BMC Medical Imaging最新文献

Objectives: To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL.

Methods: 83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman's correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models.

Results: Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966-0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895-0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953-0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828.

Conclusion: rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.

Background: To investigate how effectively clinical features and CT-based radiomic features predict the pathological grade of hepatocellular carcinoma (HCC).

Methods: We retrospectively analyzed 108 patients diagnosed with hepatocellular carcinoma who underwent pathological examination between May 2020 and May 2024 at the Second Hospital of Jilin University. All patients underwent laboratory tests and contrast-enhanced computed tomography (CECT) scanning of the liver within one month prior to pathological examination. First, we analyzed laboratory tests, such as alpha fetoprotein (AFP) and des-γ-carboxy prothrombin (PIVKA-II), to identify risk factors associated with the pathological grading of HCC. Then, we built and evaluated the performance of the clinical model. Next, we imported the arterial-phase and venous-phase images of the CECT images into the uAI Research Portal research platform for 'one-stop' processing, which included semiautomatic ROI outlining, feature extraction, dimensionality reduction, model construction and evaluation. To evaluate the model's diagnostic effectiveness, receiver operating characteristic (ROC) curves were produced, and the related accuracy, sensitivity, specificity, and area under the curve (AUC) were computed. The models were compared using the Delong test, and the clinical value of the predictive model was assessed via the use of calibration curves and decision curve analysis (DCA) to quantify the agreement between the model and the actual outcomes.

Results: Poorly differentiated hepatocellular carcinoma (pHCC) is associated with risk variables such as hepatitis C virus antibodies(HCVAb), PIVKA-II, and sex. In the training and validation cohorts, the AUC values of the clinical model were 0.719 and 0.692, respectively; those of the AP model were 0.843 and 0.773; those of the VP model were 0.806 and 0.804; those of the AP + VP model were 0.953 and 0.844; and those of the AP + VP + clinical model were 0.926 (95% CI: 0.88-0.995) and 0.863 (95% CI: 0.711-1). The DCA curves revealed that the overall net benefit of the AP + VP + clinical model was greater than that of the other models and that it had the best diagnostic results.

Conclusions: CT-based radiomic modeling combined with clinical features (sex) and laboratory tests (e.g., AFP and PIVKA-II) can reliably predict the pathological grade of HCC patients prior to surgery.

Background: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the management of cancer patients. Deep learning models excel at segmenting highly metabolic tumors but face challenges in regions with complex anatomy and normal cell uptake, such as the gastro-intestinal tract. Despite these challenges, it remains important to achieve accurate segmentation of gastro-intestinal tumors.

Methods: Here, we present an international multicenter comparative study between a novel organ-focused approach and a whole-body training method to evaluate the effectiveness of training data homogeneity in accurately identifying gastro-intestinal tumors. In the organ-focused method, the training data is limited to cases with intestinal tumors which makes the network trained with more homogeneous data and with stronger presence of intestinal tumor signals. The whole body approach extracts the intestinal tumors from the results of a model trained on the whole-body scans. Both approaches were trained using diffuse large B cell (DLBCL) patients from a large multi-center clinical trial (NCT01287741).

Results: We report an improved mean(±std) Dice score of 0.78(±0.21) for the organ-based approach on the hold-out set, compared to 0.63(±0.30) for the whole-body approach, with the p-value of less than 0.0001. At the lesion level, the proposed organ-based approach also shows increased precision, recall, and F1-score. An independent trial was used to evaluate the generalizability of the proposed method to non-Hodgkin's lymphoma (NHL) patients with follicular lymphoma (FL).

Conclusion: Given the variability in structure and metabolism across tissues in the body, our quantitative findings suggest organ-focused training enhances intestinal tumor segmentation by leveraging tissue homogeneity in the training data, contrasting with the whole-body training approach, which, by its very nature, is a more heterogeneous data set.

Objective: To establish a joint radiomics model based on T1 contrast-enhanced (T1C) imaging and diffusion-weighted imaging (DWI), and investigate its value in predicting the efficacy of high-intensity focused ultrasound (HIFU) in ablating uterine fibroids.

