Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessment of volumetric bone mineral density (vBMD) and the diagnosis of osteoporosis (OP). This study investigated the feasibility of using deep learning to establish a system for vBMD prediction and OP classification based on LDCT scans. This study included 551 subjects who underwent both LDCT and QCT examinations. First, the U-net was developed to automatically segment lumbar vertebrae from single 2D LDCT slices near the mid-vertebral level. Then, a prediction model was proposed to estimate vBMD, which was subsequently employed for detecting OP and osteopenia (OA). Specifically, two input modalities were constructed for the prediction model. The performance metrics of the models were calculated and evaluated. The segmentation model exhibited a strong correlation with manual segmentation, achieving a mean Dice similarity coefficient (DSC) of 0.974, sensitivity of 0.964, positive predictive value (PPV) of 0.985, and Hausdorff distance of 3.261 in the test set. Linear regression and Bland-Altman analysis demonstrated strong agreement between the predicted vBMD from two-channel inputs and QCT-derived vBMD, with a root mean square error of 8.958 mg/mm³ and an R² of 0.944. The areas under the curve for detecting OP and OA were 0.800 and 0.878, respectively, with an overall accuracy of 94.2%. The average processing time for this system was 1.5 s. This prediction system could automatically estimate vBMD and detect OP and OA on LDCT scans, providing great potential for the osteoporosis screening.

<b>Rationale:</b> Quantifying functional small airway disease (fSAD) requires additional expiratory computed tomography (CT) scans, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scans at total lung capacity (TLC) alone (fSAD^TLC). <b>Objectives:</b> To evaluate an AI model for estimating fSAD^TLC, compare it with dual-volume parametric response mapping fSAD (fSAD^PRM), and assess its clinical associations and repeatability in chronic obstructive pulmonary disease (COPD). <b>Methods:</b> We analyzed 2,513 participants from SPIROMICS (the Subpopulations and Intermediate Outcome Measures in COPD Study). Using a randomly sampled subset (<i>n</i> = 1,055), we developed a generative model to produce virtual expiratory CT scans for estimating fSAD^TLC in the remaining 1,458 SPIROMICS participants. We compared fSAD^TLC with dual-volume fSAD^PRM. We investigated univariate and multivariable associations of fSAD^TLC with FEV₁, FEV₁/FVC ratio, 6-minute-walk distance, St. George's Respiratory Questionnaire score, and FEV₁ decline. The results were validated in a subset of patients from the COPDGene (Genetic Epidemiology of COPD) study (<i>n</i> = 458). Multivariable models were adjusted for age, race, sex, body mass index, baseline FEV₁, smoking pack-years, smoking status, and percent emphysema. <b>Measurements and Main Results:</b> Inspiratory fSAD^TLC showed a strong correlation with fSAD^PRM in SPIROMICS (Pearson's <i>R</i> = 0.895) and COPDGene (<i>R</i> = 0.897) cohorts. Higher fSAD^TLC levels were significantly associated with lower lung function, including lower postbronchodilator FEV₁ (in liters) and FEV₁/FVC ratio, and poorer quality of life reflected by higher total St. George's Respiratory Questionnaire scores independent of percent CT emphysema. In SPIROMICS, individuals with higher fSAD^TLC experienced an annual decline in FEV₁ of 1.156 ml (relative decrease; 95% confidence interval [CI], 0.613-1.699; <i>P</i> < 0.001) per year for every 1% increase in fSAD^TLC. The rate of decline in the COPDGene cohort was slightly lower at 0.866 ml/yr (relative decrease; 95% CI, 0.345-1.386; <i>P</i> < 0.001) per 1% increase in fSAD^TLC. Inspiratory fSAD^TLC demonstrated greater consistency between repeated measurements, with a higher intraclass correlation coefficient of 0.99 (95% CI, 0.98-0.99) compared with fSAD^PRM (0.83; 95% CI, 0.76-0.88). <b>Conclusions:</b> Small airway disease can be reliably assessed from a single inspiratory CT scan using generative AI, eliminating the need for an additional expiratory CT scan. fSAD estimation from inspiratory CT correlates strongly with fSAD^PRM, demonstrates a significant association with FEV₁ decline, and offers greater repeatability.

