Types Of Nodules Research Articles

A Novel Model Using Serum Thymidine Kinase 1 and Low-dose Computed Tomography Parameters to Predict Three-year Lung Cancer Risk in People with Pulmonary Nodules: A Retrospective Study.

This study was designed to develop a model of serum thymidine kinase 1 protein (STK1p) concentration in combination with low-dose computed tomography (LDCT) to predict the risk of benign pulmonary nodules progressing into lung cancer within three years in a large screening population. The study included a retrospective cohort of 6,841 individuals aged > 30 years who had LDCT-detected pulmonary nodules, but no cancer history or baseline cancer. The outcome was a lung cancer diagnosis recorded within three years after the first detection of pulmonary nodules. The adaptive least absolute shrinkage and selection operator was used to select candidate predictors and fit a logistic model. The model was validated internally by examining discrimination (area under the receiver operating characteristic curve (AUC), calibration (calibration plot)) and net benefit. A web application was developed based on the model. The results showed that the proportion of incident lung cancer cases was 0.79% (n=52). Predictors selected for the model were STK1p and three LDCT parameters (nodule size, type, and count). The AUC of the model was 0.91 (95% confidence interval (CI): 0.86, 0.96). The model had satisfactory discrimination at internal validation (AUC: 0.90 (0.84, 0.96)) and in subgroups (AUC=0.69-0.93). The high-risk group identified by the model exhibited a significantly higher three-year lung cancer risk than the low-risk group (odds ratio (OR): 66.03 (95% CI: 30.49, 162.98)). We concluded that the novel model of STK1p and LDCT parameters together can be used to accurately predict the three-year risk of lung cancer in people with pulmonary nodules.

Thyroid nodules and the risk of cancer based on fine-needle aspiration using Bethesda classification in Riyadh, Saudi Arabia

Background: The presence of thyroid nodules is common and can only be diagnosed through radiographic examination. Fine needle aspiration is an essential technique to identify the type of nodules and their malignancy categorization. This study aims to evaluate the effectiveness of the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) method in the Security Force Hospital, Riyadh, Saudi Arabia, and to estimate the frequency of Bethesda categorization in reporting fine needle aspiration sampling and predicting the malignancy risk in Riyadh. Methods: This is a retrospective chart review that included records of patients with sonographic thyroid nodules and an indicator for fine needle aspiration based on the American Thyroid Association guideline recommendations. The results of FNA were then classified based on the Bethesda system. Data analysis was executed through the Statistical Package for the Social Sciences (SPSS) version 26. Results: This study included 519 patients over three years. The average age was 49.71±12.4. 74.4% were asymptomatic, 50.7% had a thyroid nodule, while 33.9% had a multinodular goiter. 30.6% of the nodules had a size between 1 to 1.49 cm. 42% had smooth borders. The nodules most commonly had a low suspicion of malignancy by sonography, and 22.7% of the patients underwent thyroid surgery; of them, 54.2% had malignancy. Higher Bethesda classes and a sonographic pattern of intermediate suspicion were common predictors associated with a significantly higher risk of malignancy (p-value < 0.001). Conclusion: Thyroid malignancy is commonly detected by thyroid surgery; the use of Bethesda classification is a predictor of thyroid malignancy, and its application in Saudi hospitals should be improved.

Lung nodule type classification in CT images using UNet based segmentation and ANFIS based classification

Lung nodule type classification in CT images using UNet based segmentation and ANFIS based classification

Analysis of the correlation between clinical and imaging features of malignant lung nodules and pathological types.

To explore the correlation between clinical and imaging features of malignant lung nodules and pathology types. Patients with lung nodules admitted to the Affiliated Hospital of Jiangsu University from January 1, 2020 to December 31, 2020 were collected as study subjects, and all of them underwent surgical treatment and were clearly diagnosed by pathology. The correlation between clinical and imaging features and pathological types of lung cancer patients was analyzed. Among them, The pathological types of malignant pulmonary nodules are correlated with age, gender, smoking history, ground glass sign, nodule size, solid to solid ratio, lobulation sign, pleural indentation sign, hair prick sign, CEA, SCCA. The imaging features of ground glass sign and nodule size are most significantly correlated with the pathological type. It was found that, the clinical and imaging characteristics of patients with malignant lung nodules have a certain correlation with the pathological type, and gender, age, smoking history, nodule size, nodule nature, burr sign, pleural depression sign, and tumor markers are of great value for pathological typing.

