Differentiation Of Nodules Research Articles

In Search of Relevant Urinary Biomarkers for Thyroid Papillary Carcinoma and Benign Thyroid Nodule Differentiation, Targeting Metabolic Profiles and Pathways via UHPLC-QTOF-ESI+-MS Analysis

Background: Identification of specific urine metabolic profiles for patients diagnosed with papillary thyroid carcinoma (TC) vs. benign nodules (B) to identify specific biomarkers and altered pathways compared to those of healthy controls (C). Methods: Patient urine samples were collected, before surgery and after a histological confirmation of TC (n = 30) and B (n = 30), in parallel with sample collection from healthy controls (n = 20). The untargeted and semi-targeted metabolomic protocols were applied using UPLC-QTOF-ESI+-MS analysis, and the statistical analysis was performed using the Metaboanalyst 6.0 platform. The results for the blood biomarkers, previously published, were compared with the data obtained from urine sampling using the Venny algorithm and multivariate statistics. Results: Partial least squares discrimination, including VIP values, random forest graphs, and heatmaps (p < 0.05), together with biomarker analysis (AUROC ranking) and pathway analysis, suggested a specific model for the urinary metabolic profile and pathway alterations in TC and B vs. C, based on 190 identified metabolites in urine that were compared with the serum metabolites. By semi-targeted metabolomics, 10 classes of metabolites, considered putative biomarkers, were found to be responsible for specific alterations in the metabolic pathways, from polar molecules to lipids. Specific biomarkers for discrimination were identified in each class of metabolites that were either upregulated or downregulated when compared to those of the controls. Conclusions: The lipidomic window was the most relevant for identifying biomarkers related to thyroid cancer and benign conditions, since this study detected a stronger involvement of lipids and selenium-related molecules for metabolic discrimination.

Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.

With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image-assisted diagnosis system in clinical treatment is a major trend in medical development. Therefore, in order to explore the value and diagnostic accuracy of the current AI system in clinical application, we aim to compare the detection and differentiation of benign and malignant pulmonary nodules between AI system and physicians, so as to provide a theoretical basis for clinical application. Our study encompassed a cohort of 23 336 patients who underwent chest low-dose spiral CT screening for lung cancer at the Health Management Center of West China Hospital. We conducted a comparative analysis between AI-assisted reading and manual interpretation, focusing on the detection and differentiation of benign and malignant pulmonary nodules. The AI-assisted reading exhibited a significantly higher screening positive rate and probability of diagnosing malignant pulmonary nodules compared with manual interpretation (p < 0.001). Moreover, AI scanning demonstrated a markedly superior detection rate of malignant pulmonary nodules compared with manual scanning (97.2% vs. 86.4%, p < 0.001). Additionally, the lung cancer detection rate was substantially higher in the AI reading group compared with the manual reading group (98.9% vs. 90.3%, p < 0.001). Our findings underscore the superior screening positive rate and lung cancer detection rate achieved through AI-assisted reading compared with manual interpretation. Thus, AI exhibits considerable potential as an adjunctive tool in lung cancer screening within clinical practice settings.

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model

BackgroundThe diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis.Methods420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists’ diagnoses.ResultsThe DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS.ConclusionsA CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.

CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development and Validation Study.

BACKGROUND. CT is increasingly detecting thyroid nodules. Prior studies indicated a potential role of CT-based radiomics models in characterizing thyroid nodules, although these studies lacked external validation. OBJECTIVE. The purpose of this study was to develop and validate a CT-based radiomics model for the differentiation of benign and malignant thyroid nodules. METHODS. This retrospective study included 378 patients (mean age, 46.3 ± 13.9 [SD] years; 86 men, 292 women) with 408 resected thyroid nodules (145 benign, 263 malignant) from two centers (center 1: 293 nodules, January 2018 to December 2022; center 2: 115 nodules, January 2020 to December 2022) who underwent preoperative multiphase neck CT (noncontrast, arterial, and venous phases). Nodules from center 1 were divided into training (n = 206) and internal validation (n = 87) sets; all nodules from center 2 formed an external validation set. Radiologists assessed nodules for morphologic CT features. Nodules were manually segmented on all phases, and radiomic features were extracted. Conventional (clinical and morphologic CT), noncontrast CT radiomics, arterial phase CT radiomics, venous phase CT radiomics, multiphase CT radiomics, and combined (clinical, morphologic CT, and multiphase CT radiomics) models were established using feature selection methods and evaluated by ROC curve analysis, calibration-curve analysis, and decision-curve analysis. RESULTS. The combined model included patient age, three morphologic features (cystic change, "edge interruption" sign, abnormal cervical lymph nodes), and 28 radiomic features (from all three phases). In the external validation set, the combined model had an AUC of 0.923, and, at an optimal threshold derived in the training set, sensitivity of 84.0%, specificity of 94.1%, and accuracy of 87.0%. In the external validation set, the AUC was significantly higher for the combined model than for the conventional model (0.827), noncontrast CT radiomics model (0.847), arterial phase CT radiomics model (0.826), venous phase CT radiomics model (0.773), and multiphase CT radiomics model (0.824) (all p < .05). In the external validation set, the calibration curves indicated the lowest (i.e., best) Brier score for the combined model; in the decision-curve analysis, the combined model had the highest net benefit for most of the range of threshold probabilities. CONCLUSION. A combined model incorporating clinical, morphologic CT, and multiphase CT radiomics features exhibited robust performance in differentiating benign and malignant thyroid nodules. CLINICAL IMPACT. The combined radiomics model may help guide further management for thyroid nodules detected on CT.