Accurate determination of human epidermal growth factor receptor 2 (HER2) expression is critical for guiding targeted therapy in breast cancer. This study aimed to develop and validate a deep learning (DL)-based decision-making visual biomarker system (DM-VBS) for predicting HER2 status using radiomics and DL features derived from magnetic resonance imaging (MRI) and mammography (MG). Radiomics features were extracted from MRI, and DL features were derived from MG. Four submodels were constructed: Model I (MRI-radiomics) and Model III (mammography-DL) for distinguishing HER2-zero/low from HER2-positive cases, and Model II (MRI-radiomics) and Model IV (mammography-DL) for differentiating HER2-zero from HER2-low/positive cases. These submodels were integrated into a XGBoost model for ternary classification of HER2 status. Radiologists assessed imaging features associated with HER2 expression, and model performance was validated using two independent datasets from The Cancer Image Archive. A total of 550 patients were divided into training, internal validation, and external validation cohorts. Models I and III achieved an area under the curve (AUC) of 0.800-0.850 for distinguishing HER2-zero/low from HER2-positive cases, while Models II and IV demonstrated AUC values of 0.793-0.847 for differentiating HER2-zero from HER2-low/positive cases. The DM-VBS achieved average accuracy of 85.42%, 80.4%, and 89.68% for HER2-zero, -low, and -positive patients in the validation cohorts, respectively. Imaging features such as lesion size, number of lesions, enhancement type, and microcalcifications significantly differed across HER2 statuses, except between HER2-zero and -low groups. DM-VBS can predict HER2 status and assist clinicians in making treatment decisions for breast cancer.

This study aimed to compare CT features of COVID-19 and Influenza A pneumonia, develop a diagnostic differential model, and explore a prognostic model for lesion resolution. A total of 446 patients diagnosed with COVID-19 and 80 with Influenza A pneumonitis underwent baseline chest CT evaluation. Logistic regression analysis was conducted after multivariate analysis and the results were presented as nomograms. Machine learning models were also evaluated for their diagnostic performance. Prognostic factors for lesion resolution were analyzed using Cox regression after excluding patients who were lost to follow-up, with a nomogram being created. COVID-19 patients showed more features such as thickening of bronchovascular bundles, crazy paving sign and traction bronchiectasis. Influenza A patients exhibited more features such as consolidation, coarse banding and pleural effusion (P < 0.05). The logistic regression model achieved AUC values of 0.937 (training) and 0.931 (validation). Machine learning models exhibited area under the curve values ranging from 0.8486 to 0.9017. COVID-19 patients showed better lesion resolution. Independent prognostic factors for resolution at baseline included age, sex, lesion distribution, morphology, coarse banding, and widening of the main pulmonary artery. Distinct imaging features can differentiate COVID-19 from Influenza A pneumonia. The logistic discriminative model and each machine - learning network model constructed in this study demonstrated efficacy. The nomogram for the logistic discriminative model exhibited high utility. Patients with COVID-19 may exhibit a better resolution of lesions. Certain baseline characteristics may act as independent prognostic factors for complete resolution of lesions.

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.

