European Journal of Nuclear Medicine and Molecular Imaging

Targeted alpha therapy with 225Ac is a promising modality for cancer treatment, as shown in recent preclinical and clinical studies. Preclinical imaging of 225Ac is essential to fully understand the biokinetics of developmental 225Ac radiopharmaceuticals. Although single photon emission computed tomography (SPECT) is typically used to achieve this in vivo, conventional preclinical scanners struggle to provide high contrast and accurate quantification for 225Ac imaging due to the very low injected activities (below 1 kBq/g), the unfavorable gamma-ray branching ratios (<26%), and its complex decay chain with several gamma-ray emissions. In this work, we employ a compact CZT-based Compton camera to achieve quantitative tomography of 225Ac daughters in live mice at 18.5 kBq (0.5 Ci) injected activities. Three mice bearing 22Rv1 prostate cancer subcutaneous xenografts were scanned in a single bed position for 30 minutes following intravenous administration of 18.5 kBq of the Anti-CD46 225Ac-Macropa-PEG4-YS5 conjugate. We successfully visualized the in vivo biodistributions of the 225Ac daughters 221Fr and 213Bi, distinguishing between the tumor xenograft and the central organs. Quantification of the tumor uptake from the images revealed an activity as low as 1.1 kBq. In vivo activity quantification computed from the images was compared to ex vivo biodistribution measurements using a gamma counter after harvesting the tissue, showing strong agreement.

The lack of non-invasive, quantitative methods to track the location and proliferation of transplanted cells hampers the clinical translation of cardiac regenerative therapies. Here, we establish a PET reporter for tracking human induced pluripotent stem cell (hiPSC)-derived cardiac cells across complementary two-dimensional (2D) and three-dimensional (3D) cardiac tissue models. Methods hiPSCs were genetically engineered using CRISPR-Cas9 to express a human-derived, anticalin-based, bioorthogonal PET reporter (DTPA-R) that specifically binds radiolabeled DTPA-metal complexes. The engineered hiPSCs were differentiated into ventricular progenitors (HVPs) and cardiomyocytes (CMs). Stability of reporter gene expression and its effects on cellular differentiation and function were evaluated. Uptake of [18F]F-DTPA*Tb by DTPA-R-positive and -negative HVPs was analyzed in vitro, and DTPA-R HVPs were evaluated in ex vivo cultured porcine myocardial slices with and without radiofrequency ablation (RFA) injury. Phantom studies were performed to evaluate PET detectability under clinically relevant large-animal and human imaging conditions and to assess how activity distribution affects signal detection in relation to DTPA-R cardiac cell-equivalent numbers. Results DTPA-R expression was maintained during the differentiation stages of hiPSC-derived cardiac cells and had no measurable impact on migration, differentiation, and functional integration. PET imaging enabled the specific detection of DTPA-R HVPs in intact and injured myocardial slices. At the initial phantom scan, the measured activity corresponded to cell-equivalent numbers of approximately 3.2 x 10^6 HVPs or 5.5 x 10^6 CMs. The compact 200 uL source remained clearly detectable at 6 h, whereas the more dispersed 1,000 uL source progressively approached background activity, indicating that PET detectability depended strongly on local activity concentration and distribution. Conclusion This study establishes a bioorthogonal PET reporter gene platform for quantitative imaging of hiPSC-derived cardiac cells, with no measurable adverse effects on the biological parameters examined, and provides a methodological foundation for future translational and in vivo imaging studies. The approach may also be applicable to other hiPSC-derived cell products beyond cardiac cell lineages.

INTRODUCTION: We report the development and validation of a novel computational tool to cross-convert plasma pTau217 results from different assays to facilitate recruitment for therapeutic trials and observational studies. METHODS: Participants underwent A{beta} PET imaging and plasma pTau217 measurement on up to three assay platforms across three cohorts. We then developed conversion equations using Deming regression -- collectively named Biomarker Convert -- and evaluated robustness using independent research cohorts. Finally, we modeled the potential impact of pTau217 screening on trial enrollment. RESULTS: While absolute plasma pTau217 concentrations differed, the assays had similar predictive characteristics for A{beta} positivity (LC-MS/MS [C2N], AUC=0.952; Janssen Simoa, AUC=0.946; Lumipulse, AUC=0.929). Biomarker Convert enabled transformation of plasma pTau217 data across assays independent of A{beta} PET and clinical status. Plasma pTau217 reduced A{beta} PET scans in the model screening paradigm by >60%. DISCUSSION: Biomarker Convert equations enable harmonization of plasma pTau217 values from immunoassay and mass spectrometry platforms, supporting direct data comparison.

