Malignant Nodules Research Articles

ThyroNet-X4 genesis: an advanced deep learning model for auxiliary diagnosis of thyroid nodules’ malignancy

Thyroid nodules are a common endocrine condition, and accurate differentiation between benign and malignant nodules is essential for making appropriate treatment decisions. Traditional ultrasound-based diagnoses often depend on the expertise of physicians, which introduces a risk of misdiagnosis. To address this challenge, this study proposes a novel deep learning model, ThyroNet-X4 Genesis, designed to automatically classify thyroid nodules as benign or malignant. Built on the ResNet architecture, the model enhances feature extraction by incorporating grouped convolutions and using larger convolution kernels, improving its ability to analyze thyroid ultrasound images. The model was trained and validated using publicly available thyroid ultrasound imaging datasets, and its generalization was further tested using an external validation dataset from HanZhong Central Hospital. The ThyroNet-X4 Genesis model achieved 85.55% and 71.70% accuracy on the internal training and validation sets, respectively, and 67.02% accuracy on the external validation set. These results surpass those of other mainstream models, highlighting its potential for clinical use in thyroid nodule classification. This work underscores the growing role of deep learning in thyroid nodule diagnosis and provides a foundation for future research in high-performance medical diagnostic models.

18F]FDG PET/CT versus Dynamic Contrast-Enhanced CT for the diagnosis of solitary pulmonary Nodule: A Head-to-Head comparative Meta-Analysis.

This head-to-head comparative meta-analysis aimed to evaluate the comparative diagnostic efficacy of [18F]FDG PET/CT and dynamic contrast-enhanced CT(DCE-CT) for the differentiation between malignant and benign pulmonary nodules. An extensive search was conducted in the PubMed, Embase, and Web of Science to identify available publications up to March 23, 2024. Studies were included if they evaluated the diagnostic efficacy of [18F]FDG PET/CT and DCE-CT for the characterization of pulmonary nodules. Sensitivity and specificity were assessed using the inverse variance method, followed by transformation via the Freeman-Tukey double inverse sine transformation. The quality of the included studies utilizing the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Seven articles involving 1,183 patients were included in the meta-analysis. The sensitivity of [18F]FDG PET/CT was comparable to that of DCE-CT (0.88 vs. 0.87, P = 0.95). Similarly, the specificity of [18F]FDG PET/CT was not significantly different from that of DCE-CT (0.63 vs. 0.54, P = 0.47). No significant publication bias was detected for any outcome (Egger's test: all P > 0.05). For DCE-CT, meta-regression analysis identified the mean lesion size of pulmonary nodules (<20mm vs.≥20mm, P=0.01) as a potential source of heterogeneity. Meanwhile, the number of patients (<100 vs.≥100, P<0.01) for PET/CT may also contribute to the heterogeneity. Our meta-analysis indicates that [18F]FDG PET/CT demonstrates similar sensitivity and specificity to DCE-CT for the diagnosis of pulmonary nodules. However, the number of the head-to-head studies were relatively small, further larger sample prospective research is required to confirm these findings.

Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer.

Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model. To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model. Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings. With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer. Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

Development and biological evaluation of a novel CEACAM6-targeted PET tracer for distinguishing malignant nodules in early-stage lung adenocarcinoma.

