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Background Hashimoto's thyroiditis (HT) is a common benign thyroid disease that often coexists with papillary thyroid carcinoma (PTC). Owing to the diffuse changes in the thyroid caused by HT, PTCs can be challenging to detect using conventional imaging modalities such as ultrasound and CT. The aim of this study was to develop a radiomics model based on nonenhanced CT (NECT) to predict the presence of PTC in the patients with HT, thereby improving early diagnostic accuracy. Materials and methods This retrospective study included pathologically confirmed HT patients with or without PTC who underwent NECT scans within 30 days before surgery from January 2017 to April 2023 at Hospital I and Hospital II. The patients from hospital I were divided randomly at a ratio of 8:2 into a training cohort and an internal validation cohort. The patients from hospital II were assigned to the external validation cohort. Radiomic features were extracted using PyRadiomics. Intraclass correlation coefficient, Pearson correlation and LASSO analyses were conducted to reduce the dimensionality of the radiomic features. Four machine learning algorithms, including logistic regression (LR), naive bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP) classifiers, were employed to develop and validate the prediction models based on the remaining features. Results A total of 130 patients, 89 from Hospital I [71 in the training cohort and 18 internal validation cohort] and 41 from Hospital II [external validation cohort], were included. Six features with nonzero coefficients were retained by the LASSO algorithm for inclusion in the machine learning models. In the external validation cohort, the LR, NB, SVM, and MLP models obtained AUCs of 0.736, 0.690, 0.751 and 0.783, respectively. The MLP model performed the best in the external validation cohort, with an area under the curve of 0.783, a sensitivity of 0.643, and a specificity of 0.923. Conclusion A radiomics model based on NECT could identify PTCs in patients with HT and had the potential to enhance early diagnosis and intervention for these patients.

To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593–0.9996) in the training cohort and 0.909 (95% CI 0.7921–1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.

Purpose This study aimed to develop and validate a CT-based radiomics nomogram for predicting the progression-free survival (PFS) of epithelial ovarian cancer (EOC). Materials and methods A total of 144 EOC patients were retrospectively enrolled from two hospitals and The Cancer Genome Atlas and The Cancer Imaging Archive, divided into a training set ( n  = 101) and a test set ( n  = 43) using a 7:3 ratio. Radiomic features were extracted from contrast enhanced CT images. The radiomics score (rad-score) was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression. Clinical semantic features with P  < 0.05 in multivariate Cox regression were combined with rad-score to develop radiomics nomogram. The predictive performance of the nomogram was assessed using the concordance index (C-index) and calibration curves. Results Multivariate Cox regression analysis revealed that the International Federation of Obstetrics and Gynecology stage and residual tumor are significant predictors of PFS. Twelve radiomic features were selected by LASSO Cox regression. The combined model demonstrated superior predictive performance, with a C-index of 0.78 (95% CI: 0.689–0.889) in the training set, and 0.73 (95% CI: 0.572–0.886) in the test set. The combined model outperformed the clinical and radiomics models in predicting 1-, 3-, and 5-year PFS, with area under curves of 0.850 (95% CI: 0.722–0.943), 0.828 (95% CI: 0.722–0.901), and 0.845 (95% CI: 0.722–0.943), respectively. Calibration curves of the radiomic nomogram for prediction of 1-year, 3-year, 5-year PFS showed excellent calibrations in both training and test sets. Conclusion The combined model integrating rad-score and clinical semantic features effectively evaluates PFS in EOC patients. The radiomics nomogram provides a non-invasive, simple, and feasible method to predict PFS in EOC patients, which may facilitate clinical decision-making.

This study aimed to assess the feasibility of the deep learning in generating T2 weighted (T2W) images from diffusion-weighted imaging b0 images. This retrospective study included 53 patients who underwent head magnetic resonance imaging between September 1 and September 4, 2023. Each b0 image was matched with a corresponding T2-weighted image. A total of 954 pairs of images were divided into a training set with 763 pairs and a test set with 191 pairs. The Hybrid-Fusion Network (Hi-Net) and pix2pix algorithms were employed to synthesize T2W (sT2W) images from b0 images. The quality of the sT2W images was evaluated using three quantitative indicators: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Normalized Mean Squared Error (NMSE). Subsequently, two radiologists were required to determine the authenticity of (s)T2W images and further scored the visual quality of sT2W images in the test set using a five-point Likert scale. The overall quality score, anatomical sharpness, tissue contrast and homogeneity were used to reflect the quality of the images at the level of overall and focal parts. The indicators of pix2pix algorithm in test set were as follows: PSNR, 20.549±1.916; SSIM, 0.702±0.0864; NMSE, 0.239±0.150. The indicators of Hi-Net algorithm were as follows: PSNR, 20.646 ± 2.194; SSIM, 0.722 ± 0.0955; NMSE, 0.469 ± 0.124. Hi-Net performs better than pix2pix, so the sT2W images obtained by Hi-Net were used for radiologist assessment. The two readers accurately identified the nature of the images at rates of 69.90% and 71.20%, respectively. The synthetic images were falsely identified as real at rates of 57.6% and 57.1%, respectively. The overall quality score, sharpness, tissue contrast, and image homogeneity of the sT2Ws images ranged between 1.63 ± 0.79 and 4.45 ± 0.88. Specifically, the quality of the brain parenchyma, skull and scalp, and middle ear region was superior, while the quality of the orbit and paranasal sinus region was not good enough. The Hi-Net is able to generate sT2WIs from low-resolution b0 images, with a better performance than pix2pix. It can therefore help identify incidental lesion through providing additional information, and demonstrates the potential to shorten the acquisition time of brain MRI during acute ischemic stroke imaging.