Amyloid-targeting therapies (ATTs) substantially reduce amyloid PET signal and slow clinical decline in early symptomatic Alzheimer disease. As ATTs enter clinical practice, PET may assess target engagement and guide treatment management, but there is no consensus on how to measure treatment-related amyloid clearance (TRAC). We compared Centiloid quantification across pipelines and visual reads for interpreting pre- and post-ATT amyloid PET. Methods: We analyzed 18F-florbetapir or 18F-florbetaben PET from 101 patients treated with lecanemab or donanemab: 88 pre-ATT and 54 post-ATT scans, including 41 paired scans. Centiloids were derived using FDA-cleared MIMneuro and two research pipelines (rPOP, CapAIBL) and compared using intraclass correlation coefficients (ICC). Clearance rates were estimated using linear mixed-effects models. Full TRAC was defined on post-ATT scans quantitatively (<11 Centiloids) or based on visual interpretation by three expert readers. Results: Treated patients were 74+/-7 years old; 57% were female, 59% were apolipoprotein E4 carriers, and 78% had mild cognitive impairment (22% mild dementia). Centiloid agreement between pipelines was equally high pre- and post-ATT (both ICCs=0.90). Among paired scans (mean treatment duration, 16.7 +/- 4.6 months), clearance estimates were similar across pipelines and greater with donanemab (n=15 patients; between -62 and -60 Centiloids/year depending on the pipeline) than lecanemab (n=26; -38 to -36 Centiloids/year; all Ps<0.005). Among 54 post-ATT scans, between 44% and 57% were <11 Centiloids depending on pipeline, with 20 (37%) meeting this criterion across all three pipelines. In contrast, only 11% to 19% were visually negative across readers, with 4 (7%) unanimously read as negative. Of the 20 post-ATT scans <11 Centiloids across all pipelines, 13 (65%) were considered positive by all readers, commonly with residual occipital binding. Conclusion: Centiloid quantification was robust across pipelines before and after ATT initiation, yielding clearance rates consistent with clinical trials. However, quantitative and visual assessments were markedly discordant after ATT, with an unusually high proportion of scans <11 Centiloids remaining visually positive, warranting cautious interpretation of post-ATT Centiloid values. Larger studies should assess the clinical relevance of these findings and further develop robust approaches to establish full TRAC.

To compare virtual noncontrast (VNC) error between deep-silicon photon-counting CT (PCCT) and dual-energy energy-integrating detector CT (EID-CT) in a matched phantom and characterize tissue-specific error in humans. This prospective exploratory study included a matched phantom experiment and pilot human evaluation. Prototype projection-domain water-density maps served as the VNC surrogate. Twenty-four vials containing 0.1--20 mg I/mL in deionized water or simulated blood were scanned with PCCT and EID-CT. Three men underwent true noncontrast and contrast-enhanced PCCT; 80 matched measurements were analyzed. Paired t tests and mixed-effects models assessed system differences and iodine associations. Three participants (mean age, 67 {+/-} 10 years; all men) were included. Mean VNC error was 29.1 HU lower with PCCT than EID-CT (95% CI, 15.5--42.7 HU; {rho} < 0.001). Error was not associated with iodine concentration for PCCT (slope, 0.03 HU per mg I/mL; 95% CI, -0.92 to 0.98; {rho} = 0.95) but increased for EID-CT (slope, 3.51 HU per mg I/mL; 95% CI, 2.56--4.46 HU; {rho} < 0.001). The slopes differed by 3.48 HU per mg I/mL ({rho} < 0.001). In humans, errors in five non-blood-pool tissues were within 5 HU. Error was not associated with iodine concentration among 61 non-blood-pool measurements (adjusted slope, -0.17 HU per mg I/mL; 95% CI, -1.22 to 0.88; {rho} = 0.75). PCCT provided VNC measurements with errors closer to zero and less dependent on iodine than EID-CT in the phantom. In humans, error was generally small in non-blood-pool tissues and was not associated with iodine concentration.