A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules

BackgroundCurrently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules.MethodsThe current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People’s Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).ResultsA cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804–0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application.ConclusionIn summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

Inhibition of caspase-11 under inflammatory conditions suppresses chondrogenic differentiation

Caspase-11 is the murine homologue of human caspases-4 and −5 and is involved in mediating the inflammatory response. However, its functions are often confused and misinterpreted with the more important and better described caspase-1. Therefore, this study focused exclusively on the specific roles of caspase-11, both in cartilage formation and in the inflammatory environment. The presence of caspase-11 during mouse limb development and in chondrogenic cell cultures was investigated by immunofluorescence detection. Subsequently, the function of caspase-11 was downregulated and the affected molecules investigated. The expression analysis applied for osteo/chondrogenesis associated factors and inflammatory cytokines. Simultaneously, morphological appearance of the micromass cultures was evaluated. The results revealed that caspase-11 is physiologically present during cartilage development, but its inhibition under physiological conditions has no significant effect on chondrogenic differentiation. However, in an inflammatory environment, inhibition and downregulation of caspase-11 leads to reduced differentiation of cartilage nodules. Additionally, reduced expression of several genes including Col2a1 and Sp7 and conversely increased expression of Mmp9 were observed. In the cytokine expression panel, a significant decrease was found in molecules that, along with the inflammatory function, may also be involved in cartilage differentiation. The findings bring new information about caspase-11 in chondrogenesis and show that its downregulation under inflammatory conditions reduces cartilage formation.

Non-invasive diagnosis of nature of pulmonary nodules using the whole genome methylation sequencing of circulating cell-free DNA.

e20038 Background: The frequent false-positive results of lung cancer screening of low-dose computed tomography (LDCT) remains a challenge. In this study, we aimed to establish a novel panel of cancer-specific methylated regions detected from circulating cell-free DNA (cfDNA) for differentiating malignant pulmonary nodules (PNs) from benign lesions in high-risk patients. Methods: We performed DNA methylation profiling by high throughput DNA bisulfite sequencing in tissue samples (nodule size &lt; 3 cm in diameter) to learn methylation patterns that differentiate cancerous tumors from benign lesions. Then we filtered out differential methylation patterns in circulating cfDNA that are consistent with tissue expression patterns and built an assay for plasma sample classification. Area under the curve was computed for each receiving operation curve, and 95% confidence intervals were also estimated by bootstrapping with 1,000 iterations. Results: We first performed the whole genome methylation sequencing of 100 tissue samples from PNs to learn cancer-specific methylation patterns, and obtained 2,406 differential hypermethylated regions and 16,028 differential hypomethylated regions. These tissue-derived DNA methylation markers were further filtered using a training set of 42 plasma samples and 11 markers were ultimately identified to build a diagnostic prediction model. This methylation panel provided promising results in an independent validation set of additional 17 plasma samples, with a sensitivity of 66.67% (95% CI: 0.25–1.00) and a specificity of 79.17% for differentiating patients with malignant tumor (N = 12) from patients with benign lesions (n = 5). A further validation study of this methylation panel in the cancer genome atlas data set demonstrated significant discriminatory performance in distinguishing patients with lung cancer from subjects without malignancy (area under the curve = 0.88). Conclusions: Our model based on the measurement of circulating cfDNA methylation provide a minimally invasive procedure for differentiation of early-stage lung cancer and nonmalignant pulmonary nodules, and may guide management of positive LDCT screening results. Additional clinical studies with larger sample size are needed to establish the robustness of this model.