Single-cell RNA sequencing reveals transdifferentiation of parathyroid chief cells into oxyphil cells in patients with uremic secondary hyperparathyroidism

The parathyroid gland is one of the main organs that regulate calcium and phosphorus metabolism. It is mainly composed of chief cells and oxyphil cells. Oxyphil cell counts are low in the parathyroid glands of healthy adults but are dramatically increased in patients with uremia and secondary hyperparathyroidism (SHPT). Increased oxyphil cell counts are related to drug treatment resistance, but the origin of oxyphil cells and the mechanism of proliferation remain unknown. Herein, three types of parathyroid nodules (chief cell nodules, oxyphil cell nodules and mixed nodules, respectively) excised from parathyroid glands of uremic SHPT patients were used for single-cell RNA sequencing (scRNA-seq), other molecular biology studies, and transplantation into nude mice. Through scRNA-seq of parathyroid mixed nodules from three patients with uremic SHPT, we established the first transcriptomic map of the human parathyroid and found a chief-to-oxyphil cell transdifferentiation characterized by gradual mitochondrial enrichment associated with the uremic milieu. Notably, the mitochondrial enrichment and cellular proliferation of chief cell and oxyphil cell nodules decreased significantly after leaving the uremic milieu via transplantation into nude mice. Remarkably, the phenotype of oxyphil cell nodules improved significantly in the nude mice as characterized by decreased mitochondrial content and the proportion of oxyphil cells to chief cells. Thus, our study provides a comprehensive single-cell transcriptome atlas of the human parathyroid and elucidates the origin of parathyroid oxyphil cells and their underlying transdifferentiating mechanism. These findings enhance our understanding of parathyroid disease and may open new treatment perspectives for patients with chronic kidney disease.

Geochemistry of buried polymetallic nodules from the eastern Pacific Ocean: Implication for the depth-controlled alteration process

Polymetallic nodules are often exposed or semi-buried in surface sediments of abyssal plains, with certain older generations of nodules being deeply buried in the sediment column. Owing to the limitations of sampling, it is unclear whether these nodules have remained intact or have been altered after burial. To explore this question, we carried out a comparative study of the mineralogy and chemistry of polymetallic nodules recovered from the surface sediment and two depth intervals within a sediment column (220–223 cm and 550–555 cm) in the Clarion–Clipperton Zone of the eastern Pacific Ocean. All of the nodules examined have obvious layered structures. The main minerals in the buried nodules are 10 Å vernadite, 7 Å phyllomanganate, and Fe-vernadite, which are characteristic of the hydrogenetic type of polymetallic nodule. However, the in situ Mn/Fe ratios suggest that some microlayers are diagenetic type. With the burial depth, the total content of rare earth elements and yttrium decreased in the bulk nodules, whereas their positive Ce anomalies and diagenetic parts gradually increase. The nodule buried at 220–223 cm sediment depth shows no obvious alterations, whereas the one buried at 550–555 cm has an approximately 200-μm-thick microlayer of continuous diagenetic growth on its surface. Additionally, this GC04_550–555 cm nodule has a microlayer with higher Cu/Ni and Si/Al ratios, located adjacent to its outer diagenetic microlayer, further indicating its alteration after burial. Therefore, the alterations of buried polymetallic nodules may be related to their burial depth. Compared with the sediment layers above and below, the bulk sediment at the 550–555 cm interval has a relatively lower Mn content and higher Fe content, which may be related to the observed alterations in the buried nodule found therein. Our findings on the alterations of polymetallic nodules after burial and the possible impact of the sedimentary environment on those changes provide a theoretical basis for establishing the burial events of these ferromanganese aggregates in the geological history of the study area.

Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach

Background and objectivesEarly detection and diagnosis of thyroid nodule types are important because they can be treated more effectively in their early stages. The types of thyroid nodules are generally stated as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), benign follicular, and papillary follicular. The risk of malignancy for AUS/FLUS is typically stated to be between 5 and 15 %, while some studies indicate a risk as high as 25 %. Without complete histology, it is difficult to classify nodules and these diagnostic operations are pricey and risky. To minimize laborious workload and misdiagnosis, recently various AI-based decision support systems have been developed. MethodsIn this study, a novel AI-based decision support system has been developed for the automated segmentation and classification of the types of thyroid nodules. This system is based on a hybrid deep-learning procedure that makes both an automatic thyroid nodule segmentation and classification tasks, respectively. In this framework, the segmentation is executed with some U-Net architectures such as ResUNet and ResUNet++ integrating with the feature extraction and upsampling with dropout operations to prevent overfitting. The nodule classification task is achieved by various deep nets architecture such as VGG-16, DenseNet121, ResNet-50, and Inception ResNet-v2 considering some accurate classification criteria such as Intersection over Union (IOU), Dice coefficient, accuracy, precision, and recall. ResultsIn analysis, a total of 880 patients with ages ranging from 10 to 90 years were included by taking the ultrasound images and demographics. The experimental evaluations showed that ResUNet++ demonstrated excellent segmentation outcomes, attaining remarkable evaluation scores including a dice coefficient of 92.4 % and a mean IOU of 89.7 %. ResNet-50 and Inception ResNet-v2 trained over the images segmented with UNets have shown better performance in terms of achieving high evaluation scores for the classification accuracy such as 96.6 % and 95.0 %, respectively. In addition, ResNet-50 and Inception ResNet-v2 classified AUS/FLUS from the images segmented with UNets with AUC=97.0 % and 96.0 %, respectively. ConclusionsThe proposed AI-based decision support system improves the automatic segmentation performance of AUS/FLUS and it has shown better performance than available approaches in the literature with respect to ACC, Jaccard and DICE losses. This system has great potential for clinical use by both radiologists and surgeons as well.

Diagnostic value of peripheral blood eosinophils for benign and malignant pulmonary nodule.

This retrospective study aims to assess the diagnostic utility of peripheral blood eosinophil counts in distinguishing between benign and malignant pulmonary nodules (PNs) prior to surgical intervention. We involved patients presenting with PNs measuring ≤30 mm as the primary CT imaging finding prior to surgical procedures at the General Hospital of Northern Theater Command in Shenyang, China, during the period spanning 2021 to 2022. Multivariable logistic regression analysis and receiver operator characteristic curve analysis, along with area under the curve (AUC) calculations, were used to determine the diagnostic value of eosinophil. A total of 361 patients with PN were included, consisting of 135 with benign PN and 226 with malignant PN. Multivariable logistic regression analysis showed that eosinophil percentage (OR = 1.909, 95% CI: 1.323-2.844, P < .001), absolute eosinophil value (OR = 0.001, 95% CI: 0.000-0.452, P = .033), tumor diameter (OR = 0.918, 95% CI: 0.877-0.959, P < .001), nodule type (OR = 0.227, 95% CI: 0.125-0.400, P < .001), sex (OR = 2.577, 95% CI: 1.554-4.329, P < .001), and age (OR = 0.967, 95% CI: 0.945-0.989, P = .004) were independently associated with malignant PN. The diagnostic value of regression model (AUC [95% CI]: 0.775 [0.725-0.825]; sensitivity: 74.3%; specificity: 71.1%) was superior to eosinophil percentage (AUC [95% CI]: 0.616 [0.556-0.677]; specificity: 66.8%; specificity: 51.1%) (Delong test: P < .001). Peripheral blood eosinophil percentage might be useful for early malignant PN diagnosis, and combining that with other characteristics might improve the diagnostic performance.

