292 malignant) with 3D CTR ≥ 50% were selected and randomly split 7:3 into training and validation cohorts. Using R programming, Lasso regression with 10-fold cross-validation filtered features, followed by univariate and multivariate logistic regression to construct the model. Its efficacy was evaluated by ROC, DCA curves and calibration plots.

Results: Lasso regression picked 18 non-zero coefficients from 108 features. Three significant factors-patient age, solid component volume and mean CT value-were identified. The logistic regression equation was formulated. In the training set, the ROC AUC was 0.721 (95%CI: 0.642-0.801); in the validation set, AUC was 0.757 (95%CI: 0.632-0.881), showing the model's stability and predictive ability.

Conclusion: The model has moderate accuracy in differentiating benign from malignant 3D CTR ≥ 50% nodules, holding clinical potential. Future efforts could explore more to improve its precision and value.

Clinical trial number: Not applicable.

Objective: This study aims to investigate the predictive effectiveness of bedside lung ultrasound score (LUS) in conjunction with rapid shallow breathing index (RSBI) and oxygenation index (P/F ratio) for weaning pediatric patients from mechanical ventilation.

Methods: This was a retrospective study. Eighty-two critically ill pediatric patients, who were admitted to the Pediatric Intensive Care Unit (PICU) and underwent mechanical ventilation from January 2023 to April 2024, were enrolled in this study. Prior to weaning, all patients underwent bedside LUS, with concurrent measurements of their RSBI and P/F ratio. Patients were followed up for weaning outcomes and categorized into successful and failed weaning groups based on these outcomes. Differences in clinical baseline data, LUS scores, RSBI and P/F ratios between the two groups were compared. The predictive value of LUS scores, RSBI and P/F ratios for weaning outcomes was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC).

Results: Out of the 82 subjects, 73 (89.02%) successfully weaned, while 9 (10.98%) failed. No statistically significant differences were observed in age, gender, BMI, and respiratory failure-related comorbidities between the successful and failed weaning groups (P > 0.05). Compared to the successful weaning group, the failed weaning group exhibited longer hospital and intubation durations, higher LUS and RSBI, and lower P/F ratios, with statistically significant differences (P < 0.05). An LUS score ≥ 15.5 was identified as the optimal cutoff for predicting weaning failure, with superior predictive power compared to RSBI and P/F ratios. The combined use of LUS, RSBI and P/F ratios for predicting weaning outcomes yielded a larger area under the curve, indicating higher predictive efficacy.

Conclusion: The LUS demonstrates a high predictive value for the weaning outcomes of pediatric patients on mechanical ventilation.

Aim: This study aimed to evaluate the effect of maternal vitamin D use during intrauterine life on fetal bone development using ultrasonographic image processing techniques.

Materials and methods: We evaluated 52 pregnant women receiving vitamin D supplementation and 50 who refused vitamin D supplementation. Ultrasonographic imaging was performed on the fetal clavicle at 37-40 weeks of gestation. The fetal clavicle images were compared with adult male clavicle images. The texture features obtained from these images were used for analysis.

Results: No difference was observed in bone formation and destruction markers between the two groups. However, the texture analysis of ultrasonographic images revealed similarities in the characteristics of fetal clavicles in pregnant women receiving vitamin D supplementation and those of adult male clavicles.

Conclusions: Vitamin D supplementation in pregnancy has significant positive effects on fetal bone maturation besides contributing to maternal bone health. Texture feature analyses using ultrasonographic images successfully demonstrated fetal bone maturation.

Background: Cognitive networks impairments are common in neuropsychiatric disorders like Attention Deficit Hyperactivity Disorder (ADHD), bipolar disorder (BD), and schizophrenia (SZ). While previous research has focused on specific brain regions, the role of the procedural memory as a type of long-term memory to examine cognitive networks impairments in these disorders remains unclear. This study investigates alterations in resting-state functional connectivity (rs-FC) within the procedural memory network to explore brain function associated with cognitive networks in patients with these disorders.

