In multi-task learning (MTL), gradient conflict poses a significant challenge. Effective methods for addressing this problem, including PCGrad, CAGrad, and GradNorm, in their original implementations are computationally demanding, which significantly limits their application in modern large models and transformers. We propose Gradient Conductor (GCond), a method that builds upon PCGrad principles by combining them with gradient accumulation and an adaptive arbitration mechanism. We evaluated GCond on self-supervised learning tasks using MobileNetV3-Small and ConvNeXt architectures on the ImageNet 1K dataset and a combined head and neck CT scan dataset, comparing the proposed method against baseline linear combinations and state-of-the-art gradient conflict resolution methods. The stochastic mode of GCond achieved a two-fold computational speedup while maintaining optimization quality, and demonstrated superior performance across all evaluated metrics, achieving lower L1 and SSIM losses compared to other methods on both datasets. GCond exhibited high scalability, being successfully applied to both compact models (MobileNetV3-Small) and large architectures (ConvNeXt-tiny and ConvNeXt-Base). It also showed compatibility with modern optimizers such as AdamW and Lion/LARS. Therefore, GCond offers a scalable and efficient solution to the problem of gradient conflicts in multi-task learning.

In non-small cell lung cancer (NSCLC), non-invasive alternatives to biopsy-dependent driver mutation analysis are needed. We reviewed the effectiveness of radiomics alone or with clinical data and assessed the performance of artificial intelligence (AI) models in predicting oncogene mutation status. A PRISMA-compliant literature review for studies predicting oncogene mutation status in NSCLC patients using radiomics was conducted by a multidisciplinary team. Meta-analyses evaluating the performance of AI-based models developed with CT-derived radiomics features alone or combined with clinical data were performed. A meta-regression to analyze the influence of different predictors was also conducted. Of 890 studies identified, 124 evaluating models for the prediction of epidermal growth factor-1 (EGFR), anaplastic lymphoma kinase (ALK), and Kirsten rat sarcoma virus (KRAS) mutations were included in the systematic review, of which 51 were meta-analyzed. The AI algorithms' sensitivity/false positive rate (FPR) in predicting mutation status using radiomics-based models was 0.754 (95% CI 0.727-0.780)/0.344 (95% CI 0.308-0.381) for EGFR, 0.754 (95% CI 0.638-0.841)/0.225 (95% CI 0.163-0.302) for ALK and 0.475 (95% CI 0.153-0.820)/0.181 (95% CI 0.054-0.461) for KRAS. A meta-analysis of combined models was possible for EGFR mutation, revealing a sensitivity of 0.806 (95% CI 0.777-0.833) and a FPR of 0.315 (95% CI 0.270-0.364). No statistically significant results were obtained in the meta-regression. Radiomics-based models may offer a non-invasive alternative for determining oncogene mutation status in NSCLC. Further research is required to analyze whether clinical data might boost their performance. Question Can imaging-based radiomics and artificial intelligence non-invasively predict oncogene mutation status to improve diagnosis in non-small cell lung cancer (NSCLC)? Findings Radiomics-based models achieved high performance in predicting mutation status in NSCLC; adding clinical data showed limited improvement in predictive performance. Clinical relevance Radiomics and AI tools offer a non-invasive strategy to support molecular profiling in NSCLC. Validation studies addressing clinical and methodological aspects are essential to ensure their reliability and integration into routine clinical practice.

The use of cementless total knee arthroplasty (TKA) has significantly increased over the past decade. However, there is no objective criteria or consensus on parameters for patient selection for cementless TKA. The purpose of this study was to develop a machine learning model based on patient and radiographic parameters that could identify patients indicated for cementless TKA. We developed an explainable recommendation model using multiple patient and radiographic parameters (BMI, Age, Gender, Hounsfield Units [HU] from CT for density of tibia). The predictive model was trained on medical, operative, and radiographic data of 217 patients who underwent primary TKA. HU density measurements of four quadrants of the proximal tibia were obtained at region of interest on preoperative CT scans. which were then incorporated into the model as a surrogate for bone mineral density. The model employs Local Interpretable Model-agnostic Explanations in combination with bagging ensemble techniques for artificial neural networks. Model testing on the 217-patient cohort included 22 cemented and 38 cementless TKA cases. The model successfully identified 19 cemented patients (sensitivity: 86.4%) and 37 cementless patients (specificity: 97.4%) with an AUC = 0.94. Use of cementless TKA has grown significantly. There are currently no standard radiographic criteria for patient selection. Our machine learning model demonstrated 97.4% specificity and should improve with more training data. Future improvements will include incorporating additional cases and developing automated HU extraction techniques.

