Diagnostics

Background: Medical imaging, especially computed tomography and magnetic resonance imaging, is essential in clinical care of patients with renal cell carcinoma (RCC). Artificial intelligence (AI) research into computer-aided diagnosis, staging and treatment planning needs curated and annotated datasets. Across literature, The Cancer Genome Atlas (TCGA) datasets are widely used for model training and validation. However, re-annotation is often necessary due to limited access to public annotations, raising entry barriers and hindering comparison with prior work. Methods: We screened 1915 CT scans from three TCGA-RCC databases and employed a segmentation model to annotate kidney lesion. After a meta-data-based exclusion step, we hosted a reader study with all papillary (n=56), chromophobe (n=27) and 200 randomly selected clear cell RCC cases. Two students quality checked and corrected the data as well as annotated tumors and cysts. Uncertain cases were checked by a board-certified radiologist. Results: After data exclusion and quality control a total of 142 annotated CT scans from 101 patients (26 female, 75 male, mean age 56 years) remained. This includes 95 CTs with clear cell RCC, 29 with papillary RCC and 18 with chromophobe RCC. Images and voxel-level annotations of kidneys and lesions are open sourced at https://zenodo.org/records/19630298. Conclusion: By making the annotations open-source, we encourage accessible and reproducible AI research for renal cell carcinoma. We invite other researchers who have previously annotated any of these cohorts to share their annotations.

BackgroundMicrosatellite stability status determination is important for prognostication and therapeutic decision making in colorectal cancer management, but the conventional methods for this assessment are not readily available, especially in low- and middle-income countries. Deep learning (DL) models have been proposed for addressing this problem; however, potential computational cost due to model complexity and inadequate explainability may limit their adoption in low-resource settings. This study explored the potential of explainable lightweight models for detection of microsatellite instability in colorectal cancer. MethodsDL models were trained using a public dataset of colorectal cancer histology images and then used to classify a set of test images into one of two classes: microsatellite instability or microsatellite stability. The models were compared for efficiency. Gradient-weighted class activation mapping (Grad-CAM) was used to interpret the models decision making. ResultsThe simpler convolutional neural network (CNN) trained from scratch had modest performance (accuracy=0.757, area under receiver-operating characteristic curve [AUROC]=0.840). With an attention mechanism added, these values increased, but specificity and sensitivity reduced. Pretrained models performed better than the ones trained from scratch, and EfficientNet_B0 had the best balance of high performance and low computational requirements (accuracy=0.936, AUROC=0.990, negative predictive value=0.923, specificity=0.953, 4,010,000 trainable parameters, 0.38 gigaFLOPs). However, a simple CNN model with attention mechanism had the best interpretability based on Grad-CAM. ConclusionThis study demonstrated that DL models that are lightweight when compared to previously proposed ones can be useful for colorectal cancer microsatellite instability screening in resource-limited settings while balancing performance and computational efficiency.

Accurate histopathological differentiation between High-Grade Serous Carcinoma (HGSC) and Low-Grade Serous Carcinoma (LGSC) remains a critical yet challenging aspect of ovarian cancer diagnosis due to their similar morphology and different clinical outcomes. This study presents a deep learning framework that uses custom attention mechanisms, including the Convolutional Block Attention Module (CBAM), Squeeze-and-Excitation (SE) blocks, and a Differential Attention module within five CNN architectures for automated binary classification of ovarian cancer subtypes from H&E WSI patches. Although individual models achieved higher accuracy, the ensemble stacking framework with a shallow MLP meta-learner delivered the best overall performance, with a ROC-AUC of 0.9211, an accuracy of 0.85, and F1-scores of 0.84 and 0.85 across both subtypes. These findings demonstrate that attention-guided feature recalibration combined with ensemble stacking provides robust and clinically interpretable discrimination of ovarian carcinoma subtypes.

