To address the limitations of traditional CT-guided pulmonary nodule interventions, such as excessive radiation exposure, prolonged procedure times, and limited precision, we developed an electromagnetic navigation surgical robotic system (ENSRS) to enhance accuracy, efficiency, and safety in percutaneous procedures. The ENSRS integrates artificial intelligence to automate the segmentation of pulmonary nodules and surrounding anatomical structures, generating a detailed surgical environment. A customized path-planning algorithm facilitates minimally invasive access, whereas submillimeter localization using fiducial markers ensures precise coordinate registration. Adaptive multicalibration strategies and robust safety protocols enhance procedural reliability. System performance was evaluated through phantom and animal experiments, with comparisons to traditional CTguided techniques. The ENSRS achieved a groove localization error of 0.51 ± 0.27 mm across 63 patches and a classification accuracy of 100%. In phantom studies, it demonstrated significantly reduced puncture error (0.81 ± 0.98 mm vs. 3.50 ± 2.88 mm, p < 0.0001), required fewer CT scans (1.02 ± 0.25 vs. 1.53 ± 0.92) and shortened puncture times (39.01 ± 29.71 s). In animal experiments, ENSRS achieved improved accuracy (0.33 ± 0.74 mm vs. 1.86 ± 0.99 mm, p = 0.015). The safety outcomes were comparable between the groups, with one pneumothorax reported each. ENSRS improves the precision, efficiency, and safety of pulmonary nodule interventions, outperforming traditional CT-guided methods in phantom and animal models. This system offers a promising approach to pulmonary interventions by combining robotic precision with intelligent planning and tracking, potentially enhancing outcomes in minimally invasive procedures.

Lung cancer is a leading cause of cancer-related mortality worldwide. Accurate staging of mediastinal lymph nodes is a crucial step in determining appropriate treatment approaches. Current noninvasive diagnostic methods do not provide sufficient accuracy to confidently decide on surgery without histological confirmation. Our study aimed to develop a artificial intelligence model for the precise prediction of N2 lymph node metastasis. We retrospectively analyzed 1489 patients who underwent standard cervical mediastinoscopy at our department, including 472 patients diagnosed with non-small cell lung cancer. We developed three distinct prediction models for N2 lymph node station metastasis: one using standard statistical analysis, another utilizing an image processing deep learning algorithm with thoracic CT, and the third employing various machine learning methods with clinicopathological and radiological data. We compared diagnostic accuracy, area under the curve (AUC), sensitivity, and specificity rates, as well as the F1-score of all models. Linear discriminant analysis, quadratic discriminant analysis, Gaussian naive Bayes, and artificial neural networks all surpassed 90% accuracy. The linear support vector machine demonstrated the highest performance, with an accuracy of 95.7%, an AUC of 93.5%, and an F1-score of 92%, respectively and outperformed the logistic regression-based statistical model, which reached an accuracy of 90.6% and an AUC of 85.7%. Machine learning models outperformed standard statistical analysis models in predicting N2 lymph node metastasis. Implementing these machine learning prediction models might greatly improve the accuracy of mediastinal lymph node metastasis detection, thereby enhancing clinical decision making and patient outcomes.

Mechanical thrombectomy (MT) has been recognized as a groundbreaking intervention for acute ischemic stroke (AIS) resulting from large vessel occlusion (LVO). Traditional imaging parameters frequently fall short in synthetically encapsulating the heterogeneity of thrombi and the dynamics of collateral circulation. This study seeks to investigate the integration of venous-phase clot radiomics features with arterial-level collateral scores obtained from color-coded multi-phase CT angiography (mCTA) to predict neurological improvement (NI) following MT in LVO-AIS patients. A retrospective analysis was conducted on a series of adult patients with LVO-AIS who underwent mCTA followed by MT. Radiomic features were extracted from the peak-venous and late-venous phases of the mCTA. Subsequently, a machine learning algorithm was employed to develop radiomic models. The regional leptomeningeal collateral (rLMC) score, derived from color-coded mCTA maps, was documented to assess arterial-level collateral status. Another fusion model integrating clinical, collateral, and radiomics data was constructed using logistic regression to predict NI status. The study included 110 AIS patients in which the rLMC score was significantly higher in the NI group compared to the non-NI group (P<0.001). The clot-based radiomics model exhibited good predictive performance, with AUC values of 0.986 (training set) and 0.831 (test set) for the peak-venous phase. The fusion model based on peak venous phase data, incorporating clinical parameters, rLMC score, and radiomics features, showed superior predictive accuracy (AUC: 0.992 in training set, 0.889 in test set). Corresponding DCA indicate that the combined model demonstrates the optimal potential clinical benefits. The integration of venous-phase clot radiomics features with arterial-level collateral scores and clinical parameters effectively predicts NI after MT in AIS patients.

