Biomedical Physics & Engineering Express

Background Artificial intelligence computer-aided diagnosis (AI CAD) algorithms for screening mammography have shown promise, but independent head-to-head comparisons of commercial algorithms on large, diverse cohorts remain limited. Methods 786,124 mammography screening exam performed from January 1 2022 to December 31 2023 were identified from electronic medical records. Three FDA-cleared commercial algorithms (A, B, D) and the open-source academic model Mirai (C), were applied to the four standard screening views. Breast cancer within 12 months was ascertained through cancer registry linkage. Algorithms were compared by AUROC; sensitivity, specificity, and PPV at matched AI-positive rates of 5%, 10%, 15%, 20%, 25%; and the proportion of false negatives classified as AI-positive. Results Of 707,922 examinations with complete data, 4,436 were associated with cancer. Radiologists recalled 6.8% of examinations, with a sensitivity of 67.2%. Algorithm D had the highest AUROC (0.849), followed by algorithm B (0.818), Mirai (0.817), and algorithm A (0.815). Algorithm D also had the highest sensitivity at every AI-positive rate, from 55.3% (95% CI: 54.1, 56.6) at 5% to 77.9% (95% CI: 76.9, 79.0) at 25%, with CIs that did not overlap those of the other algorithms. No algorithm matched radiologist sensitivity at AI-positive rates of 10% or less. Algorithm B flagged a proportion of radiologist false negatives similar to that of algorithm D from 5% through 20% (20.8% vs 20.5% at 5%), despite its lower standalone sensitivity. Conclusions AI CAD algorithms differed meaningfully in performance on the same large, diverse screening examination cohort, and an open-source academic model performed comparably to two of the three commercial products. Independent local validation at clinically relevant operating points should precede adoption of mammography AI, and algorithm choice and threshold should be matched to the intended workflow.

Accurate breast lesion segmentation in ultrasound is difficult because speckle noise, heterogeneous lesion appearance, and poorly defined margins can obscure clinically relevant boundaries. This work presents UGB-SegNet, an uncertainty-guided breast ultrasound segmentation network that combines four components: multi-scale convolutional block attention (CBAM) at three EfficientNet-B0 encoder stages, a learnable feature pyramid network for adaptive multi-scale fusion, an entropy-guided decoder that routes fine-grained encoder information toward uncertain regions, and a boundary-aware composite loss with deep supervision. The model was evaluated on the public BUSI dataset (647 annotated lesion images) using a stratified 454/64/129 train/validation/test split. On the held-out test set, UGB-SegNet achieved 81.30% Dice, 68.49% IoU, 81.71% sensitivity, 98.14% specificity, and 15.63-pixel HD95. Under identical training conditions it outperformed five directly trained baselines: U-Net, Attention U-Net, SegNet, DeepLabV3+, and TransUNet. A five-fold resampling experiment over the development set with the held-out test set fixed yielded a mean Dice of 76.36% +/- 1.66% (95% CI 74.90-77.81). Reported ablations attribute gains of +2.06 Dice points to multi-scale CBAM, +1.52 to learnable FPN fusion, +1.22 to the uncertainty-guided decoder, and +1.24 to boundary-aware training. UGB-SegNet contains 14.00M parameters and runs at 62.4 FPS at 224 x 224 resolution on the tested NVIDIA GPU, providing a compact accuracy-efficiency trade-off for future prospective clinical validation.

Head and neck radiation oncology, with its complex locality and epidemiology, requires various imaging techniques to achieve sufficient visualization. Through collaboration with medical physicists, radiation therapists, radiation oncologists, and neuroradiologists, our group clinically implemented a standard set of magnetic resonance imaging (MRI) sequences on an MR-simulation scanner for three anatomical regions: skull base, oral cavity, and larynx/pharynx. A total of 41 patients with head and neck cancer and 3 volunteers were scanned on either a 1.5T or 3T MR-simulation scanner. Sequences described include Dixon-based approaches, SPAIR-based approaches, short T2-based approaches, and quantitative imaging methods. We describe and outline the steps to optimize these notable MRI sequences to aid in successful implementation in a radiation oncology clinic.