Focal cortical dysplasias (FCDs) are one of the most common structural causes of drug-resistant epilepsy in children but are frequently subtle and difficult to detect on conventional MRI. Many automated lesion detection methods have therefore been proposed to support neuroradiological assessment. In this study, we externally validated two recently developed deep-learning approaches for FCD detection, MELD Graph and 3D-nnUNet, in a pediatric cohort. In this retrospective single-center study, brain MRI scans of 71 children evaluated for epilepsy were analyzed, including 35 MRI-positive patients with suspected FCD and 36 MRI-negative cases based on the primary radiology reports. Both models were applied to standard 3D T1-weighted and 3D FLAIR images. Detected lesions were reviewed by an experienced pediatric neuroradiologist and classified as true positive, false positive, or false negative. Clinical semiology and EEG findings were additionally evaluated for cases with false-positive detections. At the lesion level, MELD Graph achieved a precision of 0.85 and recall of 0.52, while 3D-nnUNet achieved a precision of 0.91 and recall of 0.48. In the MRI-negative patients, MELD Graph produced more false-positive detections than 3D-nnUNet (0.53 vs. 0.14 false-positive lesions per patient). At the patient level, MELD Graph showed slightly higher sensitivity than 3D-nnUNet (0.63 vs. 0.54), whereas 3D-nnUNet demonstrated markedly higher specificity (0.86 vs. 0.56). Improved FLAIR image quality was associated with trends toward improved model performance. Both models demonstrated high precision but moderate sensitivity, indicating that they are valuable decision-support tools but cannot replace expert neuroradiological evaluation. Optimized MRI acquisition protocols are needed to further improve automated lesion detection in pediatric epilepsy.

BackgroundInvasive lobular breast cancer (ILC) is the most commonly diagnosed special histological subtype of breast cancer (BC). Metastatic ILC (mILC) is less sensitive to FDG-PET imaging and often metastasizes to unusual sites --peritoneum, gastrointestinal (GI) tract, ovaries, urinary tract, and orbit--which may go unrecognized after a long disease-free interval. Some metastatic sites cause nonspecific symptoms, like abdominal/epigastric pain, with numerous published case reports of mILC misdiagnosed as gastric cancer. These atypical BC metastatic sites may lead to late and/or misdiagnosis, thereby delaying effective treatments. ObjectiveWe developed a patient survey to investigate the patient-reported prevalence of delayed diagnosis or misdiagnosis of mILC and their potential impact upon treatment outcomes. MethodsA 45-question survey was developed and piloted with breast cancer researchers, clinical oncologists, and patient advocates. This IRB-approved survey was then distributed to patients with ILC. Analyses including data QC and visualization were conducted in R using descriptive statistics. Incomplete or inconsistent responses were excluded, and summary statistics were stratified by four common mILC sites to highlight subgroup differences. Results525 patient surveys were completed, with 450 patients diagnosed with ILC, and of those 321 diagnosed with mILC. For those with mILC, 33.3% (n=107) were diagnosed with de novo mILC at initial presentation. Of the patients diagnosed with mILC, 32.1% (n=103) presented with other medical conditions at diagnosis. Misdiagnosis was reported by 26.2% (n=84) of patients with mILC, and of these cases, 31% (n=26) had [≥]2 misdiagnoses. The top 5 misdiagnoses were bone-related condition (24.7%), benign breast condition (23.4%), another type of BC (7.8%), diagnostic delay (7.8%), and menopause related (5.2%). 44.5% of patients waited [≥]1 year for an accurate diagnosis. 49 patients were treated for their misdiagnosis, and 6 received incorrect cancer treatments. The most frequently reported contributors to delayed or misdiagnosis were inconclusive imaging, providers lack of ILC knowledge, and initial misdiagnosis. Of the 321 patients with mILC, 138 (42.9%) reported symptoms before diagnosis; the most common were back pain (16.5%), fatigue/malaise (14.9%), GI symptoms (11.8%), bloating (8.4%), and weight loss (8.1%). Although 40% of patients reported having a mammogram at the time of their initial misdiagnosis, ILC was detected in only 20.5% (24/116) of these cases, and mammography detected only 5 (25%) of the 20 de novo mILC cases. Patients reported additional diagnostic testing within 1-3 months of their initial mammogram, includingbiopsy, ultrasound (US), and MRI. 47.9% of patients were in active BC surveillance after curative intent therapy at the time of their mILC diagnosis; however, no statistical difference was seen in time to diagnosis versus those patients not under surveillance. ConclusionOur survey results underscore the urgent need to improve diagnostic strategies for mILC. Addressing delays and diagnostic errors in mILC is critical to optimizing treatment strategies and improving patient outcomes.

