Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research. We investigated 92 SSc recipients (mean age 51 years ± 10) who underwent bilateral LTx between 2007 and 2020. PGD was defined as grade 3 PGD at 72 h post-LTx. A comprehensive set of CT image features was automatically computed from recipient chest CT scans using deep learning algorithms. Volumetric analysis of recipients' lungs and chest cavity was used to estimate lung-size matching. Four machine learning (ML) algorithms were developed to predict PGD, including multivariate logistic regression, support vector machine (SVM), random forest classifier (RFC), and multilayer perceptron (MLP). PGD was significantly associated with BMI >30 kg/m² (p = 0.009), African American race (p = 0.011), lower Preop FEV1 (p = 0.002) and FVC (p = 0.004), longer waitlist time (p = 0.014), higher lung allocation score (LAS) (p = 0.028), and interstitial lung disease (p = 0.050). From CT analysis, PGD was significantly associated with decreased lung volume (p < 0.001), increased heart-chest cavity volume ratio (p < 0.001), epicardial (p = 0.033) and total heart (p = 0.049) adipose tissue, and five cardiopulmonary features (p < 0.050). Oversized donor allografts estimated using CT analysis were significantly associated with PGD (p < 0.050). The MLP model achieved a maximum AUROC of 0.85 (95% CI: 0.81-0.88) in predicting PGD with four features: Preop FEV1, heart-chest cavity volume ratio, waitlist time, and donor to recipient chest cavity volume ratio. CT-derived features are significantly associated with PGD, and models incorporating these features can predict PGD in SSc recipients.

This study aims to construct a survival prognosis prediction model for muscle-invasive bladder cancer based on CT imaging features. A total of 91 patients with muscle-invasive bladder cancer were sourced from the TCGA and TCIA dataset and were divided into a training group (64 cases) and a validation group (27 cases). Additionally, 54 patients with muscle-invasive bladder cancer were retrospectively collected from our hospital to serve as an external test group; their enhanced CT imaging data were analyzed and processed to identify the most relevant radiomic features. Five distinct machine learning methods were employed to develop the optimal radiomics model, which was then combined with clinical data to create a nomogram model aimed at accurately predicting the overall survival (OS) of patients with muscle-invasive bladder cancer. The model's performance was ultimately assessed using various evaluation methods, including the ROC curve, calibration curve, decision curve, and Kaplan-Meier (KM) analysis. Eight radiomic features were identified for modeling analysis. Among the models evaluated, the Gradient Boosting Machine (GBM) In the prediction of OS performed the best. the 2-year AUCs were 0.859, 95% CI (0.767-0.952) for the training group, 0.850, 95% CI (0.705-0.995) for the validation group, and 0.700, 95% CI (0.520-0.880) for the external test group. The 3-year AUCs were 0.809, 95% CI (0.704-0.913) for the training group, 0.895, 95% CI (0.768-1.000) for the validation group, and 0.730, 95% CI (0.569-0.891) for the external test group. The nomogram model incorporating clinical data achieved superior results, the AUCs for predicting 2-year OS were 0.913 (95% CI: 0.83-0.98) for the training group, 0.86 (95% CI: 0.78-0.96) for the validation group, and 0.778 (95% CI: 0.69-0.94) for the external test group; for predicting 3-year OS, the AUCs were 0.837 (95% CI: 0.83-0.98) for the training group, 0.982 (95% CI: 0.84-1.0) for the validation group, and 0.785 (95% CI: 0.75-0.96) for the external test group. The calibration curve demonstrated excellent calibration of the model, while the decision curve and KM analysis indicated that the model possesses substantial clinical utility. The GBM model, based on the radiomic features of enhanced CT imaging, holds significant potential for predicting the prognosis of patients with muscle-invasive bladder cancer. Furthermore, the combined model, which incorporates clinical features, demonstrates enhanced performance and is beneficial for clinical decision-making.