Background and Objective: Micro-ultrasound (MicroUS) is an emerging high-resolution imaging modality for prostate cancer diagnosis and real-time biopsy guidance, but its automated analysis is limited by labor-intensive expert annotation. We aimed to develop and evaluate a foundation model for data-efficient MicroUS image analysis. Methods: We developed muFM, a MicroUS-specific foundation model pretrained using DINOv2-style self-supervised learning on 1,736,391 unlabeled images from 780 patients. The model was fine-tuned and evaluated in three downstream cohorts for prostate capsule segmentation (public, 75 patients), urethra segmentation (institutional, 105 patients), and prostate cancer lesion localization (institutional, 190 patients). Under limited annotation, muFM was compared with randomly initialized models and foundation models pretrained on natural, medical, and ultrasound images. Evaluation used the Dice scores and 95th-percentile Hausdorff distance (HD95) for segmentation and free-response receiver operating characteristic analysis for lesion localization. Key Findings and Limitations: muFM improved performance across all three tasks, with the largest gains under limited annotation. With 10% of training scans, muFM achieved Dice scores of 93.8% for prostate capsule segmentation and 58.4% for urethra segmentation, versus 87.9% and 47.0% for the strongest comparators. Corresponding HD95 values were 2.0 and 4.3 mm. For lesion localization, muFM outperformed other models with approximately 0.75 sensitivity at two false-positive detections per 3D volume. Limitations include retrospective single-institution pretraining and lack of explicit 3D modeling. Conclusions and Clinical Implications: MicroUS-specific self-supervised pretraining improved data-efficient image analysis across prostate and urethra segmentation, and lesion localization. This approach may reduce annotation requirements for developing automated tools in MicroUS image analysis.

Purpose: To evaluate whether visually assessed high-quality prostate MRI, as defined by the Prostate Imaging Quality (PI-QUAL) scoring system, is sufficient to ensure consistent AI model performance, or whether hidden acquisition-related technical effects introduce variability that cannot be detected through visual inspection. Methods: This retrospective study included 568 bpMRI scans from seven institutions, classified using PI-QUAL v2 into high-quality (scores 2-3) and low-quality (score 1) groups. Quantitative image quality features were extracted using MRQy from T2w and ADC sequences. Three experiments were conducted on high-quality scans: (1) unsupervised clustering to identify hidden technical batch effects; (2) V-Net models (M) for prostate capsule segmentation on T2w using single-cluster and balanced multi-cluster training strategies; and (3) VoxelMorph-based deformable registration (M2) to align ADC maps to T2w images. Performance metrics included DSC, precision, recall, volume ratio, HD95, ASSD, and TRE. Kruskal-Wallis tests assessed cross-cluster variability, Two One-Sided Tests (TOST) evaluated performance equivalence, and bootstrap resampling quantified training efficiency. Results: Clustering identified four clusters among 277 high-quality bpMRI scans. Single-cluster models showed variable performance (DSC=0.41-0.85) with significant batch effects (p<0.001), even with 140 training samples. The balanced multi-cluster model (n=60) eliminated batch effects (p=0.075), achieved equivalent performance (p<0.001), and demonstrated 2.23-fold greater training efficiency despite using 2.3-fold fewer samples. Registration performance also varied significantly across clusters (TRE=0.86-5.47 mm, p<0.001). Conclusion: Acquisition-related batch effects significantly impact AI generalizability in prostate MRI, even among high-quality scans. Quantitative clustering enables detection of technical variations and supports diversity-aware training strategies that achieve equivalent performance with 2.3-fold fewer samples while eliminating batch effects.