To evaluate whether eight-bin deep-silicon photon-counting CT can quantify iodine and gadolinium separately and distinguish radiopaque embolic beads from vascular contrast in tumor phantoms. In this phantom study, two-input decomposition of scanner-generated iodine and water images was compared with three- and five-material decomposition of five reconstructed energy-bin images in 56 common test vials, including nine simulated-blood preparations. One tantalum and one bismuth vial were also tested with five-material decomposition. A separately calibrated two-input method using scanner-generated 70-keV and iodine images was applied to four static 3D-printed tumor vascular models: iodine-only and gadolinium-only controls and two models with iodine-containing beads and gadolinium-filled channels. Concentration errors were summarized by bias and root mean square error (RMSE). For iodine, RMSE was 0.40, 0.73, and 1.12 mg I/mL with two-input, three-material, and five-material decomposition, respectively; corresponding gadolinium RMSEs were 0.57, 0.55, and 0.46 mg Gd/mL. Two-input biases were +0.01 mg I/mL and +0.29 mg Gd/mL. Five-material decomposition distinguished tantalum and bismuth in the separate test vials. Beads and vascular gadolinium were spatially separated in the tumor models. In the two bead-containing models, gadolinium measured 32.56 {+/-} 8.02 and 37.02 {+/-} 7.93 mg Gd/mL (mean {+/-} spatial SD), compared with the prepared 35.00 mg Gd/mL. Deep-silicon photon-counting CT separately visualized radiopaque embolic beads and quantified vascular gadolinium from a single acquisition. Recalibrated scanner-generated images supported quantitative dual-contrast imaging, while research energy-bin decomposition distinguished additional materials.

Nanoparticle albumin-bound paclitaxel (Abraxane) was among the first clinically approved nanomedicines and is a first-line chemotherapeutic. It enhances therapeutic efficacy and reduces side effects of paclitaxel. Despite its extensive clinical use, mechanisms of Abraxane's therapeutic effects are the subject of ongoing study. This work posits a new mechanism for Abraxane's effects in lung cancers and metastasis. Tracing the albumin component of Abraxane with radioisotopes or fluorophores, we find that Abraxane homes to the lungs. Abraxane's lung tropism is elevated by inflammation and is eliminated in complement protein C3 knockout mice, showing that complement pathway drives the uptake. Flow cytometry and histology show that neutrophils are the dominant cell type taking up Abraxane in the lungs. We demonstrate Abraxane's tropism to pulmonary neutrophils in mice and in human donor lungs. We note previously undocumented side effects associated with Abraxane's lung tropism, including pro-thrombotic and pro-inflammatory responses in acute lung inflammation. We show that Abraxane's efficacy against lung metastases is affected when lung uptake is eliminated by C3 knockout. Our results provide insight into why Abraxane is effective in lung cancer and metastases. These findings may be used to improve outcomes in patients receiving Abraxane and help design next-generation cancer nanomedicines.

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.

Purpose Dynamic [11C]UCB-J PET can simultaneously provide information about synaptic density and relative perfusion in Parkinson's disease (PD). We investigated whether these complementary biomarkers could explain a higher percentage of motor symptom severity variation in PD and inform other PD-specific physiological alterations and symptomatology. Methods Binding potential (BPND) and relative perfusion (R1) images were derived 30 PD and 30 controls imaged with [11C]UCB-J PET. A spatial covariance analysis was used on R1 to derive PDRP-a known metabolic network alteration in PD. Linear models related PD-motor symptom severity to PDRP expression and nigral synaptic losses. Next, whole-brain cluster analyses identified physiological correlates for PD-tremor severity, a prominent motor symptom that does not correlate with dopaminergic losses. Lastly, inter-individual regional correlation alterations in PD were examined for both BPND and R1. Results PDRP was identified from R1, with higher expression in PD (p=1.7x10-5, Cohen's d=1.2), and its expression correlated with MDS-UPDRS III (r=0.57, p=0.0009). Combining PDRP expression scores with synaptic density in the substantia nigra explained 48% of the total variation in MDS-UPDRS Part III, twice as much as previously reported using nigral synaptic density alone. Two significant large clusters (pk<1x10-6) were identified in the occipital-parietal areas where R1 was negatively correlated with tremor severity (r ~ -0.65, p<0.001). Extensive correlation alterations were observed in synaptic density (prominently involving pallidum) and perfusion (prominently involving thalamus). Conclusions Combining synaptic and perfusion outcomes for [11C]UCB-J PET explains more physiological alterations associated with PD.