Early detection of breast cancer remains essential for improving clinical outcomes, and complementary non-invasive approaches are needed to support existing screening methods, particularly for women with dense breast tissue. We have previously reported plasma lipid biomarker discovery using untargeted high-resolution liquid chromatography tandem mass spectrometry (LC-MS/MS). In this study, we performed biomarker confirmation and developed machine-learning models applied to targeted plasma lipid measurements for the non-invasive detection of early-stage breast cancer across international cohorts with independent external validation. Targeted LC-MS/MS was used to quantify candidate lipid panels in plasma samples from European discovery cohorts (n = 554) and an independent Australian cohort (n = 266) used for external validation. Data-driven feature selection identified a 15-lipid panel with strong performance in European cohorts (AUC >= 0.94). External validation prior to confidence stratification yielded 76% sensitivity, 64% specificity, and an AUC of 0.81 in the Australian validation cohort. Clinical assay development requires iterative panel and model testing to support translational feasibility and performance in the intended-use population. An analytically viable panel, excluding lipids requiring complex and costly synthesis, achieved comparable accuracy with improved assay robustness. Confidence-based analysis showed enhanced performance for predictions made with moderate to high confidence, with sensitivity up to 89% and AUC up to 0.85, suggesting that ongoing research should focus on strategies to enhance diagnostic model confidence. Importantly, model predictions were independent of breast density, tumour size, grade, subtype, and morphology, indicating biological specificity of the lipid signature. These results demonstrate that calibrated machine-learning models applied to plasma lipid biomarkers can support non-invasive breast cancer detection. Expanding training datasets to include greater diversity will further improve performance in the ongoing development of this lipid-based detection approach.

Objective: To demonstrate the proof of principle that machine learning (ML) can be used to quantify Gleason Pattern (GP) 4 on digitized biopsy slides using multiple measurement approaches, allowing direct comparison of their prognostic performance. Methods: We assembled a convenience sample of 726 patients with grade group 2-4 prostate cancer on systematic biopsy who underwent radical prostatectomy between 2014 and 2023. Digitized biopsy slides were analyzed using a machine-learning algorithm (PAIGE-AI) to quantify GP4 using multiple measurement approaches, particularly with respect to how gaps between cancer foci (interfocal stroma) were handled. GP4 extent was quantified using linear measurements or a pixel-based area metric. Discrimination of each GP4 quantification approach, along with Grade Group (GG), was assessed for adverse radical prostatectomy pathology and biochemical recurrence. Results: We identified 15 different quantification approaches and observed differences between their discrimination. The highest discrimination was in the pixel-counting method (AUC 0.648). GP4 quantification outperformed GG for predicting adverse pathology (AUC 0.627 vs 0.608). Amount of GP3 was non-predictive once GP4 was known. These findings were consistent for BCR. Conclusions: We were able to measure slides using 15 distinct measurement approaches and replicated prior findings using ML to quantify GP4. Our findings support the use of ML as a research tool to compare different GP4 quantification approaches. We intend to use our method on larger cohorts to determine with which measurement approach best predicts oncologic outcome.