Objectives: To develop and validate pixel data analysis (PDA) for reconstructing pixel-level shear-wave velocity (SWV) from saved Holder-Optimized Elastography (HOE) elastograms and to determine whether whole-field subcutaneous heterogeneity provides a quantitative signature of lymphatic obstruction compared with visual high-velocity-area (HVA) counting and sparse manual region-of-interest (ROI) sampling. Materials and Methods: In this cross-sectional study, 110 women after breast cancer surgery underwent lymphoscintigraphy and HOE, a pressure-minimized acquisition framework that preserves conventionally captured SWV information while additionally revealing spatial abnormalities attenuated by handheld acquisition. Reconstruction validation used 200 paired ROIs from 49 PACS-retrieved images spanning nearly the full displayed SWV range; reproducibility was assessed across 96 matched anatomical sites from 6 participants undergoing independently reacquired HOE examinations. PDA, visual HVA counting, and manual ROI sampling were evaluated on the same HOE-acquired elastograms in task-specific common complete-case cohorts. Results: PDA reconstructed machine-reported SWV with near-zero error (mean difference, +0.0009 m/s; mean absolute error, 0.0075 m/s; 95% limits of agreement, -0.0202 to +0.0221 m/s). For subcutaneous variance, interobserver/intraobserver ICCs were 0.718/0.799 for PDA and 0.161/0.560 for manual ROI sampling. In the primary no-versus-partial comparison (n = 93), forearm-pooled PDA subcutaneous variance ratio yielded the highest observed AUC (0.859; 95% CI, 0.772 - 0.947), compared with 0.820 for visual HVA counting and 0.769 for manual ROI sampling. PDA's subcutaneous variance ratio remained a leading feature across all three comparisons (AUC, 0.859 - 0.878), including partial-versus-total grading, where all three frameworks showed high discrimination. Conclusions: PDA accurately reconstructs pixel-level SWV from saved HOE elastograms and enables reproducible whole-field quantification of superficial SWE heterogeneity. Subcutaneous variance ratio was the leading quantitative PDA feature across lymphatic-obstruction comparisons and was particularly informative in partial obstruction. The same approach also enables retrospective quantitative reanalysis of compatible archived elastograms.

Electrocutaneous stimulation provides a means to warn workers of potential hazards. Our previous parameter studies were conducted at rest or under controlled external influences such as vibration, temperature, and humidity, whereas actual work tasks have not yet been addressed. As a step towards greater practical applicability, we conducted two studies in which a circumferential electrocutaneous warning signal was applied to the upper right arm while participants performed a reading task, screw-driving, and polishing. In study 1 (n = 32), the pulse interval was adjusted individually to evoke a vibrating sensation, and the intolerance threshold was determined. In study 2 (n = 29), a fixed pulse interval of 36 ms was applied to all participants, the warning threshold was determined in addition to the intolerance threshold, and muscle twitches were documented both by participant report and by an independent observer. Study 1 demonstrated that electrocutaneous warning during work tasks is feasible, with a median intolerance threshold higher during polishing (16.5 mA) than during reading or screw-driving (14 mA each). In study 2, the fixed parameter setting elicited the intended pulsating or vibrating sensation at rest in 89 % of the participants in both presentations. The median warning threshold was 8 mA and did not differ between the three work tasks, whereas the intolerance threshold was lower during reading (18 mA) than during screw-driving and polishing (20 mA each). Muscle twitches were largely absent at the warning threshold and became frequent towards the intolerance threshold, with a ventral dominance of their location in both studies. An individual parametrization is therefore not required for the majority of users when a binary warning is to be conveyed, and operating the system slightly above the warning threshold provides a usable amplitude range while minimizing muscle twitches. Future work will focus on electrode optimization and on the objective measurement of motor responses.

Accurate hippocampal electric field (E-field) prediction is important for individualized electroconvulsive therapy (ECT) dosing and emerging deep-brain stimulation approaches such as temporal interference. Because hippocampal fields are far weaker than those beneath the stimulation electrodes, they are highly sensitive to tissue interfaces, conductivity contrasts, and numerical discretization, so numerical error can rival the physical uncertainties of anatomy and conductivity. We investigate this numerical component of hippocampal dosimetry using individualized head models from eleven ECT patients. Identical head40 segmentations, comprising 40 tissue compartments including the bilateral hippocampi, were simulated with three independent numerical methods: a charge-based boundary-element fast multipole method (BEM-FMM), a voxel-based hexahedral finite-element solver, and a conformal tetrahedral finite-element solver. For each, resolution was increased systematically and convergence assessed from the average hippocampal E-field magnitude. At the highest attainable resolutions, the three methods agreed closely, differing by 6%-7% in the left hippocampus and 9%-12% in the right. At default or coarse resolutions, however, they disagreed by 28.8% on average and up to 50.3% in the right hippocampus. Numerical convergence should therefore be established before differences are attributed to anatomical or conductivity uncertainty, and independent-solver agreement provides a practical basis for reliable hippocampal dosimetry.