Diffusion models have demonstrated remarkable generative capabilities in image processing tasks. We propose a Sparse condition Temporal Rewighted Integrated Distribution Estimation guided diffusion model (STRIDE) for sparse-view CT reconstruction. Specifically, we design a joint training mechanism guided by sparse conditional probabilities to facilitate the model effective learning of missing projection view completion and global information modeling. Based on systematic theoretical analysis, we propose a temporally varying sparse condition reweighting guidance strategy to dynamically adjusts weights during the progressive denoising process from pure noise to the real image, enabling the model to progressively perceive sparse-view information. The linear regression is employed to correct distributional shifts between known and generated data, mitigating inconsistencies arising during the guidance process. Furthermore, we construct a dual-network parallel architecture to perform global correction and optimization across multiple sub-frequency components, thereby effectively improving the model capability in both detail restoration and structural preservation, ultimately achieving high-quality image reconstruction. Experimental results on both public and real datasets demonstrate that the proposed method achieves the best improvement of 2.58 dB in PSNR, increase of 2.37\% in SSIM, and reduction of 0.236 in MSE compared to the best-performing baseline methods. The reconstructed images exhibit excellent generalization and robustness in terms of structural consistency, detail restoration, and artifact suppression.

Diagnosing pulmonary ground-glass nodules (GGNs) on chest CT imaging remains challenging in clinical practice. Moreover, different stages of GGNs may require different clinical treatments. Hence, we sought to predict the progressive state of pulmonary GGNs (absorption or persistence) for accurate clinical treatment and decision-making. We retrospectively enrolled 672 patients (absorption group: 299; control group: 373) from two medical centres from January 2017 to March 2023. Clinical information and radiomic features extracted from regions of interest of all patients on chest CT imaging were collected. All patients were randomly divided into training and test sets at a ratio of 7:3. Three models were constructed-Rad-score (Model 1), clinical factor (Model 2), and clinical factors and Rad-score (Model 3)-to identify GGN progression. In the test dataset, two radiologists (with over 8 years of experience in chest imaging) evaluated the models' performance. Receiver operating characteristic curves, accuracy, sensitivity, and specificity were analysed. In the test set, the area under the curve (AUC) of Model 1 and Model 2 was 0.907 [0.868-0.946] and 0.918 [0.88-0.955], respectively. Model 3 achieved the best predictive performance, with an AUC of 0.959 [0.936-0.982], an accuracy of 0.881, a sensitivity of 0.902, and a specificity of 0.856. The intraclass correlation coefficient of Model 3 (0.86) showed better performance than radiologists (0.83 and 0.71). We developed and validated a radiomics-based machine-learning method that achieved good performance in predicting the progressive state of GGNs on initial computed tomography. The model may improve follow-up management of GGNs.

Image-based modeling is essential for understanding cardiovascular hemodynamics and advancing the diagnosis and treatment of cardiovascular diseases. Constructing patient-specific vascular models remains labor-intensive, error-prone, and time-consuming, limiting their clinical applications. This study introduces a deep-learning framework that automates the creation of simulation-ready vascular models from medical images. The framework integrates a segmentation module for accurate voxel-based vessel delineation with a surface deformation module that performs anatomically consistent and unsupervised surface refinements guided by medical image data. The integrated pipeline addresses key limitations of existing methods, enhancing geometric accuracy and computational efficiency. Evaluated on public datasets, it achieves state-of-the-art segmentation performance while substantially reducing manual effort and processing time. The resulting vascular models exhibit anatomically accurate and visually realistic geometries, effectively capturing both primary vessels and intricate branching patterns. In conclusion, this work advances the scalability and reliability of image-based computational modeling, facilitating broader applications in clinical and research settings.