Methods: This multicenter retrospective study included 195 patients with uterine fibroids. Their data were divided into training (n = 120), internal test (n = 30), and external test (n = 45) sets. The radiomic features were extracted from T1C and DWI sequences. Logistic regression was used to develop the T1C, DWI, integration, and joint models, and receiver operating characteristic curves were used to assess model performance. The Delong test was used to compare the predictive efficacies of different models, and the best model was used for external validation and development of the nomogram.

Results: Eight T1C features, six DWI features, and three imaging features were retained for the modeling. The areas under the curve were 0.852 and 0.769 for the integrated model on the training and internal test sets, respectively; 0.857 and 0.824 for the joint model on the training and internal test sets, respectively, which were higher than those of the single-sequence model; and 0.857 for the joint model on the external test set.

Conclusions: A joint radiomics model based on T1C and DWI data can effectively predict the efficacy of HIFU for ablating uterine fibroids and guide the development of individualized clinical treatment plans.

Objectives: The classification of malignant breast nodules into four categories according to the Breast Imaging Reporting and Data System (BI-RADS) presents significant variability, posing challenges in clinical diagnosis. This study investigates whether a nomogram prediction model incorporating automated breast ultrasound system (ABUS) can improve the accuracy of differentiating benign and malignant BI-RADS 4 breast nodules.

Methods: Data were collected for a total of 257 nodules with breast nodules corresponding to BI-RADS 4 who underwent ABUS examination and for whom pathology results were obtained from January 2019 to August 2022. The participants were divided into a benign group (188 cases) and a malignant group (69 cases) using a retrospective study method. Ultrasound imaging features were recorded. Logistic regression analysis was used to screen the clinical and ultrasound characteristics. Using the results of these analyses, a nomogram prediction model was established accordingly.

Results: Age, distance between nodule and nipple, calcification and C-plane convergence sign were independent risk factors that enabled differentiation between benign and malignant breast nodules (all P < 0.05). A nomogram model was established based on these variables. The area under curve (AUC) values for the nomogram model, age, distance between nodule and nipple, calcification, and C-plane convergence sign were 0.86, 0.735, 0.645, 0.697, and 0.685, respectively. Thus, the AUC value for the model was significantly higher than a single variable.

Conclusions: A nomogram based on the clinical and ultrasound imaging features of ABUS can be used to improve the accuracy of the diagnosis of benign and malignant BI-RADS 4 nodules. It can function as a relatively accurate predictive tool for sonographers and clinicians and is therefore clinically useful. ADVANCES IN KNOWLEDGE STATEMENT: we retrospectively analyzed the clinical and ultrasound characteristics of ABUS BI-RADS 4 nodules and established a nomogram model to improve the efficiency of the majority of ABUS readers in the diagnosis of BI-RADS 4 nodules.

Background: Radial artery plaque (RAP) can influence the function of arterial conduits after revascularization and hinder the maturation of arteriovenous fistulas in patients undergoing hemodialysis patients. However, the preferred in vivo sites for RAP development have not been systematically investigated. This study measured and evaluated RAP to map the distribution of RAP in the radial artery (RA) using optical coherence tomography (OCT).

Methods: OCT images of the entire RA in 300 patients at 1 mm intervals were analyzed to assess RAP phenotypes and measure the distance of RAP from the radial artery ostium. The RA was evenly divided into three segments: proximal, middle, and distal. Patients were categorized into two groups: the RAP group (n = 68) and the non-RAP group (n = 232).

Results: Among the 300 patients with 300 radial arteries studied, 68 patients (22.7%) developed 180 distinct RAPs. The distal segment was the most susceptible to RAP formation (51 patients; 17.0%).In plaque level analysis, Most RAPs (55%) were located ≥ 150 mm from the RA ostium. The distal segment exhibited a significantly higher median cumulative plaque index compared with the proximal and middle segments (p = 0.031). Logistic regression analysis identified aging, smoking, diabetes mellitus, and multi-vessel coronary disease (MVCD) as independent risk factors for RAP occurrence.