This study presents a systematic investigation of the brain's own microwave emissions in the 0.85-5.0 GHz range, aiming to validate microwave encephalography (MWEG) as a novel tool for cognitive state assessment. Early experiments (2016-2019) using comparisons with background noise, a control phantom, and different antenna types confirmed the endogenous origin of the recorded signals. In a 2021 experiment (118 measurements on one subject, right and left occipital areas), we observed a significant functional hemispheric asymmetry: the number of "substantial spikes" was significantly higher in darkness for the right hemisphere (p<0.001) and higher in light for the left hemisphere (p<0.001). A 2025 group study (three subjects) confirmed the asymmetry (p<0.05 for right, p<0.01 for left). We describe the signal processing pipeline, including spike detection and statistical analysis. Two complementary mathematical models are presented: an energy-based model linking optical input to microwave emission via neural activity modes, and an information-based model interpreting spectral changes in terms of structural pattern overlap with memory engrams. An estimation of the physical dimensions of possible brain resonators (2-22 mm) is provided. The results are discussed in the context of hemispheric specialization and the information commutation theory. This work opens new perspectives for developing low cost, contactless brain monitoring systems.

Black participants remain underrepresented in electroencephalography (EEG) research, partly because current EEG devices work poorly with dense, coily Type 4 Afro-textured hair. Research-grade EEG typically wires electrodes on a cap, and therefore the electrodes often fail to reach the scalp through dense, Afro-textured hair. Protocols that ask Black participants to unbraid or restyle protective hairstyles add burdens that discourage enrollment in research studies. To address this, we designed and tested 3D-printed attachments that fit onto an EEG electrode and anchored it directly in the hair, using the hairstyle itself as the mounting structure. We fabricated three design configurations for natural Afro-textured hair, cornrows/braids, and twists/microlocs. Seventeen healthy adults with Type 4a-4c hair used electrode attachments that matched their hairstyle. During a single visit, EEG was recorded with 16 conventional cap electrodes defined by the international 10-20 system and with 16 electrodes using novel electrode attachments anchored directly to their hair. Participants completed resting and walking trials with each system. Electrode-to-scalp distance and gel volume were measured during setup. Electrode disconnection, impedance, signal quality (i.e. peak alpha power and aperiodic (1/f) component), and comfort level were measured throughout the data collection. Compared with the conventional cap, the novel attachments reduced electrode-to-scalp distance, gel use, and impedance. Twenty percent of the conventional cap electrodes failed to connect to the scalp compared with 0.1% of novel electrodes. Impedance rose across trials for the conventional cap, whereas the novel attachments showed a smaller increase and maintained lower impedance throughout. Signal quality was significantly better using the novel attachments, while comfort level did not differ between systems. In conclusion, hair-anchored electrode attachment may present a feasible alternative to conventional cap-based EEG for people with Type 4 hair, yielding more usable channels and better signal quality, and supporting more inclusive EEG acquisition.

