Uterine sarcoma is a rare disease whose association with body composition parameters is poorly understood. This study explored the impact of body composition parameters on overall survival with uterine sarcoma. This multicenter study included 52 patients with uterine sarcomas treated at three Japanese hospitals between 2007 and 2023. A semi-automatic segmentation program based on deep learning analyzed transaxial CT images at the L3 vertebral level, calculating body composition parameters as follows: area indices (areas divided by height squared) of skeletal muscle, visceral and subcutaneous adipose tissue (SMI, VATI, and SATI, respectively); skeletal muscle density; and the visceral-to-subcutaneous fat area ratio (VSR). The optimal cutoff values for each parameter were calculated using maximally selected rank statistics with several p value approximations. The effects of body composition parameters and clinical data on overall survival (OS) and cancer-specific survival (CSS) were analyzed. Univariate Cox proportional hazards regression analysis revealed that advanced stage (III-IV) and high VSR were unfavorable prognostic factors for both OS and CSS. Multivariate Cox proportional hazard regression analysis revealed that advanced stage (III-IV) (hazard ratios (HRs), 4.67 for OS and 4.36 for CSS, p < 0.01), and high VSR (HRs, 9.36 for OS and 8.22 for CSS, p < 0.001) were poor prognostic factors for both OS and CSS. Added values were observed when the VSR was incorporated into the OS and the CSS prediction models. Increased VSR and tumor stage are significant predictors of poor overall survival in patients with uterine sarcoma.

Following the successful paradigm shift of large language models, leveraging pre-training on a massive corpus of data and fine-tuning on different downstream tasks, generalist models have made their foray into computer vision. The introduction of Segment Anything Model (SAM) set a milestone on segmentation of natural images, inspiring the design of a multitude of architectures for medical image segmentation. In this survey we offer a comprehensive and in-depth investigation on generalist models for medical image segmentation. We start with an introduction on the fundamentals concepts underpinning their development. Then, we provide a taxonomy on the different declinations of SAM in terms of zero-shot, few-shot, fine-tuning, adapters, on the recent SAM 2, on other innovative models trained on images alone, and others trained on both text and images. We thoroughly analyze their performances at the level of both primary research and best-in-literature, followed by a rigorous comparison with the state-of-the-art task-specific models. We emphasize the need to address challenges in terms of compliance with regulatory frameworks, privacy and security laws, budget, and trustworthy artificial intelligence (AI). Finally, we share our perspective on future directions concerning synthetic data, early fusion, lessons learnt from generalist models in natural language processing, agentic AI and physical AI, and clinical translation.

Dental implant surgery has become a prevalent treatment option for patients with single tooth defects. However, the success of this surgery relies heavily on precise planning and execution. This study investigates the application of artificial intelligence (AI) in assisting the planning process of dental implant surgery for single tooth defects. Single tooth defects in the oral cavity pose a significant challenge in restorative dentistry. Dental implant restoration has emerged as an effective solution for rehabilitating such defects. However, the complexity of the procedure and the need for accurate treatment planning necessitate the integration of advanced technologies. In this study, we propose the utilisation of AI to enhance the precision and efficiency of implant surgery planning for single tooth defects. A total of twenty patients with single tooth loss were enrolled. Cone-beam computed tomography (CBCT) and intra-oral scans were obtained and imported into the AI-dentist software for 3D reconstruction. AI assisted in implant selection, tooth position identification, and crown fabrication. Evaluation included subjective verification and objective assessments. A paired samples t-test was used to compare planning times (dentist vs. AI), with a significance level of p < 0.05. Twenty patients (9 male, 11 female; mean age 59.5 ± 11.86 years) with single missing teeth participated in this study. Implant margins were carefully positioned: 3.05 ± 1.44 mm from adjacent roots, 2.52 ± 0.65 mm from bone plate edges, 3.05 ± 1.44 mm from sinus/canal, and 3.85 ± 1.23 mm from gingival height. Manual planning (21.50 ± 4.87 min) was statistically significantly slower than AI (11.84 ± 3.22 min, p < 0.01). Implant planning met 100% buccolingual/proximal/distal bone volume criteria and 90% sinus/canal distance criteria. Two patients required sinus lifting and bone grafting due to insufficient bone volume. This study highlights the promising role of AI in enhancing the precision and efficiency of dental implant surgery planning for single tooth defects. Further studies are necessary to validate the effectiveness and safety of AI-assisted planning in a larger patient population.