Differential diagnosis of thyroid nodules by DCE-MRI based on compressed sensing volumetric interpolated breath-hold examination: A feasibility study

ObjectivesTo explore the potential and performance of quantitative and semi-quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on compressed sensing volumetric interpolated breath-hold (CS-VIBE) examination in the differential diagnosis of thyroid nodules. Materials and methodsA total of 208 patients with 259 thyroid nodules scheduled for surgery operation were prospectively recruited. All participants underwent routine and DCE-MRI. DCE-MRI quantitative parameters [Ktrans, Kep, Ve], semi-quantitative parameters [wash-in, wash-out, time to peak (TTP), arrival time (AT), peak enhancement intensity (PEI), and initial area under curve in 60 s (iAUC)] and time-intensity curve (TIC) types were analyzed. Differential diagnostic performances were assessed using area under the receiver operating characteristic curve (AUC) and compared with the Delong test. ResultsKtrans, Kep, Ve, wash-in, wash-out, PEI and iAUC were statistically significantly different between malignant and benign nodules (P < 0.001). Among these parameters, ROC analysis revealed that Ktrans showed the highest diagnostic performance in the differentiation of benign and malignant nodules, followed by wash-in. ROC analysis also revealed that Ktrans achieved the best diagnostic performance for distinguishing papillary thyroid carcinoma (PTC) from non-PTC, follicular adenoma (FA) from non-FA, nodular goiter (NG) from non-NG, with AUC values of 0.854, 0.895 and 0.609, respectively. Type III curve is frequently observed in benign thyroid nodules, accounting for 77.4% (82/106). While malignant nodules are more common in type II, accounting for 57.5% (88/153). ConclusionThyroid examination using CS-VIBE based DCE-MRI is a feasible, non-invasive method to identify benign and malignant thyroid nodules and pathological types.

Comparison of ultrasonography findings and fine needle aspiration biopsy results in benign-malign differentiation of thyroid nodules

Aims: The aim of this study was to compare the ultrasonography findings and cytology-histopathology findings of thyroid nodules undergoing a fine needle aspiration biopsy. Methods: The records of patients with thyroid nodules who underwent a fine needle aspiration biopsy between January 2023 and February 2024 in the Radiology Clinic were retrospectively analyzed. Patients with malign, benign, and atypia of undetermined significance on pathology were evaluated. The correlation and concordance between ultrasonography, cytology, and histopathology findings were investigated. Results: A total of 114 patients with biopsy pathology results of 75 (65.7%) benign, 21 (18.4%) malign, and 18 (15.7%) atypia of uncertain significance were evaluated. The study included 87 (76%) female and 27 (24%) male patients. The mean age of all patients was 53.04 years. There was no significant correlation between age, gender, nodule location, and pathology result. The presence of contour irregularity and microcalcification in the nodule was significant for malignancy (p&lt;0.001). Conclusion: Ultrasonography is an effective diagnostic method to differentiate between benign-malign thyroid neoplasms. Ultrasound-guided fine needle aspiration biopsy has a high diagnostic rate. It is helpful in determining the subsequent clinical management option for most patients.

Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts

This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID-19 and other benign and malignant lung nodules. The findings suggest the potential complementary role of radiomics in patients with COVID-19 pneumonia exhibiting lung nodules and suspicion of concurrent lung pathologies. The clinical relevance lies in the utilization of radiomics analysis for feature extraction and classification, contributing to the enhanced differentiation of lung nodules, particularly in the context of COVID-19.

Identification and characterization of the TetR family transcriptional regulator NffT in Rhizobium johnstonii.