To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spinal MRI sequences using the denoising diffusion probabilistic model (DDPM) and to compare its performance against one baseline model pix2pix and three advanced models. A total of 1195 consecutive spinal tumor patients who underwent contrast-enhanced MRI at two hospitals were divided into a training set (n = 809, 49 ± 17 years, 437 men), an internal test set (n = 203, 50 ± 16 years, 105 men), and an external test set (n = 183, 52 ± 16 years, 94 men). Input sequences were T1- and T2-weighted images, and T2 fat-saturation images. The output was T1CE images. In the test set, one radiologist read the virtual images and marked all visible enhancing lesions. Results were evaluated using sensitivity (SE) and false discovery rate (FDR). We compared differences in lesion size and enhancement degree between reference and virtual images, and calculated signal-to-noise (SNR) and contrast-to-noise ratios (CNR) for image quality assessment. In the external test set, the mean squared error was 0.0038±0.0065, and structural similarity index 0.78±0.10. Upon evaluation by the reader, the overall SE of the generated T1CE images was 94% with FDR 2%. There was no difference in lesion size or signal intensity ratio between the reference and generated images. The CNR was higher in the generated images than the reference images (9.241 vs. 4.021; P＜0.001). The proposed DDPM demonstrates potential as an alternative to gadolinium contrast in spinal MRI examinations of oncologic patients.

Accurately registering intraoral and cone beam computed tomography (CBCT) scans in patients with metal artifacts poses a significant challenge. Whether a cloud-based platform trained for artificial intelligence (AI)-driven segmentation can improve registration is unclear. The purpose of this clinical study was to validate a cloud-based platform trained for the AI-driven segmentation of prosthetic crowns on CBCT scans and subsequent multimodal intraoral scan-to-CBCT registration in the presence of high metal artifact expression. A dataset consisting of 30 time-matched maxillary and mandibular CBCT and intraoral scans, each containing at least 4 prosthetic crowns, was collected. CBCT acquisition involved placing cotton rolls between the cheeks and teeth to facilitate soft tissue delineation. Segmentation and registration were compared using either a semi-automated (SA) method or an AI-automated (AA). SA served as clinical reference, where prosthetic crowns and their radicular parts (natural roots or implants) were threshold-based segmented with point surface-based registration. The AA method included fully automated segmentation and registration based on AI algorithms. Quantitative assessment compared AA's median surface deviation (MSD) and root mean square (RMS) in crown segmentation and subsequent intraoral scan-to-CBCT registration with those of SA. Additionally, segmented crown STL files were voxel-wise analyzed for comparison between AA and SA. A qualitative assessment of AA-based crown segmentation evaluated the need for refinement, while the AA-based registration assessment scrutinized the alignment of the registered-intraoral scan with the CBCT teeth and soft tissue contours. Ultimately, the study compared the time efficiency and consistency of both methods. Quantitative outcomes were analyzed with the Kruskal-Wallis, Mann-Whitney, and Student t tests, and qualitative outcomes with the Wilcoxon test (all α=.05). Consistency was evaluated by using the intraclass correlation coefficient (ICC). Quantitatively, AA methods excelled with a 0.91 Dice Similarity Coefficient for crown segmentation and an MSD of 0.03 ±0.05 mm for intraoral scan-to-CBCT registration. Additionally, AA achieved 91% clinically acceptable matches of teeth and gingiva on CBCT scans, surpassing SA method's 80%. Furthermore, AA was significantly faster than SA (P<.05), being 200 times faster in segmentation and 4.5 times faster in registration. Both AA and SA exhibited excellent consistency in segmentation and registration, with ICC values of 0.99 and 1 for AA and 0.99 and 0.96 for SA, respectively. The novel cloud-based platform demonstrated accurate, consistent, and time-efficient prosthetic crown segmentation, as well as intraoral scan-to-CBCT registration in scenarios with high artifact expression.

Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation, which suffer from low specificity and high interobserver variability. This study aimed to develop and validate an artificial intelligence (AI)-driven diagnostic model to improve diagnostic accuracy and reduce variability. A total of 1,861 consecutive postmenopausal women were enrolled from two centers between April 2021 and April 2024. Super-resolution (SR) technique was applied to enhance image quality before feature extraction. Radiomics features were extracted using Pyradiomics, and deep learning features were derived from convolutional neural network (CNN). Three models were developed: (1) R model: radiomics-based machine learning (ML) algorithms; (2) CNN model: image-based CNN algorithms; (3) DLR model: a hybrid model combining radiomics and deep learning features with ML algorithms. Using endometrium-level regions of interest (ROI), the DLR model achieved the best diagnostic performance, with an area under the receiver operating characteristic curve (AUROC) of 0.893 (95% CI: 0.847-0.932), sensitivity of 0.847 (95% CI: 0.692-0.944), and specificity of 0.810 (95% CI: 0.717-0.910) in the internal testing dataset. Consistent performance was observed in the external testing dataset (AUROC 0.871, sensitivity 0.792, specificity 0.829). The DLR model consistently outperformed both the R and CNN models. Moreover, endometrium-level ROIs yielded better results than uterine-corpus-level ROIs. This study demonstrates the feasibility and clinical value of AI-enhanced ultrasound analysis for EC detection. By integrating radiomics and deep learning features with SR-based image preprocessing, our model improves diagnostic specificity, reduces false positives, and mitigates operator-dependent variability. This non-invasive approach offers a more accurate and reliable tool for EC screening in postmenopausal women. Not applicable.

This study constructed an interpretable machine learning model based on multi-parameter MRI sub-region habitat radiomics and clinicopathological features, aiming to preoperatively evaluate the microsatellite instability (MSI) status of rectal cancer (RC) patients. This retrospective study recruited 291 rectal cancer patients with pathologically confirmed MSI status and randomly divided them into a training cohort and a testing cohort at a ratio of 8:2. First, the K-means method was used for cluster analysis of tumor voxels, and sub-region radiomics features and classical radiomics features were respectively extracted from multi-parameter MRI sequences. Then, the synthetic minority over-sampling technique method was used to balance the sample size, and finally, the features were screened. Prediction models were established using logistic regression based on clinicopathological variables, classical radiomics features, and MSI-related sub-region radiomics features, and the contribution of each feature to the model decision was quantified by the Shapley-Additive-Explanations (SHAP) algorithm. The area under the curve (AUC) of the sub-region radiomics model in the training and testing groups was 0.848 and 0.8, respectively, both better than that of the classical radiomics and clinical models. The combined model performed the best, with AUCs of 0.908 and 0.863 in the training and testing groups, respectively. We developed and validated a robust combined model that integrates clinical variables, classical radiomics features, and sub-region radiomics features to accurately determine the MSI status of RC patients. We visualized the prediction process using SHAP, enabling more effective personalized treatment plans and ultimately improving RC patient survival rates.

This study aims to develop and validate an ultrasoundbased habitat imaging and peritumoral radiomics model for predicting high-risk capsule characteristics for recurrence of pleomorphic adenoma (PA) of the parotid gland while also exploring the optimal range of peritumoral region. Retrospective analysis was conducted on 325 patients (171 in training set, 74 in validation set and 80 in testing set) diagnosed with PA at two medical centers. Univariate and multivariate logistic regression analyses were performed to identify clinical risk factors. The tumor was segmented into four habitat subregions using K-means clustering, with peri-tumor regions expanded at thicknesses of 1/3/5mm. Radiomics features were extracted from intra-tumor, habitat subregions, and peritumoral regions respectively to construct predictive models, integrating three machine learning classifiers: SVM, RandomForest, and XGBoost. Additionally, a combined model was developed by incorporating peritumoral features and clinical factors based on habitat imaging. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). SHAP analysis was employed to improve the interpretability. The RandomForest model in habitat imaging consistently outperformed other models in predictive performance, with AUC values of 0.881, 0.823, and 0.823 for the training set, validation set, and testing set respectively. Incorporating peri-1mm features and clinical factors into the combined model slightly improved its performance, resulting in AUC values of 0.898, 0.833, and 0.829 for each set. The calibration curves and DCA exhibited excellent fit for the combined model while providing great clinical net benefit. The combined model exhibits robust predictive performance in identifying high-risk capsule characteristics for recurrence of PA in the parotid gland. This model may assist in determining optimal surgical margin and assessing patients' prognosis.