Low-dose CT (LDCT) screening effectively reduces lung adenocarcinoma (LUAD) mortality. However, accurately evaluating the malignant potential of indeterminate lung nodules remains a challenge. Carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6), a potential biomarker for distinguishing benign pulmonary nodules from LUAD, may be leveraged for noninvasive positron emission tomography (PET) imaging to aid LUAD diagnosis. This study utilized mRNA, protein, and survival datasets of LUAD patients, along with an animal model of malignant pulmonary nodules, to investigate CEACAM6 expression specificity and its correlation with LUAD. Targeting ligands for CEACAM6 were designed using the Rosetta platform, labeled with [68Ga]Ga, and screened through high-throughput PET imaging to identify the optimal tracer. CEACAM6 was found to be specifically overexpressed in LUAD and was significantly associated with poor prognosis and disease progression. In vivo, [68Ga]Ga-NODA-P3 demonstrated high specificity for delineating CEACAM6-positive A549 xenografts, a LUAD model, via PET imaging, achieving a highest target-to-background ratio of 7.68 ± 0.44. Region of interest (ROI) analysis showed significantly higher tracer uptake in A549 xenografts compared to CEACAM6-negative Huh7 xenografts (a hepatocellular carcinoma model) at 30min post-injection (1.81 ± 0.10%ID/g vs. 0.54 ± 0.06%ID/g). Pre-treatment with an excess of unlabeled NODA-P3 significantly reduced tumor uptake to 0.52 ± 0.07%ID/g. These preclinical findings indicate that [68Ga]Ga-NODA-P3 is a candidate radiotracer for the non-invasive visualization of CEACAM6-positive LUAD, demonstrating favorable imaging contrast. Although the current tumor uptake limits its immediate clinical application, ongoing optimization efforts are expected to improve its efficacy, enabling earlier and more accurate diagnosis of malignant pulmonary nodules in LUAD.

Enhancing Liver Nodule Visibility and Diagnostic Classification Using Ultrasound Local Attenuation Coefficient Slope Imaging.

B-mode ultrasound (US) presents challenges in accurately detecting and distinguishing between benign and malignant liver nodules. This study utilized quantitative US local attenuation coefficient slope (LACS) imaging to address these limitations. This is a prospective, cross-sectional study in adult patients with definable solid liver nodules at US conducted from March 2021 to December 2023. The composite reference standard included histopathology when available or magnetic resonance imaging. LACS images were obtained using a phantom-free method. Nodule visibility was assessed by computing the contrast-to-noise ratio (CNR). Classification accuracy for differentiating benign and malignant lesions was assessed with the area under the receiver operating characteristic curve (AUC), along with sensitivity and specificity. The study enrolled 97 patients (age: 62 y ± 13 [standard deviation]), with 57.0% malignant and 43.0% benign observations (size: 26.3 ± 18.9 mm). LACS images demonstrated higher CNR (12.3 dB) compared to B-mode (p < 0.0001). The AUC for differentiating nodules and liver parenchyma was 0.85 (95% confidence interval [CI]: 0.79-0.90), with higher values for malignant (0.93, CI: 0.88-0.97) than benign nodules (0.76, CI: 0.66-0.87). A LACS threshold of 0.94 dB/cm/MHz provided a sensitivity of 0.83 (CI: 0.74-0.89) and a specificity of 0.82 (CI: 0.73-0.88). LACS mean values were higher (p < 0.0001) in malignant (1.28 ± 0.27 dB/cm/MHz) than benign nodules (0.98 ± 0.19 dB/cm/MHz). LACS imaging improves nodule visibility and provides better differentiation between benign and malignant liver nodules, showing promise as a diagnostic tool.

Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning

IntroductionDiagnosing the types of malignant lymphoma could help determine the most suitable treatment, anticipate the probability of recurrence and guide long-term monitoring and follow-up care.MethodsWe evaluated the differences in benign, lymphoma and metastasis superficial lymph nodes using ultrasonography and tissue metabolomics.ResultsOur findings indicated that three ultrasonographic features, blood supply pattern, cortical echo, and cortex elasticity, hold potential in differentiating malignant lymph nodes from benign ones, and the shape and corticomedullary boundary emerged as significant indicators for distinguishing between metastatic and lymphoma groups. Metabolomics revealed the difference in metabolic profiles among lymph nodes. We observed significant increases in many amino acids, organic acids, lipids, and nucleosides in both lymphoma and metastasis groups, compared to the benign group. Specifically, the lymphoma group exhibited higher levels of nucleotides (inosine monophosphate and adenosine diphosphate) as well as glutamic acid, and the metastasis group was characterized by higher levels of carbohydrates, acylcarnitines, glycerophospholipids, and uric acid. Linear discriminant analysis coupled with these metabolites could be used for differentiating lymph nodes, achieving recognition rates ranging from 87.4% to 89.3%, outperforming ultrasonography (63.1% to 75.4%).DiscussionOur findings could contribute to a better understanding of malignant lymph node development and provide novel targets for therapeutic interventions.