Mineralogy and Conditions of Formation Genesis of Aggregates of Natural and Sulfide Minerals of the Poldnevskoe Demantoid Deposit (Middle Urals)

Polymineral aggregates of rounded shapes (“nodules”) composed of native and sulfide minerals of Cu, Ni, Fe, Ag, and other elements from vein magnetite–calcite–chrysotile rocks with jewelry demantoid in the Korkodinskoe hypermafic massif are described. A common feature of the six identified types of native sulfide nodules, composed of native copper, heazlewoodite, pentlandite, cuprite, and other native sulfide minerals, is their spheroidal shape, which makes them similar to individual grains of other gangue minerals (calcite, magnetite, etc.). In heazlewoodite–pentlandite nodules, specific symplectites of mercuric silver and nickel copper in heazlewoodite, as well as awaruite in Co–pentlandite, were found. The matching set of ore minerals in the host serpentinite vein mass (native copper, mercuric silver, heazlewoodite, pentlandite, awaruite) and nodules from the vein material indicates their genetic connection and the conjugation of demantoid mineralization with the evolving processes of serpentinization. It was established that the nodules formed at temperatures below 380°C under reducing conditions at very low sulfur fugacity values (10–17–10–27 bar) and oxygen (10–30 bar at 200°C to 10–21 bar at 350°C). For heazlewoodite–pentlandite nodules, such conditions persisted throughout the entire time of their formation, while, for other nodules, the reducing conditions of early parageneses were replaced by oxidative conditions in late parageneses, which is recorded by the replacement of native copper with cuprite. It is assumed that the features of the morphology and structure of native sulfide nodules and the presence of symplectite intergrowths of ore minerals in them are associated with specific conditions created during the decompression of the crust-mantle mixture rising to the surface in the fault zone. The source of the metals was a deep, high-temperature fluid interacting with mafic and ultramafic rocks under reducing conditions at a low water-to-rock ratio.

EP04.04-06 Low-Dose CT Iterative Reconstructions can Impact Lung Nodule Measurements and Type Classification

EP04.04-06 Low-Dose CT Iterative Reconstructions can Impact Lung Nodule Measurements and Type Classification

Incorporation of CEUS and SWE parameters into a multivariate logistic regression model for the differential diagnosis of benign and malignant TI-RADS 4 thyroid nodules.

To investigate the diagnostic value of contrast-enhanced ultrasound (CEUS) quantitative analysis parameters combined with shear wave elastography (SWE) quantitative parameters in the differentiation of benign and malignant ACR TI-RADS category 4 thyroid nodules and to provide a more effective reference for clinical work. We analyzed 187 category 4 nodules, including 132 nodules in the development cohort and 55 nodules in the validation cohort, divided the development cohort into benign and malignant groups, and analyzed the differences in all CEUS and SWE quantitative parameters between the two groups. We selected the highest AUC of the two parameters, performed binary logistic regression analysis with the ACR TI-RADS score and constructed a diagnostic model. ROC curves were applied to evaluate their diagnostic efficacy. 1) The diagnostic model had an AUC of 0.926, sensitivity of 87.5%, specificity of 86.8%, diagnostic threshold of 3, accuracy of 87.12%, positive predictive value of 86.15%, and negative predictive value of 88.06%. 2) The diagnostic model had an AUC of 0.890 in the validation cohort, sensitivity of 81.5%, specificity of 79.6%, and accuracy of 80.00%. The combined multiparameter construction of the nodule diagnostic model can effectively improve the diagnostic efficacy of 4 types of thyroid nodules and provide a new reference index for clinical diagnostic work.

DeepNet model empowered cuckoo search algorithm for the effective identification of lung cancer nodules.