Opportunistic computed tomography (CT) screening for the evaluation of sarcopenia and myosteatosis has been gaining emphasis. A fully automated artificial intelligence (AI)-integrated system for body composition assessment on CT scans is a prerequisite for effective opportunistic screening. However, no study has evaluated the implementation of fully automated AI systems for opportunistic screening in real-world clinical practice for routine health check-ups. The aim of this study is to evaluate the performance and clinical utility of a fully automated AI-integrated system for body composition assessment on opportunistic CT during routine health check-ups. This prospective multicenter study included 537 patients who underwent routine health check-ups across 3 institutions. Our AI algorithm models are composed of selecting L3 slice and segmenting muscle and fat area in an end-to-end manner. The AI models were integrated into the Picture Archiving and Communication System (PACS) at each institution. Technical success rate, processing time, and segmentation accuracy in Dice similarity coefficient were assessed. Body composition metrics were analyzed across age and sex groups. The fully automated AI-integrated system successfully retrieved anonymized CT images from the PACS, performed L3 selection and segmentation, and provided body composition metrics, including muscle quality maps and muscle age. The technical success rate was 100% without any failed cases requiring manual adjustment. The mean processing time from CT acquisition to report generation was 4.12 seconds. Segmentation accuracy comparing AI results and human expert results was 97.4%. Significant age-related declines in skeletal muscle area and normal-attenuation muscle area were observed, alongside increases in low-attenuation muscle area and intramuscular adipose tissue. Implementation of the fully automated AI-integrated system significantly enhanced opportunistic sarcopenia screening, achieving excellent technical success and high segmentation accuracy without manual intervention. This system has the potential to transform routine health check-ups by providing rapid and accurate assessments of body composition.

Purpose/ObjectiveCurrent radiotherapy (RT) planning workflows rely on pre-treatment simulation CT (sCT), which can significantly delay treatment initiation, particularly in resource-constrained settings. While diagnostic CT (dCT) offers a potential alternative for expedited planning, inherent geometric discrepancies from sCT in patient positioning and table curvature limit its direct use for accurate RT planning. This study presents a novel AI-based method designed to overcome these limitations by generating synthetic simulation CT (ssCT) directly from standard dCT for spinal palliative RT, aiming to eliminate the need for sCT and accelerate the treatment workflow. Materials/MethodsssCTs were generated using two neural network models to adjust spine position and correct table curvature. The neural networks use a three-layer structure (ReLU activation), optimized by Adam with MSE loss and MAE metrics. The models were trained on paired dCT and sCT images from 30 patients undergoing palliative spine radiotherapy from a safety-net hospital, with 22 cases used for training and 8 for testing. To explore institutional dependence, the models were also tested on 7 patients from an academic medical center (AMC). To evaluate ssCT accuracy, both ssCT and dCT were aligned with sCT using the same frame of reference rigid registration on bone windows. Dosimetric differences were assessed by comparing dCT vs. sCT and ssCT vs. sCT, quantifying deviations in dose-volume histogram (DVH) metrics, including Dmean, Dmax, D95, D99, V100, V107, and root-mean-square (RMS) differences. The imaging and plan quality was assessed by four radiation oncologists using a Likert score. The Wilcoxon signed-rank test was used to determine whether there is a significant difference between the two methods. ResultsFor the safety-net hospital cases, the generated ssCT demonstrated significantly improved geometric and dosimetric accuracy compared to dCT. ssCT reduced the mean difference in key dosimetric parameters (e.g., Dmean difference decreased from 2.0% for dCT vs. sCT to 0.57% for ssCT vs. sCT with significant improvement under the Wilcoxon signed-rank test) and achieved a significant reduction in the RMS difference of DVH curves (from 6.4% to 2.2%). Furthermore, physician evaluations showed that ssCT was consistently rated as significantly superior for treatment planning images (mean scores improving from "Acceptable" for dCT to "Good to Perfect" for ssCT), reflecting improved confidence in target and tissue positioning. In the academic medical-center cohort--where technologists already apply meticulous pre-scan alignment--ssCT still yielded statistically significant, though smaller, improvements in several dosimetric endpoints and in observer ratings. ConclusionOur AI-driven approach successfully generates ssCT from dCT that achieves geometric and dosimetric accuracy comparable to sCT for spinal palliative RT planning. By specifically addressing critical discrepancies like spine position and table curvature, this method offers a robust approach to bypass the need for dedicated sCT simulations. This advancement has the potential to significantly streamline the RT workflow, reduce treatment uncertainties, and accelerate time to treatment, offering a highly promising solution for improving access to timely and accurate radiotherapy, especially in limited-resource environments.