Shear Wave Elastography as an Supplement Tool in Differentiating Benign and Malignant Axillary Lymph Nodes.

To determine the efficacy of quantitative shear wave elastography in differentiating benign and malignant axillary lymph nodes (ALN). Exactly 127 lymph nodes from 127 patients with clinically palpable axillary swelling were examined by both B-mode sonography and elastography from November 2022 to March 2024. Gray-scale sonograms were evaluated based on: the short-axis diameter, shape, hilum, maximum cortical thickness, and border of the ALN. Shear wave elastography determined the mean elasticity modulus (E-mean) and elasticity ratio (E-ratio). Fine needle aspiration cytology or histopathological examination was kept as the gold standard and diagnostic performance shear wave elastography was compared. The data showed that out of 127 lymph nodes, 77 (60.6%) were benign and 50 (39.4%) were malignant based on pathological results. The E-mean for malignant lymph nodes (mean, 73.15 kPa) was higher than that for benign lymph nodes (mean, 21.47 kPa; P < .001). The area under the receiver operating characteristic curve for E-ratio in predicting malignant and benign lymph nodes was 0.897 (95% CI: 0.839-0.955). The E-ratio for malignant lymph nodes was also higher (mean, 10.2) than for benign nodes (mean, 2.95; P < .001). The area under the receiver operating characteristic curve for E-ratio in predicting malignant and benign lymph nodes was 0.816 (95% CI: 0.733-0.899). Our results showed a significant association between tissue elasticity and pathological correlation.

An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs)

BackgroundAn increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers.MethodsWe retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building.ResultsAmong LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99).ConclusionsThese results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.

Natural History and Long Term Follow-Up of Incidental Thyroid Nodules on Computed Tomography Imaging.

incidental thyroid nodules (ITNs) are found in up to 25% of CT scans. Increased use of cross-sectional imaging has contributed to the increased incidence of thyroid cancer worldwide. ITNs pose a management dilemma since nodule malignancy rate is 5-15% but most cancers are indolent and prognosis in differentiated thyroid cancer is excellent. Study aims are to determine prevalence of ITNs ≥1 cm on CT scans, evaluate reporting practices, assess for emergence of clinically evident thyroid cancer during 13-year follow-up and assess interim nodule growth and clinical outcomes in nodules that were further investigated. direct image review of 1499 consecutive CT scans that included the thyroid, performed during January 2009 in a large NHS health board was performed. Clinical data up to January 2022 was analysed in 150 patients with at least one ITN > =1cm. ITN prevalence was 11% with mean patient age 70 years and mean nodule diameter 17.5 mm. 30% of ITNs were mentioned in the CT report. During the follow-up period 11% proceeded to thyroid ultrasound, 5% fine needle aspiration and 2% diagnostic hemithyroidectomy with no thyroid malignancy found. 120 patients (80%) were deceased by the study endpoint, none from thyroid malignancy. No patients presented with clinically evident thyroid malignancy during follow-up. None of150 ITN cases developed clinically evident thyroid malignancy in a 13-year follow-up period with 80% of patients deceased by the study endpoint from non-thyroid causes. This would suggest that ITNs detected on CT do not require further investigation unless malignant appearances or significant clinical concern for thyroid malignancy.

A Machine Learning-Based Radiomics Model for the Differential Diagnosis of Benign and Malignant Thyroid Nodules in F-18 FDG PET/CT: External Validation in the Different Scanner.