Deep learning image reconstruction (DLIR) technology effectively improves the image quality while maintaining spatial resolution. The impact of DLIR on the quantification of coronary artery calcium (CAC) is still unclear. The purpose of this study was to investigate the effect of DLIR on the quantification of coronary calcium in high-risk populations. A retrospective study was conducted on patients who underwent coronary artery CT angiography (CCTA) at our hospital(China) from February 2022 to September 2022. Raw data were reconstructed with filtered back projection (FBP) reconstruction, 40% and 80% level adaptive statistical iterative reconstruction-veo (ASiR-V 40%, ASiR-V 80%) and low, medium and high-level deep learning algorithm (DLIR-L, DLIR-M, and DLIR-H). Calculate and compare the signal-to-noise and contrast-to-noise ratio, volumetric score, mass scores, and Agaston score of 6 sets of images. There were 178 patients, female (107), mean age (62.43 ± 9.26), and mean BMI (25.33 ± 3.18) kg/m². Compared with FBP, the image noise of ASiR-V and DLIR was significantly reduced (P < 0.001). There was no significant difference in Agaston score, volumetric score, and mass scores among the six reconstruction algorithms (all P > 0.05). Bland-Altman diagram indicated that the Agatston scores of the five reconstruction algorithms showed good agreement with FBP, with DLIR-L(AUC, 110.08; 95% CI: 26.48, 432.92;)and ASIR-V40% (AUC,110.96; 95% CI: 26.23, 431.34;) having the highest consistency with FBP. Compared with FBP, DLIR and ASiR-V improve CT image quality to varying degrees while having no impact on Agatston score-based risk stratification. CACS is a powerful tool for cardiovascular risk stratification, and DLIR can improve image quality without affecting CACS, making it widely applicable in clinical practice.

The development of artificial intelligence has revolutionized the field of dentistry. Medical image segmentation is a vital part of AI applications in dentistry. This technique can assist medical practitioners in accurately diagnosing diseases. The detection of the maxillary sinus (MS), such as dental implants, tooth extraction, and endoscopic surgery, is important in the surgical field. The accurate segmentation of MS in radiological images is a prerequisite for diagnosis and treatment planning. This study aims to investigate the feasibility of applying a CNN algorithm based on the U-Net architecture to facilitate MS segmentation of individuals from the Chinese population. A total of 300 CBCT images in the axial, coronal, and sagittal planes were used in this study. These images were divided into a training set and a test set at a ratio of 8:2. The marked regions (maxillary sinus) were labelled for training and testing in the original images. The training process was performed for 40 epochs using a learning rate of 0.00001. Computation was performed on an RTX GeForce 3060 GPU. The best model was retained for predicting MS in the test set and calculating the model parameters. The trained U-Net model achieved yield segmentation accuracy across the three imaging planes. The IoU values were 0.942, 0.937 and 0.916 in the axial, sagittal and coronal planes, respectively, with F1 scores across all planes exceeding 0.95. The accuracies of the U-Net model were 0.997, 0.998, and 0.995 in the axial, sagittal and coronal planes, respectively. The trained U-Net model achieved highly accurate segmentation of MS across three planes on the basis of 2D CBCT images among the Chinese population. The AI model has shown promising application potential for daily clinical practice. Not applicable.

<b>Objective</b>  The aim of this study was to explore an innovative approach for developing deep learning (DL) algorithm for renal cell carcinoma (RCC) detection and subtyping on computed tomography (CT): clear cell RCC (ccRCC) versus non-ccRCC using two-dimensional (2D) neural network architecture and feature consistency modules. <b>Materials and Methods</b>  This retrospective study included baseline CT scans from 196 histopathologically proven RCC patients: 143 ccRCCs and 53 non-ccRCCs. Manual tumor annotations were performed on axial slices of corticomedullary phase images, serving as ground truth. After image preprocessing, the dataset was divided into training, validation, and testing subsets. The study tested multiple 2D DL architectures, with the FocalNet-DINO demonstrating highest effectiveness in detecting and classifying RCC. The study further incorporated spatial and class consistency modules to enhance prediction accuracy. Models' performance was evaluated using free-response receiver operating characteristic curves, recall rates, specificity, accuracy, F1 scores, and area under the curve (AUC) scores. <b>Results</b>  The FocalNet-DINO architecture achieved the highest recall rate of 0.823 at 0.025 false positives per image (FPI) for RCC detection. The integration of spatial and class consistency modules into the architecture led to 0.2% increase in recall rate at 0.025 FPI, along with improvements of 0.1% in both accuracy and AUC scores for RCC classification. These enhancements allowed detection of cancer in an additional 21 slices and reduced false positives in 126 slices. <b>Conclusion</b>  This study demonstrates high performance for RCC detection and classification using DL algorithm leveraging 2D neural networks and spatial and class consistency modules, to offer a novel, computationally simpler, and accurate DL approach to RCC characterization.

To quantitatively characterize the degree of perirenal fat adhesion using artificial intelligence in renal cell carcinoma. This retrospective study analyzed a total of 596 patients from three cohorts, utilizing corticomedullary phase computed tomography urography (CTU) images. The nnUNet v2 network combined with numerical computation was employed to segment the perirenal fat region. Pyradiomics algorithms and a computed tomography foundation model were used to extract features from CTU images separately, creating single-modality predictive models for identifying perirenal fat adhesion. By concatenating the Pyradiomics and foundation model features, an early fusion multimodal predictive signature was developed. The prognostic performance of the single-modality and multimodality models was further validated in two independent cohorts. The nnUNet v2 segmentation model accurately segmented both kidneys. The neural network and thresholding approach effectively delineated the perirenal fat region. Single-modality models based on radiomic and computed tomography foundation features demonstrated a certain degree of accuracy in diagnosing and identifying perirenal fat adhesion, while the early feature fusion diagnostic model outperformed the single-modality models. Also, the perirenal fat adhesion score showed a positive correlation with surgical time and intraoperative blood loss. AI-based radiomics and foundation models can accurately identify the degree of perirenal fat adhesion and have the potential to be used for surgical risk assessment.

