Objective: Thermal ablation is a minimally invasive procedure for the treatment of pulmonary malignancy, but the intraoperative measure of complete ablation of the tumor is mainly based on the subjective judgment of clinicians without quantitative criteria. This study aimed to develop and validate an intraoperative computed tomography (CT)-based radiomic nomogram to predict complete ablation of pulmonary malignancy. Methods: This study enrolled 104 individual lesions from 92 patients with primary or metastatic pulmonary malignancies, which were randomly divided into training cohort (n=74) and verification cohort (n=30). Radiomics features were extracted from the original CT images when the study clinicians determined the completion of the ablation surgery. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted for the dimensionality reduction of high-dimensional data and feature selection. The prediction model was developed based on the radiomics signature combined with the independent clinical predictors by multiple logistic regression analysis. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. Decision curve analysis (DCA) was applied to estimate the clinical usefulness and net benefit of the nomogram for decision making. Results: Thirteen CT features were selected to construct radiomics prediction model, which exhibits good predictive performance for determination of complete ablation of pulmonary malignancy. The AUCs of a CT-based radiomics nomogram that integrated the radiomics signature and the clinical predictors were 0.88 (95% CI 0.80-0.96) in the training cohort and 0.87 (95% CI: 0.71–1.00) in the validation cohort, respectively. The radiomics nomogram was well calibrated in both the training and validation cohorts, and it was highly consistent with complete tumor ablation. DCA indicated that the nomogram was clinically useful. Conclusion: A CT-based radiomics nomogram has good predictive value for determination of complete ablation of pulmonary malignancy intraoperatively, which can assist in decision-making.

This study aims to develop a prediction model based on non-contrast computed tomography (NCCT) images to differentiate uric acid stones from non-uric acid stones before treatment. This study retrospectively enrolled 195 patients from Quzhou People’s Hospital between 2022 and 2024 who underwent dual-energy CT scans with confirmed renal stone composition. The patients were randomly divided into a training set (156 cases) and a test set (39 cases) in an 8:2 ratio. Regions of interest (ROIs) were manually delineated slice-by-slice on NCCT images to extract radiomic features. Feature dimensionality reduction and selection were performed using intraclass correlation coefficient (ICC), Spearman rank correlation coefficients, and least absolute shrinkage and selection operator (LASSO) regression. Radiomics and clinical models were developed using logistic regression (LR), support vector machine (SVM), multilayer perceptron (MLP), ExtraTrees, and LightGBM algorithms. Finally, a combined model was constructed by integrating the selected radiomic features with clinically significant risk factors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), while clinical utility was assessed through decision curve analysis (DCA). Model interpretability was examined using Shapley additive explanations (SHAP). A total of 1834 radiomic features were extracted from each ROI. After feature dimensionality reduction and selection, 6 radiomic features remained for characterizing stone composition. The clinical, radiomics, and combined models all demonstrated favorable discriminatory power for uric acid stones. The AUC values of the three models in the training set were 0.744 (95% CI: 0.666–0.822), 0.831 (95% CI: 0.766–0.897), and 0.886 (95% CI: 0.834–0.938), respectively, and 0.778 (95% CI: 0.631–0.925), 0.634 (95% CI: 0.454–0.777), and 0.805 (95% CI: 0.666–0.944) in the test set. DeLong’s test indicated that in the training set, the performance of the combined model was significantly superior to both the clinical model and the radiomics model (0.886 vs. 0.744, p < 0.001; 0.886 vs. 0.831, p = 0.015). Decision curve analysis also demonstrated its potential clinical utility. SHAP analysis revealed that texture features were important factors in predicting uric acid stones. The combined model based on NCCT performs well in distinguishing uric acid stones and can provide effective references for treatment decisions. Not applicable.

