We extend the Hybrid Unet Transformer (HUT) foundation model, which combines the advantages of the CNN and Transformer architectures with a noisy self-supervised approach, and demonstrate it in an ischemic stroke lesion segmentation task. We introduce a self-supervised approach using a noise anchor and show that it can perform better than a supervised approach under a limited amount of annotated data. We supplement our pre-training process with an additional unannotated CT perfusion dataset to validate our approach. Compared to the supervised version, the noisy self-supervised HUT (HUT-NSS) outperforms its counterpart by a margin of 2.4% in terms of dice score. HUT-NSS, on average, gained a further margin of 7.2% dice score and 28.1% Hausdorff Distance score over the state-of-the-art network USSLNet on the CT perfusion scans of the Ischemic Stroke Lesion Segmentation (ISLES2018) dataset. In limited annotated data sets, we show that HUT-NSS gained 7.87% of the dice score over USSLNet when we used 50% of the annotated data sets for training. HUT-NSS gained 7.47% of the dice score over USSLNet when we used 10% of the annotated datasets, and HUT-NSS gained 5.34% of the dice score over USSLNet when we used 1% of the annotated datasets for training. The code is available at https://github.com/vicsohntu/HUTNSS_CT .

Clinical staging on CT has several biases, and a radiogenomics approach could be proposed. The study aimed to test the performance of a radiogenomics approach in identifying high-risk colon cancer. ATTRACT is a multicentric trial, registered in ClinicalTrials.gov (NCT06108310). Three hundred non-metastatic colon cancer patients were retrospectively enrolled and divided into two groups, high-risk and no-risk, according to the pathological staging. Radiological evaluations were performed by two abdominal radiologists. For 151 patients, we achieved genomics. The baseline CT scans were used to evaluate the radiological assessment and to perform 3D cancer segmentation. One expert radiologist used open-source software to perform the volumetric cancer segmentations on baseline CT scans in the portal phase (3DSlicer v4.10.2). Implementing the classical LASSO with a machine-learning library method was used to select the optimal features to build Model 1 (clinical-radiological plus radiomic feature, 300 patients) and Model 2 (Model 1 plus genomics, 151 patients). The performance of clinical-radiological interpretation was assessed regarding the area under the curve (AUC), sensitivity, specificity, and accuracy. The average performance of Models 1 and 2 was also calculated. In total, 262/300 were classified as high-risk and 38/300 as no-risk. Clinical-radiological interpretation by the two radiologists achieved an AUC of 0.58-0.82 (95% CI: 0.52-0.63 and 0.76-0.85, p < 0.001, respectively), sensitivity: 67.9-93.8%, specificity: 47.4-68.4%, and accuracy: 65.3-90.7%, respectively. Model 1 yielded AUC: 0.74 (95% CI: 0.61-0.88, p < 0.005), sensitivity: 86%, specificity: 48%, and accuracy: 81%. Model2 reached AUC: 0.84, (95% CI: 0.68-0.99, p < 0.005), sensitivity: 88%, specificity: 63%, and accuracy: 84%. The radiogenomics model outperformed radiological interpretation in identifying high-risk colon cancer. Question Can this radiogenomic model identify high-risk stages II and III colon cancer in a preoperative clinical setting? Findings This radiogenomics model outperformed both the radiomics and radiological interpretations, reducing the risk of improper staging and incorrect treatment options. Clinical relevance The radiogenomics model was demonstrated to be superior to radiological interpretation and radiomics in identifying high-risk colon cancer, and could therefore be promising in stratifying high-risk and low-risk patients.

The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed as pancreatic cancer. Identification of pancreatic tumors early is significant for enhancing survival rate and providing appropriate treatment. Thus, an efficient Secretary Wolf Bird Optimization (SeWBO)_Efficient DenseNet is presented for pancreatic tumor detection using Computed Tomography (CT) scans. Firstly, the input pancreatic CT image is accumulated from a database and subjected to image preprocessing using a bilateral filter. After this, lesion is segmented by utilizing Parallel Reverse Attention Network (PraNet), and hyperparameters of PraNet are enhanced by using the proposed SeWBO. The SeWBO is designed by incorporating Wolf Bird Optimization (WBO) and the Secretary Bird Optimization Algorithm (SBOA). Then, features like Complete Local Binary Pattern (CLBP) with Discrete Wavelet Transformation (DWT), statistical features, and Shape Local Binary Texture (SLBT) are extracted. Finally, pancreatic tumor detection is performed by SeWBO_Efficient DenseNet. Here, Efficient DenseNet is developed by combining EfficientNet and DenseNet. Moreover, the proposed SeWBO_Efficient DenseNet achieves better True Negative Rate (TNR), accuracy, and True Positive Rate (TPR), of 93.596%, 94.635%, and 92.579%.