High pitch helical Computed Tomography (CT) scanning significantly reduces radiation dose while improving temporal resolution, offering substantial clinical benefits. However, the incomplete scanning data commonly leads to artifacts in the reconstructed images, degrading image quality and potentially affecting clinical diagnosis. Existing high pitch reconstruction methods primarily operate within the image domain or combine image-domain networks with traditional iterative algorithms, yet their performance remains limited. To address such limitations, we propose Delta-Net, a deep dual-domain alternating iterative optimization network for high pitch helical CT reconstruction. We introduce a novel optimization objective and develop an alternating iterative optimization framework, where each sub iteration consists of projection domain correction and image domain refinement. To enhance generalization and robustness, deep neural networks are employed to learn domain-specific priors, which are incorporated as regularization terms, with all hyper-parameters automatically optimized during training. Specifically, the image domain residual refinement network (IRN) and projection domain consistency enhanced network (PCN) regularize the intermediate results across both domains. Additionally, to improve the capability of artifact suppression and structure restoration, a structure-aware joint loss is tailored for the optimization of Delta-Net. Quantitative and qualitative evaluations on clinical datasets demonstrate that Delta-Net outperforms other competitive methods in artifact suppression, fine structure recovery, and generalization.

Timely detection and monitoring of abdominal aortic aneurysms (AAA) are necessary to prevent ruptures and decrease mortality. Artificial intelligence (AI)-based algorithms can automatically detect the presence of AAA on imaging and radiology reports. The goal of this study is to examine the impact of AI utilization on AAA detection and care while comparing it to historical standard of care. AI-based AAA detection and measurement algorithm was deployed in the healthcare system. The software can be used as a phone application and a desktop analytical tool. The team (vascular surgeons, radiologists, and nurses) gets notifications when AAA ≥5cm is detected on any CT imaging in the network. It also generates monthly lists of all patients with AAA for the team to review. A workflow to ensure timely referral and evaluation was established. All CT reports prior to the software deployment were analyzed for the AAA presence using natural language processing of radiology reports. Patients with imaging for known AAA monitoring and AAA screening were excluded. Patients were divided into two groups: "pre-AI" and "post-AI" (prior to and post implementation of AI-driven protocol, respectively). The study compared patient and imaging characteristics, initial evaluation and long-term follow-up, and the timeline between AI-detected scans and AAA repairs. A subgroup analysis to assess the time to evaluation for AAA measuring ≥ 4 cm was performed. The primary outcome was initial evaluation after incidental detection of AAA. Patient and imaging characteristics were similar in both groups. A greater proportion of patients underwent initial AAA evaluation after implementation of AI-assisted AAA care (42% vs 18%, p<0.001). There was a trend for a shorter evaluation timeline for patients in the post-AI protocol group (22 days vs 83 days, p=0.1). Most patients in both groups were seen by vascular surgeons for the initial AAA evaluation and during long-term follow-up. Similar proportions of patients in both groups were treated with statin, aspirin and antiplatelet medical therapy at the time of initial evaluation. A greater proportion of patients in the post-AI protocol group had long-term follow-up (45% vs 30%, p=0.004) and had scheduled appointments for long-term AAA monitoring (99% vs 65%, p<0.001). The implementation of the AI-assisted AAA detection and care protocol significantly increased proportion of patients receiving initial AAA evaluation and long-term follow-up care. It also correlated with a decreased timeline to initial evaluation, and for AAA measuring ≥5cm, it shortened the time from detection to repair.

