Clinical evaluation of Artificial Intelligence (AI)-based Precise Image (PI) algorithm in brain imaging remains limited. PI is a deep-learning reconstruction (DLR) technique that reduces image noise while maintaining a familiar Filtered Back Projection (FBP)-like appearance at low doses. This study aims to compare PI, Iterative Reconstruction (IR), and FBP-in improving image quality and enhancing lesion detection in 1.0 mm thin-slice brain computed tomography (CT) images. A retrospective analysis was conducted on brain non-contrast CT scans from August to September 2024 at our institution. Each scan was reconstructed using four methods: routine 5.0 mm FBP (Group A), thin-slice 1.0 mm FBP (Group B), thin-slice 1.0 mm IR (Group C), and thin-slice 1.0 mm PI (Group D). Subjective image quality was assessed by two radiologists using a 4- or 5‑point Likert scale. Objective metrics included contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and image noise across designated regions of interest (ROIs). 60 patients (65.47 years ± 18.40; 29 males and 31 females) were included. Among these, 39 patients had lesions, primarily low-density lacunar infarcts. Thin-slice PI images demonstrated the lowest image noise and artifacts, alongside the highest CNR and SNR values (p < 0.001) compared to Groups A, B, and C. Subjective assessments revealed that both PI and IR provided significantly improved image quality over routine FBP (p < 0.05). Specifically, Group D (PI) achieved superior lesion conspicuity and diagnostic confidence, with a 100% detection rate for lacunar lesions, outperforming Groups B and A. PI reconstruction significantly enhances image quality and lesion detectability in thin-slice brain CT scans compared to IR and FBP, suggesting its potential as a new clinical standard.

Early detection and precise preoperative staging of early gastric cancer (EGC) are critical. Therefore, this study aims to develop a deep learning model using portal venous phase CT images to accurately distinguish EGC without lymph node metastasis. This study included 3164 patients with gastric cancer (GC) who underwent radical surgery at two medical centers in China from 2006 to 2019. Moreover, 2.5D radiomic data and multi-instance learning (MIL) were novel approaches applied in this study. By basing the selection of features on 2.5D radiomic data and MIL, the ResNet101 model combined with the XGBoost model represented a satisfactory performance for diagnosing pT1N0 GC. Furthermore, the 2.5D MIL-based model demonstrated a markedly superior predictive performance compared to traditional radiomics models and clinical models. We first constructed a deep learning prediction model based on 2.5D radiomics and MIL for effectively diagnosing pT1N0 GC patients, which provides valuable information for the individualized treatment selection.

<b>BACKGROUND</b>. Coronary artery calcium (CAC) scoring is used to stratify acute coronary syndrome (ACS) risk. Nonetheless, patients with a CAC score of zero (CAC₀) remain at risk from noncalcified plaque components. <b>OBJECTIVE</b>. The purpose of this study was to explore CTA-derived coronary artery plaque characteristics in symptomatic patients with CAC₀ who subsequently have ACS through comparisons with patients with a CAC score greater than 0 (CAC_{> 0}) who subsequently have ACS as well as with patients with CAC₀ who do not subsequently have ACS. <b>METHODS</b>. This study entailed a secondary retrospective analysis of prior prospective registry data. The international multicenter CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter) registry collected longitudinal observational data on symptomatic patients who underwent clinically indicated coronary CTA from January 2004 to May 2010. ICONIC (Incident Coronary Syndromes Identified by CT) was a nested cohort study conducted within CONFIRM that identified patients without known coronary artery disease (CAD) at the time of CTA who did and did not subsequently have ACS (i.e., the ACS and control groups, respectively) and who were propensity matched in a 1:1 ratio on the basis of CAD risk factors and CAD severity on CTA. The present ICONIC substudy selected matched patients in the ACS and control groups who both had documented CAC scores. CTA examinations were analyzed using artificial intelligence software for automated quantitative plaque assessment. In the ACS group, invasive angiography findings were used to identify culprit lesions. <b>RESULTS</b>. The present study included 216 patients (mean age, 55.6 years; 91 women and 125 men), with 108 patients in each of the ACS and control groups. In the ACS group, 23% (<i>n</i> = 25) of patients had CAC₀. In the ACS group, culprit lesions in the subsets of patients with CAC₀ and CAC_{> 0} showed no significant differences in fibrous, fibrofatty, or necrotic-core plaque volumes (<i>p</i> > .05). In the CAC₀ subset, patients with ACS, compared with control patients, had greater mean (± SD) fibrous plaque volume (29.4 ± 42.0 vs 5.5 ± 15.2 mm³, <i>p</i> < .001), fibrofatty plaque volume (27.3 ± 52.2 vs 1.3 ± 3.7 mm³, <i>p</i> < .001), and necrotic-core plaque volume (2.8 ± 6.4 vs 0.0 ± 0.1 mm³, <i>p</i> < .001). <b>CONCLUSION</b>. After propensity-score matching, 23% of patients with ACS had CAC₀. Patients with CAC₀ in the ACS and control groups showed significant differences in volumes of noncalcified plaque components. <b>CLINICAL IMPACT</b>. Methods that identify and quantify noncalcified plaque forms may help characterize ACS risk in symptomatic patients with CAC₀.

