Magnetic resonance imaging (MRI) offers multiple sequences that provide complementary views of brain anatomy and pathology. However, real-world datasets often exhibit variability in sequence availability due to clinical and logistical constraints. This variability complicates radiological interpretation and limits the generalizability of machine learning models that depend on a consistent multimodal input. Here, we propose an anatomy-guided, modality-agnostic framework to assess disease-related abnormalities in brain MRI, leveraging structural context to ensure robustness in diverse input configurations. Central to our approach is Region ModalMix (RMM), an augmentation strategy that integrates anatomical priors during training to improve model performance under missing or variable modality conditions. Using the BraTS 2020 dataset (n = 369), our framework outperformed state-of-the-art methods, achieving a 9.68 mm average reduction in 95th percentile Hausdorff Distance (HD95) and a 1.36 percentage point improvement in Dice Similarity Coefficient (DSC) over baselines with only one available modality. To evaluate out-of-distribution generalization, we tested RMM on the MU-Glioma-Post dataset (n = 593), which includes heterogeneous post-operative glioma cases. Despite distribution shifts, RMM maintained strong performance, reducing HD95 by 18.24 mm and improving DSC by 9.54% points in the most severe missing-modality scenario. Our framework is applicable to multimodal neuroimaging pipelines, enabling more generalizable abnormality detection under heterogeneous data availability.

Advances in fetal three-dimensional (3D) ultrasound (US) and magnetic resonance imaging (MRI) have revolutionized the study of fetal brain development, enabling detailed analysis of brain structures and growth. Despite their complementary capabilities, these modalities capture fundamentally different physical signals, potentially leading to systematic differences in image-derived phenotypes (IDPs). Here, we evaluate the agreement of IDPs between US and MRI by comparing the volumes of eight brain structures from 90 subjects derived using deep-learning algorithms from majority same-day imaging (days between scans: mean = 1.2, mode = 0 and max = 4). Excellent agreement (intra-class correlation coefficient, <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>ICC</mi> <mo>></mo> <mn>0.75</mn></mrow> <annotation>$$ ICC>0.75 $$</annotation></semantics> </math> ) was observed for the cerebellum, cavum septum pellucidum, thalamus, white matter and deep grey matter volumes, with significant correlations <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <mfenced><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </mfenced> </mrow> <annotation>$$ \left(p<0.001\right) $$</annotation></semantics> </math> for most structures, except the ventricular system. Bland-Altman analysis revealed some systematic biases: intracranial and cortical plate volumes were larger on US than MRI, by an average of <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mn>35</mn> <mspace></mspace> <msup><mi>cm</mi> <mn>3</mn></msup> </mrow> <annotation>$$ 35\ {\mathrm{cm}}^3 $$</annotation></semantics> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mn>4.1</mn> <mspace></mspace> <msup><mi>cm</mi> <mn>3</mn></msup> </mrow> <annotation>$$ 4.1\ {\mathrm{cm}}^3 $$</annotation></semantics> </math> , respectively. Finally, we found the labels of the brainstem and ventricular system were not comparable between the modalities. These findings highlight the necessity of structure-specific adjustments when interpreting fetal brain IPDs across modalities and underscore the complementary roles of US and MRI in advancing fetal neuroimaging.