More cases of thyroid micro-nodules have been diagnosed annually in recent years because of advancements in diagnostic technologies and increased public health awareness. To explore the application value of various machine learning (ML) algorithms based on dual-layer spectral computed tomography (DLCT) quantitative parameters in distinguishing benign from malignant thyroid micro-nodules. All 338 thyroid micro-nodules (177 malignant micro-nodules and 161 benign micro-nodules) were randomly divided into a training cohort (n = 237) and a testing cohort (n = 101) at a ratio of 7:3. Four typical radiological features and 19 DLCT quantitative parameters in the arterial phase and venous phase were measured. Recursive feature elimination was employed for variable selection. Three ML algorithms-support vector machine (SVM), logistic regression (LR), and naive Bayes (NB)-were implemented to construct predictive models. Predictive performance was evaluated via receiver operating characteristic (ROC) curve analysis. A variable set containing 6 key variables with "one standard error" rules was identified in the SVM model, which performed well in the training and testing cohorts (area under the ROC curve (AUC): 0.924 and 0.931, respectively). A variable set containing 2 key variables was identified in the NB model, which performed well in the training and testing cohorts (AUC: 0.882 and 0.899, respectively). A variable set containing 8 key variables was identified in the LR model, which performed well in the training and testing cohorts (AUC: 0.924 and 0.925, respectively). And nine ML models were developed with varying variable sets (2, 6, or 8 variables), all of which consistently achieved AUC values above 0.85 in the training, cross validation (CV)-Training, CV-Validation, and testing cohorts. Artificial intelligence-based DLCT quantitative parameters are promising for distinguishing benign from malignant thyroid micro-nodules.

Classifying chondroid tumors is an essential step for effective treatment planning. Recently, with the advances in computer-aided diagnosis and the increasing availability of medical imaging data, automated tumor classification using deep learning shows promise in assisting clinical decision-making. In this study, we propose a hybrid approach that integrates deep learning and radiomics for chondroid tumor classification. First, we performed tumor segmentation using the nnUNetv2 framework, which provided three-dimensional (3D) delineation of tumor regions of interest (ROIs). From these ROIs, we extracted a set of radiomics features and deep learning-derived features. After feature selection, we identified 15 radiomics and 15 deep features to build classification models. We developed 5 machine learning classifiers including Random Forest, XGBoost, Gradient Boosting, LightGBM, and CatBoost for the classification models. The approach integrating features from radiomics, ROI-originated deep learning features, and clinical variables yielded the best overall classification results. Among the classifiers, CatBoost classifier achieved the highest accuracy of 0.90 (95% CI 0.90-0.93), a weighted kappa of 0.85, and an AUC of 0.91. These findings highlight the potential of integrating 3D U-Net-assisted segmentation with radiomics and deep learning features to improve classification of chondroid tumors.

The aim of this study was to develop a radiomics model based on cone beam CT (CBCT) to differentiate odontogenic cysts (OCs), odontogenic keratocysts (OKCs), and ameloblastomas (ABs). In this retrospective study, CBCT images were collected from 300 patients diagnosed with OC, OKC, and AB who underwent histopathological diagnosis. These patients were randomly divided into training (70%) and test (30%) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into random forest model, support vector classifier (SVC) model, logistic regression model, and a soft VotingClassifier based on the above 3 algorithms. The performance of the models was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). The optimal model among these was then used to establish the final radiomics prediction model, whose performance was evaluated using the sensitivity, accuracy, precision, specificity, and F1 score in both the training cohort and the test cohort. The 6 optimal radiomics features were incorporated into a soft VotingClassifier. Its performance was the best overall. The AUC values of the One-vs-Rest (OvR) multi-classification strategy were AB-vs-Rest 0.963; OKC-vs-Rest 0.928; OC-vs-Rest 0.919 in the training cohort and AB-vs-Rest 0.814; OKC-vs-Rest 0.781; OC-vs-Rest 0.849 in the test cohort. The overall accuracy of the model in the training cohort was 0.757, and in the test cohort was 0.711. The VotingClassifier model demonstrated the ability of the CBCT radiomics to distinguish the multiple types of diseases (OC, OKC, and AB) in the jaw and may have the potential to diagnose accurately under non-invasive conditions.