Current Medical Imaging Reviews最新文献

Background: Ingestion of gastrointestinal foreign bodies (FB) is a common clinical problem worldwide. Approximately 10-20% of FBs require an endoscopic procedure for removal, and < 1% require surgery.

Case description: An 89-year-old male with Alzheimer's disease was hospitalized because of abdominal pain, abdominal distention, vomiting for three days, and cessation of bowel movements for six days. Abdominal computed tomography (CT) scan showed a small intestinal obstruction and an atypical FB in the small intestine. A pill and remaining plastic casing were removed from the small intestine during surgery. FB is a square with four sharp acute angles at its edge. The patient was discharged after two weeks of treatment, and no recurrence or complications were observed during the 6- month follow-up.

Conclusion: Atypical intestinal FBs may cause misdiagnosis and easily lead to serious complications. Therefore, an appropriate radiological examination, such as CT, is necessary for unexplained intestinal obstruction. Symptomatic intestinal FBs should be actively removed to avoid serious complications.

Background Classifying brain tumors with extraordinary precision using images is critical for prognosis and treatment planning. The aberrant proliferation of brain cells characterizes brain tumors. Variations in neuronal development may occur among individuals. The classification of tumors as benign or malignant is contingent upon their rate of growth. A benign tumor remains localized at its site of origin; one that has spread to distant sites is malignant. Brain tumor identification may be difficult due to the unique characteristics of brain tumor cells. Objective This study presents a method that methodically improves the identification of brain tumor cells and the analysis of functional structures through the utilization of sample training that incorporates features extracted from Magnetic Resonance Imaging (MRI) images. In the image enhancement phase, the color information of the MRI image is converted to greyscale, and its margins are sharpened to facilitate the detection of finer details. For specialists or general practitioners to accurately diagnose life-threatening conditions, such as brain tumors, medical images are required. Picture denoising has been identified in recent research as a potentially fruitful area of study. It is critical to perform image cleanup while preserving the sharpness of the boundaries. Methods In this research, a Prompt Multi Level Segmentation Denoising model with a Fragile Correlated Feature Subset (PMLSD-FCFS) model is proposed for accurate denoising of MRI images and to extract the most relevant features set by applying a feature dimensionality reduction model for better brain tumor predictions. Results The proposed model achieves 98.2% accuracy in Multi-Level Image Segmentation and 98.4% accuracy in Fragile Correlated Feature Subset Generation. Conclusion The experimental findings indicated that the model proposed exhibits superior performance compared to the traditional algorithms. Furthermore, it successfully eliminates the noise from the MRI images, and most relevant features are only considered for brain tumor detection, thereby enhancing the accuracy of classification.

Objective: The primary objective of this comparative investigation was to examine the qualitative attributes of image reconstructions utilizing two distinct algorithms, namely OSEM and HYPER Iterative, in total-body 18F- FDG PET/CT under various acquisition durations and injection activities.

Methods: An initial assessment was executed using a NEMA phantom to compare image quality engendered by OSEM and HYPER Iterative algorithms. Parameters such as BV, COV, and CRC were meticulously evaluated. Subsequently, a prospective cohort study was conducted on 50 patients, employing both reconstruction algorithms. The study was compartmentalized into distinct acquisition time and dosage groups. Lesions were further categorized into three size-based groups. Quantifiable metrics including SD of noise, SUV_max, SNR, and TBR were computed. Additionally, the differences in values, namely ΔSUV_max, ΔTBR, %ΔSUV_max, %ΔSD, and %ΔSNR, between OSEM and HYPER Iterative algorithms were also calculated.

Results: The HYPER Iterative algorithm showed reduced BV and COV compared to OSEM in the phantom study, with constant acquisition time. In the clinical study, lesion SUV_max, TBR, and SNR were significantly elevated in images reconstructed using the HYPER Iterative algorithm in comparison to those generated by OSEM (p < 0.001). Furthermore, an amplified increase in SUV_max was predominantly discernible in lesions with dimensions less than 10 mm. Metrics such as %ΔSNR and %ΔSD in HYPER Iterative exhibited improvements correlating with reduced acquisition times and dosages, wherein a more pronounced degree of enhancement was observable in both ΔSUV_max and ΔTBR.

Conclusion: The HYPER Iterative algorithm significantly improves SUV_max and reduces noise level, with particular efficacy in lesions measuring ≤ 10 mm and under conditions of abbreviated acquisition times and lower dosages.

Background: In the realm of cancer studies,the differences among the biological behavior of oligometastatic prostate cancer (OPCa), localized prostate cancer (LPCa), and widely prostate cancer (WPCa) are still unclear.

Objectives: The purpose of our study was to assess the clinical and intravoxel incoherent motion (IVIM) parameters of tumor burden in OPCa. In addition, the correlation between clinical and IVIM parameters and the prostate-specific antigen nadir (PSAN) and time to nadir (TTN) during initial androgen deprivation therapy (ADT) in OPCa was explored. It was found that the IVIM parameters could effectively differentiate LPCa and WPCa, as well as LPCa and OPC. Moreover, Gleason score (GS) was positively correlated with PSAN, while prostate volume was positively correlated with TTN.