Intracranial aneurysms (IAs) are common vascular pathologies with a risk of fatal rupture. Human assessment of rupture risk is error prone, and treatment decision for unruptured IAs often rely on expert opinion and institutional policy. Therefore, we aimed to develop a computer-assisted aneurysm rupture prediction framework to help guide the decision-making process and create future decision criteria. This retrospective study included 335 patients with 500 IAs, of the 500 IAs studied, 250 were labeled as ruptured and 250 as unruptured. A skilled radiologist and a neurosurgeon visually examined the computed tomography angiography (CTA) images and labeled the IAs. For external validation we included 24 IAs, 10 ruptured and 15 unruptured, imaged with 3D rotational angiography (3D-RA) from the Aneurisk dataset. The pretrained nnU-net model was used for automated vessel segmentation, which was fed to pretrained PointNet++ models for vessel labeling and aneurysm segmentation. From these the latent keypoint representations were extracted as vessel shape and aneurysm shape features, respectively. Additionally, conventional features such as IAs morphological measurements, location and patient data, such as age, sex, were used for training and testing eight machine learning models for rupture status classification. The top-performing model, a random forest with feature selection, achieved an area under the receiver operating curve of 0.851, an accuracy of 0.782, a sensitivity of 0.804, and a specificity of 0.760. This model used 14 aneurysm shape features, seven conventional features, and one vessel shape feature. On the external dataset, it achieved an AUC of 0.805. While aneurysm shape features consistently contributed significantly across the classification models, vessel shape features contributed a small portion. Our proposed automated artificial intelligence framework could assist in clinical decision-making by assessing aneurysm rupture risk using screening tests, such as CTA and 3D-RA.

Carotid artery disease is a major cause of stroke and is frequently evaluated using Carotid CT Angiography (CTA). However, the associated radiation exposure and contrast agent use raise concerns, particularly for high-risk patients. Recent advances in Deep Learning Image Reconstruction (DLIR) offer new potential to enhance image quality under low-dose conditions. This study aimed to evaluate the effectiveness of the DLIR-H algorithm in improving image quality of 40 keV Virtual Monoenergetic Images (VMI) in dual-energy CTA (DE-CTA) while minimizing radiation dose and contrast agent usage. A total of 120 patients undergoing DE-CTA were prospectively divided into four groups: one control group using ASIR-V and three experimental groups using DLIR-L, DLIR-M, and DLIR-H algorithms. All scans employed a "triple-low" protocol-low radiation, low contrast volume, and low injection rate. Objective image quality was assessed via CT values, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Subjective image quality was evaluated using a 5-point Likert scale. The DLIR-H group showed the greatest improvements in image quality, with significantly reduced noise and increased SNR and CNR, particularly at complex vascular sites such as the carotid bifurcation and internal carotid artery. Radiation dose and contrast volume were reduced by 15.6 % and 17.5 %, respectively. DLIR-H also received the highest subjective image quality scores. DLIR-H significantly enhances DE-CTA image quality under ultra-low-dose conditions, preserving diagnostic detail while reducing patient risk. DLIR-H supports safer and more effective carotid imaging, especially for high-risk groups like renal-impaired patients and those needing repeated scans, enabling wider clinical use of ultra-low-dose protocols.