A multiplex diagnostic test is evaluated for self-reported long COVID associated persistent symptoms and a poor recovery from a SARS-CoV-2 infection. A mass-standardised concentration of total antibodies (AC), high-quality (HQ) antibodies and percentage of HQ antibodies (HQ%) is assessed against a spectrum of spike proteins to the SARS-CoV-2 variants: Wuhan, , {delta}, and the Omicron variants BA.1, BA.2, BA.2.12.1, BA.2.75, BA.5, CH.1.1, BQ.1.1 and XBB.1.5 in three cohorts. A cohort of control patients (n = 46) recovered (CC) and a cohort of self-declared long COVID patients (n = 113) (LCC). A nested Receiver Operating Characteristic (ROC) analysis, performed for the variant with lowest HQ concentration in the spectrum, produced an area under the curve and AUC = 0.61 (0.53-0.70) for the CC vs LCC cohorts. For the LCC cohort, the cut-off thresholds for AC = 0.8 mg/L, HQ = 1.5 mg/L and HQ% of 34% were determined, leading to a 71% sensitivity and 66% specificity derived by the Youden metric. The cohorts may be fully classified based on ROC and outlier analysis to give an incidence of persistent virus 62% (95% CI 52% - 71%), hyperimmune 12% (95% CI 7% - 20%) and unclassified, 26% (95% CI 18% - 35%). The overall diagnostic accuracy for both the hyper and hypo immune is 69%. All clinical interventions can now be tailored for the heterogenous long COVID patient cohort.

Definitive radiotherapy (RT) for prostate cancer (PC) with dose intensification and/or focal boosting has excellent oncologic outcomes, but many patients experience adverse events. Dose escalation to the whole prostate improves outcomes at the expense of increased late adverse events. Intraprostatic recurrence after definitive RT typically occurs at the site of the primary tumor, suggesting that dose to the site of the dominant lesion is an important predictor of future failure. The efficacy and safety of tumor-focused RT compared to that of standard RT for definitive treatment of localized PC has not been assessed. RadTARGET (RAdiation Dose TAiloRing Guided by Enhanced Targeting) is a phase II randomized trial that aims to demonstrate superior safety of image-guided, tumor-focused RT compared to standard RT for acute genitourinary (GU) or gastrointestinal (GI) in the setting of definitive RT for intermediate- and high-risk PC. The study intervention is image-guided, tumor-focused RT with dose intensification of cancer visible on imaging and dose de-intensification to remaining prostate. Patients will be randomized to two arms: those who receive standard RT dose and those that receive tumor-focused RT. The study population will be patients with intermediate- or high-risk PC planning to undergo definitive RT with or without systemic therapy. The primary endpoint to compare between randomized arms is acute GU or GI grade [≥]2 adverse events. Participant and study duration are 5 years and 8 years, respectively. RadTARGET will compare the efficacy and safety of tumor-focused RT to that of standard RT for definitive treatment of localized PC. We hypothesize that the tumor-focused approach will substantially reduce adverse events after prostate RT while retaining high efficacy. If this hypothesis is confirmed, we will conclude that a phase III randomized control trial is warranted to formally establish oncologic non-inferiority compared to the current standard of whole-gland dose escalation.

Background: Radiographic detection of caries lesions adjacent to restorations is challenging due to limitations of two-dimensional imaging and difficulties distinguishing true lesions from restorative or anatomical radiolucencies. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) have been introduced to assist radiographic interpretation; however, different AI tools may yield variable diagnostic outputs, and their comparative performance remains unclear. Objective: To compare the diagnostic performance of commercial and experimental AI algorithms for detecting secondary caries lesions on bitewings. Methods: This cross-sectional diagnostic accuracy study included 200 anonymized bitewings comprising 885 restored tooth surfaces. A consensus group reference standard identified all surfaces with a caries lesion and classified each lesion by type (primary/secondary) and depth (enamel-only/dentin-involved). Five commercial (Second Opinion, CranioCatch, Diagnocat, DIO Inteligencia, and Align X-ray Insights) and three experimental (Mask R-CNN-based and Mask DINO-based) systems were tested. Diagnostic performance was expressed through sensitivity, specificity, and overall accuracy (95% CI). Comparisons used generalized estimating equations, adjusted for clustered data. Results: Specificity was high across all systems (0.957-0.986), confirming accurate recognition of non-carious surfaces, whereas sensitivity was moderate (0.327-0.487), reflecting frequent missed detections of enamel and dentin lesions. Accuracy ranged from 0.882 to 0.917, with no significant differences among models (p >= 0.05). Confounding factors, such as radiographic overlapping, marginal restoration defects, and cervical artifacts, were the main sources of misclassification. Conclusions: AI algorithms, regardless of architecture or commercial status, showed similar diagnostic capabilities and a conservative detection profile, favoring specificity over sensitivity. Improvements in dataset diversity, labeling precision, and explainability may further enhance reliability for secondary caries detection. Clinical Significance: AI-based CDSSs assist clinicians by providing consistent detection. Their high specificity is particularly valuable in minimizing unnecessary invasive treatments (overtreatment), though they should be used as adjuncts rather than a replacement for expert judgment.