In recent years, the application of artificial intelligence (AI) in medical image analysis has drawn increasing attention in clinical studies of gynecologic tumors. This study presents the development and prospects of AI applications to assist in the treatment of gynecological oncology. The Web of Science database was screened for articles published until August 2023. "artificial intelligence," "deep learning," "machine learning," "radiomics," "radiotherapy," "chemoradiotherapy," "neoadjuvant therapy," "immunotherapy," "gynecological malignancy," "cervical carcinoma," "cervical cancer," "ovarian cancer," "endometrial cancer," "vulvar cancer," "Vaginal cancer" were used as keywords. Research articles related to AI-assisted treatment of gynecological cancers were included. A total of 317 articles were retrieved based on the search strategy, and 133 were selected by applying the inclusion and exclusion criteria, including 114 on cervical cancer, 10 on endometrial cancer, and 9 on ovarian cancer. Among the included studies, 44 (33%) focused on prognosis prediction, 24 (18%) on treatment response prediction, 13 (10%) on adverse event prediction, five (4%) on dose distribution prediction, and 47 (35%) on target volume delineation. Target volume delineation and dose prediction were performed using deep Learning methods. For the prediction of treatment response, prognosis, and adverse events, 57 studies (70%) used conventional radiomics methods, 13 (16%) used deep Learning methods, 8 (10%) used spatial-related unconventional radiomics methods, and 3 (4%) used temporal-related unconventional radiomics methods. In cervical and endometrial cancers, target prediction mostly included treatment response, overall survival, recurrence, toxicity undergoing radiotherapy, lymph node metastasis, and dose distribution. For ovarian cancer, the target prediction included platinum sensitivity and postoperative complications. The majority of the studies were single-center, retrospective, and small-scale; 101 studies (76%) had single-center data, 125 studies (94%) were retrospective, and 127 studies (95%) included Less than 500 cases. The application of AI in assisting treatment in gynecological oncology remains limited. Although the results of AI in predicting the response, prognosis, adverse events, and dose distribution in gynecological oncology are superior, it is evident that there is no validation of substantial data from multiple centers for these tasks.

Artificial intelligence (AI) is having an increasing impact on healthcare. In ultrasound-guided regional anesthesia (UGRA), commercially available devices exist that augment traditional grayscale ultrasound imaging by highlighting key sono-anatomical structures in real-time. We review the latest evidence supporting this emerging technology and consider the opportunities and challenges to its widespread deployment. The existing literature is limited and heterogenous, which impedes full appraisal of systems, comparison between devices, and informed adoption. AI-based devices promise to improve clinical practice and training in UGRA, though their impact on patient outcomes and provision of UGRA techniques is unclear at this early stage. Calls for standardization across both UGRA and AI are increasing, with greater clinical leadership required. Emerging AI applications in UGRA warrant further study due to an opaque and fragmented evidence base. Robust and consistent evaluation and reporting of algorithm performance, in a representative clinical context, will expedite discovery and appropriate deployment of AI in UGRA. A clinician-focused approach to the development, evaluation, and implementation of this exciting branch of AI has huge potential to advance the human art of regional anesthesia.