Cardiac substructures are essential in thoracic radiation therapy planning to minimize risk of radiation-induced heart disease. Deep learning (DL) offers efficient methods to reduce contouring burden but lacks generalizability across different modalities and overlapping structures. This work introduces and validates a Modality-AGnostic Image Cascade (MAGIC) for comprehensive and multi-modal cardiac substructure segmentation. MAGIC is implemented through replicated encoding and decoding branches of an nnU-Net-based, U-shaped backbone conserving the function of a single model. Twenty cardiac substructures (heart, chambers, great vessels (GVs), valves, coronary arteries (CAs), and conduction nodes) from simulation CT (Sim-CT), low-field MR-Linac, and cardiac CT angiography (CCTA) modalities were manually delineated and used to train (n=76), validate (n=15), and test (n=30) MAGIC. Twelve comparison models (four segmentation subgroups across three modalities) were equivalently trained. All methods were compared for training efficiency and against reference contours using the Dice Similarity Coefficient (DSC) and two-tailed Wilcoxon Signed-Rank test (threshold, p<0.05). Average DSC scores were 0.75(0.16) for Sim-CT, 0.68(0.21) for MR-Linac, and 0.80(0.16) for CCTA. MAGIC outperforms the comparison in 57% of cases, with limited statistical differences. MAGIC offers an effective and accurate segmentation solution that is lightweight and capable of segmenting multiple modalities and overlapping structures in a single model. MAGIC further enables clinical implementation by simplifying the computational requirements and offering unparalleled flexibility for clinical settings.

Medical image segmentation is crucial for clinical diagnosis, yet existing models are limited by their reliance on explicit human instructions and lack the active reasoning capabilities to understand complex clinical questions. While recent advancements in multimodal large language models (MLLMs) have improved medical question-answering (QA) tasks, most methods struggle to generate precise segmentation masks, limiting their application in automatic medical diagnosis. In this paper, we introduce medical image reasoning segmentation, a novel task that aims to generate segmentation masks based on complex and implicit medical instructions. To address this, we propose MedSeg-R, an end-to-end framework that leverages the reasoning abilities of MLLMs to interpret clinical questions while also capable of producing corresponding precise segmentation masks for medical images. It is built on two core components: 1) a global context understanding module that interprets images and comprehends complex medical instructions to generate multi-modal intermediate tokens, and 2) a pixel-level grounding module that decodes these tokens to produce precise segmentation masks and textual responses. Furthermore, we introduce MedSeg-QA, a large-scale dataset tailored for the medical image reasoning segmentation task. It includes over 10,000 image-mask pairs and multi-turn conversations, automatically annotated using large language models and refined through physician reviews. Experiments show MedSeg-R's superior performance across several benchmarks, achieving high segmentation accuracy and enabling interpretable textual analysis of medical images.

Medical image segmentation is a fundamental and key technology in computer-aided diagnosis and treatment. Previous methods can be broadly classified into three categories: convolutional neural network (CNN) based, Transformer based, and hybrid architectures that combine both. However, each of them has its own limitations, such as restricted receptive fields in CNNs or the computational overhead caused by the quadratic complexity of Transformers. Recently, the Receptance Weighted Key Value (RWKV) model has emerged as a promising alternative for various vision tasks, offering strong long-range modeling capabilities with linear computational complexity. Some studies have also adapted RWKV to medical image segmentation tasks, achieving competitive performance. However, most of these studies focus on modifications to the Vision-RWKV (VRWKV) mechanism and train models from scratch, without exploring the potential advantages of leveraging pre-trained VRWKV models for medical image segmentation tasks. In this paper, we propose Med-URWKV, a pure RWKV-based architecture built upon the U-Net framework, which incorporates ImageNet-based pretraining to further explore the potential of RWKV in medical image segmentation tasks. To the best of our knowledge, Med-URWKV is the first pure RWKV segmentation model in the medical field that can directly reuse a large-scale pre-trained VRWKV encoder. Experimental results on seven datasets demonstrate that Med-URWKV achieves comparable or even superior segmentation performance compared to other carefully optimized RWKV models trained from scratch. This validates the effectiveness of using a pretrained VRWKV encoder in enhancing model performance. The codes will be released.

