Full head-to-head comparison of ultrasonography and CT scan in volumetric quantification of pleural effusion: a systematic review and meta-analysis.

Pleural effusion is a very common clinical finding. Quantifying pleural effusion volume and its response to treatment over time has become increasingly important for clinicians, which is currently performed via computed tomography (CT) or drainage. To determine and compare ultrasonography (US), CT, and drainage agreements in pleural effusion volumetry. Protocol pre-registration was performed a priori at ( https://osf.io/rnugd/ ). We searched PubMed, Web of Science, Embase, and Cochrane Library for studies up to January 7, 2024. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), QUADAS-C, and Consensus-based Standards for the selection of health Measurement Instruments (COSMIN). Volumetric performances of CT, US, and drainage in assessment of pleural effusion volume were evaluated through both aggregated data (AD) and individual participant data (IPD) analyses. Certainty of evidence was evaluated using Grading of Recommendations, Assessment, Development, and Evaluations (GRADE). Six studies were included with 446 pleural effusion lesions. AD results showed a perfect level of agreement with pooled Pearson correlation and intraclass correlation coefficient (ICC) of 0.933 and 0.948 between US and CT. IPD results demonstrated a high level of agreement between US and CT, with Finn's coefficient, ICC, concordance correlation coefficient (CCC), and Pearson correlation coefficient values of 0.856, 0.855, 0.854, and 0.860, respectively. Also, both results showed an overall perfect level of agreement between US and drainage. As for comparing the three combinations, US vs. CT and US vs. drainage were both superior to CT vs. drainage, suggesting the US is a good option for pleural effusion volumetric assessment. Ultrasound provides a highly reliable, to-the-point, cost-effective, and noninvasive method for the assessment of pleural effusion volume and is a great alternative to CT or drainage.

AdaRes: A deep learning-based model for ultrasound image denoising: Results of image quality metrics, radiomics, artificial intelligence, and clinical studies.

PURPOSE: The quality of ultrasound images is degraded by speckle and Gaussian noises. This study aims to develop a deep-learning (DL)-based filter for ultrasound image denoising. METHODS: A novel DL-based filter using adaptive residual (AdaRes) learning was proposed. Five image quality metrics (IQMs) and 27 radiomics features were used to evaluate denoising results. The effect of our proposed filter, AdaRes, on four pre-trained convolutional neural network (CNN) classification models and three radiologists was assessed. RESULTS: AdaRes filter was tested on both natural and ultrasound image databases. IQMs results indicate that AdaRes could remove noises in three different noise levels with the highest performances. In addition, a radiomics study proved that AdaRes did not distort tissue textures and it could preserve most radiomics features. AdaRes could also improve the performance classification using CNNs in different settings. Finally, AdaRes also improved the mean overall performance (AUC) of three radiologists from 0.494 to 0.702 in the classification of benign and malignant lesions. CONCLUSIONS: AdaRes filtered out noises on ultrasound images more effectively and can be used as an auxiliary preprocessing step in computer-aided diagnosis systems. Radiologists may use it to remove unwanted noises and improve the ultrasound image quality before the interpretation.

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer. METHODS: An in-house deep learning-based model called "ClymphNet" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively. RESULTS: Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts. CONCLUSION: A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.

Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi-Center Study.

OBJECTIVES: Ultrasound is widely used in diagnosing carpal tunnel syndrome (CTS). However, the limitations of ultrasound in CTS detection are the lack of objective measures in the detection of nerve abnormality and the operator-dependent nature of ultrasound imaging. Therefore, in this study, we developed and proposed externally validated artificial intelligence (AI) models based on deep-radiomics features. METHODS: We have used 416 median nerves from 2 countries (Iran and Colombia) for the development (112 entrapped and 112 normal nerves from Iran) and validation (26 entrapped and 26 normal nerves from Iran, and 70 entrapped and 70 normal nerves from Columbia) of our models. Ultrasound images were fed to the SqueezNet architecture to extract deep-radiomics features. Then a ReliefF method was used to select the clinically significant features. The selected deep-radiomics features were fed to 9 common machine-learning algorithms to choose the best-performing classifier. The 2 best-performing AI models were then externally validated. RESULTS: Our developed model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.910 (88.46% sensitivity, 88.46% specificity) and 0.908 (84.62% sensitivity, 88.46% specificity) with support vector machine and stochastic gradient descent (SGD), respectively using the internal validation dataset. Furthermore, both models consistently performed well in the external validation dataset, and achieved an AUC of 0.890 (85.71% sensitivity, 82.86% specificity) and 0.890 (84.29% sensitivity and 82.86% specificity), with SVM and SGD models, respectively. CONCLUSION: Our proposed AI models fed with deep-radiomics features performed consistently with internal and external datasets. This justifies that our proposed system can be employed for clinical use in hospitals and polyclinics.

Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?

OBJECTIVES: The tumor microenvironment (TME) consists of cellular and noncellular components which enable the tumor to interact with its surroundings and plays an important role in the tumor progression and how the immune system reacts to the malignancy. In the present study, we investigate the diagnostic potential of the TME in differentiating benign and malignant lesions using image quantification and machine learning. METHODS: A total of 229 breast lesions and 220 cervical lymph nodes were included in the study. A group of expert radiologists first performed medical imaging and segmented the lesions, after which a rectangular mask was drawn, encompassing all of the contouring. The mask was extended in each axis up to 50%, and 29 radiomics features were extracted from each mask. Radiomics features that showed a significant difference in each contour were used to develop a support vector machine (SVM) classifier for benign and malignant lesions in breast and lymph node images separately. RESULTS: Single radiomics features extracted from extended contours outperformed radiologists' contours in both breast and lymph node lesions. Furthermore, when fed into the SVM model, the extended models also outperformed the radiologist's contour, achieving an area under the receiver operating characteristic curve of 0.887 and 0.970 in differentiating breast and lymph node lesions, respectively. CONCLUSIONS: Our results provide convincing evidence regarding the importance of the tumor periphery and TME in medical imaging diagnosis. We propose that the immediate tumor periphery should be considered for differentiating benign and malignant lesions in image quantification studies.

Radiofrequency ablation of parathyroid adenomas causing primary hyperparathyroidism: A report of 27 patients.

PURPOSE: To study treatment outcome of parathyroid adenomas using ultrasound-guided radiofrequency ablation. METHODS: Twenty-seven patients with a single adenoma of the parathyroid gland were included in the study. Using color Doppler ultrasonography, the lesion and its characteristics were determined, and dextrose was injected to dissect the gland from the surrounding structures. The ablation process was done with 6-12 watts of power. RESULTS: No complications were seen in any of the subjects. A significant reduction was seen in serum parathyroid hormone (PTH) and calcium levels after treatment. PTH levels showed a median decrease of 13.8%, and a median decrease of 8.2% was seen in serum calcium levels (p < 0.001). Phosphorus levels did not change significantly after treatment. In 1-month follow-up of patients, the lesion size had decreased considerably. In long-term follow-up, 11 of 20 patients having subsequent imaging had indistinguishable lesions. CONCLUSION: Our results showed that RFA of parathyroid adenomas caused a significant reduction in biomedical indicators of disease and resulted in a significant reduction or disappearance of the lesion in the majority of the patients while having no considerable complications.

COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings.

OBJECTIVES: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients. METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases. RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier. CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis. KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.

Application of shear wave elastography and median nerve cross-section area in the diagnosis and staging of carpal tunnel syndrome: a case-control study.

PURPOSE: Imaging is gaining a more prominent role in the diagnosis of carpal tunnel syndrome (CTS), especially ultrasonography. Shear wave elastography (SWE) is a novel qualitative method to study mechanical changes in tissue. In this study, we aim to assess the role of SWE in diagnosing and staining of the disease. MATERIAL AND METHODS: A total of 124 wrists were included in the study. Seventy wrists had CTS, and 54 were included as the control group. All of the wrists with CTS had staging done with nerve conduction study. All patients underwent ultrasonography by an expert radiologist and had the SWE and cross-section of the median nerve determined. These values were compared among the 2 groups and different stages of CTS. The receiver operating characteristic curve was utilized to assess the diagnostic role of each of the variables. RESULTS: Cross-section area (CSA) and SWE were significantly different between the 2 groups (p = 0.0001). CSA was also significantly different among various stages of CTS. SWE was not significantly different among moderate and severe stages of CTS. Both of the variables had a good ability to distinguish mild CTS from healthy wrists (p = 0.0001). CONCLUSION: SWE can be used in diagnosing CTS and in the staging of the disease.