Background Depression serves as a prodromal symptom of dementia, and individuals with depression exhibit a significantly higher risk of developing dementia. The aim of this study is to develop and validate a novel dementia risk prediction tool among middle-aged and elderly individuals with depression based on machine learning algorithms. Methods This study included 31,587 middle-aged and elderly individuals with depression who did not have a diagnosis of dementia at baseline from a large UK population-based prospective cohort. A rigorous variable selection strategy was employed to identify risk and protective factors of dementia from an initial pool of 190 candidate variables, ultimately retaining 27 variables. Eight distinct data analysis strategies were utilized to develop and validate the dementia risk prediction model. The DeLong's test was applied to compare the statistical differences between different models. Results During a median follow-up of 7.98 years, 896 incident dementia cases were identified among study participants. In model development employing an 8:2 data split (fivefold cross-validation for training), the Adaboost classifier achieved the optimal performance (AUC 0.861 ± 0.003), followed by XGBoost (AUC 0.839 ± 0.005) and CatBoost (AUC 0.828 ± 0.007) classifiers. To facilitate community generalization and clinical applicability, we develop a simplified model through a forward feature subset selection algorithm, retaining 12 variables. The simplified model maintained robust performance, with AdaBoost achieving the highest discriminative ability (AUC 0.859 ± 0.002), followed by XGBoost (AUC 0.835 ± 0.001) and CatBoost (AUC 0.821 ± 0.005). The DeLong's test revealed no statistically significant difference in AUC values between models using 12 and 27 variables ( p  = 0.278). For practical implementation, we deployed the optimal model to a web application for visualization and dementia risk assessment, named DRP-Depression. Conclusions We developed a practical and easy-to-promote risk prediction model based on machine learning algorithms, and deployed it to a web application to provide a new and convenient tool for dementia risk prediction in the middle-aged and elderly individuals with depression.

Background Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset. Methods A total of 201 EOC patients from three centers, divided into a training cohort ( n  = 106), internal ( n  = 46) and external ( n  = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test. Results Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort ( P  < 0.001). Conclusions The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

The aim of this study is to assess the effectiveness of conventional and two additional functional markers derived from standard cardiac magnetic resonance (CMR) images in detecting the occurrence of late gadolinium enhancement (LGE) in patients with secondary cardiac amyloidosis (CA) related to multiple myeloma (MM). This study retrospectively included 32 patients with preserved ejection fraction (EF) who had MM-CA diagnosed consecutively. Conventional left ventricular (LV) function markers and two additional functional markers, namely myocardial contraction fraction (MCF) and LV long-axis strain (LAS), were obtained using commercial cardiac post-processing software. Logistic regression analyses and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive performances. (1) There were no notable distinctions in clinical features between the LGE+ and LGE− groups, with the exception of a reduced systolic blood pressure in the former (105.60 ± 18.85 mmHg vs. 124.50 ± 20.95 mmHg, P  = 0.022). (2) Patients with MM-CA presented with intractable heart failure with preserved ejection fraction (HFpEF). The LVEF in the LGE+ group exhibited a greater reduction (54.27%, IQR 51.59–58.39%) in comparison to the LGE− group (P < 0.05). And MM-CA patients with LGE+ had significantly higher LVMI (90.15 ± 23.69 g/m 2 ), lower MCF (47.39%, IQR 34.28–54.90%), and the LV LAS were more severely damaged (− 9.94 ± 3.42%) than patients with LGE− (all P values < 0.05). (3) The study found that MCF exhibited a significant independent association with LGE, as indicated by an odds ratio of 0.89 ( P  < 0.05). The cut-off value for MCF was determined to be 64.25% with a 95% confidence interval ranging from 0.758 to 0.983. The sensitivity and specificity of this association were calculated to be 95% and 83%, respectively. MCF is a simple reproducible predict marker of LGE in MM-CA patients. It is a potentially CMR-based method that promise to reduce scan times and costs, and boost the accessibility of CMR.

