To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endometrial cancer patients, and to examine the effect of gene expression variability on image texture features. We generated two datasets in this retrospective and exploratory study. The first, with 123 histopathologically confirmed patient cases, was used to develop an endometrial cancer segmentation model. The second dataset, including 249 patients for MMR and 179 for TP53 mutation prediction, was derived from PET/CT exams and immunohistochemical analysis. A PET-based Attention-U Net network was used for segmentation, followed by region-growing with co-registered PET and CT images. Feature models were constructed using PET, CT, and combined data, with model selection based on performance comparison. Our segmentation model achieved 99.99% training accuracy and a dice coefficient of 97.35%, with validation accuracy at 99.93% and a dice coefficient of 84.81%. The combined PET + CT model demonstrated superior predictive power for both genes, with AUCs of 0.8146 and 0.8102 for MMR, and 0.8833 and 0.8150 for TP53 in training and test sets, respectively. MMR-related protein heterogeneity and TP53 expression differences were predominantly seen in PET images. An efficient deep learning algorithm for endometrial cancer segmentation has been established, highlighting the enhanced predictive power of integrated PET and CT radiomics for MMR and TP53 expression. The study underscores the distinct influences of MMR and TP53 gene expression on tumor characteristics.

Automatic segmentation of cervical tumors is important in quantitative analysis and radiotherapy planning. A parallel encoder U-Net (PEU-Net) integrating the multi-modality information of PET/MRI was proposed to segment cervical tumor, which consisted of two parallel encoders with the same structure for PET and MR images. The features of the two modalities were extracted separately and fused at each layer of the decoder. Res2Net module on skip connection aggregated the features of various scales and refined the segmentation performance. PET/MRI images of 165 patients with cervical cancer were included in this study. U-Net, TransUNet, and nnU-Net with single or multi-modality (PET or/and T2WI) input were used for comparison. The Dice similarity coefficient (DSC) with volume data, DSC and the 95th percentile of Hausdorff distance (HD95) with tumor slices were calculated to evaluate the performance. The proposed PEU-Net exhibited the best performance (DSC_3d: 0.726 ± 0.204, HD₉₅: 4.603 ± 4.579 mm), DSC_2d (0.871 ± 0.113) was comparable to the best result of TransUNet with PET/MRI (0.873 ± 0.125). The networks with multi-modality input outperformed those with single-modality images as input. The results showed that the proposed PEU-Net could use multi-modality information more effectively through the redesigned structure and achieved competitive performance.

Previous studies have highlighted the prominent role of age in lung cancer risk, with signs of lung aging visible in computed tomography (CT) imaging. This study aims to characterize lung aging using quantitative radiomic features extracted from five delineated lung lobes and explore how age contributes to lung cancer development through these features. We analyzed baseline CT scans from the Wenling lung cancer screening cohort, consisting of 29,810 participants. Deep learning-based segmentation method was used to delineate lung lobes. A total of 1,470 features were extracted from each lobe. The minimum redundancy maximum relevance algorithm was applied to identify the top 10 age-related radiomic features among 13,137 never smokers. Multiple regression analyses were used to adjust for confounders in the association of age, lung lobar radiomic features, and lung cancer. Linear, Cox proportional hazards, and parametric accelerated failure time models were applied as appropriate. Mediation analyses were conducted to evaluate whether lobar radiomic features mediate the relationship between age and lung cancer risk. Age was significantly associated with an increased lung cancer risk, particularly among current smokers (hazard ratio = 1.07, P = 2.81 × 10^- 13). Age-related radiomic features exhibited distinct effects across lung lobes. Specifically, the first order mean (mean attenuation value) filtered by wavelet in the right upper lobe increased with age (β = 0.019, P = 2.41 × 10^- 276), whereas it decreased in the right lower lobe (β = -0.028, P = 7.83 × 10^- 277). Three features, namely wavelet_HL_firstorder_Mean of the right upper lobe, wavelet_LH_firstorder_Mean of the right lower lobe, and original_shape_MinorAxisLength of the left upper lobe, were independently associated with lung cancer risk at Bonferroni-adjusted P value. Mediation analyses revealed that density and shape features partially mediated the relationship between age and lung cancer risk while a suppression effect was observed in the wavelet first order mean of right upper lobe. The study reveals lobe-specific heterogeneity in lung aging patterns through radiomics and their associations with lung cancer risk. These findings may contribute to identify new approaches for early intervention in lung cancer related to aging. Not applicable.