Predictors of coronavirus disease 19 (COVID-19) pneumonitis outcome based on computed tomography (CT) imaging obtained prior to hospitalization: a retrospective study.

PURPOSE: Computed tomography (CT) has been utilized as a diagnostic modality in the coronavirus disease 19 (COVID-19), while some studies have also suggested a prognostic role for it. This study aimed to assess the diagnostic and prognostic value of computed tomography (CT) imaging in COVID-19 patients. METHODS: This was a retrospective study of fifty patients with COVID-19 pneumonia. Twenty-seven patients survived, while 23 passed away. CT imaging was performed in all of the patients on the day of admission. Imaging findings were interpreted based on current guidelines by two expert radiologists. Imaging findings were compared between surviving and deceased patients. Lung scores were assigned to patients based on CT chest findings. Then, the receiver operating characteristic curve was used to determine cutoff values for lung scores. RESULTS: The common radiologic findings were ground-glass opacities (82%) and airspace consolidation (42%), respectively. Air bronchogram was more commonly seen in deceased patients (p = 0.04). Bilateral and multilobar involvement was more frequently found in deceased patients (p = 0.049 and 0.014, respectively). The mean number of involved lobes was 3.46 ± 1.80 lobes in surviving patients and 4.57 ± 0.60 lobes in the deceased patients (p = 0.009). The difference was statistically significant. The area under the curve for a lung score cutoff of 12 was 0.790. CONCLUSION: Air bronchogram and bilateral and multilobar involvement were more frequently seen in deceased patients and may suggest a poor outcome for COVID-19 pneumonia.

CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold.

OBJECTIVES: The aim of this study was to evaluate if the analysis of sonographic parameters could predict if a thyroid nodule was hot or cold. METHODS: Overall, 102 thyroid nodules, including 51 hyperfunctioning (hot) and 51 hypofunctioning (cold) nodules, were evaluated in this study. Twelve sonographic features (i.e., seven B-mode and five Doppler features) were extracted for each nodule type. The isthmus thickness, nodule volume, echogenicity, margin, internal component, microcalcification, and halo sign features were obtained in the B-mode, while the vascularity pattern, resistive index (RI), peak systolic velocity, end diastolic velocity, and peak systolic/end diastolic velocity ratio (SDR) were determined, based on Doppler ultrasounds. All significant features were incorporated in the computer-aided diagnosis (CAD) system to classify hot and cold nodules. RESULTS: Among all sonographic features, only isthmus thickness, nodule volume, echogenicity, RI, and SDR were significantly different between hot and cold nodules. Based on these features in the training dataset, the CAD system could classify hot and cold nodules with an area under the curve (AUC) of 0.898. Also, in the test dataset, hot and cold nodules were classified with an AUC of 0.833. CONCLUSIONS: 2D sonographic features could differentiate hot and cold thyroid nodules. The CAD system showed a great potential to achieve it automatically. KEY POINTS: • Cold nodules represent higher volume (p = 0.005), isthmus thickness (p = 0.035), RI (p = 0.020), and SDR (p = 0.044) and appear hypoechogenic (p = 0.010) in US. • Nodule volume with an AUC of 0.685 and resistive index with an AUC of 0.628 showed the highest classification potential among all B-mode and Doppler features respectively. • The proposed CAD system could distinguish hot nodules from cold ones with an AUC of 0.833 (sensitivity 90.00%, specificity 70.00%, accuracy 80.00%, PPV 87.50%, and NPV 75.00%).

A Hybrid Multilayer Filtering Approach for Thyroid Nodule Segmentation on Ultrasound Images.