Temporomandibular disorders (TMDs) are frequently associated with posterior condylar displacement; however, early prediction of this displacement remains a significant challenge. Therefore, in this study, we aimed to develop and evaluate a predictive model for bilateral posterior condylar displacement. In this retrospective observational study, 166 cone-beam computed tomography images were examined and categorized into two groups based on condyle positions as observed in the sagittal images of the joint space: those with bilateral posterior condylar displacement and those without. Three machine-learning algorithms-Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Extreme Gradient Boosting (XGBoost)-were used to identify risk factors and establish a risk assessment model. Calibration curves, receiver operating characteristic curves, and decision curve analyses were employed to evaluate the accuracy of the predictions, differentiation, and clinical usefulness of the models, respectively. Articular eminence inclination (AEI) and age were identified as significant risk factors for bilateral posterior condylar displacement. The area under the curve values for the LASSO and Random Forest models were both > 0.7, indicating satisfactory discriminative ability of the nomogram. No significant differences were observed in the differentiation and calibration performance of the three models. Clinical utility analysis revealed that the LASSO regression model, which incorporated age, AEI, A point-nasion-B point (ANB) angle, and facial height ratio (S-Go/N-Me), demonstrated superior net benefit compared to the other models when the probability threshold exceeded 45%. Patients with a steeper AEI, insufficient posterior vertical distance (S-Go/N-Me), an ANB angle ≥ 4.7°, and older age are more likely to experience bilateral posterior condylar displacement. The prognostic nomogram developed and validated in this study may assist clinicians in assessing the risk of bilateral posterior condylar displacement.

Lung cancer is one of the main threats to global health, among lung diseases. Low-Dose Computed Tomography (LDCT) provides significant benefits for its screening but also brings new diagnostic challenges that require close attention. By searching the Web of Science core collection, we selected articles and reviews published in English between 2005 and June 2024 on topics such as "Low-dose", "CT image", and "Lung". These literatures were analyzed by bibliometric method, and CiteSpace software was used to explore the cooperation between countries, the cooperative relationship between authors, highly cited literature, and the distribution of keywords to reveal the research hotspots and trends in this field. The number of LDCT research articles show a trend of continuous growth between 2019 and 2022. The United States is at the forefront of research in this field, with a centrality of 0.31; China has also rapidly conducted research with a centrality of 0.26. The authors' co-occurrence map shows that research teams in this field are highly cooperative, and their research questions are closely related. The analysis of highly cited literature and keywords confirmed the significant advantages of LDCT in lung cancer screening, which can help reduce the mortality of lung cancer patients and improve the prognosis. "Lung cancer" and "CT" have always been high-frequency keywords, while "image quality" and "low dose CT" have become new hot keywords, indicating that LDCT using deep learning techniques has become a hot topic in early lung cancer research. The study revealed that advancements in CT technology have driven in-depth research from application challenges to image processing, with the research trajectory evolving from technical improvements to health risk assessments and subsequently to AI-assisted diagnosis. Currently, the research focus has shifted toward integrating deep learning with LDCT technology to address complex diagnostic challenges. The study also presents global research trends and geographical distributions of LDCT technology, along with the influence of key research institutions and authors. The comprehensive analysis aims to promote the development and application of LDCT technology in pulmonary disease diagnosis and enhance diagnostic accuracy and patient management efficiency. The future will focus on LDCT reconstruction algorithms to balance image noise and radiation dose. AI-assisted multimodal imaging supports remote diagnosis and personalized health management by providing dynamic analysis, risk assessment, and follow-up recommendations to support early diagnosis.