Significance: Fluorescence imaging remains largely qualitative and device-specific, limiting reproducibility and intersystem comparisons. SI-traceable radiometric characterization requires calibrated sources suitable for transferring known radiance to imaging systems. Aim: Establish and evaluate a compact, calibrated solid-state radiometric emitter target (RET) for SI-traceable radiance transfer, and assess methods for evaluating radiance invariance, imager responsivity, and extension of radiance transfer to digitally defined regions of interest (ROIs). Approach: The radiance invariance of an LED-based RET was evaluated by measuring its radiance across varying source-to-aperture distances and collection apertures. A previously described radiance-transfer formalism based on calibrated source radiance and collection geometry was implemented to determine imager responsivity (R{lambda}), which was evaluated across varying working distances and lens apertures. Grubbs and MAD outlier tests and one-way ANOVA were used to assess radiance and responsivity invariance. Finally, responsivity obtained using digitally defined ROIs was compared with matched physical apertures to assess ROI-based radiance transfer. Results: The measured RET radiance remained invariant across the tested source-to-aperture distances and collection apertures, with no significant differences observed across distance (p = 0.88) or aperture (p = 0.15). Imager responsivity remained stable across working distances for both tested lens configurations (p = 0.48 and p > 0.99). Responsivity was also invariant across lens apertures, except for deviations at f/11 that were consistent with practical aperture tolerances. Digitally defined ROIs reproduced responsivity values obtained using matched physical apertures, demonstrating that the radiance-transfer approach can be extended to image-defined regions. Conclusions: The presented methods provide an approach for evaluating compact LED-based sources for invariant-radiance emission and suitability for SI-traceable radiance transfer. The characterized RET supported stable imager responsivity across changes in imaging geometry and extension to digitally defined ROIs. These methods can be adapted to other LED-based sources, with further angular characterization and uncertainty analysis needed to establish broader applicability.

Objectives: Intraoperative ultrasound (IOUS) is an increasingly valuable real-time imaging modality in neurosurgery, enabling visualization of brain parenchyma, tumor margins, ventricular anatomy, and vascular structures during cranial procedures. Despite its clinical relevance, the development of artificial intelligence (AI)-assisted interpretation tools for neurosurgical IOUS has been limited by the scarcity of large, annotated datasets. Manual pixel-level annotation of ultrasound video frames is labor-intensive, requires specialized neuroanatomical expertise, and scales poorly. To address this bottleneck, we developed a proof-of-concept interactive semi-automated annotation pipeline designed to explore the feasibility of scalable, expert-guided labeling for intraoperative neurosurgical IOUS. Methods: We implemented the system as a napari-based graphical interface coupled with a scikit-learn Random Forest (RF) pixel classifier. Real intraoperative neurosurgical ultrasound videos from five craniotomy cases were used as training material. A neurosurgical expert annotated a minimal set of representative frames using brush-based labeling to define anatomical classes of interest. For each labeled pixel, a feature vector was computed comprising raw intensity, multi-scale Gaussian filtering ({sigma} = 1, 2, 3), Laplacian of Gaussian, and Sobel edge magnitude. The RF classifier was trained on accumulated annotations using class-balanced weighting and out-of-bag (OOB) validation, then applied across full video sequences. An incremental accumulator module enabled multi-case training. Segmentation outputs were exported as TIFF overlays for expert quality review. Results: Across five intraoperative cases, sparse expert annotation of a small number of representative frames per case was sufficient to train case-specific Random Forest classifiers that propagated labels across the full video sequences. Out-of-bag estimation served as an internal coherence check during training, and the classifier additionally produced per-class probability maps that exposed regions of uncertainty at acoustic boundaries. On expert qualitative review, the auto-segmented frames were anatomically coherent, with the classifier reliably distinguishing anechoic fluid compartments from echogenic tissue interfaces; residual misclassification was concentrated in low-contrast boundary regions. Conclusions: This proof-of-concept study demonstrates that semi-automated, expert-guided annotation of intraoperative neurosurgical ultrasound is technically feasible using classical machine learning. By combining neurosurgical domain expertise with an interactive, incremental annotation workflow applied to real intraoperative acquisitions, this framework represents a foundational step toward scalable generation of labeled datasets for neurosurgical IOUS. Future directions include formal multi-rater validation, expanded multi-class anatomical labeling, and eventual migration to deep learning architectures as annotated datasets grow.