OBJECTIVES: Speckle noise is the main factor that degrades ultrasound image contrast and segmentation failure. Determining an effective filter can reduce speckle noise and improve segmentation performances. The aim of this study was to define a useful filter to improve the segmentation outcome. METHODS: Twelve filters, including median, hybrid median (Hmed), Fourier Butterworth, Fourier ideal, wavelet (Wlet), homomorphic Fourier Butterworth, homomorphic Fourier ideal, homomorphic wavelet (Hmp_Wlet), frost, anisotropic diffusion, probabilistic patch-based (PPB), and homogeneous area filters, were used to find the best filter(s) to prepare thyroid nodule segmentation. A receiver operating characteristic (ROC) analysis was used for filter evaluation in the nodule segmentation process. Accordingly, 10 morphologic parameters were measured from segmented regions to find the best parameters that predict the segmentation performance. RESULTS: The best segmentation performance was reached by using 4 hybrid filters that mainly contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters. The area under the ROC curve for these filters ranged from 0.900 to 0.943 in comparison with the original image, with an area under the curve of 0.685. From 10 morphologic parameters, the area, convex area, equivalent diameter, solidity, and extent can evaluate segmentation performance. CONCLUSIONS: Hybrid filters that contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters have a high potential to provide good conditions for thyroid nodule segmentation in ultrasound images. In addition to an ROC analysis, morphometry of a segmented region can be used to evaluate segmentation performances.

A novel computerised quantification of thyroid vascularity in the differentiation of malignant and benign thyroid nodules.

PURPOSE: Only five percent of thyroid nodules are malignant. It is important to find reliable and at the same time non-invasive methods to identify high-risk nodules. The aim of this study was to determine the diagnostic validity of a morphologic feature-oriented approach of ultrasound study for the identification of malignant thyroid nodules. MATERIAL AND METHODS: Seventy-one thyroid nodules in 71 consecutive patients were evaluated with both ultrasonography (US) and US-assisted fine needle aspiration biopsy (FNAB). Thyroid grey-scale and power Doppler US were performed, and a Windows-based software was designed to process power Doppler US (PDUS) images that were recorded directly by the US device. We provided a histogram graph of coloured pixels and calculated the Malignancy Index to identify the probability of malignancy for each thyroid nodule. RESULTS: Thirty-six nodules (50.7%) were determined to be malignant in FNAB. Area under the receiver operating curve was 0.91 (95% CI: 0.85-0.98) for PDUS-based malignancy index in differentiating malignant thyroid nodules from benign ones. The best cut-off point for malignancy index was determined to be 0.092, with a sensitivity of 86.1% and specificity of 80% in identifying malignant nodules. CONCLUSIONS: This PDUS-driven malignancy index using a contour-finding algorithm approach could accurately and reliably differentiate malignant and benign thyroid nodules. As a pre-FNAB assessment, the malignancy index may be able to reduce the number of patients with nodular thyroid disease undergoing this invasive procedure.

A Clinical Decision Support System Using Ultrasound Textures and Radiologic Features to Distinguish Metastasis From Tumor-Free Cervical Lymph Nodes in Patients With Papillary Thyroid Carcinoma.

OBJECTIVES: This study investigated the potential of a clinical decision support approach for the classification of metastatic and tumor-free cervical lymph nodes (LNs) in papillary thyroid carcinoma on the basis of radiologic and textural analysis through ultrasound (US) imaging. METHODS: In this research, 170 metastatic and 170 tumor-free LNs were examined by the proposed clinical decision support method. To discover the difference between the groups, US imaging was used for the extraction of radiologic and textural features. The radiologic features in the B-mode scans included the echogenicity, margin, shape, and presence of microcalcification. To extract the textural features, a wavelet transform was applied. A support vector machine classifier was used to classify the LNs. RESULTS: In the training set data, a combination of radiologic and textural features represented the best performance with sensitivity, specificity, accuracy, and area under the curve (AUC) values of 97.14%, 98.57%, 97.86%, and 0.994, respectively, whereas the classification based on radiologic and textural features alone yielded lower performance, with AUCs of 0.964 and 0.922. On testing the data set, the proposed model could classify the tumor-free and metastatic LNs with an AUC of 0.952, which corresponded to sensitivity, specificity, and accuracy of 93.33%, 96.66%, and 95.00%. CONCLUSIONS: The clinical decision support method based on textural and radiologic features has the potential to characterize LNs via 2-dimensional US. Therefore, it can be used as a supplementary technique in daily clinical practice to improve radiologists' understanding of conventional US imaging for characterizing LNs.