Accurate aortic diameter measurements are essential for diagnosis, surveillance, and procedural planning in aortic disease. Semi-automatic methods remain widely used but require manual corrections, which can be time-consuming and operator-dependent. Artificial intelligence (AI)-driven fully automatic methods may offer improved efficiency and measurement accuracy. This study aims to validate a fully automatic method against a semi-automatic approach using computed tomography angiography (CTA) and non-contrast CT scans. A monocentric retrospective comparative study was conducted on patients who underwent endovascular aortic repair (EVAR) for infrarenal, juxta-renal or thoracic aneurysms and a control group. Maximum aortic wall-to-wall diameters were measured before and after repair using a fully automatic software (PRAEVAorta2®, Nurea, Bordeaux, France) and compared to measurements performed by two vascular surgeons using a semi-automatic approach on CTA and non-contrast CT scans. Correlation coefficients (Pearson's R) and absolute differences were calculated to assess agreement. A total of 120 CT scans (60 CTA and 60 non-contrast CT) were included, comprising 23 EVAR, 4 thoracic EVAR, 1 fenestrated EVAR, and 4 control cases. Strong correlations were observed between the fully automatic and semi-automatic measurements in both CTA and non-contrast CT. For CTA, correlation coefficients ranged from 0.94 to 0.96 (R² = 0.88-0.92), while for non-contrast CT, they ranged from 0.87 to 0.89 (R² = 0.76-0.79). Median absolute differences in aortic diameter measurements varied between 1.1 mm and 4.2 mm across the different anatomical locations. The fully automatic method demonstrated a significantly faster processing time, with a median execution time of 73 seconds (IQR: 57-91) compared to 700 (IQR: 613-800) for the semi-automatic method (p < 0.001). The fully automatic method demonstrated strong agreement with semi-automatic measurements for both CTA and non-contrast CT, before and after endovascular repair in different aortic locations, with significantly reduced analysis time. This method could improve workflow efficiency in clinical practice and research applications.

<b>BACKGROUND.</b> The importance of including the thoracic aortic calcification (TAC), in addition to coronary artery calcification (CAC), in prognostic assessments has been difficult to determine, partly due to greater challenge in performing standardized TAC assessments. <b>OBJECTIVE.</b> The purpose of this study was to evaluate long-term prognostic implications of TAC assessed using artificial intelligence (AI)-based quantification on routine chest CT in a screening population. <b>METHODS.</b> This retrospective study included 7404 asymptomatic individuals (median age, 53.9 years; 5875 men, 1529 women) who underwent nongated noncontrast chest CT as part of a national general health screening program at one of two centers from January 2007 to December 2014. A commercial AI program quantified TAC and CAC using Agatston scores, which were stratified into categories. Radiologists manually quantified TAC and CAC in 2567 examinations. The role of AI-based TAC categories in predicting major adverse cardiovascular events (MACE) and all-cause mortality (ACM), independent of AI-based CAC categories as well as clinical and laboratory variables, was assessed by multivariable Cox proportional hazards models using data from both centers and concordance statistics from prognostic models developed and tested using center 1 and center 2 data, respectively. <b>RESULTS.</b> AI-based and manual quantification showed excellent agreement for TAC and CAC (concordance correlation coefficient: 0.967 and 0.895, respectively). The median observation periods were 7.5 years for MACE (383 events in 5342 individuals) and 11.0 years for ACM (292 events in 7404 individuals). When adjusted for AI-based CAC categories along with clinical and laboratory variables, the risk for MACE was not independently associated with any AI-based TAC category; risk of ACM was independently associated with AI-based TAC score of 1001-3000 (HR = 2.14, <i>p</i> = .02) but not with other AI-based TAC categories. When prognostic models were tested, the addition of AI-based TAC categories did not improve model fit relative to models containing clinical variables, laboratory variables, and AI-based CAC categories for MACE (concordance index [C-index] = 0.760-0.760, <i>p</i> = .81) or ACM (C-index = 0.823-0.830, <i>p</i> = .32). <b>CONCLUSION.</b> The addition of TAC to models containing CAC provided limited improvement in risk prediction in an asymptomatic screening population undergoing CT. <b>CLINICAL IMPACT.</b> AI-based quantification provides a standardized approach for better understanding the potential role of TAC as a predictive imaging biomarker.