Computed Tomography (CT) is widely acknowledged as the gold standard for diagnosing thoracic diseases. However, the accuracy of interpretation significantly depends on radiologists' expertise. Large Language Models (LLMs) have shown considerable promise in various medical applications, particularly in radiology. This study aims to assess the performance of leading LLMs in analyzing unstructured chest CT reports and to examine how different questioning methodologies and fine-tuning strategies influence their effectiveness in enhancing chest CT diagnosis. This retrospective analysis evaluated 13,489 chest CT reports encompassing 13 common thoracic conditions across pulmonary, cardiovascular, pleural, and upper abdominal systems. Five LLMs (Claude-3.5-Sonnet, GPT-4, GPT-3.5-Turbo, Gemini-Pro, Qwen-Max) were assessed using dual questioning methodologies: multiple-choice and open-ended. Radiologist-curated datasets underwent rigorous preprocessing, including RadLex terminology standardization, multi-step diagnostic validation, and exclusion of ambiguous cases. Model performance was quantified via Subjective Answer Accuracy Rate (SAAR), Reference Answer Accuracy Rate (RAAR), and Area Under the Receiver Operating Characteristic (ROC) Curve analysis. GPT-3.5-Turbo underwent fine-tuning (100 iterations with one training epoch) on 200 high-performing cases to enhance diagnostic precision for initially misclassified conditions. GPT-4 demonstrated superior performance with the highest RAAR of 75.1% in multiple-choice questioning, followed by Qwen-Max (66.0%) and Claude-3.5 (63.5%), significantly outperforming GPT-3.5-Turbo (41.8%) and Gemini-Pro (40.8%) across the entire patient cohort. Multiple-choice questioning consistently improved both RAAR and SAAR for all models compared to open-ended questioning, with RAAR consistently surpassing SAAR. Model performance demonstrated notable variations across different diseases and organ conditions. Notably, fine-tuning substantially enhanced the performance of GPT-3.5-Turbo, which initially exhibited suboptimal results in most scenarios. This study demonstrated that general-purpose LLMs can effectively interpret chest CT reports, with performance varying significantly across models depending on the questioning methodology and fine-tuning approaches employed. For surgical practice, these findings provided evidence-based guidance for selecting appropriate AI tools to enhance preoperative planning, particularly for thoracic procedures. The integration of optimized LLMs into surgical workflows may improve decision-making efficiency, risk stratification, and diagnostic speed, potentially contributing to better surgical outcomes through more accurate preoperative assessment.

Computed tomography is an inadequate method for detecting myocardial focal scar (MFS) due to its moderate density resolution, which is insufficient for distinguishing MFS from artificial beam-hardening (BH). Virtual monochromatic images (VMIs) of dual-energy coronary computed tomography angiography (DECCTA) provide a variety of diagnostic information with significant potential for detecting myocardial lesions. The aim of this study was to assess whether radiomics analysis in VMIs of DECCTA can help distinguish MFS from BH. A prospective cohort of patients who were suspected with an old myocardial infarction was assembled at two different centers between Janurary 2021 and June 2024. MFS and BH segmentation and radiomics feature extraction and selection were performed on VMIs images, and four machine learning classifiers were constructed using selected strongest features. Subsequently, an independent validation was conducted, and a subjective diagnosis of the validation set was provided by an radiologist. The AUC was used to assess the performance of the radiomics models. The training set included 57 patients from center 1 (mean age, 54 years +/- 9, 55 men), and the external validation set included 10 patients from center 2 (mean age, 59 years +/- 10, 9 men). The radiomics models exhibited the highest AUC value of 0.937 (expressed at 130 keV VMIs), while the radiologist demonstrated the highest AUC value of 0.734 (expressed at 40 keV VMIs). The integration of radiomic features derived from VMIs of DECCTA with machine learning algorithms has the potential to improve the efficiency of distinguishing MFS from BH.