In computed tomography (CT), achieving high image quality while minimizing radiation exposure remains a key clinical challenge. This paper presents CAPRI-CT, a novel causal-aware deep learning framework for Causal Analysis and Predictive Reasoning for Image Quality Optimization in CT imaging. CAPRI-CT integrates image data with acquisition metadata (such as tube voltage, tube current, and contrast agent types) to model the underlying causal relationships that influence image quality. An ensemble of Variational Autoencoders (VAEs) is employed to extract meaningful features and generate causal representations from observational data, including CT images and associated imaging parameters. These input features are fused to predict the Signal-to-Noise Ratio (SNR) and support counterfactual inference, enabling what-if simulations, such as changes in contrast agents (types and concentrations) or scan parameters. CAPRI-CT is trained and validated using an ensemble learning approach, achieving strong predictive performance. By facilitating both prediction and interpretability, CAPRI-CT provides actionable insights that could help radiologists and technicians design more efficient CT protocols without repeated physical scans. The source code and dataset are publicly available at https://github.com/SnehaGeorge22/capri-ct.

To develop and assess the performance of an anatomically based multitask deep learning radiomics nomogram (AMDRN) system to predict implant failure risk before maxillary sinus floor elevation (MSFE) while incorporating automated segmentation of key anatomical structures. We retrospectively collected patients' preoperative cone beam computed tomography (CBCT) images and electronic medical records (EMRs). First, the nn-UNet v2 model was optimized to segment the maxillary sinus (MS), Schneiderian membrane (SM), and residual alveolar bone (RAB). Based on the segmentation mask, a deep learning model (3D-Attention-ResNet) and a radiomics model were developed to extract 3D features from CBCT scans, generating the DL Score, and Rad Score. Significant clinical features were also extracted from EMRs to build a clinical model. These components were then integrated using logistic regression (LR) to create the AMDRN model, which includes a visualization module to support clinical decision-making. Segmentation results for MS, RAB, and SM achieved high DICE coefficients on the test set, with values of 99.50% ± 0.84%, 92.53% ± 3.78%, and 91.58% ± 7.16%, respectively. On an independent test set, the Clinical model, Radiomics model, 3D-DL model, and AMDRN model achieved prediction accuracies of 60%, 76%, 82%, and 90%, respectively, with AMDRN achieving the highest AUC of 93%. The AMDRN system enables efficient preoperative prediction of implant failure risk in MSFE and accurate segmentation of critical anatomical structures, supporting personalized treatment planning and clinical risk management.