Symbiotic nitrogen fixation (SNF) by rhizobia is not only the main natural bionitrogen-source for organisms but also a green process leveraged to increase the fertility of soil for agricultural production. However, an insufficient understanding of the regulatory mechanism of SNF hinders its practical application. During SNF, nifA-fixA signaling is essential for the biosynthesis of nitrogenases and electron transfer chain proteins. In the present study, the TetR regulator NffT, whose mutation increased fixA expression, was discovered through a fixA-promoter-β-glucuronidase fusion assay performed with Rhizobium johnstonii. Real-time quantitative PCR analysis showed that nffT deletion increased the expression of symbiotic genes including nifA and fixA in nifA-fixA signaling, and fixL, fixK, fnrN, and fixN9 in fixL-fixN signaling. nffT overexpression resulted in disordered nodules and reduced nitrogen-fixing efficiency. Electrophoretic mobility shift assays revealed that NffT directly regulated the transcription of RL0091-93, which encode an ATP-binding ABC transporter predicted to be involved in carbohydrate transport. Purified His-tagged NffT bound to a 68 bp DNA sequence located -32 to -99 bp upstream of RL0091-93 and NffT deletion significantly increased the expression of RL0091-93. nffT-promoter-β-glucuronidase fusion assay indicated that nffT expression was regulated by the cobNTS genes and cobalamin. Mutations in cobNTS significantly decreased the expression of nffT, and cobalamin restored its expression. These results revealed that NffT affects nodule development and nitrogen-fixing reaction by participating in a complex regulatory network of symbiotic and carbohydrate metabolic genes and, thus, plays a pivotal regulatory role during symbiosis of R. johnstonii-Pisum sativum.IMPORTANCESymbiotic nitrogen fixation (SNF) by rhizobia is a green way to maintain soil fertility without causing environmental pollution or consuming chemical energy. A detailed understanding of the regulatory mechanism of this complex process is essential for promoting sustainable agriculture. In this study, we discovered the TetR-type regulator NffT, which suppressed the expression of fixA in Rhizobium johnstonii. Furthermore, NffT was confirmed to play pleiotropic roles in R. johnstonii-Pisum sativum symbiosis; specifically, it inhibited rhizobial growth, nodule differentiation, and nitrogen-fixing reactions. We revealed that NffT indirectly affected R. johnstonii-P. sativum symbiosis by participating in a complex regulatory network of symbiotic and carbohydrate metabolic genes. Furthermore, cobalamin, a chemical molecule, was reported for the first time to be involved in TetR-type protein transcription during symbiosis. Thus, NffT identification connects SNF regulation with genetic, metabolic, and chemical signals and provides new insights into the complex regulation of SNF, laying an experimental basis for the targeted construction of rhizobial strains with highly efficient nitrogen-fixing capacity.

Establishment and verification of a prediction model based on clinical characteristics and computed tomography radiomics parameters for distinguishing benign and malignant pulmonary nodules.

The radiographic classification of pulmonary nodules into benign versus malignant categories is a pivotal component of early lung cancer diagnosis. The present study aimed to investigate clinical and computed tomography (CT) clinical-radiomics nomogram for preoperative differentiation of benign and malignant pulmonary nodules. This retrospective study included 342 patients with pulmonary nodules who underwent high-resolution CT (HRCT) examination. We assigned them to a training dataset (n=239) and a validation dataset (n=103). There are 1781 tumor characteristics quantified by extracted features from the lesion segmented from patients' CT images. The features with poor reproducibility and high redundancy were removed. Then a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to further select features and build radiomics signatures. The independent predictive factors were identified by multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability. The performance and clinical utility of the clinical-radiomics nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). After dimension reduction by the LASSO algorithm and multivariate logistic regression, four radiomic features were selected, including original_shape_Sphericity, exponential_glcm_Maximum Probability, log_sigma_2_0_mm_3D_glcm_Maximum Probability, and ogarithm_firstorder_90Percentile. Multivariate logistic regression showed that carcinoembryonic antigen (CEA) [odds ratio (OR) 95% confidence interval (CI): 1.40 (1.09-1.88)], CT rad score [OR (95% CI): 2.74 (2.03-3.85)], and cytokeratin-19-fragment (CYFRA21-1) [OR (95% CI): 1.80 (1.14-2.94)] were independent influencing factors of malignant pulmonary nodule (all P<0.05). The clinical-radiomics nomogram combining CEA, CYFRA21-1 and radiomics features achieved an area of curve (AUC) of 0.85 and 0.76 in the training group and verification group for the prediction of malignant pulmonary nodules. The clinical-radiomics nomogram demonstrated excellent agreement and practicality, as evidenced by the calibration curve and DCA. The clinical-radiomics nomogram combined of CT-based radiomics signature, along with CYFRA21-1 and CEA, demonstrated strong predictive ability, calibration, and clinical usefulness in distinguishing between benign and malignant pulmonary nodules. The use of CT-based radiomics has the potential to assist clinicians in making informed decisions prior to biopsy or surgery while avoiding unnecessary treatment for non-cancerous lesions.

The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study.

Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis. A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features. Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets. This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs.

Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules

PurposeTo develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules.MethodsA retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC).ResultsThe combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896.ConclusionsA multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.

Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT

PurposeTo investigate the clinical value of radiomic analysis on [18F]FDG and [18F]FLT PET on the differentiation of [18F]FDG-avid benign and malignant pulmonary nodules (PNs).MethodsData of 113 patients with inconclusive PNs based on preoperative [18F]FDG PET/CT who underwent additional [18F]FLT PET/CT scans within a week were retrospectively analyzed in the present study. Three methods of analysis including visual analysis, radiomic analysis based on [18F]FDG PET/CT images alone, and radiomic analysis based on dual-tracer PET/CT images were evaluated for differential diagnostic value of benign and malignant PNs.ResultsA total of 678 radiomic features were extracted from volumes of interest (VOIs) of 123 PNs. Fourteen valuable features were thereafter selected. Based on a visual analysis of [18F]FDG PET/CT images, the diagnostic accuracy, sensitivity, and specificity were 61.6%, 90%, and 28.8%, respectively. For the test set, the area under the curve (AUC), sensitivity, and specificity of the radiomic models based on [18F]FDG PET/CT plus [18F]FLT signature were equal or better than radiomics based on [18F]FDG PET/CT only (0.838 vs 0.810, 0.778 vs 0.778, 0.750 vs 0.688, respectively).ConclusionRadiomic analysis based on dual-tracer PET/CT images is clinically promising and feasible for the differentiation between benign and malignant PNs.Clinical relevance statementRadiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT.Key points• Radiomics brings new insights into the differentiation of benign and malignant pulmonary nodules beyond the naked eyes.• Dual-tracer imaging shows the biological behaviors of cancerous cells from different aspects.• Radiomics helps us get to the histological view in a non-invasive approach.Graphical

Machine Learning Model as a Useful Tool for Prediction of Thyroid Nodules Histology, Aggressiveness and Treatment-Related Complications.

Thyroid nodules are very common, 5-15% of which are malignant. Despite the low mortality rate of well-differentiated thyroid cancer, some variants may behave aggressively, making nodule differentiation mandatory. Ultrasound and fine-needle aspiration biopsy are simple, safe, cost-effective and accurate diagnostic tools, but have some potential limits. Recently, machine learning (ML) approaches have been successfully applied to healthcare datasets to predict the outcomes of surgical procedures. The aim of this work is the application of ML to predict tumor histology (HIS), aggressiveness and post-surgical complications in thyroid patients. This retrospective study was conducted at the ENT Division of Eastern Piedmont University, Novara (Italy), and reported data about 1218 patients who underwent surgery between January 2006 and December 2018. For each patient, general information, HIS and outcomes are reported. For each prediction task, we trained ML models on pre-surgery features alone as well as on both pre- and post-surgery data. The ML pipeline included data cleaning, oversampling to deal with unbalanced datasets and exploration of hyper-parameter space for random forest models, testing their stability and ranking feature importance. The main results are (i) the construction of a rich, hand-curated, open dataset including pre- and post-surgery features (ii) the development of accurate yet explainable ML models. Results highlight pre-screening as the most important feature to predict HIS and aggressiveness, and that, in our population, having an out-of-range (Low) fT3 dosage at pre-operative examination is strongly associated with a higher aggressiveness of the disease. Our work shows how ML models can find patterns in thyroid patient data and could support clinicians to refine diagnostic tools and improve their accuracy.

Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules

In an observational study conducted from 2016 to 2021, we assessed the utility of radiomics in differentiating between benign and malignant lung nodules detected on computed tomography (CT) scans. Patients in whom a final diagnosis regarding the lung nodules was available according to histopathology and/or 2017 Fleischner Society guidelines were included. The radiomics workflow included lesion segmentation, region of interest (ROI) definition, pre-processing, and feature extraction. Employing random forest feature selection, we identified ten important radiomic features for distinguishing between benign and malignant nodules. Among the classifiers tested, the Decision Tree model demonstrated superior performance, achieving 79% accuracy, 75% sensitivity, 85% specificity, 82% precision, and 90% F1 score. The implementation of the XGBoost algorithm further enhanced these results, yielding 89% accuracy, 89% sensitivity, 89% precision, and an F1 score of 89%, alongside a specificity of 85%. Our findings highlight tumor texture as the primary predictor of malignancy, emphasizing the importance of texture-based features in computational oncology. Thus, our study establishes radiomics as a powerful, non-invasive adjunct to CT scans in the differentiation of lung nodules, with significant implications for clinical decision-making, especially for indeterminate nodules, and the enhancement of diagnostic and predictive accuracy in this clinical context.