Osteoporosis is a prevalent skeletal disorder characterized by reduced bone mineral density (BMD) and structural deterioration, resulting in increased fracture risk. Early diagnosis is crucial to prevent fractures and improve patient outcomes. This study investigates the diagnostic utility of morphometric and cortical indices derived from cone-beam computed tomography (CBCT) for identifying osteoporotic postmenopausal women who were candidates for dental implant therapy, with dual-energy X-ray absorptiometry (DXA) used as the reference standard. This cross-sectional study included 71 postmenopausal women, aged 50-79 years, who underwent CBCT imaging at the Oral and Maxillofacial Radiology Department of Hamadan University of Medical Sciences between 2022 and 2024. Participants with systemic conditions affecting bone metabolism were excluded. The morphometric indices-Computed Tomography Mandibular Index (CTMI), Computed Tomography Index Superior (CTI(S)), Computed Tomography Index Inferior (CTI(I)), and Computed Tomography Cortical Index (CTCI)-were measured at the mental foramen and antegonial regions using OnDemand3D Dental software. Bone mineral density (BMD) was assessed by DXA scans of the lumbar spine and femoral neck. In addition to traditional statistical analyses (Pearson's correlation and one-way ANOVA with LSD test), a multilayer perceptron (MLP) neural network model was employed to evaluate the diagnostic power of CBCT indices. DXA results based on the femoral neck T-scores categorized 38 patients as normal, 32 as osteopenic, and one as osteoporotic, while lumbar spine T-scores identified 38 normal, 22 osteopenic, and 11 osteoporotic patients. Significant differences (p < 0.05) were observed in most CBCT-derived indices, with the CTMI index demonstrating the most marked variation, especially between normal and osteoporotic groups (p < 0.001). Moreover, significant positive correlations were found between the CBCT indices and DXA T-scores across the lumbar spine, femoral neck, and total hip regions. The neural network model achieved an overall diagnostic accuracy of 75%, with the highest predictive importance attributed to antegonial CTCI and CTMI indices. This study highlights the significant correlation between CBCT-derived radiomorphometric indices such as CTMI, CTI(S), CTI(I), and CTCI at the mental foramen and antegonial regions and bone mineral density (BMD) in postmenopausal women. CBCT, particularly the CTMI index in the antegonial region, offers a cost-effective, non-invasive method for early osteoporosis detection, providing a valuable alternative to traditional screening methods.

Computed tomography (CT) analysis of lung morphology has significantly advanced our understanding of acute respiratory distress syndrome (ARDS). During the Coronavirus Disease 2019 (COVID-19) pandemic, CT imaging was widely utilized to evaluate lung injury and was suggested as a tool for predicting patient outcomes. However, data specifically focused on patients with ARDS admitted to intensive care units (ICUs) remain limited. This retrospective study analyzed patients admitted to ICUs between March 2020 and November 2022 with moderate to severe COVID-19 ARDS. All CT scans performed within 48 h of ICU admission were independently reviewed by three experts. Lung injury severity was quantified using the CT Severity Score (CT-SS; range 0-25). Patients were categorized as having severe disease (CT-SS ≥ 18) or non-severe disease (CT-SS < 18). The primary outcome was all-cause mortality at 90 days. Secondary outcomes included ICU mortality and medical complications during the ICU stay. Additionally, we evaluated a computer-assisted CT-score assessment using artificial intelligence software (CT Pneumonia Analysis^®, SIEMENS Healthcare) to explore the feasibility of automated measurement and routine implementation. A total of 215 patients with moderate to severe COVID-19 ARDS were included. The median CT-SS at admission was 18/25 [interquartile range, 15-21]. Among them, 120 patients (56%) had a severe CT-SS (≥ 18), while 95 patients (44%) had a non-severe CT-SS (< 18). The 90-day mortality rates were 20.8% for the severe group and 15.8% for the non-severe group (p = 0.35). No significant association was observed between CT-SS severity and patient outcomes. In patients with moderate to severe COVID-19 ARDS, systematic CT assessment of lung parenchymal injury was not a reliable predictor of 90-day mortality or ICU-related complications.

This study investigates the use of list-mode (LM) maximum a posteriori (MAP) expectation maximization (EM) incorporating prior information predicted by a convolutional neural network for image reconstruction in fast neutron (FN)-based proton therapy range verification.&#xD;Approach. A conditional generative adversarial network (pix2pix) was trained on progressively noisier data, where detector resolution effects were introduced gradually to simulate realistic conditions. FN data were generated using Monte Carlo simulations of an 85 MeV proton pencil beam in a computed tomography (CT)-based lung cancer patient model, with range shifts emulating weight gain and loss. The network was trained to estimate the expected two-dimensional (2D) ground truth FN production distribution from simple back-projection images. Performance was evaluated using mean squared error (MSE), structural similarity index (SSIM), and the correlation between shifts in predicted distributions and true range shifts. &#xD;Main results. Our results show that pix2pix performs well on noise-free data but suffers from significant degradation when detector resolution effects are introduced. Among the LM-MAP-EM approaches tested, incorporating a mean prior estimate into the reconstruction process improved performance, with LM-MAP-EM using a mean prior estimate outperforming naïve LM maximum likelihood EM (LM-MLEM) and conventional LM-MAP-EM with a smoothing quadratic energy function in terms of SSIM. &#xD;Significance. Findings suggest that deep learning techniques can enhance iterative reconstruction for range verification in proton therapy. However, the effectiveness of the model is highly dependent on data quality, limiting its robustness in high-noise scenarios.&#xD.