Noninvasive and precise tools for treatment response estimation in patients with high-risk hepatocellular carcinoma (HCC) who could benefit from hepatic arterial infusion chemotherapy (HAIC) plus lenvatinib and humanized programmed death receptor-1 inhibitors (PD-1) (HAIC-LEN-PD1) are lacking. This study aimed to evaluate the predictive potential of intratumoral and peritumoral radiomics for preoperative treatment response assessment to HAIC-LEN-PD1 in high-risk HCC cases. Totally 630 high-risk HCC cases administered HAIC-LEN-PD1 at three institutions were retrospectively identified and assigned to training, validation and external test sets. Totally 1834 radiomic features were, respectively, obtained from intratumoral and peritumoral regions and radiomics models were established using five classifiers. Based on the optimal model, a nomogram was developed and evaluated using areas under the curves (AUCs), calibration curves and decision curve analysis (DCA). Overall survival (OS) and progression-free survival (PFS) were assessed by Kaplan-Meier curves. The Intratumoral + Peritumoral 10 mm (Intra + Peri10) radiomics models were superior to the intratumor models and peritumor models, with AUCs of 0.919 (95%CI 0.889-0.949) in the training set, 0.874 (95%CI 0.812-0.936) in validation set and 0.893 (95%CI 0.839-0.948) in external test sets. The nomogram had good calibration ability and clinical value, with the AUCs of 0.936 (95%CI 0.907-0.965) in the training set, 0.878 (95%CI 0.916-0.940) in validation set and 0.902 (95%CI 0.848-0.957) in external test sets. The Kaplan-Meier analysis showed that high-score patients had significantly shorter OS and PFS than the low-score patients (median OS: 11.7 vs. 29.6 months, the whole set, p < 0.001; median PFS: 6.0 vs. 12.0 months, the whole set, p < 0.001). The Intra + Peri10 model can effectively predict the treatment response of high-risk HCC cases administered HAIC-LEN-PD1. The nomogram could provide an effective tool to evaluate the treatment response and risk stratification.

Detecting Pulmonary Embolism (PE) is critical for effective patient care, and Artificial Intelligence (AI) has shown promise in supporting radiologists in this task. Integrating AI into radiology workflows requires not only evaluation of its diagnostic accuracy but also assessment of its acceptance among clinical staff. This study aims to evaluate the performance of an AI algorithm in detecting pulmonary embolisms (PEs) on contrast-enhanced computed tomography pulmonary angiograms (CTPAs) and to assess the level of acceptance of the algorithm among radiology department staff. This retrospective study analyzed anonymized computed tomography pulmonary angiography (CTPA) data from a university clinic. Surveys were conducted at three and nine months after the implementation of a commercially available AI algorithm designed to flag CTPA scans with suspected PE. A thoracic radiologist and a cardiac radiologist served as the reference standard for evaluating the performance of the algorithm. The AI analyzed 59 CTPA cases during the initial evaluation and 46 cases in the follow-up assessment. In the first evaluation, the AI algorithm demonstrated a sensitivity of 84.6% and a specificity of 94.3%. By the second evaluation, its performance had improved, achieving a sensitivity of 90.9% and a specificity of 96.7%. Radiologists' acceptance of the AI tool increased over time. Nevertheless, despite this growing acceptance, many radiologists expressed a preference for hiring an additional physician over adopting the AI solution if the costs were comparable. Our study demonstrated high sensitivity and specificity of the AI algorithm, with improved performance over time and a reduced rate of unanalyzed scans. These improvements likely reflect both algorithmic refinement and better data integration. Departmental feedback indicated growing user confidence and trust in the tool. However, many radiologists continued to prefer the addition of a resident over reliance on the algorithm. Overall, the AI showed promise as a supportive "second-look" tool in emergency radiology settings. The AI algorithm demonstrated diagnostic performance comparable to that reported in similar studies for detecting PE on CTPA, with both sensitivity and specificity showing improvement over time. Radiologists' acceptance of the algorithm increased throughout the study period, underscoring its potential as a complementary tool to physician expertise in clinical practice.

Accurate preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) remains challenging. Current imaging biomarkers show limited predictive performance. To develop a deep learning model based on preoperative multiphase CT images of tumors and lesser omental adipose tissue (LOAT) for predicting MVI status and to analyze associated survival outcomes. This retrospective study included pathologically confirmed HCC patients from two medical centers between 2016 and 2023. A dual-branch feature fusion model based on ResNet18 was constructed, which extracted fused features from dual-phase CT images of both tumors and LOAT. The model's performance was evaluated on both internal and external test sets. Logistic regression was used to identify independent predictors of MVI. Based on MVI status, patients in the training, internal test, and external test cohorts were stratified into high- and low-risk groups, and overall survival differences were analyzed. The model incorporating LOAT features outperformed the tumor-only modality, achieving an AUC of 0.889 (95% CI: [0.882, 0.962], P=0.004) in the internal test set and 0.826 (95% CI: [0.793, 0.872], P=0.006) in the external test set. Both results surpassed the independent diagnoses of three radiologists (average AUC=0.772). Multivariate logistic regression confirmed that maximum tumor diameter and LOAT area were independent predictors of MVI. Further Cox regression analysis showed that MVI-positive patients had significantly increased mortality risks in both the internal test set (Hazard Ratio [HR]=2.246, 95% CI: [1.088, 4.637], P=0.029) and external test set (HR=3.797, 95% CI: [1.262, 11.422], P=0.018). This study is the first to use a deep learning framework integrating LOAT and tumor imaging features, improving preoperative MVI risk stratification accuracy. Independent prognostic value of LOAT has been validated in multicenter cohorts, highlighting its potential to guide personalized surgical planning.