Trans-arterial radioembolization using yttrium-90 (Y-90) microspheres treats unresectable liver cancer with radiation. The tumor-to-parenchyma dose ratio is mainly governed by the patient-specific hepatic arterial flow distribution, which transports the microspheres. Even though computational fluid dynamics (CFD) simulation can predict this flow distribution, its high computational cost and time limit intraprocedural application. To overcome this computational limitation, a physics-informed neural network (PINN) surrogate was developed to provide rapid predictions of flow distribution in a patient-specific hepatic artery model. The surrogate maps spatial coordinates to the velocity vector U with Cartesian components u, v, w and the static pressure p. Reference velocity and pressure fields came from a converged finite-element CFD solve of the same geometry at about 1.05 x 105 nodes; a controlled fraction was used to supervise the training while the remainder was held out for validation. Using the NVIDIA PhysicsNeMo framework, we compared four coordinate networks (MLP, Modified Fourier, Multi-Scale Fourier and SIREN) under soft and hard inlet enforcement across a supervised data sweep (data fraction df = 0 to 0.20) and repeated df = 0.20 at a matched 300,000-step budget. Predicted flow splits were propagated through an established Y-90 dose-kernel pipeline. Accuracy is governed primarily by the supervised-data fraction, not the architecture: at df [&ge;] 0.10 the backbones agree to within one to two R2 points, and R2(u) rises from {approx} 0 under pure physics (df = 0) to {approx} 0.95 by df = 0.01 for the best backbones, saturating near 0.97 to 0.99 by df = 0.20. At a matched 300,000-step budget the Multi-Scale Fourier network leads (R2(u) = 0.978) ahead of SIREN (R2(u) = 0.976) and the MLP (R2(u) = 0.968); the best value seen (R2(u) = 0.985, Modified Fourier) needed 1.8 times that budget. On the df = 0.20 checkpoint the per-outlet flow split matches CFD (r = 0.999, L1 = 4.4 percentage points) and the derived dosimetry agrees to within 2.4% for perfused volume, mean dose and every isodose volume. At df = 0 training collapses toward the trivial near-zero state, which satisfies continuity, momentum and the zero-pressure outlets exactly and is ruled out only by the inlet and data terms; this, not architecture choice, is the limiting failure mode. Once trained, the network evaluates the full 105,284-node field in about 2 s on a four-core CPU. The supervised-data fraction, with a practical floor between df = 0.05 and 0.10, is the primary design lever for such hepatic-arterial PINN surrogates.

Introduction: Resection margins in oral squamous cell carcinoma (OSCC) are frequently inadequate. This study investigated the accuracy and diagnostic performance of ex-vivo 7T MRI against histology. Methods: Fifty OSCC specimens underwent ex-vivo 7T MRI. Tumours were segmented on corresponding histology and twice on MRI: blinded to histology (radiologist segmentation; clinical practice) and using histology (histology-MR segmentation; MR-based ground truth). Resection margins were measured in five regions and compared with histology. Results: Anterior and posterior margins were generally smaller on MRI than on histology (maximum mean difference -2.5 mm), whereas cranial, caudal and deep margins were generally larger on MRI (mean differences <1 mm). For close margins (<5 mm), sensitivity was 95% for histology-MR segmentations and 91% for radiologist segmentations, with 75% specificity for both. Conclusion: Ex-vivo 7T MRI accurately measures resection margins and detects close margins with high sensitivity. A future clinical trial will determine whether intraoperative implementation reduces inadequate margins and adjuvant treatment.

Background: Accurate patient understanding of lung cancer screening (LCS) results is critical for engagement and follow-up adherence, given the currently low screening uptake. While artificial intelligence (AI) systems show promise in facilitating patient communication, its effective use in clinical settings requires appropriate invocation of imaging tools and self-regulation by deferring certain questions to physicians. To this end, we developed and evaluated a planner-executor style multimodal agentic system to answer simulated patient questions about LCS CT results. Methods: This retrospective study utilized 116 LCS CT reports and images collected from a tertiary academic hospital. The system employed a multi-agent architecture (planner and executor) to mimic clinical reasoning. We developed a large language model (LLM)-based question generation framework to prepare a comprehensive set of 699 simulated patient questions, categorized as answerable and defer-to-doctor to assess self-regulation. Performance was evaluated on tool-calling, self-regulation accuracy (ability to correctly defer out-of-scope questions to a human provider) assessed by LLM-as-a-judge, and clinical quality assessed by a reader performance study with four physicians evaluating a subset of 100 responses. Results: The system demonstrated high overall tool-calling accuracy of 93.1% (646/694; 95% CI: 91.2%, 95.0%) and self-regulation accuracy of 92.6% (462/499; 90.2%, 94.8%). For answerable questions, the percentage of responses receiving perfect 5.0 scores across all readers included 78.9% (180/228) for clinical accuracy (inter-rater agreement: Gwet's AC2=0.92), and 73.2% (167/228) for patient understandability (0.86). For defer-to-doctor questions, the percentage of responses included 77.3% (133/172) for clinical accuracy (0.80) and 70.3% (121/172) for patient understandability (0.78). Conclusions: The planner-executor style multi-modal agentic system reliably answers patient-specific questions about LCS. Its high tool-calling and self-regulation performance demonstrates its potential as a safe and effective digital communication facilitator in LCS programs.