Globally, lung cancer is a highly harmful type of cancer. An efficient diagnosis system can enable pathologists to recognize the type and nature of lung nodules and the mode of therapy to increase the patient's chance of survival. Hence, implementing an automatic and reliable system to segment lung nodules from a computed tomography (CT) image is useful in the medical industry. This study develops a novel fully convolutional deep neural network (hereafter called DeepNet) model for segmenting lung nodules from CT scans. This model includes an encoder/decoder network that achieves pixel-wise image segmentation. The encoder network exploits a Visual Geometry Group (VGG-19) model as a base architecture, while the decoder network exploits 16 upsampling and deconvolution modules. The encoder used in this model has a very flexible structural design that can be modified and trained for any resolution based on the size of input scans. The decoder network upsamples and maps the low-resolution attributes of the encoder. Thus, there is a considerable drop in the number of variables used for the learning process as the network recycles the pooling indices of the encoder for segmentation. The Thresholding method and the cuckoo search algorithm determines the most useful features when categorizing cancer nodules. The effectiveness of the intended DeepNet model is cautiously assessed on the real-world database known as The Cancer Imaging Archive (TCIA) dataset and its effectiveness is demonstrated by comparing its representation with some other modern segmentation models in terms of selected performance measures. The empirical analysis reveals that DeepNet significantly outperforms other prevalent segmentation algorithms with 0.962 ± 0.023% of volume error, 0.968 ± 0.011 of dice similarity coefficient, 0.856 ± 0.011 of Jaccard similarity index, and 0.045 ± 0.005s average processing time.

Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study.

Broad generalization of radiomics-assisted models may be impeded by concerns about variability. This study aimed to evaluate the merit of combatting batch effect (ComBat) harmonization in reducing the variability of voxel size-related radiomics in both phantom and clinical study in comparison with image resampling correction method. A pulmonary phantom with 22 different types of nodules was scanned by computed tomography (CT) with different voxel sizes. The variability of voxel size-related radiomics features was evaluated using concordance correlation coefficient (CCC), dynamic range (DR), and intraclass correlation coefficient (ICC). ComBat and image resampling compensation methods were used to reduce variability of voxel size-related radiomics. The percentage of robust radiomics features was compared before and after optimization. Pathologically differential diagnosis of invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) (AIS-MIA group) was used for clinical validation in 134 patients. Before optimization, the number of excellent features in the phantom and clinical data was 26.12% and 32.31%, respectively. The excellent features were increased after image resampling and ComBat correction. For clinical optimization, the effect of the ComBat compensation method was significantly better than that of image resampling, with excellent features reaching 90.96% and poor features only amounting to 4.96%. In addition, the hierarchical clustering analysis showed that the first-order and shape features had better robustness than did texture features. In clinical validation, the area under the curve (AUC) of the testing set was 0.865 after ComBat correction. The ComBat harmonization can optimize voxel size-related CT radiomics variability in pulmonary nodules more efficiently than image resampling harmonization.

Performance of Radiomics Based Multi-Parameter Models Derived from Lung CT in Differentiating Benign Non-Inflammatory, Inflammatory and Malignant Pulmonary Nodule

Pulmonary nodules are a significant clinical issue that require accurate and efficient diagnosis. This study constructed a machine learning model, combining radiomics features of chest CT sequences with three types of microvascular density (MVD) values, to differentiate benign, inflammatory and malignant pulmonary nodules. A total of 100 patients with lung nodules on CT images and corresponding pathological results were retrospectively included in the study. The MVD values and radiomics features were calculated and extracted based on the segmented nodules. Univariate correlation analysis and principal component analysis were performed to select radiomics features. Combined MVD values and selected radiomics features, we conducted a logistic regression classification model. The area under the curve (AUC) was applied to show model performance. Our model reached an AUC value of 0.867 when tested on independent datasets. The performance of the model to differentiate benign, malignant and inflammatory nodules reached AUC values of only 0.908, 0.833, and 0.730, respectively. We conducted a prediction model that shows promising results in distinguishing three different types of lung nodules.

Ultrasound characteristics of follicular and parafollicular thyroid neoplasms: diagnostic performance of artificial neural network

BackgroundUltrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain features observable by ultrasound have recently been equated with potential malignancy. This retrospective cohort study was conducted to test the hypothesis that radiomics of the four categorical divisions (medullary [MTC], papillary [PTC], or follicular [FTC] carcinoma and follicular thyroid adenoma [FTA]) demonstrate distinctive sonographic characteristics. Using an artificial neural network model for proof of concept, these sonographic features served as input.MethodsA total of 148 patients were enrolled for study, all with confirmed thyroid pathology in one of the four named categories. Preoperative ultrasound profiles were obtained via standardized protocols. The neural network consisted of seven input neurons; three hidden layers with 50, 250, and 100 neurons, respectively; and one output layer.ResultsRadiomics of contour, structure, and calcifications differed significantly according to nodule type (p = 0.025, p = 0.032, and p = 0.0002, respectively). Levels of accuracy shown by artificial neural network analysis in discriminating among categories ranged from 0.59 to 0.98 (95% confidence interval [CI]: 0.57–0.99), with positive and negative predictive ranges of 0.41–0.99 and 0.78–0.97, respectively.ConclusionsOur data indicate that some MTCs, PTCs, FTCs, and FTAs have distinctive sonographic characteristics. However, a significant overlap of these characteristics may impede an explicit classification. Further prospective investigations involving larger patient and nodule numbers and multicenter access should be pursued to determine if neural networks of this sort are beneficial, helping to classify neoplasms of the thyroid gland.

Initial ablation radio predicting volume reduction from microwave ablation of benign thyroid nodules.

Our research sought to investigate the relationship between initial ablation ratio (IAR) and internal composition of benign thyroid nodules treated by microwave ablation (MWA). Patients who underwent MWA at the Affiliated Hospital of Jiangsu University from January 2018 to December 2022 were enrolled in our research. All the patients were followed up for at least one year. We analyzed the relationship between IAR at 1 month of solid nodules (solid >90%), predominantly solid nodules (90% >solid > 75%), mixed solid alongside cystic nodules (75% >solid > 50%) as well as volume reduction rate (VRR) at 1, 3, 6 and 12 months follow-up. The mean IAR of the solid nodules (solid >90%) was 94.32±7.87%,#x0025;, that of the predominantly solid nodules (90% >solid > 75%) and mixed solid alongside cystic nodules (75% >solid > 50%) were 86.51±6.66% and 75.19±4.97%,#x0025;, respectively. Almost all the thyroid nodules were significantly decreased in size after MWA. After 12 months of MWA treatment, the average volume of the aforementioned thyroid nodules decreased from 8.69±8.79 to 1.84±3.11 ml, 10.94±9.07 to 2.58±3.34 ml, 9.92±6.27 to 0.25±0.42 ml, respectively. The mean symptom and cosmetic scores of the nodules showed significant (p < 0.000) improvement. The rates of the complications or side effects of MWA against the above-mentioned nodule types were 8.3% (3/36), 3.2% (1/31) and 0% (0/36), respectively. The application of the IAR to quantify the success rate of thyroid nodule microwaves in the short term demonstrated that IAR was related to the internal components of the nodule. Although the IAR was not high when the thyroid component was mixed solid and cystic nodules (75% >solid > 50%), the final therapeutic effect was still satisfactory.

Dense Swin Transformer for Classification of Thyroid Nodules.

As an early sign of thyroid cancer, thyroid nodules are the most common nodular lesions. As a non-invasive imaging method, ultrasound is widely used in the diagnosis of benign and malignant thyroid nodules. As there is no obvious difference in appearance between the two types of thyroid nodules, and the contrast with the surrounding muscle tissue is too low, it is difficult to distinguish the benign and malignant nodules. Therefore, a dense nodal Swin-Transformer(DST) method for the diagnosis of thyroid nodules is proposed in this paper. Image segmentation is carried out through patch, and feature maps of different sizes are constructed in four stages, which consider different information of each layer of features. In each stage block, a dense connection mechanism is used to make full use of multi-layer features and effectively improve the diagnostic performance. The experimental results of multi-center ultrasound data collected from 17 hospitals show that the accuracy of the proposed method is 87.27%, the sensitivity is 88.63%, and the specific effect is 85.16%, which verifies that the proposed algorithm has the potential to assist clinical practice.

Analysis of risk factors for intra-cystic hemorrhage in microwave ablation of partially cystic thyroid nodules.