Left ventricular non-compaction is a cardiac condition marked by excessive trabeculae in the left ventricle's inner wall. Although various methods exist to measure these structures, the medical community still lacks consensus on the best approach. Previously, we developed DL-LVTQ, a tool based on a UNet neural network, to quantify trabeculae in this region. In this study, we expand the dataset to include new patients with Titin cardiomyopathy and healthy individuals with fewer trabeculae, requiring retraining of our models to enhance predictions. We also propose ViTUNeT, a neural network architecture combining U-Net and Vision Transformers to segment the left ventricle more accurately. Additionally, we train a YOLOv8 model to detect the ventricle and integrate it with ViTUNeT model to focus on the region of interest. Results from ViTUNet and YOLOv8 are similar to DL-LVTQ, suggesting dataset quality limits further accuracy improvements. To test this, we analyze MRI images and develop a method using two YOLOv8 models to identify and remove problematic images, leading to better results. Combining YOLOv8 with deep learning networks offers a promising approach for improving cardiac image analysis and segmentation.

Over the past decade, Medical Image Segmentation (MIS) using Deep Neural Networks (DNNs) has achieved significant performance improvements and holds great promise for future developments. This paper presents a comprehensive study on MIS based on DNNs. Intelligent Vision Systems are often evaluated based on their output levels, such as Data, Information, Knowledge, Intelligence, and Wisdom (DIKIW),and the state-of-the-art solutions in MIS at these levels are the focus of research. Additionally, Explainable Artificial Intelligence (XAI) has become an important research direction, as it aims to uncover the "black box" nature of previous DNN architectures to meet the requirements of transparency and ethics. The study emphasizes the importance of MIS in disease diagnosis and early detection, particularly for increasing the survival rate of cancer patients through timely diagnosis. XAI and early prediction are considered two important steps in the journey from "intelligence" to "wisdom." Additionally, the paper addresses existing challenges and proposes potential solutions to enhance the efficiency of implementing DNN-based MIS.

Human brain fiber tractography using diffusion magnetic resonance imaging is a crucial stage in mapping brain white matter structures, pre-surgical planning, and extracting connectivity patterns. Accurate and reliable tractography, by providing detailed geometric information about the position of neural pathways, minimizes the risk of damage during neurosurgical procedures. Both tractography itself and its post-processing steps such as bundle segmentation are usually used in these contexts. Many approaches have been put forward in the past decades and recently, multiple data-driven tractography algorithms and automatic segmentation pipelines have been proposed to address the limitations of traditional methods. Several of these recent methods are based on learning algorithms that have demonstrated promising results. In this study, in addition to introducing diffusion MRI datasets, we review learning-based algorithms such as conventional machine learning, deep learning, reinforcement learning and dictionary learning methods that have been used for white matter tract, nerve and pathway recognition as well as whole brain streamlines or whole brain tractogram creation. The contribution is to discuss both tractography and tract recognition methods, in addition to extending previous related reviews with most recent methods, covering architectures as well as network details, assess the efficiency of learning-based methods through a comprehensive comparison in this field, and finally demonstrate the important role of learning-based methods in tractography.