•The dynamic functional network framework overlooks the continuous impact of non-dynamic dependencies, which dominants the fluctuations of regional correlations, within its connection measurements resulting in a relatively stable spatiotemporal pattern to model the communication process in the human brain.•We propose the activation network framework based on the functional connectivity activity, capturing the potential time-specific dependency fluctuations, to establish a new spatiotemporal pattern of brain network.•The activation network reveals a different spatiotemporal connection mode that presents a more effective connectivity pattern with temporal evolution, largely invisible to the dynamic functional network.•The successful application of this approach to autism spectrum disorders and coronavirus disease classification demonstrates its feasibility for extracting communication dynamics. Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated \"ground truth\" validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.

Purpose To explore the added value of quantitative parameters derived from routine chest contrast-enhanced computed tomography (CECT) in distinguishing malignant from benign solid pulmonary nodules (SPNs). Methods Eighty-one SPNs pathologically confirmed as benign or malignant with preoperative nonenhanced chest and CECT scans were retrospectively analyzed. Quantitative parameters [CT attenuation value of nonenhanced phase (AV N ), value of arterial phase (AV A ), value of venous phase (AV V ), their differentials (△AV A−N , △AV V−N , △AV V−A ), diameter] and qualitative CT features [lobulation, spiculation, vacuolar sign, pleural depression sign, vascular convergence, edge clarity] were obtained. Inter-group comparisons for clinical/imaging variables used t-tests/Mann-Whitney U tests or Chi-square/Fisher’s tests. Three multivariate logistic regression models (qualitative, quantitative, and combined models) were developed and evaluated through five-fold cross-validation, DeLong tests (Bonferroni-corrected α = 0.0167), decision/calibration curves, and Bootstrap-based threshold sensitivity analysis (1000 iterations; 0.1–0.9 thresholds). Subgroup ROC analyses assessed age/diameter effects (stratified by mean/median). Results Malignant SPNs showed higher age and greater AV A, AV N , AV V , △AV A−N , △AV V−N , diameter ( P  < 0.05). AV V showed the strongest discriminatory power among quantitative parameters (AUC = 0.779). The qualitative model incorporated vascular convergence, pleural depression sign, and lobulation, while the quantitative model included AV V . Cross-validation yielded mean AUCs of 0.877 ± 0.019, 0.790 ± 0.081, and 0.900 ± 0.042 for the qualitative, quantitative, and combined model respectively. The combined model surpassed the qualitative model ( P  = 0.016), demonstrating better calibration and decision curve performance. Bootstrap analysis identified 0.4 as the optimal sensitivity-specificity threshold. Subgroup AUCs were 0.945/0.860 (mean-age strara) and 0.903/0.912 (median-diameter strata). Conclusions Quantitative CECT parameters, particularly AV V , aid in discriminating malignant SPNs. Combining AV V with qualitative features enhances diagnostic accuracy for malignancy risk assessment.

Background Advances in nanotechnology have introduced novel methodologies for immunotherapy targeting hepatocellular carcinoma (HCC). This study aims to develop a knowledge map and identify potential research hotspots in this field through bibliometric analysis. Methods Publications on HCC immunotherapy utilizing nanotechnology from 2005 to 2024 were obtained from the Web of Science Core Collection (WoSCC). Data visualization and statistical analysis were primarily conducted using VOSviewer, CiteSpace, R software, and Microsoft Office Excel 2021. Results The systematic search initially retrieved 600 publications from WoSCC, of which 10 were excluded during screening, yielding 590 publications on nanotechnology applications in HCC immunotherapy. Notably, the annual publication count surged from 44 papers in 2020 to 159 papers in 2024, representing an impressive 261% increase over this four-year period. China emerged as the foremost country in publishing volume and international collaborations, although its articles receive relatively few average citations. The USA, ranking second in output, achieves the highest average citation count. The timeline visualization of reference reveals three distinct phases (2005–2009, 2010–2018, and 2019–2024) with the following seven reference clusters: #0 cancer chitosan nanoparticle, #1 hepatocellular carcinoma, #2 antigen presentation, #3 carbon nanotube, #4 targeting PD-L1 expression, #5 natural killer cell, and #6 liver metastases. “Cancer microenvironment”, “drug delivery”, “immunogenic cell death”, and “combination therapy” showed extremely high occurrence frequencies. “mRNA”, “mRNA delivery”, “metabolism” were the key keywords that have emerged recently. Conclusion Nanotechnology-based immunotherapy field has evolved from foundational nanoparticle development (2005–2009) to immune microenvironment modulation (2010–2018) and now embraces mRNA-based precision immunotherapy (2019–2024). Future efforts may prioritize combinatorial nano-immunotherapies integrating metabolic regulation and AI-optimized delivery.