Conflicting evidence on scatter correction (SC) methods plagues quantitative myocardial perfusion SPECT (MPI), hindering standardized clinical protocols. This simulation study, utilizing the SIMIND Monte Carlo program and a highly realistic 4D XCAT phantom, systematically evaluates Dual Energy Window (DEW, with k=0.5) and Triple Energy Window (TEW) SC techniques. We uniquely investigate their performance across various photopeak window widths (2, 4, and 6 keV) and novel overlapped/non overlapped configurations specifically for Tc 99m MPI parameters largely unexplored in realistic cardiac models. Images were reconstructed with OSEM under uncorrected (UC), SC, and combined attenuation and scatter corrected (ACSC) conditions. Quantitative analysis focused on signal to noise ratio (SNR), contrast to noise ratio (CNR), defect contrast, and relative noise to background (RNB). Our findings consistently show ACSC's superior performance in CNR, SNR, and defect contrast, confirming its critical role. Interestingly, SC alone reduced noise but compromised defect contrast relative to UC, highlighting a potential trade-off without attenuation correction. Crucially, this study reveals minimal influence of photopeak window width and overlap configuration on image quality, and no significant difference between DEW and TEW across most metrics. These results provide essential evidence for optimizing quantitative MPI protocols, suggesting that for Tc 99m, the choice between DEW and TEW, and specific window settings, may be less critical than ensuring robust attenuation correction.

Accurate segmentation of nasal and paranasal sinus structures from CT scans is critical for surgical planning and treatment evaluation in rhinology. However, the complex anatomical topology and thin-wall boundaries of these structures pose significant challenges for automated segmentation methods. We propose AFS-DSN (Adaptive Frequency-Spatial Dual-Stream Network), a novel deep learning architecture that integrates multi-scale wavelet decomposition with spatial feature learning for binary segmentation of the nasal cavity complex. Our method employs a dual-stream encoder with frequency branch utilizing three wavelet scales (db1, db2, db4) to capture 24 frequency sub-bands, enabling enhanced boundary detection in anatomically challenging regions. Cross-domain attention and adaptive routing mechanisms dynamically fuse spatial and frequency features based on local tissue characteristics. We formulate the task as binary segmentation where all five anatomical structures (maxillary sinus, sphenoid sinus, ethmoid sinus, frontal sinus, and nasal cavity) are treated as a unified foreground region against the background, prioritizing clinical boundary detection over individual structure differentiation. Evaluated on the NasalSeg dataset (130 CT volumes) with a 70/15/15 train/validation/test split, AFS-DSN achieves 94.34% {+/-} 2.30% overall Dice coefficient with statistically significant improvements in thin-wall regions (91.34% vs. 90.57% baseline, p=0.004) and statistically significant improvement in Surface Dice at 1mm tolerance (0.874 vs. 0.868 baseline, p=0.010), demonstrating enhanced boundary precision while maintaining sub-second inference time, making the method suitable for surgical planning applications where sub-millimeter accuracy is clinically relevant. To address concerns regarding model complexity, we further introduce AFS-DSN-Lite, a parameter-efficient variant (27.41M parameters) that achieves comparable performance (94.37% Dice) through depthwise separable convolutions, and validate robustness via 3-fold cross-validation (mean Dice: 94.59% {+/-} 0.31%).