Differentiation between metastatic and tumour-free cervical lymph nodes in patients with papillary thyroid carcinoma by grey-scale sonographic texture analysis.

PURPOSE: Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, and cervical lymph nodes (LNs) are the most common extrathyroid metastatic involvement. Early detection and reliable diagnosis of LNs can lead to improved cure rates and management costs. This study explored the potential of texture analysis for texture-based classification of tumour-free and metastatic cervical LNs of PTC in ultrasound imaging. MATERIAL AND METHODS: A total of 274 LNs (137 tumour-free and 137 metastatic) were explored using the texture analysis (TA) method. Up to 300 features were extracted for texture analysis in three normalisations (default, 3sigma, and 1-99%). Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features were classified by the first nearest neighbour classifier. RESULTS: Normalisation reflected improvement on the performance of the classifier; hence, the features under 3sigma normalisation schemes through FFPA (fusion Fisher plus the probability of classification error [POE] + average correlation coefficients [ACC]) features indicated high performance in classifying tumour-free and metastatic LNs with a sensitivity of 99.27%, specificity of 98.54%, accuracy of 98.90%, positive predictive value of 98.55%, and negative predictive value of 99.26%. The area under the receiver operating characteristic curve was 0.996. CONCLUSIONS: TA was determined to be a reliable method with the potential for characterisation. This method can be applied by physicians to differentiate between tumour-free and metastatic LNs in patients with PTC in conventional ultrasound imaging.

PRIMARY HYPERPARATHYROIDISM: COMPARING BETWEEN SOLID AND CYSTIC ADENOMAS AND THE EFFICACY OF ULTRASOUND AND SINGLE-PHOTON EMISSION COMPUTED TOMOGRAPHY IN THEIR DIAGNOSIS.

OBJECTIVE: Primary hyperparathyroidism (PHPT) is a disorder that results from abnormal functioning of the parathyroid glands. The purpose of this study was to compare cystic and solid adenomas by analyzing different variables associated with PHPT and parathyroid adenomas (age, calcium, phosphorus, and parathyroid hormone [PTH] levels, adenoma volume) while comparing the efficacy of ultrasound and single-photon emission computed tomography in differentiating between both types of adenoma. METHODS: From 152 patients diagnosed with PHPT between January 2013 and 2014, only 109 patients who had positive ultrasonographic findings for single parathyroid adenoma were included in the study. RESULTS: A total of 26 patients had cystic adenomas and 83 patients had solid adenomas. Sestamibi (MIBI) was negative in 50% of the cystic adenoma group and 27.7% of the solid adenoma group, with an overall technetium-MIBI efficacy of 67%. Age, phosphorus level, and adenoma volume were significantly higher in patients with cystic adenomas (P = .001, P = .02, and P = .02, respectively), whereas calcium and PTH levels were significantly higher in patients with solid adenomas (P = .02, P = .038, respectively). MIBI had a significant correlation with PTH levels (P = .031) and adenoma volume (P = .05) only in patients with solid adenomas. No significant correlation was found between sex and type of parathyroid adenoma. CONCLUSION: The current study is the first to compare age, PTH levels, and adenoma volume between cystic and solid adenoma patients, providing more information for the poorly understood pathology of cystic adenomas. Our findings showed that age and calcium and PTH levels are significantly higher in patients with solid adenomas, whereas adenoma volume and phosphorus levels are significantly higher in patients with cystic adenomas.

Classification of Benign and Malignant Thyroid Nodules Using Wavelet Texture Analysis of Sonograms.