Accurate diagnosis is essential to avoid unnecessary procedures for thyroid incidentalomas (TIs). Advances in radiomics and machine learning applied to medical imaging offer promise for assessing thyroid nodules. This study utilized radiomics analysis on F-18 FDG PET/CT to improve preoperative differential diagnosis of TIs. A total of 152 patient cases were retrospectively analyzed and split into training and validation sets (7:3) using stratification and randomization. The least absolute shrinkage and selection operator (LASSO) algorithm identified nine radiomics features from 960 candidates to construct a radiomics signature predictive of malignancy. Performance of the radiomics score was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). In the training set, the radiomics score achieved an AUC of 0.794 (95% CI: 0.703-0.885, p < 0.001). Validation was performed on internal and external datasets, yielding AUCs of 0.702 (95% CI: 0.547-0.858, p = 0.011) and 0.668 (95% CI: 0.500-0.838, p = 0.043), respectively. These results demonstrate that the selected nine radiomics features effectively differentiate malignant thyroid nodules. Overall, the radiomics model shows potential as a valuable predictive tool for thyroid cancer in patients with TIs, supporting improved preoperative decision-making.

Shear wave elastography for thyroid nodule evaluation in patients with chronic autoimmune thyroiditis.

Shear wave elastography (SWE) is a valuable tool in discerning the malignancy risk of thyroid nodules. This study investigates whether 2D-SWE can reliably differentiate malignant thyroid nodules in patients with chronic autoimmune thyroiditis (CAT), despite the challenges posed by fibrosis, which can increase tissue stiffness and complicate diagnosis. This retrospective observational study evaluated 130 thyroid nodules (91 benign, 39 malignant) in patients with underlying CAT using conventional ultrasound (B-mode) and 2D-SWE with SuperSonic Mach30 equipment (Supersonic Imagine, Aix-en-Provence, France). Pathology reports served as the reference standard. Out of the 130 nodules, 30% were malignant, and 70% were benign. 2D-SWE proved to be an excellent distinguisher between benign and malignant nodules. Malignant nodules had significantly higher elasticity indices compared to benign nodules (mean elasticity index: 47.2 kPa vs. 18.1 kPa, p < 0.0001; maximum elasticity index: 75 kPa vs. 26.2 kPa, p < 0.0001). The mean elasticity index was the most reliable elastographic parameter (AUC 0.907, sensitivity 87.2% [95% CI: 77.3-94.0%], specificity 84.6% [95% CI: 75.4-91.5%], and NPV 93.9% for a cut-off value of 30.5 kPa). Our results confirm that 2D-SWE can accurately diagnose malignant thyroid nodules in cases with CAT (p < 0.0001), supporting its use as a complementary tool to conventional ultrasound.

Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis

ObjectiveIn clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy.MethodsData of 2,591 pulmonary nodules from five medical centers (Zhengzhou People’s Hospital, etc.) were collected. Applying exclusion criteria, 370 nodules (78 benign, 292 malignant) with 3D CTR ≥ 50% were selected and randomly split 7:3 into training and validation cohorts. Using R programming, Lasso regression with 10-fold cross-validation filtered features, followed by univariate and multivariate logistic regression to construct the model. Its efficacy was evaluated by ROC, DCA curves and calibration plots.ResultsLasso regression picked 18 non-zero coefficients from 108 features. Three significant factors—patient age, solid component volume and mean CT value—were identified. The logistic regression equation was formulated. In the training set, the ROC AUC was 0.721 (95%CI: 0.642–0.801); in the validation set, AUC was 0.757 (95%CI: 0.632–0.881), showing the model’s stability and predictive ability.ConclusionThe model has moderate accuracy in differentiating benign from malignant 3D CTR ≥ 50% nodules, holding clinical potential. Future efforts could explore more to improve its precision and value.Clinical trial numberNot applicable.