Conclusions: RAP was observed in 22.7% of patients with acute coronary syndrome (ACS), with a predominant localization in the distal segment, both at the patient and plaque level. Significant risk factors included aging, smoking, diabetes mellitus, and MVCD.

Background: Adhesive small bowel obstruction (ASBO) is a common emergency that requires prompt medical attention, and the timing of surgical intervention poses a considerable challenge. Although computed tomography (CT) is widely used, its effectiveness in accurately identifying bowel strangulation is limited. The potential of radiomics models to predict the necessity for surgical resection in ASBO cases is not yet fully explored.

Objectives: The aim of this study is to identify risk factors for surgical resection in patients with ASBO and to develop a predictive model that integrates radiomic features with clinical data. This model designed to estimate the likelihood of surgical intervention and aid in clinical decision-making for acute ASBO cases.

Methods: From January 2019 to February 2022, we enrolled 188 ASBO patients from our hospital, dividing them randomly into a training cohort (n = 131) and a test cohort (n = 57) using a 7:3 ratio. We collected baseline clinical data and extracted radiomic features from CT images to compute a radiomic score (Rad-score). A nomogram was developed that combines clinical characteristics and Rad-score. The performance of clinical, radiomic, and combined nomogram models was evaluated in both cohorts.

Results: Of the 188 patients, 92 underwent surgical resection, while 96 did not. The nomogram integrated factors such as white blood cell count, duration of obstruction, and preoperative infection indicators (fever, tachycardia, peritonitis), along with CT findings (elevated wall density, thickened wall, mesenteric fluid, ascites, bowel wall gas, small bowel feces, and hyperdensity of mesenteric fat) (p < 0.1). This combined model accurately predicted the need for surgical resection, with area under the curve (AUC) values of 0.761 (95% CI, 0.628-0.893) for the test cohort. Calibration curves showed strong agreement between predicted and observed outcomes, and decision curve analysis validated the model's utility for acute ASBO cases.

Conclusion: We developed and validated a CT-based nomogram that combines radiomic features with clinical data to predict the risk of surgical resection in ASBO patients. This tool offers valuable support for treatment planning and decision-making in emergent situations.

Background: Differentiated thyroid cancer (DTC) accounts for the majority of thyroid cancers. The preoperative diagnosis of extrathyroidal extension (ETE) in DTC patients is highly important. However, two-dimensional ultrasound (2D-US) has several limitations in diagnosing ETE. This study aimed to evaluate the efficiency of OmniView of three-dimensional ultrasound (3D-OmniView) in assessing the ETE of DTC patients compared with that of 2D-US.

Methods: Patients who underwent thyroid surgery for nodules adjacent to the thyroid capsule between February 2016 and January 2018 were prospectively enrolled in this study. Both 2D-US and 3D-OmniView were used to evaluate ETE of thyroid nodules. The definition for ETE in ultrasound images was capsule disruption, or capsule disruption and surrounding tissue invasion. Intraoperative and pathological findings of ETE were considered positive. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the ROC curve (AUC) were calculated.

Results: A total of 176 DTC nodules from 137 patients were included in this study. ETE was identified in 67.0% of the nodules. The sensitivity, accuracy, NPV and AUC of 3D-OmniView for predicting ETE were significantly greater than those of 2D-US. The sensitivity and specificity of 2D-US and 3D-OmniView were 51.7% and 79.7%, respectively (P < 0.001), and 81.0% and 82.8%, respectively (P = 0.809). Both 2D-US and 3D-OmniView showed better efficacy in evaluating ETE in nodules > 1 cm than in evaluating ETE in nodules ≤ 1 cm.

Conclusion: 3D-OmniView was more precise in predicting ETE of DTC nodules than 2D-US. 3D-OmniView is recommended for further evaluation of all thyroid nodules adjacent to the thyroid capsule. ETE was easier to detect by ultrasound for nodules > 1 cm than for nodules ≤ 1 cm.