The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multiple myeloma (MM) remains incompletely elucidated. This study aims to validate novel low-dose CT-based methods for quantifying myeloma bone marrow (BM) volume in the appendicular skeleton of MM patients undergoing [¹⁸F]FDG PET/CT. Seventy-two newly diagnosed, transplantation eligible MM patients enrolled in the randomised phase 3 GMMG-HD7 trial underwent whole-body [¹⁸F]FDG PET/CT prior to treatment and after induction therapy with either isatuximab plus lenalidomide, bortezomib, and dexamethasone or lenalidomide, bortezomib, and dexamethasone alone. Two CT-based methods using the Medical Imaging Toolkit (MITK 2.4.0.0, Heidelberg, Germany) were used to quantify BM infiltration in the appendicular skeleton: (1) Manual approach, based on calculation of the highest mean CT value (CTv) within bony canals. (2) Semi-automated approach, based on summation of CT values across the appendicular skeleton to compute cumulative CT values (cCTv). PET/CT data were analyzed visually and via standardized uptake value (SUV) metrics, applying the Italian Myeloma criteria for PET Use (IMPeTUs). Additionally, an AI-based method was used to automatically derive whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) from PET scans. Post-induction, all patients were evaluated for minimal residual disease (MRD) using BM multiparametric flow cytometry. Correlation analyses were performed between imaging data and clinical, histopathological, and cytogenetic parameters, as well as treatment response. Statistical significance was defined as p < 0.05. At baseline, the median CTv (manual) was 26.1 Hounsfield units (HU) and the median cCTv (semi-automated) was 5.5 HU. Both CT-based methods showed weak but significant correlations with disease burden indicators: CTv correlated with BM plasma cell infiltration (r = 0.29; p = 0.02) and β2-microglobulin levels (r = 0.28; p = 0.02), while cCTv correlated with BM plasma cell infiltration (r = 0.25; p = 0.04). Appendicular CT values further demonstrated significant associations with PET-derived parameters. Notably, SUVmax values from the BM of long bones were strongly correlated with both CTv (r = 0.61; p < 0.001) and moderately with cCTv (r = 0.45; p < 0.001). Patients classified as having increased [¹⁸F]FDG uptake in the BM (Deauville Score ≥ 4), according to the IMPeTUs criteria, exhibited significantly higher CTv and cCTv values compared to those with Deauville Score <4 (p = 0.002 for both). AI-based analysis of PET data revealed additional weak-to-moderate significant associations, with MTV correlating with CTv (r = 0.32; p = 0.008) and cCTv (r = 0.45; p < 0.001), and TLG showing correlations with CTv (r = 0.36; p = 0.002) and cCTv (r = 0.46; p < 0.001). Following induction therapy, CT values decreased significantly from baseline (median CTv = -13.8 HU, median cCTv = 5.2 HU; p < 0.001 for both), and CTv significantly correlated with SUVmax values from the BM of long bones (r = 0.59; p < 0.001). In parallel, the incidence of follow-up pathological PET/CT scans, SUV values, Deauville Scores, and AI-derived MTV and TLG values showed a significant reduction after therapy (all p < 0.001). No significant differences in CTv, cCTv, or PET-derived metrics were observed between MRD-positive and MRD-negative patients. Novel CT-based quantification approaches for assessing BM involvement in the appendicular skeleton correlate with key clinical and PET parameters in MM. As low-dose, standardized techniques, they show promise for inclusion in MM imaging protocols, potentially enhancing assessment of disease extent and treatment response.

Amyloid PET/CT is essential for quantifying amyloid-beta (Aβ) deposition in Alzheimer's disease (AD), with the Centiloid (CL) scale standardizing measurements across imaging centers. However, MRI-based CL pipelines face challenges: high cost, contraindications, and patient burden. To address these challenges, we developed a deep learning-based CT parcellation pipeline calibrated to the standard CL scale using CT images from PET/CT scans and evaluated its performance relative to standard pipelines. A total of 306 participants (23 young controls [YCs] and 283 patients) underwent 18 F-florbetaben (FBB) PET/CT and MRI. Based on visual assessment, 207 patients were classified as Aβ-positive and 76 as Aβ-negative. PET images were processed using the CT parcellation pipeline and compared to FreeSurfer (FS) and standard pipelines. Agreement was assessed via regression analyses. Effect size, variance, and ROC analyses were used to compare pipelines and determine the optimal CL threshold relative to visual Aβ assessment. The CT parcellation showed high concordance with the FS and provided reliable CL quantification (R² = 0.99). Both pipelines demonstrated similar variance in YCs and effect sizes between YCs and ADCI. ROC analyses confirmed comparable accuracy and similar CL thresholds, supporting CT parcellation as a viable MRI-free alternative. Our findings indicate that the CT parcellation pipeline achieves a level of accuracy similar to FS in CL quantification, demonstrating its reliability as an MRI-free alternative. In PET/CT, CT and PET are acquired sequentially within the same session on a shared bed and headrest, which helps maintain consistent positioning and adequate spatial alignment, reducing registration errors and supporting more reliable and precise quantification.