The Application of Three-dimensional Shear Wave Elastography in the Detection of Inguinal Lymph Node Metastasis in Gynecological Malignancies

Background: Accurate assessment of lymph node involvement is essential for prognosis and guiding treatment decisions in gynecological cancers, as it directly influences therapeutic strategies and patient outcomes. However, conventional imaging techniques, such as ultrasound and computed tomography (CT), demonstrate limited accuracy in determining the nature of lymph nodes. This study implements three-dimensional shear wave elastography (3D-SWE), an innovative technique that quantifies the stiffness of inguinal lymph nodes (ILN) in patients with gynecological cancers. This approach offers a more comprehensive assessment of ultrasound elastography compared to traditional methods. Methods: This retrospective study evaluated 120 ILNs from patients with gynecological cancers who underwent conventional ultrasound (US), two-dimensional shear wave elastography (2D-SWE), and multiplanar 3D-SWE. The diagnostic performance was analyzed using receiver operating characteristic (ROC) methodology, enabling a comprehensive comparison of the sensitivity and specificity of 3D-SWE relative to conventional ultrasound and 2D-SWE. Pathology results from lymph node resections or biopsies served as the reference standard. Results: Final pathology confirmed 65 metastatic and 55 benign lymph nodes. The maximum (Emax) and average (Emean) elasticity values from 2D-SWE and 3D-SWE for malignant lymph nodes were significantly higher than those for benign lymph nodes (p &lt; 0.0001). ROC analysis indicated that for identifying metastatic lymph nodes, the area under the curve (AUC) values were 0.798 for 2D-SWE Emax, and 0.828, 0.839, and 0.816 for the transverse, sagittal, and coronal planes of 3D-SWE Emax, respectively. The differences between these AUC values were not statistically significant (p &gt; 0.05). Furthermore, Emax values consistently surpassed Emean values in terms of characterization accuracy across modalities. Although no significant differences were observed between 2D-SWE and 3D-SWE, the sagittal view Emax yielded the best AUC, indicating optimal diagnostic precision overall. Conclusions: 3D-SWE, particularly using the sagittal Emax parameter, effectively distinguishes benign lymph nodes from metastatic ones by quantitatively evaluating the stiffness of ILNs in relation to gynecological tumors. Serving as a dependable diagnostic method for lymph nodes, 3D-SWE may help reduce the necessity for invasive biopsy procedures in some cases.

Malignant Risk Assessment of Cystic-Solid Thyroid Nodules Based on Multimodal Ultrasound Features: A Systematic Review and Meta-Analysis

Background: The malignant risk of cystic-solid thyroid nodules may be underestimated in the ultrasound assessment. Objective: This systematic review and meta-analysis aimed to evaluate the value of multimodal ultrasound characteristics in the malignant risk assessment of cystic-solid thyroid nodules. Methods: We conducted a comprehensive search of PubMed, Web of Science, and Cochrane Library databases for studies depicting the ultrasound characteristics of cystic-solid thyroid nodules published prior to October 2023. The Review Manager 5.4 software was utilized to evaluate the ultrasound features suggestive of malignancy and to determine their sensitivity and specificity. Additionally, the Sata 12.0 software was utilized to construct summary receiver operating characteristic curves (SROC), estimate the area under the curve (AUC), and evaluate any potential publication bias. Results: This review included 16 studies comprising 5,655 cystic-solid thyroid nodules. Nine ultrasound features were identified as risk factors for tumor malignancy. Among the ultrasound features, microcalcification in the solid portion, heterogeneous hypoenhancement on Contrast-Enhanced Ultrasound (CEUS), and sharp angles in the solid portion exhibited higher malignant predictive value in cystic-solid thyroid nodules, with AUC values of 0.91, 0.84, and 0.81, respectively. Conclusion: Our findings indicate that microcalcification and sharp angles in the solid part of the nodule, along with heterogeneous hypoenhancement on contrast-enhanced ultrasound (CEUS), can better predict malignant cystic-solid thyroid nodules. The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42024602893).

UMAM-NET: ultrasound thyroid nodule malignancy grading network based on multi-subnet attention mechanism

UMAM-NET: ultrasound thyroid nodule malignancy grading network based on multi-subnet attention mechanism

The value of non-punctate echogenic foci in the ultrasonic diagnosis of thyroid nodules.