Numerous studies have been conducted to investigate touch on the hand and forearm, but less is known about the rest of the body, particularly the torso and breasts, which play an important role in affective touch. We conducted psychophysical tests on three skin sites on the torso: upper breast, nipple-areola complex (NAC), and under the breast, as well as on two control sites: palm and forearm. We studied touch pleasantness and intensity by applying gentle caress-like strokes using a soft brush, and measured temperature and tactile detection thresholds. The results show an inverted U-shaped relationship for pleasantness over increasing stroking velocity for all skin sites, although we found some differences between sites, particularly at 30 cm.s-1, where participants found stroking on the NAC to be more intense and women additionally found it to be less pleasant. For temperature detection, we found that the NAC was more sensitive in men. For tactile detection, we mainly observed that the areola was far less sensitive than the other areas. Together, these results provide insights into tactile perception in the torso on a fundamental level, which can be applied to understanding how to regain sensations after mastectomy.

Background: This study compared functional and cognitive trajectories in older women treated for early breast cancer with matched women without cancer. Methods: Data were derived from two Dutch cohorts: the CLIMB study (women aged 75 years and older with operable breast cancer) and the ISCOPE study (community-dwelling women aged 75 years and older without cancer). Breast cancer patients were matched 1:2 to controls on age and baseline functional or cognitive status. Functional status was assessed using the Groningen Activity Restriction Scale (GARS), and cognitive function using the Mini-Mental State Examination (MMSE). Longitudinal changes over 12 months were analyzed using both unadjusted and confounder-adjusted linear mixed models. Results: 168 breast cancer patients and 336 matched breast cancer-free controls were included with a mean age of 80 years. Most breast cancer patients had stage 0-I or II disease and received surgery and/or radiotherapy; 48% received adjuvant endocrine therapy, chemotherapy was rare (<1%). At baseline, both cohorts were comparable in age and functional or cognitive status, although there was a higher comorbidity burden in the control cohort. No significant differences in functional or cognitive decline were observed between the cohorts over 12 months. Conclusions: In older women, early breast cancer and its treatment did not lead to accelerated decline in functional status or cognitive function within the first year compared to matched older women without cancer. These findings suggest that, for relatively fit older patients, breast cancer treatment may not accelerate functional decline in the short term.

Background: Software tools can support MRI diagnosis of focal cortical dysplasias (FCDs) - subtle, surgically-treatable lesions that underlie drug-resistant focal epilepsy (DRFE). MELD-graph, a widely used open-source tool combining surface-based morphometry with a convolutional neural network, was developed using 1.5T and 3T MRI data. 7T MRI is increasingly used for lesion detection in DRFE. This study investigated whether MELD-graph is robust to domain shift at 7T. Methods: We collated paired 3T and 7T T1-weighted MRI, and FLAIR where available, from 23 patients with FCD (6 histologically-verified, 17 identified radiologically) and 39 healthy controls at four epilepsy centres. Manual lesion labels were used as ground truth. MELD-graph was run on 3T images, on 7T images with the default pipeline, and on 7T images with adaptations for 7T MRI (SPM bias-field correction, SynthStrip skull-stripping, sub-millimetric FreeSurfer reconstruction), with and without ComBat harmonisation. FCD detection performance and surface morphometry features were compared. Results: Sensitivity for FCD detection was 65% (15/23) at 3T, 48% (11/23) for 7T default and 52% (12/23) for 7T adapted, with Combat harmonisation. Among radiologically 3T-negative patients, 2/8 were detected at 3T and 3/8 at 7T; and among histologically confirmed FCD, 3/6 at 3T and 1/6 at 7T. Specificity was significantly lower at 7T (default 39%, adapted 45%) than 3T (76%). False-positives predominantly occurred in the basal temporal lobes. ComBat harmonisation improved specificity in all conditions and largely compensated for field-strength dependent differences in surface morphometry features. Adding FLAIR at 7T increased false-positive predictions. Conclusion: Sensitivity is comparable but specificity of MELD-graph is lower at 7T compared to 3T. Adaptations for 7T MRI can reduce segmentation errors but do not significantly improve FCD detection, while harmonisation improves performance. The adapted pipeline is released open-source. 7T MELD-graph outputs may inform radiologist review in challenging cases but require cautious interpretation.