Kidney lesion subtype identification is essential for precise diagnosis and personalized treatment planning. However, achieving reliable classification remains challenging due to factors such as inter-patient anatomical variability, incomplete multi-phase CT acquisitions, and ill-defined or overlapping lesion boundaries. In addition, genetic and ethnic morphological variations introduce inconsistent imaging patterns, reducing the generalizability of conventional deep learning models. To address these challenges, we introduce a unified framework called Phase-aware Cross-Scale U-MAMba and Switch Atrous Bifovea EfficientNet B7 (PCU-SABENet), which integrates multi-phase reconstruction, fine-grained lesion segmentation, and robust subtype classification. The PhaseGAN-3D synthesizes missing CT phases using binary mask-guided inter-phase priors, enabling complete four-phase reconstruction even under partial acquisition conditions. The PCU segmentation module combines Contextual Attention Blocks, Cross-Scale Skip Connections, and uncertainty-aware pseudo-labeling to delineate lesion boundaries with high anatomical fidelity. These enhancements help mitigate low contrast and intra-class ambiguity. For classification, SABENet employs Switch Atrous Convolution for multi-scale receptive field adaptation, Hierarchical Tree Pooling for structure-aware abstraction, and Bi-Fovea Self-Attention to emphasize fine lesion cues and global morphology. This configuration is particularly effective in addressing morphological diversity across patient populations. Experimental results show that the proposed model achieves state-of-the-art performance, with 99.3% classification accuracy, 94.8% Dice similarity, 89.3% IoU, 98.8% precision, 99.2% recall, a phase-consistency score of 0.94, and a subtype confidence deviation of 0.08. Moreover, the model generalizes well on external datasets (TCIA) with 98.6% accuracy and maintains efficient computational performance, requiring only 0.138 GFLOPs and 8.2 ms inference time. These outcomes confirm the model's robustness in phase-incomplete settings and its adaptability to diverse patient cohorts. The PCU-SABENet framework sets a new standard in kidney lesion subtype analysis, combining segmentation precision with clinically actionable classification, thus offering a powerful tool for enhancing diagnostic accuracy and decision-making in real-world renal cancer management.

Current measures of adiposity have limitations. Artificial intelligence (AI) models may accurately and efficiently estimate body composition (BC) from routine imaging. To assess the association of AI-derived BC compartments from magnetic resonance imaging (MRI) with cardiometabolic outcomes. Prospective cohort study. UK Biobank (UKB) observational cohort study. 33 432 UKB participants with no history of diabetes, myocardial infarction, or ischemic stroke (mean age, 65.0 years [SD, 7.8]; mean body mass index [BMI], 25.8 kg/m² [SD, 4.2]; 52.8% female) who underwent whole-body MRI. An AI tool was applied to MRI to derive 3-dimensional (3D) BC measures, including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle (SM), and SM fat fraction (SMFF), and then calculate their relative distribution. Sex-stratified associations of these relative compartments with incident diabetes mellitus (DM) and major adverse cardiovascular events (MACE) were assessed using restricted cubic splines. Adipose tissue compartments and SMFF increased and SM decreased with age. After adjustment for age, smoking, and hypertension, greater adiposity and lower SM proportion were associated with higher incidence of DM and MACE after a median follow-up of 4.2 years in sex-stratified analyses; however, after additional adjustment for BMI and waist circumference (WC), only elevated VAT proportions and high SMFF (top fifth percentile in the cohort for each) were associated with increased risk for DM (respective adjusted hazard ratios [aHRs], 2.16 [95% CI, 1.59 to 2.94] and 1.27 [CI, 0.89 to 1.80] in females and 1.84 [CI, 1.48 to 2.27] and 1.84 [CI, 1.43 to 2.37] in males) and MACE (1.37 [CI, 1.00 to 1.88] and 1.72 [CI, 1.23 to 2.41] in females and 1.22 [CI, 0.99 to 1.50] and 1.25 [CI, 0.98 to 1.60] in males). In addition, in males only, those in the bottom fifth percentile of SM proportion had increased risk for DM (aHR for the bottom fifth percentile of the cohort, 1.96 [CI, 1.45 to 2.65]) and MACE (aHR, 1.55 [CI, 1.15 to 2.09]). Results may not be generalizable to non-Whites or people outside the United Kingdom. Artificial intelligence-derived BC proportions were strongly associated with cardiometabolic risk, but after BMI and WC were accounted for, only VAT proportion and SMFF (both sexes) and SM proportion (males only) added prognostic information. None.