The aim of this study is to identify risk factors of intra-cystic hemorrhage in microwave ablation of mixed solid and cystic microwave ablation s, and to design a preoperative nomogram to predict the risk value of intraoperative bleeding with the goal of individualizing the surgical approach toward different types of cystic and solid thyroid nodules. A total of 241 patients with cystic-solid thyroid nodules who underwent ultrasound-guided percutaneous microwave ablation were retrospectively divided into a bleeding group and a non-bleeding group to compare the diameter, cystic proportion, cystic fluid nature, color Doppler flow imaging, Contrast-enhanced ultrasound (CEUS) findings, and operative methods. Based on univariate and multivariate analysis, the important risk factors of nodular intracapsular hemorrhage in the ablation procedure were projected to a nomogram for predicting the possibility of intraoperative hemorrhage in the thyroid cystic solid nodules. Intra-cystic hemorrhage was developed in 37 cases during the ablation of mixed thyroid nodules with a total incidence of 15% (37/241). Significant differences were found statistically between the two groups on the diameter of the lesions, CEUS findings, the cystic fluid ratio, and operative methods (P = 0.000, P = 0.001, P = 0.024, P = 0.002). The possibility of intraoperative nodular intracapsular hemorrhage was predicted by the model based on the risk factors with the accuracy of 81% and prediction consistency index (C-index) of 0.78. A new and efficient prediction model was developed based on the identified risk factors for intracapsular hemorrhage during microwave ablation of mixed thyroid nodules, which will aid in the development of targeted surgical planning for different types of cystic thyroid nodules, thus reducing the risk of hemorrhage during ablation.

Incidence of Thyroid Carcinoma in Large (≥4cm) Benign Thyroid Nodules: A Retrospective Single Center Study

Objective: The purpose of our study is to see the incidence of thyroid carcinoma in large (≥4cm) Fine Needle Aspiration Cytology (FNAC) proven benign thyroid nodules. Study Design: Retrospective cross-sectional study Place &amp; duration of Study: Department of General Surgery, Recep Tayyip Erdogan Hospital, Muzaffargarh from August 2014 to May 2021 Methodology: 238 patients with large benign thyroid nodules who underwent thyroid surgery were included in the study. The type of thyroid nodules (solitary or dominant), size of thyroid nodule (mm) and histopathology of the thyroid nodules after surgery were collected. The data was analyzed by IBM Statistical Package for Social Sciences (IBM SPSS Statistics for Windows version 20.0. Armonk, New York). Tables were constructed using Microsoft Excel 2018. Results: A total of 238 patients with large benign thyroid nodules were included in the study. 156 patients (65.9%) had thyroid nodule size in the range of 40mm to 50mm rest of the patients had nodule size above 50mm. The incidence of thyroid carcinoma was 14.3% (34/238). There was no significant effect of age, gender &amp; type of thyroid nodule on risk of thyroid carcinoma. Conclusion: The risk of thyroid carcinoma in large (≥4cm) benign thyroid nodules is significant and there is a need to change our approach in their management to diagnose and manage them timely. Therefore, large (≥4cm) thyroid nodules may be considered for surgery even if preoperative FNAC is benign.

Comparison of microwave alone and combined with ethanol ablation for different types of benign mixed thyroid nodules.

To evaluate the efficacy and safety of microwave ablation (MWA) plus ethanol ablation (EA) for different types of benign mixed thyroid nodules. A total of 81 patients with 81 benign mixed thyroid nodules were enrolled into the study; 39 were divided to the MWA group and 42 to the combined group (MWA combined with EA). Nodule ablation rate, volume reduction rate (VRR) and surgical complications of all patients were analyzed before and after treatment. The mean ablation rate were 86.49 ± 6.68% and 90.09 ± 5.79% in the microwave and combined groups respectively, and the ablation rate of nodule decreased as the nodule volume increased. For nodules ≥15 ml in volume, the mean ablation rate of the combined group was higher than that of the microwave group (all P < 0.05). The mean VRR at 12 months postoperatively was 89.58 ± 4.32% in the microwave group and 92.92 ± 3.49% in the combined group, showing statistical significantly different between both arms (P = 0.001). The combined group decreased in volume more significantly than the microwave group for nodules with 20-50% or 50-80% cystic proportions or >15 ml in volume (all P < 0.05). The complication rate was 23.08% and 2.38% respectively. MWA combined with EA is more effective than MWA for treating mixed thyroid nodules. MWA combined with EA may be the first approach for nodules with >20% cystic proportions or volume >15 ml.