This study aims to explore the feasibility to automate the application process of nomograms in clinical medicine, demonstrated through the task of preoperative pleural invasion prediction in non-small cell lung cancer patients using PET/CT imaging. The automatic pipeline involves multimodal segmentation, feature extraction, and model prediction. It is validated on a cohort of 1116 patients from two medical centers. The performance of the feature-based diagnostic model outperformed both the radiomics model and individual machine learning models. The segmentation models for CT and PET images achieved mean dice similarity coefficients of 0.85 and 0.89, respectively, and the segmented lung contours showed high consistency with the actual contours. The automatic diagnostic system achieved an accuracy of 0.87 in the internal test set and 0.82 in the external test set, demonstrating comparable overall diagnostic performance to the human-based diagnostic model. In comparative analysis, the automatic diagnostic system showed superior performance relative to other segmentation and diagnostic pipelines. The proposed automatic diagnostic system provides an interpretable, automated solution for predicting pleural invasion in non-small cell lung cancer.

Agentic Artificial Intelligence (AI) systems leveraging Large Language Models (LLMs) exhibit significant potential for complex reasoning, planning, and tool utilization. We demonstrate that a specialized computer vision system can be built autonomously from a natural language prompt using Agentic AI methods. This involved extending SimpleMind (SM), an open-source Cognitive AI environment with configurable tools for medical image analysis, with an LLM-based agent, implemented using OpenManus, to automate the planning (tool configuration) for a particular computer vision task. We provide a proof-of-concept demonstration that an agentic system can interpret a computer vision task prompt, plan a corresponding SimpleMind workflow by decomposing the task and configuring appropriate tools. From the user input prompt, "provide sm (SimpleMind) config for lungs, heart, and ribs segmentation for cxr (chest x-ray)"), the agent LLM was able to generate the plan (tool configuration file in YAML format), and execute SM-Learn (training) and SM-Think (inference) scripts autonomously. The computer vision agent automatically configured, trained, and tested itself on 50 chest x-ray images, achieving mean dice scores of 0.96, 0.82, 0.83, for lungs, heart, and ribs, respectively. This work shows the potential for autonomous planning and tool configuration that has traditionally been performed by a data scientist in the development of computer vision applications.

BackgroundHepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under- diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for all-cause mortality prediction. MethodsThis study included 27039 consecutive patients [57% male] with MPI scans from nine sites. We used an AI model to segment liver and spleen on low dose CTAC scans and quantify the liver measures, and the difference of liver minus spleen (LmS) measures. HS was defined as mean liver attenuation < 40 Hounsfield units (HU) or LmS attenuation < -10 HU. Additionally, we used seven sites to develop an AI liver risk index (LIRI) for comprehensive hepatic assessment by integrating the hepatic measures and two external sites to validate its improved prognostic value and generalizability for all-cause mortality prediction over HS. FindingsMedian (interquartile range [IQR]) age was 67 [58, 75] years and body mass index (BMI) was 29.5 [25.5, 34.7] kg/m2, with diabetes in 8950 (33%) patients. The algorithm identified HS in 6579 (24%) patients. During median [IQR] follow-up of 3.58 [1.86, 5.15] years, 4836 (18%) patients died. HS was associated with increased mortality risk overall (adjusted hazard ratio (HR): 1.14 [1.05, 1.24], p=0.0016) and in subpopulations. LIRI provided higher prognostic value than HS after adjustments overall (adjusted HR 1.5 [1.32, 1.69], p<0.0001 vs HR 1.16 [1.02, 1.31], p=0.0204) and in subpopulations. InterpretationsAI-based hepatic measures automatically identify HS from CTAC scans in patients undergoing MPI without additional radiation dose or physician interaction. Integrated liver assessment combining multiple hepatic imaging measures improved risk stratification for all-cause mortality. FundingNational Heart, Lung, and Blood Institute/National Institutes of Health. Research in context Evidence before this studyExisting studies show that fully automated hepatic quantification analysis from chest computed tomography (CT) scans is feasible. While hepatic measures show significant potential for improving risk stratification and patient management, CT attenuation correction (CTAC) scans from patients undergoing myocardial perfusion imaging (MPI) have rarely been utilized for concurrent and automated volumetric hepatic analysis beyond its current utilization for attenuation correction and coronary artery calcium burden assessment. We conducted a literature review on PubMed and Google Scholar on April 1st, 2025, using the following keywords: ("liver" OR "hepatic") AND ("quantification" OR "measure") AND ("risk stratification" OR "survival analysis" OR "prognosis" OR "prognostic prediction") AND ("CT" OR "computed tomography"). Previous studies have established approaches for the identification of hepatic steatosis (HS) and its prognostic value in various small- scale cohorts using either invasive biopsy or non-invasive imaging approaches. However, CT-based non- invasive imaging, existing research predominantly focuses on manual region-of-interest (ROI)-based hepatic quantification from selected CT slices or on identifying hepatic steatosis without comprehensive prognostic assessment in large-scale and multi-site cohorts, which hinders the association evaluation of hepatic steatosis for risk stratification in clinical routine with less precise estimates, weak statistical reliability, and limited subgroup analysis to assess bias effects. No existing studies investigated the prognostic value of hepatic steatosis measured in consecutive patients undergoing MPI. These patients usually present with multiple cardiovascular risk factors such as hypertension, dyslipidemia, diabetes and family history of coronary disease. Whether hepatic measures could provide added prognostic value over existing cardiometabolic factors is unknown. Furthermore, despite the diverse hepatic measures on CT in existing literature, integrated AI-based assessment has not been investigated before though it may improve the risk stratification further over HS. Lastly, previous research relied on dedicated CT scans performed for screening purposes. CTAC scans obtained routinely with MPI had never been utilized for automated HS detection and prognostic evaluation, despite being readily available at no additional cost or radiation exposure. Added value of this studyIn this multi-center (nine sites) international (three countries) study of 27039 consecutive patients undergoing myocardial perfusion imaging (MPI) with PET or SPECT, we used an innovative artificial intelligence (AI)- based approach for automatically segmenting the entire liver and spleen volumes from low-dose ungated CT attenuation correction (CTAC) scans acquired during MPI, followed by the identification of hepatic steatosis. We evaluated the added prognostic value of several key hepatic metrics--liver measures (mean attenuation, coefficient of variation (CoV), entropy, and standard deviation), and similar measures for the difference of liver minus spleen (LmS)--derived from volumetric quantification of CTAC scans with adjustment for existing clinical and MPI variables. A HS imaging criterion (HSIC: a patient has moderate or severe hepatic steatosis if the mean liver attenuation is < 40 Hounsfield unit (HU) or the difference of liver mean attenuation and spleen mean attenuation is < -10 HU) was used to detect HS. These hepatic metrics were assessed for their ability to predict all-cause mortality in a large-scale and multi-center patient cohort. Additionally, we developed and validated an eXtreme Gradient Boosting decision tree model for integrated liver assessment and risk stratification by combining the hepatic metrics with the demographic variables to derive a liver risk index (LIRI). Our results demonstrated strong associations between the hepatic metrics and all-cause mortality, even after adjustment for clinical variables, myocardial perfusion, and atherosclerosis biomarkers. Our results revealed significant differences in the association of HS with mortality in different sex, age, and race subpopulations. Similar differences were also observed in various chronic disease subpopulations such as obese and diabetic subpopulations. These results highlighted the modifying effects of various patient characteristics, partially accounting for the inconsistent association observed in existing studies. Compared with individual hepatic measures, LIRI showed significant improvement compared to HSIC-based HS in mortality prediction in external testing. All these demonstrate the feasibility of HS detection and integrated liver assessment from cardiac low-dose CT scans from MPI, which is also expected to apply for generic chest CT scans which have coverage of liver and spleen while prior studies used dedicated abdominal CT scans for such purposes. Implications of all the available evidenceRoutine point-of-care analysis of hepatic quantification can be seamlessly integrated into all MPI using CTAC scans to noninvasively identify HS at no additional cost or radiation exposure. The automatically derived hepatic metrics enhance risk stratification by providing additional prognostic value beyond existing clinical and imaging factors, and the LIRI enables comprehensive assessment of liver and further improves risk stratification and patient management.

Agentic Artificial Intelligence (AI) systems leveraging Large Language Models (LLMs) exhibit significant potential for complex reasoning, planning, and tool utilization. We demonstrate that a specialized computer vision system can be built autonomously from a natural language prompt using Agentic AI methods. This involved extending SimpleMind (SM), an open-source Cognitive AI environment with configurable tools for medical image analysis, with an LLM-based agent, implemented using OpenManus, to automate the planning (tool configuration) for a particular computer vision task. We provide a proof-of-concept demonstration that an agentic system can interpret a computer vision task prompt, plan a corresponding SimpleMind workflow by decomposing the task and configuring appropriate tools. From the user input prompt, "provide sm (SimpleMind) config for lungs, heart, and ribs segmentation for cxr (chest x-ray)"), the agent LLM was able to generate the plan (tool configuration file in YAML format), and execute SM-Learn (training) and SM-Think (inference) scripts autonomously. The computer vision agent automatically configured, trained, and tested itself on 50 chest x-ray images, achieving mean dice scores of 0.96, 0.82, 0.83, for lungs, heart, and ribs, respectively. This work shows the potential for autonomous planning and tool configuration that has traditionally been performed by a data scientist in the development of computer vision applications.