Visceral pleural invasion (VPI) indicates poor prognosis in non-small cell lung cancer (NSCLC), and upgrades T classification of NSCLC from T1 to T2 when accompanied by VPI. This study aimed to develop and validate deep learning models for the accurate prediction of VPI in patients with NSCLC, and to compare the performance of two-dimensional (2D), three-dimensional (3D), and hybrid 3D models. This retrospective study included consecutive patients with pathologically confirmed lung tumor between June 2017 and September 2022. The clinical data and preoperative imaging features of these patients were investigated and their relationships with VPI were statistically compared. Elastic fiber staining analysis results were the gold standard for diagnosis of VPI. The data of non-VPI and VPI patients were randomly divided into training cohort and validation cohort based on 8:2 and 6:4, respectively. The EfficientNet-B0_2D model and Double-head Res2Net/_F6/_F24 models were constructed, optimized and verified using two convolutional neural network model architectures-EfficientNet-B0 and Res2Net, respectively, by extracting the features of original CT images and combining specific clinical-CT features. The receiver operating characteristic curve, the area under the curve (AUC), and confusion matrix were utilized to assess the diagnostic efficiency of models. Delong test was used to compare performance between models. A total of 1931 patients with NSCLC were finally evaluated. By univariate analysis, 20 clinical-CT features were identified as risk predictors of VPI. Comparison of the diagnostic efficacy among the EfficientNet-b0_2D, Double-head Res2Net, Res2Net_F6, and Res2Net_F24 combined models revealed that Double-head Res2Net_F6 model owned the largest AUC of 0.941 among all models, followed by Double-head Res2Net (AUC=0.879), Double-head Res2Net_F24 (AUC=0.876), and EfficientNet-b0_2D (AUC=0.785). The three 3D-based models showed comparable predictive performance in the validation cohort and all outperformed the 2D model (EfficientNet-B0_2D, all P＜0.05). It is feasible to predict VPI in NSCLC with the predictive models based on deep learning, and the Double-head Res2Net_F6 model fused with six clinical-CT features showed greatest diagnostic efficacy.

Diagnosis of carotid plaques from head and neck CT angiography (CTA) scans is typically time-consuming and labor-intensive, leading to limited studies and unpleasant results in this area. The objective of this study is to develop a deep-learning-based model for detection and segmentation of carotid plaques using CTA images. CTA images from 1061 patients (765 male; 296 female) with 4048 carotid plaques were included and split into a 75% training-validation set and a 25% independent test set. We built a workflow involving three modified deep learning networks: a plain U-Net for coarse artery segmentation, an Attention U-Net for fine artery segmentation, a dual-channel-input ConvNeXt-based U-Net architecture for plaque segmentation, and post-processing to refine predictions and eliminate false positives. The models were trained on the training-validation set using five-fold cross-validation and further evaluated on the independent test set using comprehensive metrics for segmentation and plaque detection. The proposed workflow was evaluated in the independent test set (261 patients with 902 carotid plaques) and achieved a mean dice similarity coefficient (DSC) of 0.91±0.04 in artery segmentation, and 0.75±0.14/0.67±0.15 in plaque segmentation per artery/patient. The model detected 95.5% (861/902) plaques, including 96.6% (423/438), 95.3% (307/322), and 92.3% (131/142) of calcified, mixed, and soft plaques, with less than one (0.63±0.93) false positive plaque per patient on average. This study developed an automatic detection and segmentation deep learning-based CAP-Net for carotid plaques using CTA, which yielded promising results in identifying and delineating plaques.