This study aimed to evaluate the value of contrast-enhanced computed tomography (CT) imaging radiomics in distinguishing malignant lesions from benign ones in the orbit. A retrospective analysis was conducted on CT imaging data from 139 patients with orbital tumor lesions, all of whom underwent contrast-enhanced CT scans within 2 weeks before diagnosis. Of these, 45 cases were benign lesions and 94 were malignant lesions. Radiomic features were extracted from the contrast-enhanced CT images, and 12 features were selected through the minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression methods. The selected features were used to build models using logistic regression, Naive Bayes Classifier (NaiveBayes), support vector machine (SVM), Extra Trees Classifier (ExtraTrees), and multilayer perceptron, with the best-performing model identified. Multivariate logistic regression was employed to identify clinical risk factors for malignant orbital lesions, and a nomogram model was developed by combining radiomic features and clinical variables. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve. Among the 3 machine learning models, the SVM model demonstrated the best predictive performance and robustness across datasets. Therefore, the SVM model was used to construct the nomogram. The nomogram achieved area under the receiver operating characteristic curve values of 0.957 and 0.833 in the training and testing cohorts, respectively, both of which were higher than 0.80. The performance of the nomogram was significantly superior to that of the clinical model (De-long test, P < .05), but no statistically significant difference was observed when compared to the radiomics model (De-long test, P > .05). Contrast-enhanced CT radiomics can effectively differentiate between malignant and benign orbital lesions. Both the nomogram and radiomics models exhibited high predictive performance, offering valuable insights for clinical decision-making.

Introduction: Prolonged mechanical ventilation in intensive care units (ICUs) leads to increased morbidity, higher mortality rates, and elevated healthcare costs. Predicting successful weaning from mechanical ventilation with accuracy is essential for optimizing resource use and improving patient outcomes. The International Classification of Functioning, Disability and Health (ICF) framework offers a holistic perspective on health conditions and can be adapted to identify key predictors of weaning readiness. This study aims to develop a Delphi-based core predictor set for weaning in critically ill patients, utilizing the ICF model.Methods and analysis: The core predictor set development comprises three steps: (1) Literature review and expert consultation to gather weaning predictors, (2) Predictor alignment with ICF categories per established rules, and (3) Three-round Delphi survey with a multidisciplinary team. A systematic review across major databases will be conducted to identify predictors related to weaning predictors in critically ill adults from cohort studies, trials, and reviews. Predictors will then be categorized within ICF domains. A multidisciplinary expert panel will evaluate the relevance of each predictor using a 9-point Likert scale to achieve consensus.Discussion: This study will contribute to the development of a standardized, evidence-based predictor set for weaning readiness in critically ill patients. Using the ICF framework, this study aims to encompass the complex factors that influence weaning, thereby enabling personalized care plans and improving weaning outcomes. The Delphi methodology guarantees a thorough, iterative process for building consensus by integrating diverse clinical perspectives.Conclusion: The proposed Delphi-based study protocol aims to establish a core set of predictors for weaning in the ICU setting, guided by the ICF model. Successful implementation of this predictor set could enhance decision-making around weaning trials, reduce unnecessary ventilation days, and ultimately improve patient outcomes and healthcare efficiency. Future validation and implementation studies will be essential to confirm the utility and generalizability of this predictor set in clinical practice.

Rationale: Carcinoid tumors arising from mature cystic teratomas of the ovary (MCTO) are exceptionally rare and often misdiagnosed as benign teratomas on imaging studies, which can lead to delayed or inappropriate treatment. The purpose of this study is to raise awareness of this rare condition by presenting a case of a carcinoid tumor originating from MCTO, highlighting its unique imaging features and clinical presentation, and discussing the importance of accurate preoperative diagnosis. Patient concerns: A 59-year-old woman was admitted to the hospital following an ultrasound physical examination that revealed an anterior uterine parenchymal mass. She exhibited no obvious abnormal clinical symptoms. Diagnosis: CT and MRI of the pelvis revealed a cystic-solid soft tissue mass with small calcified foci and a significant cystic fatty component. There was a marked enhancement of the solid component in the pelvis off to the right, leading to the consideration of a teratoma. Interventions: The final clinical preoperative diagnosis was an ovarian teratoma. Pelvic tumor resection was performed after the exclusion of contraindications to surgery. Postoperative pathology revealed a carcinoid tumor arising from MCTO. Limitations: This study is limited by the fact that it reports a single case, which makes it difficult to generalize the findings to a larger population. Additionally, the rarity of this condition means that there is limited literature available for comparison, which could provide a broader understanding of the potential challenges in diagnosing similar cases. The retrospective nature of the case also restricts the ability to conduct a more extensive analysis of potential risk factors. Outcomes: The patient did not receive adjuvant therapy post-surgery. During the 36-month follow-up period, the tumor showed no signs of recurrence or metastasis. The follow-up was concluded without further complications, indicating a favorable outcome. Lessons: Mature cystic teratoma is a common ovarian tumor, but its association with carcinoid tumors is very rare and prone to preoperative misdiagnosis. The imaging presentation of this case is characteristic and has significant diagnostic implications for this disease.