Although not included in the eighth edition of the American Joint Committee on Cancer Staging System, there is growing evidence suggesting that imaging-based extranodal extension (iENE) is associated with worse outcomes in HPV-associated oropharyngeal carcinoma (OPC). Key challenges with iENE include the lack of standardized criteria, reliance on radiological expertise, and interreader variability. To develop an artificial intelligence (AI)-driven pipeline for lymph node segmentation and iENE classification using pretreatment computed tomography (CT) scans, and to evaluate its association with oncologic outcomes in HPV-positive OPC. This was a single-center cohort study conducted at a tertiary oncology center in Montreal, Canada, of adult patients with HPV-positive cN+ OPC treated with up-front (chemo)radiotherapy from January 2009 to January 2020. Participants were followed up until January 2024. Data analysis was performed from March 2024 to April 2025. Pretreatment planning CT scans along with lymph node gross tumor volume segmentations performed by expert radiation oncologists were extracted. For lymph node segmentation, an nnU-Net model was developed. For iENE classification, radiomic and deep learning feature extraction methods were compared. iENE classification accuracy was assessed against 2 expert neuroradiologist evaluations using area under the receiver operating characteristic curve (AUC). Subsequently, the association of AI-predicted iENE with oncologic outcomes-ie, overall survival (OS), recurrence-free survival (RFS), distant control (DC), and locoregional control (LRC)-was assessed. Among 397 patients (mean [SD] age, 62.3 [9.1] years; 80 females [20.2%] and 317 males [79.8%]), AI-iENE classification using radiomics achieved an AUC of 0.81. Patients with AI-predicted iENE had worse 3-year OS (83.8% vs 96.8%), RFS (80.7% vs 93.7%), and DC (84.3% vs 97.1%), but similar LRC. AI-iENE had significantly higher Concordance indices than radiologist-assessed iENE for OS (0.64 vs 0.55), RFS (0.67 vs 0.60), and DC (0.79 vs 0.68). In multivariable analysis, AI-iENE remained independently associated with OS (adjusted hazard ratio [aHR], 2.82; 95% CI, 1.21-6.57), RFS (aHR, 4.20; 95% CI, 1.93-9.11), and DC (aHR, 12.33; 95% CI, 4.15-36.67), adjusting for age, tumor category, node category, and number of lymph nodes. This single-center cohort study found that an AI-driven pipeline can successfully automate lymph node segmentation and iENE classification from pretreatment CT scans in HPV-associated OPC. Predicted iENE was independently associated with worse oncologic outcomes. External validation is required to assess generalizability and the potential for implementation in institutions without specialized imaging expertise.

Liver fibrosis represents the accumulation of excessive extracellular matrix caused by sustained hepatic injury. It disrupts normal lobular architecture and function, increasing the chances of cirrhosis and liver failure. Precise staging of fibrosis for early diagnosis and intervention is often invasive, which carries risks and complications. To address this challenge, recent advances in artificial intelligence-based liver segmentation and fibrosis staging offer a non-invasive alternative. As a result, the CARE 2025 Challenge aimed for automated methods to quantify and analyse liver fibrosis in real-world scenarios, using multi-centre, multi-modal, and multi-phase MRI data. This challenge included tasks of precise liver segmentation (LiSeg) and fibrosis staging (LiFS). In this study, we developed an automated pipeline for both tasks across all the provided MRI modalities. This pipeline integrates pseudo-labelling based on multi-modal co-registration, liver segmentation using deep neural networks, and liver fibrosis staging based on shape, textural, appearance, and directional (STAD) features derived from segmentation masks and MRI images. By solely using the released data with limited annotations, our proposed pipeline demonstrated excellent generalisability for all MRI modalities, achieving top-tier performance across all competition subtasks. This approach provides a rapid and reproducible framework for quantitative MRI-based liver fibrosis assessment, supporting early diagnosis and clinical decision-making. Code is available at https://github.com/YangForever/care2025_liver_biodreamer.