To evaluate the diagnostic value of different subtypes of non-punctate echogenic foci in thyroid malignancy. Retrospective research of 342 thyroid nodules with calcification was performed. The echogenic foci were divided into punctate echogenic foci (type I) and non-punctate echogenic foci (type II), and type II were further divided into four subtypes: macrocalcification (type IIa), continuous peripheral calcification (type IIb), discontinuous peripheral calcification (type IIc) and isolated calcification (type IId). Postoperative histopathological results were used as the gold standard to evaluate the correlation between non-punctate echogenic foci subtypes and thyroid malignancy. The malignant risk of nodules with echogenic foci was type I (82.1%) > type IIa (66.2%) > type IIc (52.9%) > type IId (16.7%) > type IIb (13.9%), P < 0.001. Type I and type IIa echogenic foci were independent risk factors for thyroid cancer (OR = 16.593, 7.785). Solid, hypoechogenicity/marked hypoechogenicity and a single lesion in a unilateral thyroid lobe were independently associated with malignant thyroid nodules with macrocalcification(OR = 6.825, 40.042, 5.201). Irregular margins and uneven calcification thickness were independent factors for malignant thyroid nodules with peripheral calcification (OR = 5.676, 2.750). Type IIa echogenic foci could independently predict thyroid malignancy. The diagnostic value of non-punctate echogenic foci depended on the differentiated combination of ultrasound characteristics. Type IIa nodules with solid composition, irregular margins, and a single lesion in a unilateral thyroid lobe implied a higher risk of malignancy; peripheral calcified nodules with irregular margins and uneven calcification thickness suggested an increased risk of malignancy.

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy.

Non-invasive assessment of pulmonary nodule malignancy remains a critical challenge in lung cancer diagnosis. Traditional methods often lack precision in differentiating benign from malignant nodules, particularly in the early stages. This study introduces an approach using multifractal spectrum analysis to quantitatively evaluate pulmonary nodule characteristics. A fractal-based protocol was developed to process computed tomography (CT)-digital imaging and communications in medicine (DICOM) data, enabling three-dimensional (3D) visualization and analysis of pulmonary nodule's multifractal spectrum. The method involves 3D volume reconstruction, precise ROI delineation, and calculation of fractal dimensions across multiple scales. Multifractal spectra were computed for both early-stage and late-stage lung adenocarcinoma nodules, with comparative analysis performed using data tip tool quantification. Analysis revealed that the fractal dimension of a pulmonary nodule's 3D digital matrix varies continuously with different voxel scales, forming a distinctive multifractal spectrum. Significant differences were observed between early-stage and late-stage nodules. Late-stage nodules demonstrated a wider scale range (longer X-axis) and higher extreme points in their multifractal spectra. These distinctions were quantitatively confirmed, indicating the method's potential for precise staging. The multifractal spectrum analysis provides a highly significant and precise quantitative method for staging pulmonary nodules, effectively differentiating between benign and malignant cases. This non-invasive technique shows promise for improving early diagnosis and accurate staging of lung cancer, potentially enhancing clinical decision-making in pulmonary oncology.

Discrimination of Benign and Malignant Thyroid Nodules through Comparative Analyses of Human Saliva Samples via Metabolomics and Deep-Learning-Guided Label-free SERS.

Thyroid nodules are a very common entity. The overall prevalence in the populace is estimated to be around 65-68%, among which a small portion (less than 5%) is malignant (cancerous). Therefore, it is important to discriminate benign thyroid nodules from malignant thyroid nodules. In this study, an equal number of participants with benign and malignant thyroid nodules (N = 10/group) were recruited. Saliva samples were collected from each participant, and SERS spectra were acquired, followed by validation using a metabolomics approach. An additional equal number of patients (N = 40/group) were recruited to construct diagnostic models. The performance of various machine learning (ML) algorithms was assessed using multiple evaluation metrics. Finally, the reliability of the optimal model was tested using blind test data (N = 10/group for benign and malignant thyroid nodules). The results showed a consistent trend between the SERS metabolic profile and the metabolites identified through MS analysis. The Multi-ResNet algorithm was optimal, achieving a 95% accuracy in sample discrimination. Additionally, blind test data sets yielded an overall accuracy of 83%. In summary, the deep-learning-guided SERS technique holds great potential in the accurate discrimination of benign and malignant thyroid nodules via human saliva samples, which facilitates the noninvasive diagnosis of malignant thyroid nodules in clinical settings.