Hematologic toxicity (HT) is a common and serious side effect for advanced cervical cancer patients undergoing chemoradiotherapy. Accurately predicting HT can significantly improve patient management and treatment outcomes. This study aims to develop and evaluate interpretable machine learning models that use radiomic and dosimetric features to predict HT in advanced cervical cancer patients. Retrospectively collected general clinical data, planning CT images, and dose files from 205 patients with advanced cervical cancer who underwent chemoradiotherapy, and classified them according to the severity of HT. Radiomics and dosiomics features were extracted from the same region of interest, and feature selection was performed using a random forest algorithm. Radiomics models, dosiomics models, and hybrid models were then constructed based on extreme gradient boosting trees. Sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the classification performance of the models. Finally, SHAP values were used to perform interpretability analysis on the best model to enhance the transparency and practicality of the model. The sensitivity, specificity, and AUC values for the radiomics model were 0.42, 0.86, and 0.78, respectively, while those for the dosiomics model were 0.50, 0.90, and 0.74. In contrast, the hybrid model exhibited superior classification performance with sensitivity, specificity, and AUC values of 0.50, 0.83, and 0.83, respectively. Compared to the standalone radiomics and dosiomics models, the hybrid model demonstrated enhanced classification capability. Interpretability analysis based on SHAP values not only provided a ranking of feature importance and the distribution of feature impacts on model outputs but also elucidated the specific decision-making processes influenced by these features and the interactions between them. This enables clinicians to gain a more intuitive understanding of the model's decisions. For patients with advanced cervical cancer undergoing chemoradiotherapy, the integration of radiomics and dosiomics features can significantly enhance the classification performance of predictive models, thereby holding considerable potential for refining patient treatment strategies. Interpretability analysis based on SHAP values can aid clinicians in more readily understanding the model's decisions, thus promoting the effective implementation of the model in clinical practice.

Mixture-of-Experts (MoE) architectures have significantly contributed to scalable machine learning by enabling specialized subnetworks to tackle complex tasks efficiently. However, traditional MoE systems lack domain-specific constraints essential for medical imaging, where anatomical structure and regional disease heterogeneity strongly influence pathological patterns. Here, we introduce Regional Expert Networks (REN), the first anatomically-informed MoE framework tailored specifically for medical image classification. REN leverages anatomical priors to train seven specialized experts, each dedicated to distinct lung lobes and bilateral lung combinations, enabling precise modeling of region-specific pathological variations. Multi-modal gating mechanisms dynamically integrate radiomics biomarkers and deep learning (DL) features (CNN, ViT, Mamba) to weight expert contributions optimally. Applied to interstitial lung disease (ILD) classification, REN achieves consistently superior performance: the radiomics-guided ensemble reached an average AUC of 0.8646 +/- 0.0467, a +12.5 percent improvement over the SwinUNETR baseline (AUC 0.7685, p = 0.031). Region-specific experts further revealed that lower-lobe models achieved AUCs of 0.88-0.90, surpassing DL counterparts (CNN: 0.76-0.79) and aligning with known disease progression patterns. Through rigorous patient-level cross-validation, REN demonstrates strong generalizability and clinical interpretability, presenting a scalable, anatomically-guided approach readily extensible to other structured medical imaging applications.