The development of therapeutics builds on testing their efficiency in vitro. To optimize gene therapies, for example, fluorescent reporters expressed by treated cells are typically utilized as readouts. Traditionally, their global fluorescence signal has been used as an estimate of transduction efficiency. However, analysis in individual cells within a living 3D tissue remains a challenge. Readout on a single-cell level can be realized via fluo-rescence-based flow cytometry at the cost of tissue dissociation and loss of spatial information. Complementary, spatial information is accessible via immunofluorescence of fixed samples. Both approaches impede time-dependent studies on the delivery of the vector to the cells. Here, quantitative 3D characterization of viral transduction efficiencies in living retinal organoids is introduced. The approach combines quantified gene delivery efficiency in space and time, leveraging human retinal organ-oids, engineered adeno-associated virus (AAV) vectors, confocal live imaging, and deep learning-based image segmentation. The integration of these tools in an organoid imaging and analysis pipeline allows quantitative testing of future treatments and other gene delivery methods. It has the potential to guide the development of therapies in biomedical applications.

BackgroundCoeliac disease, an autoimmune disorder affecting approximately 1% of the global population, is typically diagnosed on a duodenal biopsy. However, inter-pathologist agreement on coeliac disease diagnosis is only around 80%. Existing machine learning solutions designed to improve coeliac disease diagnosis often lack interpretability, which is essential for building trust and enabling widespread clinical adoption. ObjectiveTo develop an interpretable AI model capable of segmenting key histological structures in duodenal biopsies, generating explainable segmentation masks, estimating intraepithelial lymphocyte (IEL)-to-enterocyte and villus-to-crypt ratios, and diagnosing coeliac disease. DesignSemantic segmentation models were trained to identify villi, crypts, IELs, and enterocytes using 49 annotated 2048x2048 patches at 40x magnification. IEL-to-enterocyte and villus-to-crypt ratios were calculated from segmentation masks, and a logistic regression model was trained on 172 images to diagnose coeliac disease based on these ratios. Evaluation was performed on an independent test set of 613 duodenal biopsy scans from a separate NHS Trust. ResultsThe villus-crypt segmentation model achieved a mean PR AUC of 80.5%, while the IEL-enterocyte model reached a PR AUC of 82%. The diagnostic model classified WSIs with 96% accuracy, 86% positive predictive value, and 98% negative predictive value on the independent test set. ConclusionsOur interpretable AI models accurately segmented key histological structures and diagnosed coeliac disease in unseen WSIs, demonstrating strong generalization performance. These models provide pathologists with reliable IEL-to-enterocyte and villus-to-crypt ratio estimates, enhancing diagnostic accuracy. Interpretable AI solutions like ours are essential for fostering trust among healthcare professionals and patients, complementing existing black-box methodologies. What is already known on this topicPathologist concordance in diagnosing coeliac disease from duodenal biopsies is consistently reported to be below 80%, highlighting diagnostic variability and the need for improved methods. Several recent studies have leveraged artificial intelligence (AI) to enhance coeliac disease diagnosis. However, most of these models operate as "black boxes," offering limited interpretability and transparency. The lack of explainability in AI-driven diagnostic tools prevents widespread adoption by healthcare professionals and reduces patient trust. What this study addsThis study presents an interpretable semantic segmentation algorithm capable of detecting the four key histological structures essential for diagnosing coeliac disease: crypts, villi, intraepithelial lymphocytes (IELs), and enterocytes. The model accurately estimates the IEL-to-enterocyte ratio and the villus-to-crypt ratio, the latter being an indicator of villous atrophy and crypt hyperplasia, thereby providing objective, reproducible metrics for diagnosis. The segmentation outputs allow for transparent, explainable decision-making, supporting pathologists in coeliac disease diagnosis with improved accuracy and confidence. This study presents an AI model that automates the estimation of the IEL-to-enterocyte ratio--a labour-intensive task currently performed manually by pathologists in limited biopsy regions. By minimising diagnostic variability and alleviating time constraints for pathologists, the model provides an efficient and practical solution to streamline the diagnostic workflow. Tested on an independent dataset from a previously unseen source, the model demonstrates explainability and generalizability, enhancing trust and encouraging adoption in routine clinical practice. Furthermore, this approach could set a new standard for AI-assisted duodenal biopsy evaluation, paving the way for the development of interpretable AI tools in pathology to address the critical challenges of limited pathologist availability and diagnostic inconsistencies.