RNA analysis of patients with benign and malignant pulmonary nodules.

Pulmonary nodules are the main manifestations of early lung cancer. Non-small cell lung cancer is the most common histological type of lung cancer, and the main histological classification of non-small cell lung cancer is lung adenocarcinoma. The present study aimed to analyze the differentially expressed genes between patients with benign and malignant pulmonary nodules, and to identify potential therapeutic targets for lung adenocarcinoma. Sequencing data for benign and malignant pulmonary nodule samples and samples with no nodules were obtained from the National Center for Biotechnology Information Gene Expression Omnibus GSE135304 dataset. Differential gene analysis showed that S100 calcium binding protein P (S100P), ribonuclease A family member 2 (RNASE2), cytochrome c oxidase subunit 7C and mast cell expressed membrane protein 1 (C19orf59) were significantly upregulated among the blood samples collected from patients with malignant pulmonary nodules. Results from Kaplan-Meier plotter datasets showed that S100P, RNASE2 and C19orf59 were associated with the prognosis of lung cancer. RNASE2 expression was positively associated with nodule size and negatively associated with lung cancer prognosis. Moreover, RNASE2 was highly expressed in lung adenocarcinoma tissues compared with that in normal tissues. CCK-8 and Transwell assays indicated that overexpressed RNASE2 promoted the proliferation, migration and invasion of lung adenocarcinoma cells. In lung adenocarcinoma cells, RNASE2 was identified as a downstream target of microRNA (miR)-185-5p and was regulated by it. Inhibited cell proliferation, migration and invasion were observed following overexpression of miR-185-5p. Overexpression of RNASE2 reversed the inhibitory effect of miR-185-5p overexpression. In conclusion, in blood samples from patients with malignant pulmonary nodules and lung adenocarcinoma tissues, RNASE2 was found to be upregulated. High RNASE2 expression was associated with poor overall survival. miR-185-5p inhibited the proliferation, migration and invasion of lung adenocarcinoma cells by downregulating RNASE2 expression. These findings have implications for guiding therapeutic strategies.

Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation.

To evaluate the diagnostic value of ultrasound radiomics in distinguishing between benign and malignant breast nodules in women who have undergone silicone breast augmentation. A retrospective study was conducted of 99 breast nodules detected by ultrasound in 93 women who had undergone silicone breast augmentation. The ultrasound data were collected between 1 January 2006 and 1 September 2023. The nodules were allocated into a training set (n = 69) and a validation set (n = 30). Regions of interest (ROIs) were manually delineated using 3D Slicer software, and radiomic features were extracted and selected using Python programming. Eight machine learning algorithms were applied to build predictive models, and their performance was assessed using sensitivity, specificity, area under the ROC curve (AUC), accuracy, Brier score, and log loss. Model performance was further evaluated using ROC curves and calibration curves, while clinical utility was assessed via decision curve analysis (DCA). The random forest model exhibited superior performance in differentiating benign from malignant nodules in the validation set, achieving sensitivity of 0.765, specificity of 0.838, and an AUC of 0.787 (95% CI: 0.561-0.960). The model's accuracy, Brier score, and log loss were 0.796, 0.197, and 0.599, respectively. DCA suggested potential clinical utility of the model. Ultrasound radiomics demonstrates promising diagnostic accuracy in differentiating benign from malignant breast nodules in women with silicone breast prostheses. This approach has the potential to serve as an additional diagnostic tool for patients following silicone breast augmentation.