ObjectiveThe purpose of the present study is to describe the survival outcomes of patients with low-grade serous ovarian cancer (LGSOC) in the post-operative setting from a tertiary gynecologic oncology referral centre in Quebec, including evaluation of patient characteristics, clinical outcomes and prognostic factors. MethodsThe study included 25 patients: 1) with a post-surgical histopathologic diagnosis of a low-grade serous tumour of the ovary, 2) underwent primary cytoreductive surgery prior to adjuvant therapy, and 3) for whom clinical data was available. Clinical and demographic features were characterized by descriptive statistics. Clinical endpoints of progression-free survival (PFS) and overall survival (OS) were assessed, utilizing the Kaplan-Meier method for estimating survival probabilities. ResultsThe median age of this cohort was 61 years (range, 26-81). Median OS was 140.6 months in patients with no residual disease (R0), 71 months in patients with microscopic residual disease (R1), and 27.7 months in patients with macroscopic residual disease (R2) (p=.001). Residual disease was also found to significantly impact PFS (p=.008). Administration of adjuvant chemotherapy failed to improve survival outcomes altogether (PFS, p = .270; OS, p = .300). ConclusionsThis study supports the shifting consensus that optimal cytoreductive surgery, where feasible, is paramount for successful treatment of LGSOC. Furthermore, treatment with adjuvant chemotherapy may lead to worse survival outcomes.

BackgroundAcute basilar artery occlusion (BAO) causes devastating strokes. Despite the benefit of endovascular treatment, the optimal management remains sometimes controversial, such as for patients with mild deficits, and would benefit from robust prognostic tools. Given the dense white matter networks within the posterior fossa, we tested whether quantifying disconnections from acute diffusion-weighted imaging (DWI) could improve outcome prediction and responders to recanalization compared with conventional metrics. MethodsWe conducted a secondary analysis from a prospective multicenter stroke registry, including consecutive patients (2017-2024) with BAO and admission MRI. Ultra-high-resolution diffusion MRI was acquired in healthy participants to build normative tractograms with optimized posterior fossa quality. Patient infarcts delineated on DWI were projected onto these tractograms to estimate disconnected fiber volume. The primary outcome was 90-day modified Rankin Scale (mRS) 0-3 vs 4-6. Predictive performance of disconnected fiber volume was compared with baseline NIHSS, infarct volume, and posterior circulation ASPECTS (pc-ASPECTS) using logistic regressions and areas under receiver operating characteristic curves (AUC). Ordinal regressions tested associations across the full mRS spectrum, stratified by recanalization status. Analyses were repeated in patients with NIHSS [&le;]10. ResultsAmong 201 patients (median age 70; NIHSS 10), 97 (48.3%) had poor outcome. Despite small median infarct volume (4.75 mL), disconnected fiber volume was substantial (median 25.15 mL). Disconnected fiber volume achieved an AUC of 0.84, outperforming NIHSS (0.67; p<0.0001), infarct volume (0.75; p=0.00059), and pc-ASPECTS (0.76; p=0.0127). Low disconnected fiber volume predicted better outcomes across the full mRS (OR=0.12 [95% CI, 0.065-0.204]) and greater benefit from successful recanalization (OR=0.33 [95% CI, 0.15-0.70]). In patients with NIHSS [&le;]10 (n=102), disconnected fiber volume remained the strongest predictor (AUC=0.83). ConclusionsDisconnected fiber volume derived indirectly is a robust prognostic marker of BAO outcomes that outperforms conventional predictors and may support future treatment decisions. Registrationhttps://clinicaltrials.gov - NCT03776877.