Methods: This study analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ADHD, 49 with BD, 50 with SZ, and 50 healthy controls (HCs). A procedural memory network was defined based on the selection of 34 regions of interest (ROIs) associated with the network in the Harvard-Oxford Cortical Structural Atlas (default atlas). Multivariate region of interest to region of interest connectivity (mRRC) was used to analyze the rs-FC between the defined network regions. Significant differences in rs-FC between patients and HCs were identified (P < 0.001).

Results: ADHD patients showed increased Cereb45 l - Cereb3 r rs-FC (p = 0.000067) and decreased Cereb1 l - Cereb6 l rs-FC (p = 0.00092). BD patients exhibited increased rs-FC between multiple regions, including Claustrum r - Caudate r (p = 0.00058), subthalamic nucleus r - Pallidum l (p = 0.00060), substantia nigra l - Cereb2 l (p = 0.00082), Cereb10 r - SMA r (p = 0.00086), and Cereb9 r - SMA l (p = 0.00093) as well as decreased rs-FC in subthalamic nucleus r - Cereb6 l (p = 0.00013) and Cereb9 r - Cereb9 l (p = 0.00033). SZ patients indicated increased Caudate r- putamen l rs-FC (p = 0.00057) and decreased rs-FC in subthalamic nucleus r - Cereb6 l (p = 0.000063), and Cereb1 r - subthalamic nucleus r (p = 0.00063).

Conclusions: This study found significant alterations in rs-FC within the procedural memory network in patients with ADHD, BD, and SZ compared to HCs. These findings suggest that disrupted rs-FC within this network may related to cognitive networks impairments observed in these disorders.

Clinical trial number: Not applicable.

Background: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality.

Methods: We enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements.

Results: Scan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI.

Conclusion: Integrating deep learning-based reconstruction methods into standard clinical workflows has the potential to the promise of accelerating image acquisition, enhancing image clarity, and increasing patient throughput, thereby optimizing diagnostic efficiency.

Trial registration: Retrospectively registered.

Background: This study aims to evaluate the predictive usefulness of a habitat radiomics model based on ultrasound images for anticipating lateral neck lymph node metastasis (LLNM) in differentiated thyroid cancer (DTC), and for pinpointing high-risk habitat regions and significant radiomics traits.

Methods: A group of 214 patients diagnosed with differentiated thyroid carcinoma (DTC) between August 2021 and August 2023 were included, consisting of 107 patients with confirmed postoperative lateral lymph node metastasis (LLNM) and 107 patients without metastasis or lateral cervical lymph node involvement. An additional cohort of 43 patients was recruited to serve as an independent external testing group for this study. Patients were randomly divided into training and internal testing group at an 8:2 ratio. Region of interest (ROI) was manually outlined, and habitat analysis subregions were defined using the K-means method. The ideal number of subregions (n = 5) was determined using the Calinski-Harabasz score, leading to the creation of a habitat radiomics model with 5 subregions and the identification of the high-risk habitat model. Area under the curve (AUC) values were calculated for all models to assess their validity, and predictive model nomograms were created by integrating clinical features. The internal and external testing dataset is employed to assess the predictive performance and stability of the model.

Results: In internal testing group, Habitat 3 was identified as the high-risk habitat model in the study, showing the best diagnostic efficacy among all models (AUC(CRM) vs. AUC(Habitat 3) vs. AUC(CRM + Habitat 3) = 0.84(95%CI:0.71-0.97) vs. 0.90(95%CI:0.80-1.00) vs. 0.79(95%CI:0.65-0.93)). Moreover, integrating the Habitat 3 model with clinical features and constructing nomograms enhanced the predictive capability of the combined model (AUC = 0.95(95%CI:0.88-1.00)). In this study, an independent external testing cohort was utilized to assess the model's accuracy, yielding an AUC of 0.88 (95%CI: 0.78-0.98).

Conclusion: The integration of the High-Risk Habitats (Habitat 3) radiomics model with clinical characteristics demonstrated a high predictive accuracy in identifying LLNM. This model has the potential to offer valuable guidance to surgeons in deciding the necessity of LLNM dissection for DTC.

Clinical trial number: Not applicable.