Metal artifacts in computed tomography (CT) imaging significantly hinder diagnostic accuracy and clinical decision-making. While deep learning-based metal artifact reduction (MAR) methods have demonstrated promising progress, their clinical application is still constrained by three major challenges: (1) balancing metal artifact reduction with the preservation of critical anatomical structures, (2) effectively capturing the clinical priors of metal artifacts, and (3) dynamically adapting to polychromatic spectral variations. To address these limitations, in this paper, we propose a Self-supervised MAR method for computed Tomography (SMART) that leverages range-null space decomposition (RND) to model metal and tissue LACs separately, and employs implicit neural representation (INR) to learn their respective clinical characteristics without explicit supervision. Specifically, RND decouples metal and tissue LACs into a residual range component for metal LAC modeling, which captures metal artifacts, thus facilitating metal artifact reduction, and a null component for tissue LAC modeling, which focuses on preserving tissue details. To deal with the lack of paired data in clinical settings, we utilize INR to learn the clinical characteristics of these components in a self-supervised manner. Furthermore, SMART incorporates polychromatic spectra into the implicit representation, allowing dynamic adaptation to spectral variations across different imaging conditions. Extensive experiments on one synthetic and two clinical datasets demonstrate the strong potential of SMART in real-world scenarios. By flexibly adapting to spectral variations, it achieves superior generalizability to out-of-distribution clinical data.

Small cell lung cancer (SCLC) is an aggressive disease with diverse phenotypes that reflect the heterogeneous expression of tumor-related genes. Recent studies have shown that neuroendocrine (NE) transcription factors may be used to classify SCLC tumors with distinct therapeutic responses. The liver is a common site of metastatic disease in SCLC and can drive a poor prognosis. Here, we present a computational approach to detect and characterize metastatic SCLC (mSCLC) liver lesions and their associated NE-related phenotype as a method to improve patient management. This study utilized computed tomography scans of patients with hepatic lesions from two data sources for segmentation and classification of liver disease: (1) a public dataset from patients of various cancer types (segmentation; n = 131) and (2) an institutional cohort of patients with SCLC (segmentation and classification; n = 86). We developed deep learning segmentation algorithms and compared their performance for automatically detecting liver lesions, evaluating the results with and without the inclusion of the SCLC cohort. Following segmentation in the SCLC cohort, radiomic features were extracted from the detected lesions, and least absolute shrinkage and selection operator regression was utilized to select features from a training cohort (80/20 split). Subsequently, we trained radiomics-based machine learning classifiers to stratify patients based on their NE tumor profile, defined as expression levels of a preselected gene set derived from bulk RNA sequencing or circulating free DNA chromatin immunoprecipitation sequencing. Our liver lesion detection tool achieved lesion-based sensitivities of 66%-83% for the two datasets. In patients with mSCLC, the radiomics-based NE phenotype classifier distinguished patients as positive or negative for harboring NE-like liver metastasis phenotype with an area under the receiver operating characteristic curve of 0.73 and an F1 score of 0.88 in the testing cohort. We demonstrate the potential of utilizing artificial intelligence (AI)-based platforms as clinical decision support systems, which could help clinicians determine treatment options for patients with SCLC based on their associated molecular tumor profile. Targeted therapy requires accurate molecular characterization of disease, which imaging and AI may aid in determining.

Limited-angle computerized tomography stands for one of the most difficult challenges in imaging. Although it opens the way to faster data acquisition in industry and less dangerous scans in medicine, standard approaches, such as the filtered backprojection (FBP) algorithm or the widely used total-variation functional, often produce various artefacts that hinder the diagnosis. With the rise of deep learning, many modern techniques have proven themselves successful in removing such artefacts but at the cost of large datasets. In this paper, we propose a new model-driven approach based on the method of the approximate inverse, which could serve as new starting point for learning strategies in the future. In contrast to FBP-type approaches, our reconstruction step consists in evaluating linear functionals on the measured data using reconstruction kernels that are precomputed as solution of an auxiliary problem. With this problem being uniquely solvable, the derived limited-angle reconstruction kernel (LARK) is able to fully reconstruct the object without the well-known streak artefacts, even for large limited angles. However, it inherits severe ill-conditioning which leads to a different kind of artefacts arising from the singular functions of the limited-angle Radon transform. The problem becomes particularly challenging when working on semi-discrete (real or analytical) measurements. We develop a general regularization strategy, named constrained limited-angle reconstruction kernel (CLARK), by combining spectral filter, the method of the approximate inverse and custom edge-preserving denoising in order to stabilize the whole process. We further derive and interpret error estimates for the application on real, i.e. semi-discrete, data and we validate our approach on synthetic and real data.