Multimodal machine learning leverages complementary information from diverse data sources and has shown strong promise in medical imaging, where multimodal data is critical for clinical decision making. In glioma grading, integrating MRI modalities with clinical data can improve diagnostic accuracy, yet systematic comparisons of fusion strategies remain limited. This study evaluates early, intermediate, and late fusion approaches, addressing the question: How does the inclusion of clinical data alongside MRI modalities influence grading performance? To assess modality contributions, we design adaptable fusion layers and employ interpretability techniques, including attention-based analysis. Our results show that incorporating clinical data consistently outperforms unimodal and MRI-only baselines, with intermediate fusion yielding the most reliable gains. Beyond accuracy, the framework reveals how MRI and clinical features jointly shape predictions, underscoring the importance of both fusion design and interpretability for clinical adoption.

Background: Mucormycosis is a rapidly progressive and often fatal invasive fungal infection caused by moulds in the order, Mucorales. Early diagnosis is essential for effective clinical management; however, conventional diagnostic approaches such as culture and histopathology are slow, insensitive, and require specialist mycological expertise. Although molecular methods are available for disease detection, they are not widely accessible. At present, no enzyme immunoassay (EIA) exists for the detection of mucormycosis. Methods: A murine IgG1 monoclonal antibody (mAb), FH12, was generated against extracellular polysaccharides (EPSs) produced by Mucorales pathogens during active growth. The antibody was characterised for specificity, epitope stability, and antigen localisation using ELISA, immunoblotting, and immunofluorescence techniques. The mAb was incorporated into a Sandwich-ELISA and evaluated using culture filtrates, purified EPSs spiked into human serum, and tissue homogenates from a patient with cutaneous mucormycosis caused by Lichtheimia ramosa. Results: mAb FH12 demonstrated pan-Mucorales specificity and no cross-reactivity with other clinically relevant yeasts and moulds. The epitope recognised by FH12 is periodate-insensitive and moderately heat-stable. The Sandwich-ELISA detected EPS antigens in human serum with limits of detection ranging from pg/mL to low ng/mL levels, and successfully identified the EPS biomarker in patient tissue homogenates. Conclusion: The FH12-based Sandwich-ELISA shows high sensitivity and specificity, and has the potential to be used as a laboratory-based adjunct diagnostic test for the detection of mucormycosis in humans.

Backgound: Accurate assessment of diastolic function and left ventricular (LV) filling pressure is central to heart failure diagnosis and risk stratification. Contemporary guideline algorithms rely on complex parameters that are not consistently available in routine clinical practice. Objective: To compare the diagnostic and prognostic performance of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) and 2025 ASE guidelines with a deep learning model based on routinely acquired echocardiographic variables. Methods: This study evaluated the guideline-based algorithms and a deep learning model in participants from the Atherosclerosis Risk in Communities (ARIC) cohort (n=5450) for prognostication and two invasive hemodynamic validation cohorts from the United States (n=83) and Japan (n=130) for detection of elevated left ventricular filling pressure. Results: In the ARIC cohort, the deep learning model demonstrated superior prognostic performance compared with the 2016 and 2025 guidelines (C-index: 0.676 vs. 0.638 and 0.602, respectively; both p<0.001). Similar findings were observed among participants with preserved ejection fraction (C-index: 0.660 vs. 0.628 and 0.590; both p<0.001), with improved performance compared with the H2FPEF score (C-index: 0.660 vs. 0.607; p<0.001). In the US hemodynamic validation cohort, the deep learning model showed higher diagnostic performance than the 2025 guidelines (AUC: 0.879 vs. 0.822; p=0.041) and similar performance compared with the 2016 guidelines (AUC: 0.879 vs. 0.812; p=0.138). In the Japanese hemodynamic validation cohort, the deep learning model outperformed both guidelines (AUC: 0.816 vs. 0.634 and 0.694; both p<0.05). Conclusions: A deep learning model leveraging routinely available echocardiographic parameters demonstrated improved diagnostic and prognostic performance compared with contemporary guideline-based approaches, potentially offering a scalable alternative for assessing diastolic function and left ventricular filling pressures.