OBJECTIVES: The purpose of this study was to evaluate a computer-aided diagnostic system using texture analysis to improve radiologic accuracy for identification of thyroid nodules as malignant or benign. METHODS: The database comprised 26 benign and 34 malignant thyroid nodules. Wavelet transform was applied to extract texture feature parameters as descriptors for each selected region of interest in 3 normalization schemes (default, µ ± 3σ, and 1%-9%). Linear discriminant analysis and nonlinear discriminant analysis were used for texture analysis of the thyroid nodules. The first-nearest neighbor classifier was applied to features resulting from linear discriminant analysis. Nonlinear discriminant analysis features were classified by using an artificial neural network. Receiver operating characteristic curve analysis was used to examine the performance of the texture analysis methods. RESULTS: Wavelet features under default normalization schemes from nonlinear discriminant analysis indicated the best performance for classification of benign and malignant thyroid nodules and showed 100% sensitivity, specificity, and accuracy; the area under the receiver operating characteristic curve was 1. CONCLUSIONS: Wavelet features have a high potential for effective differentiation of benign from malignant thyroid nodules on sonography.

Classification of breast tumors using sonographic texture analysis.

OBJECTIVES: The purpose of this study was to evaluate a computer-aided diagnostic system with texture analysis to improve radiologists' accuracy in identification of breast tumors as malignant or benign. METHODS: The database included 20 benign and 12 malignant tumors. We extracted 300 statistical texture features as descriptors for each selected region of interest in 3 normalization schemes (default, µ - 3σ, and µ + 3σ, where µ and σ were the mean value and standard deviation, respectively, of the gray-level intensity and 1%-99%). Then features determined by the Fisher coefficient and the lowest probability of classification error + average correlation coefficient yielded the 10 best and most effective features. We analyzed these features under 2 standardization states (standard and nonstandard). For texture analysis of the breast tumors, we applied principle component, linear discriminant, and nonlinear discriminant analyses. First-nearest neighbor classification was performed for the features resulting from the principle component and linear discriminant analyses. Nonlinear discriminant analysis features were classified by an artificial neural network. Receiver operating characteristic curve analysis was used for examining the performance of the texture analysis methods. RESULTS: Standard feature parameters extracted by the Fisher coefficient under the default and 3σ normalization schemes via nonlinear discriminant analysis showed high performance for discrimination between benign and malignant tumors, with sensitivity of 94.28%, specificity of 100%, accuracy of 97.80%, and an area under the receiver operating characteristic curve of 0.9714. CONCLUSIONS: Texture analysis is a reliable method and has the potential to be used effectively for classification of benign and malignant tumors on breast sonography.

Enhanced staging of extrahepatic hepatocellular carcinoma metastasis through dual-tracer PET/computed tomography: a systematic review and meta-analysis.

The aim of this study was to quantify the diagnostic value of dual-tracer PET/computed tomography (CT) with 11C-acetate and fluorodeoxyglucose (FDG) in per-lesion and per-patient and its effect on clinical decision-making for choosing the most appropriate management. The study protocol is registered a priori at https://osf.io/rvm75/. PubMed, Web of Science, Embase, and Cochrane Library were searched for relevant studies until 1 June 2023. Studies regarding the review question were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess bias risk. Per-lesion and per-patient diagnostic performance were calculated for: (1) 11C-acetate alone; (2) FDG alone; and (3) dual tracer of 11C-acetate and FDG. A direct comparison of these three combinations was made. The possible sources of statistical heterogeneity were also examined. We also calculated the percentage change in clinical decision-making when dual-tracer PET/CT was added to conventional imaging routinely used for metastatic evaluation (CT/MRI). Grading of Recommendations, Assessment, Development, and Evaluations tool was used to evaluate the certainty of evidence. Eight studies including 521 patients and 672 metastatic lesions were included. Dual-tracer PET/CT had a per-lesion sensitivity of 96.3% [95% confidence interval (CI), 91.8-98.4%] and per-patient sensitivity of 95.5% (95% CI, 89.1-98.2%) which were highly superior to either of tracers alone. Per-patient specificity was 98.5% (84.1-99.9%) which was similar to either of tracers alone. Overall, 9.3% (95% CI, 4.7-13.9%) of the patients had their management beneficially altered by adding dual-tracer PET/CT to their conventional CT/MRI results. Dual-tracer PET/CT substantially outperforms single-tracer methods in detecting extrahepatic hepatocellular carcinoma metastases, evidencing its reliability and significant role in refining clinical management strategies based on robust diagnostic performance.