Methods: About 54 patients were included in this retrospective study (mean age=74±7.4 years). ADC, D, D*, and f were acquired according to the biexponential Diffusion Weighted Imaging (DWI) model. The Kruskal-Wallis test was used to test the differences in clinical and IVIM parameters among the three groups. The Receiver Operating Characteristic (ROC) curve was used to evaluate the discrimination abilities. The Area Under the Curve (AUC) was compared using the DeLong test. Furthermore, Spearman correlation analysis was performed to assess the correlation between clinical and IVIM parameters of PSAN and TTN during initial ADT with OPCa.

Results: There were significant differences among the three groups observed for age, PSA, GS, ADC, D and D* values (P<0.05). Multi-parameter pairwise comparison results showed that significant differences between LPCa and WPCa were observed for the age, PSA, GS, ADC, D and D* values (P<0.05). However, D* was different between the LPCa and OPCa groups (P=0.032). GS showed a significant positive correlation with PSAN (Rho=0.594, P=0.042), and prostate volume showed a significant positive correlation with TTN (Rho=0.777, P=0.003).

Conclusions: The IVIM parameters can effectively differentiate LPCa and WPCa, as well as LPCa and OPCa. Moreover, there was a certain trend in their distribution, which could reflect the tumor burden of PCa.

Objective: To develop and validate a radiomics-clinical nomogram for the detection of the acquired T790M mutation in patients with advanced non-small cell lung cancer (NSCLC) with resistance after the duration of first-line epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment.

Materials and methods: Thoracic CT was collected from 120 advanced NSCLC patients who suffered progression on first- or second-generation TKIs. Radiomics signatures were retrieved from the entire tumor. Pearson correlation and the least absolute shrinkage and selection operator (LASSO) regression method were adopted to choose the most suitable radiomics features. Clinical and radiological factors were assessed using univariate and multivariate analysis. Three Machine Learning (ML) models were constructed according to three classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), and RandomForest (RF), combining clinical and radiomic features. A nomogram combining clinical features and the rad score signature was built. The predictive ability of the nomogram was assessed by the ROC curve, calibration curve, and decision curve analysis (DCA).

Results: Multivariate regression analysis showed that two clinicopathological characteristics and two radiological features were highly correlated with the acquired T790M mutation, including the progression-free survival (PFS) of first-line EGFR TKIs (P = 0.029), the initial EGFR profile (P = 0.01), vascular convergence (P = 0.043), and air bronchogram (P = 0.030). The AUCs of clinical, radiomics, and combined models using RF classifiers for T790M mutation detection were 0.951 (95% confidence interval [CI] 0.911,0.991), 0.917 (95%CI 0.856,0.978), and 0.961 (95%CI 0.927,0.995) in the training cohort, respectively, higher than those of other classifier models.The calibration curve and Hosmer-Lemeshow Test showed good calibration power, and the DCA demonstrated a significant net benefit.

Conclusion: A radiomics-clinical nomogram based on CT radiomics proved valuable in non-invasively and efficiently predicting the acquired T790M mutation in patients who suffered progression on first-line TKIs.

Background: Unilateral absence of the pulmonary artery (UAPA) is a very rare congenital anomaly.

Objective: To analyze the diagnostic strategy applied to seven patients with UAPA who were examined and subsequently treated at the National Lung Hospital, Hanoi, Vietnam.

Methods: All seven patients, including three pediatric cases (1, 2, and 14 years old) and four adult cases (21, 26, 44, and 53 years old), had a history of recurrent pneumonia, and the clinical symptoms on admission included cough, progressive dyspnea, chest pain, and fatigue. The patients were initially examined clinically, followed by hematological testing, blood biochemistry testing, and chest X-ray radiology. The results suggested UAPA, so echocardiography and contrast-enhanced chest computed tomography (CT) were performed as soon as practical.

Results: The echocardiographic and CT imaging findings confirmed the suspected diagnosis of UAPA in all seven patients, which was accompanied by congenital heart disease in three patients. Three of the seven patients had mild and medium pulmonary hypertension. All seven patients were treated with drugs, which led to improvement in symptoms.

Conclusion: Frontal chest X-ray provided the initial signs suggesting a diagnosis of UAPA. Subsequent echocardiography and contrast-enhanced chest CT were effective diagnostic tools for fast and accurate confirmation of UAPA.

Objective: To explore the application value of a combined model based on multi-parameter MRI radiomics and clinical features in preoperative prediction of lymphatic vascular space invasion (LVSI) in endometrial carcinoma (EC).

Methods: This retrospective study collected the clinicopathological and imaging data of 218 patients with EC in Yuncheng Central Hospital from March 2018 to May 2022. The patients were randomly divided into training group (n=152) and validation group (n= 66) according to the ratio of 7: 3. Based on the ADC, CE-sag, CE-tra, DWI, T2WI-sag-fs, T2WI-tra sequence images of each patient, the region of interest was manually segmented and the features were extracted. The four-step dimensionality reduction method based on max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and radiomics model construction. Independent predictors of clinicopathological features were screened by multivariate logistic regression analysis. The imaging model based on ADC, CE-sag, CE-tra, DWI, T2WI-sag-fs, T2WI-tra single sequence and combined sequence and the fusion model with clinicopathological features were constructed, and the nomogram was made. ROC curve, correction curve and decision analysis curve were used to evaluate the efficacy and clinical benefits of the nomogram.