Impact of the 3rd Edition of the Bethesda System for Reporting Thyroid Cytopathology on Grey Zone Categories

Introduction: Thyroid Bethesda Reporting System is a six-tiered system that aims to bring uniformity in reporting thyroid cytology and improve the communication with clinicians. The system has achieved its goal as a presurgical diagnostic method; however, it remains a screening method in the grey zone categories, namely atypia of undetermined significance (AUS) and follicular neoplasm (FN). The book recently released the 3rd edition, following the recent changes in thyroid pathology. One of the most important novelties is subgrouping AUS category and FN to be able to make a better risk stratification in these categories. Our group aims to retrospectively analyze a large dataset based on the new TBSRTC, with a focus on the grey zone categories. Methods: Only patients who underwent lobectomy or total thyroidectomy were included, allowing for direct comparison between cytological and histopathological results. Cytological evaluations, based on the TBSRTC 3rd edition, were methodically compared with their respective histopathological results, enabling a comprehensive analysis. Results: Of the 244 patients (female:male ratio = 8.8:1, mean age = 56), a total of 252 nodules were evaluated. A distinction was noted with 79 nodules (31%) diagnosed as AUS and 173 nodules (69%) as FN. Intriguingly, the risk of malignancy (ROM) for AUS-overall stood at 44.3%, with AUS-nuclear atypia at 50% and AUS-other at 43.2%. Although the AUS subdivisions did not demonstrate statistical significance, a significant disparity was observed in their distribution, with 15% as AUS-nuclear atypia compared to 85% as AUS-other. This disparity raises the question: Could AUS-other be considered the new waste-basket category in the TBSRTC 3rd edition? Using the TBSRTC 3rd edition as a base, we added a subclassification for FN nodules based on the presence or absence of papillary thyroid carcinoma (PTC) nuclear features. Our findings showed that differentiating FN with oncocytic characteristics correlated well with histological outcomes and ROMs. Though retrospective in design with inherent bias potential, our data suggest a possible improvement in PTC case segregation in the FN category when differentiating between FN nodules with and without PTC nuclear features. Conclusion: Our retrospective study sheds light on the potential advantages of the TBSRTC 3rd edition, particularly in refining the AUS and FN categories for thyroid nodules. The clear disparity in AUS subcategories raises important questions about their classification and potential future refinements. Moreover, the differentiation of FN nodules based on PTC nuclear features holds a promising approach for better risk stratification.

Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto's thyroiditis.

Previous models for differentiating benign and malignant thyroid nodules(TN) have predominantly focused on the characteristics of the nodules themselves, without considering the specific features of the thyroid gland(TG) in patients with Hashimoto's thyroiditis(HT). In this study, we analyzed the clinical and ultrasound radiomics(USR) features of TN in patients with HT and constructed a model for differentiating benign and malignant nodules specifically in this population. We retrospectively collected clinical and ultrasound data from 227 patients with TN and concomitant HT(161 for training, 66 for testing). Two experienced sonographers delineated the TG and TN regions, and USR features were extracted using Python. Lasso regression and logistic analysis were employed to select relevant USR features and clinical data to construct the model for differentiating benign and malignant TN. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis(DCA). A total of 1,162 USR features were extracted from TN and the TG in the 227 patients with HT. Lasso regression identified 14 features, which were used to construct the TN score, TG score, and TN+TG score. Univariate analysis identified six clinical predictors: TI-RADS, echoic type, aspect ratio, boundary, calcification, and thyroid function. Multivariable analysis revealed that incorporating USR scores improved the performance of the model for differentiating benign and malignant TN in patients with HT. Specifically, the TN+TG score resulted in the highest increase in AUC(from 0.83 to 0.94) in the clinical prediction model. Calibration curves and DCA demonstrated higher accuracy and net benefit for the TN+TG+clinical model. USR features of both the TG and TN can be utilized for differentiating benign and malignant TN in patients with HT. These findings highlight the importance of considering the entire TG in the evaluation of TN in HT patients, providing valuable insights for clinical decision-making in this population.

133MO Differentiation of malignant and benign lung nodules using epigenetically modified nucleosomes in plasma

133MO Differentiation of malignant and benign lung nodules using epigenetically modified nucleosomes in plasma