Background: Classification of central nervous system (CNS) tumors has become increasingly complex, raising concerns about the sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. Methods: We performed nWGS on DNA extracted from 90 adult CNS tumor samples (58 retrospective, 32 prospective) and compared the results to findings from standard of care (SoC) diagnostic work-up. Analysis was done through an automated workflow that consolidated diagnostically and therapeutically relevant genomic alterations, including copy-number variation, structural, and single-nucleotide variants, chromosomal aberrations, gene fusions, and methylation-based classification. Results: nWGS supported final diagnostic classification in all samples with >15% tumor cell content, requiring ~3 hours of hands-on library preparation, parallel sample processing, and sequencing times within 72 hours. Methylation-based classification was available within 1 hour and was concordant with the integrated final diagnosis in 89% of cases (80/90). All diagnostically relevant copy-number variations, single-nucleotide variants, and gene fusions were concordant with SoC testing. MGMT promoter methylation status matched in 94% of cases. In addition, nWGS identified prognostic and potentially actionable variants that were not reported or covered by SoC. Conclusions: nWGS delivers comprehensive genetic and epigenetic results with a fast turn-around compared to standard methods. This enables efficient, accurate, and scalable molecular diagnostics of CNS tumors using a single platform. This data supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

Machine learning models have seen a lot of success in medical image segmentation domain. However, one of the challenges that they face are confounders or shortcuts: spurious correlations or biases in the training data that affect the resulting models. One example of such confounders for surgical machine learning is the setup of surgical equipment, including tools and lighting. Using the task of identification of safe and dangerous zones of dissection in laparoscopic cholecystectomy images and videos as a use-case, we inspect two equipment-induced biases: the presence of surgical tools in the field of view and the position of lighting. We propose methods for evaluating the severity of these biases and augmentation-based methods for mitigating them. We show that our tool bias mitigations improve the models' consistency under tool movements by 9 percentage points in the most inconsistent cases, and by 4 percentage points on average. Our lighting bias mitigations help reduce fraction of true dangerous zone pixels that may be predicted as safe under light changes from 5% to 1.5%, without compromising segmentation quality.

Introduction Improved diagnostics are needed for people at risk of tuberculosis, especially adolescents. Tongue swab (TS) molecular testing has emerged as a promising strategy for tuberculosis diagnosis. We evaluated diagnostic accuracy and acceptability of Xpert MTB/RIF Ultra (Xpert) using TS samples for tuberculosis detection among adolescents. Methods We conducted a cross-sectional diagnostic accuracy study with consecutive recruitment in Vietnam. Adolescents aged 10-19 who were recommended to undergo investigation for tuberculosis and had not received tuberculosis treatment in the past years were eligible. Participants provided TS and sputum samples and completed a structured survey regarding sampling experiences. TS was tested on Xpert, with sputum tested on Xpert and liquid culture. We utilised a composite reference standard of a positive result on sputum Xpert or sputum culture to define disease status. Sensitivity, specificity, and diagnostic yield were calculated for TS Xpert. Results From July to December 2025, we enrolled 225 adolescents from Can Tho and An Giang provinces in southern Vietnam. Fewer than half (96/225, 43%) the participants exhibited a tuberculosis -like symptom, and the majority (157/225, 70%) were close contacts of a person recently diagnosed with tuberculosis. TS were collected from all adolescents, while 116 (52%) could provide mucopurulent sputum. Tuberculosis prevalence was relatively low (12/225, 5.3%). TS Xpert sensitivity (90% CI) and specificity (90% CI) were 58.3% (35.6, 78.0) and 99.5% (97.9, 99.9), respectively. Diagnostic yield among all diagnosed was 58.3% (7/12). TS sampling was highly acceptable to adolescents; the short time and simplicity of collecting TS were considered favourably. Conclusions The sensitivity and diagnostic yield of TS Xpert was relatively low among adolescents recommended for tuberculosis investigation, which includes asymptomatic individuals who may not provide high quality sputum. Specificity was excellent, and everyone could provide a TS. TS high acceptability indicates it remains a promising sample for diagnostic algorithms.