Results: There was no significant difference in general clinical data between the training and validation groups (P > 0.05). After screening the extracted features, 16 radiomics features were obtained, which were all related to LVSI in EC patients (P < 0.05). The area under the ROC curve (AUC) of the six independent sequence radiomics models in the training group was 0.807, 0.794, 0.826, 0.794, 0.828, 0.824, respectively. The AUC corresponding to the radiomics model constructed by the combined sequence was 0.884, and the diagnostic efficiency was the best, which was verified in the validation group. The AUC of the nomogram constructed by the combined radiomics model and age maximum tumor diameter(MTD), lymph node enlargement (LNE) in the training group and the validation group were 0.914 and 0.912, respectively. The correction curve shows that the nomogram has good correction performance. The decision curve suggests that taking radiomics nomogram to predict LVSI net benefit when the risk threshold is > 10% is better than considering all patients as LVSI+ or LVSI-.

Conclusion: The combined model based on multi-parametric MRI radiomics features and clinical features has good predictive value for LVSI status in EC patients.

Objective: This study aimed to investigate whether there is a correlation between quantitative parameters of dual-energy computed tomography (DECT) and the relative expression of HIF-1α in patients with non-small cell lung cancer (NSCLC) to preliminarily explore the value of DECT in evaluating the hypoxia of tumor microenvironment and tumor biological behavior and provide more information for the treatment of NSCLC.

Methods: This retrospective research included 36 patients with pathologically confirmed NSCLC who underwent dual-energy enhanced CT scans. The quantitative parameters of DECT were analyzed, including iodine concentration, water concentration, the CT values corresponding to 40keV, 70keV, 100keV, and 130keV in arterial and venous phases, and the normalized iodine concentration and the slope of the energy spectrum curve were calculated. Postoperative specimens underwent HIF immunohistochemical staining by two pathologists. Spearman correlation analysis was adopted as the statistical methodology. The data were analyzed by SPSS26.0 statistical software.

Results: Water concentration (r=0.659, P<0.001 and r= 0.632, P<0.001, the CT values corresponding to 100keV (r=0.645, P<0.001 and r= 0.566, P<0.001) and 130keV (r=0.687, P<0.001 and r= 0.682, P<0.001) in arterial and venous phases, and CT value of 70keV in arterial phase (r=0.457, P=0.005) were positively correlated with HIF-1α expression level. There was no correlation among iodine concentration, standardized iodine concentration, CT value of 40keV, λHU, and HIF-1α expression in arterial and venous levels (P >0.05).

Conclusion: The quantitative parameters of DECT have a certain correlation with HIF-1α expression in NSCLC. Moreover, it has been demonstrated that DECT can be used to predict hypoxia in tumor tissues and the prognosis of lung cancer patients.

Background: The prognosis in hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) is challenging due to heterogeneity. Radiomics may enable noninvasive outcome prediction.

Objective: This study aimed to evaluate ultrasound-based radiomics for predicting outcomes in HBV-ACLF.

Methods: We enrolled 264 HBV-ACLF patients, dividing them into a training cohort (n=184) and a validation cohort (n=80). From hepatic ultrasound images, 455 radiomic features were extracted. Radiomics-based phenotypes were identified through unsupervised hierarchical clustering. A radiomic signature was developed using a Cox-LASSO algorithm to predict 30-day mortality. Furthermore, we integrated the signature with independent clinical predictors via multivariate Cox regression to construct a combined clinical-radiomic nomogram (CCR-nomogram). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) assessed performance improvements achieved by adding radiomic features to clinical data.

Results: Both clustering and radiomic signature identified two distinct subgroups with significant differences in clinical characteristics and 30-day prognosis. In the training cohort, the signature achieved a C-index of 0.746, replicated in validation with a C-index of 0.747. The CCR-nomogram achieved C-indices of 0.834 and 0.819 for the training and validation cohorts. Incorporating radiomic features significantly improved the CCRnomogram over the signature and clinical-only models, evidenced by IDI of 0.108-0.264 and NRI of 0.292-0.540 in both cohorts (all p0.05).

Conclusion: Ultrasound-based radiomics offered prognostic information complementary to clinical data and demonstrated potential to enhance outcome prediction in HBV-ACLF.

Introduction: While pulmonary vein filling defects on CT are typically considered diagnostic for thrombus, under certain circumstances, they can be artifactual as a result of flow phenomena.

Case presentation: We report a case of a 53-year-old female with chest pain who was found to have filling defects in pulmonary vein branches on CCTA that were initially treated as thromboses. However, follow-up cardiac MRI was negative for thrombi, and pseudo-thrombosis was therefore diagnosed.

Conclusion: Pulmonary vein pseudo-thrombosis should be considered in the differential diagnosis of pulmonary vein filling defects.