Background: Conventional performance metrics do not indicate whether artificial intelligence (AI)-platforms and radiologists are right or wrong on the same examinations, nor whether their non-agreement is more often beneficial or harmful. Objective: To develop, illustrate, and apply directional metrics quantifying non-agreement between radiologists and AI-platforms against a common reference standard, and to adapt a fragility-based framework for quantifying the statistical robustness of these metrics. Methods: We defined Positive Non-Agreement (PNA, AI-platform correct, radiologist incorrect), Negative Non-Agreement (NNA, radiologist correct, AI-platform incorrect), and Net Non-Agreement (NENA, their difference). Metrics were illustrated in three hypothetical scenarios of 1,000 examinations each and applied to three previously published AI-radiologist studies on pulmonary embolism and mammography. Bootstrap confidence intervals were calculated, and a reclassification fragility index (FI) quantified the minimum case reclassification needed to remove statistical support for NENA's direction. Results: In illustration, NENA was positive in two scenarios and negative in one. In the three published studies, NENA ranged from -4.18% to +10.74% and decreased consistently with increasing radiologist experience (junior residents, +6.27%; consultants, +1.45%), a gradient driven primarily by declining PNA with modest increase in NNA. The reclassification FI was defined for all three illustrative scenarios (range, 0.10%-1.70%) and for six of the seven comparisons from these studies (range, 0.34%-4.31%), but not for the weakest comparison, whose confidence interval for NENA included zero. Conclusions: PNA, NNA, and NENA provide a directional, case-level complement to conventional performance metrics for AI-radiologist non-agreement. The accompanying reclassification FI offers a statistically grounded, generalizable method for quantifying their robustness.

Purpose: Posterior pharyngeal wall (PPW) thickening is a common structural manifestation of treatment-related internal lymphedema in patients with head and neck cancer and can be evaluated using lateral videofluoroscopic images acquired during modified barium swallow (MBS) studies. However, no standardized method currently exists for quantitative measurement. Methods: This two-phase methodological study first conducted a scoping review to characterize existing approaches to PPW thickness measurement and identify the principal factors contributing to variation in PPW thickness measurement. Eight studies were identified with variation observed across these measurement factors. An empirical reliability study was then performed using 107 MBS studies from 93 Head and Neck Cancer patients. Two independent raters annotated 10 anatomical landmarks on pre-swallow and post-swallow resting frames, and a computer-assisted algorithm quantitatively generated seven candidate PPW thickness measurements based on different vertebral reference points and measurement directions. Reliability was assessed intraclass correlation coefficients (ICCs). Results: Inter-rater reliability was highest for the measurement originating from the midpoint of C3 and extending perpendicular to the anterior C3 surface (ICC, 0.78; 95% CI, 0.72-0.83). Among the normalization references, C2-C4 vertebral length demonstrated greater reliability than C3 vertebral length, with an ICC of 0.88 (95% CI, 0.85-0.91). No significant differences were observed between pre-swallow and post-swallow PPW thickness measurements across the evaluated approaches. Conclusions: These findings provide a methodological foundation for more standardized and reliable quantification of PPW thickness, with potential to expand the role of MBS from the functional evaluation of swallowing to include quantitative characterization of pharyngeal structural abnormalities.