Deep-silicon photon-counting CT uses a detector architecture distinct from cadmium-telluride systems and has not been evaluated for pediatric head imaging. To compare attenuation-dependent performance of deep-silicon photon-counting CT (dSi-PCCT) with energy-integrating detector CT (EID-CT) and assess spectral and ultra-high-resolution (UHR) reconstructions from one acquisition. Graded-thickness cranial attenuator and pediatric head phantoms were scanned at a matched target volume CT dose index of 18 mGy. dSi-PCCT used 120 kVp and generated 50-, 65-, and 100-keV images; EID-CT used 80 and 100 kVp. We measured CT-number bias, noise, contrast, and contrast-to-noise ratio (CNR) across 0--9 mm of nominal skull thickness. Mixed-effects models assessed imaging-condition effects. We compared matched standard-resolution and UHR temporal-bone images and generated calcium- and water-density maps from the same dSi-PCCT scan. Noise increased by 4.54%, 3.07%, and 1.35% per millimeter at 50, 65, and 100 keV, respectively, versus 8.38% and 7.84% at 80 and 100 kVp. Corresponding CNR decreases were 4.17%, 3.25%, and 1.75% versus 8.94% and 8.54% (interaction {rho} < 0.001). Changes in absolute CT-number bias and contrast did not differ among conditions ({rho} = 0.92 and {rho} = 0.59). In the pediatric head phantom, model-estimated 50-keV CNR ratios were 1.299 (95% CI, 1.163--1.452) versus 80 kVp and 1.424 (95% CI, 1.275--1.592) versus 100 kVp. Across three temporal-bone edges, UHR transition widths were 58.6%-61.9% narrower than standard-resolution widths. dSi-PCCT maintained CNR more consistently as nominal skull thickness increased and provided complementary spectral and UHR pediatric head images from one acquisition.

Background: Artificial intelligence (AI) models for mammography can achieve high diagnostic performance when training and test data originate from similar populations and imaging environments, but performance often deteriorates under cross- cohort domain shift. Differences in acquisition systems, image characteristics, preprocessing, population composition, and disease prevalence can introduce distributional variation that is poorly captured by conventional within- dataset validation. Recent studies have further shown that Foundation Model representations can retain substantial dataset-specific information. However, it remains unclear how image representation influences diagnostic transfer to completely unseen cohorts and whether domain-adversarial learning provides comparable benefits across different representation types. Methods: We systematically evaluated cross-cohort mammography classification using seven independent datasets spanning diverse geographic, institutional, scanner, and imaging environments. Three image representations were compared: handcrafted radiomic features, EfficientNet-B0-derived features, and embeddings from the medical vision-language model MedSigLIP-448. Each was evaluated under three preprocessing conditions: unprocessed images, Otsu-based breast-region extraction, and Otsu extraction combined with Contrast Limited Adaptive Histogram Equalization (CLAHE). Patient-level classification was performed using a mean-pooled multilayer perceptron (MLP) baseline and an attention-based Domain-Adversarial Neural Network (DANN). Generalization was evaluated using a multi-source Leave-One-Domain-Out (LODO) framework in which six datasets were used for model development and the seventh was completely withheld as an unseen test cohort, iteratively across all seven datasets. Performance was primarily assessed by area under the receiver operating characteristic curve (AUC-ROC). Results: Cross-cohort performance depended strongly on image representation and model architecture. MedSigLIP consistently provided the highest performance on unseen cohorts. With MedSigLIP features, mean LODO AUC-ROC increased from 0.598 with MLP to 0.685 with DANN for unprocessed images, from 0.607 to 0.678 after Otsu preprocessing, and from 0.603 to 0.659 after CLAHE+Otsu preprocessing. When DANN-MLP differences were evaluated across held-out datasets and preprocessing conditions, a significant gain was observed for MedSigLIP after correction for multiple comparisons, whereas corresponding gains were not significant for Radiomics or EfficientNet-B0. A mixed-effects analysis similarly showed a positive MedSigLIP X DANN interaction, although this narrowly missed conventional statistical significance. Unprocessed images produced the highest numerical mean performance for MedSigLIP-DANN, but preprocessing itself had no significant overall effect. Notably, linear probing showed strong dataset discriminability in MedSigLIP and EfficientNet representations but little linearly recoverable dataset information in Radiomics, indicating that low dataset separability alone did not predict better diagnostic generalization. Conclusion: Cross-cohort mammography generalization depends strongly on the choice of image representation and its interaction with the classification strategy. MedSigLIP provided the most transferable representation in our multi-source unseen-domain evaluation, and the performance gain associated with DANN was concentrated in MedSigLIP rather than consistently observed across representation types. These findings suggest that the effectiveness of domain-adversarial learning is representation-dependent and that neither conventional preprocessing nor low dataset discriminability alone ensures robust transfer to unseen clinical cohorts.

Background. Automated segmentation of glioma subregions on multiparametric MRI can be improved by changing either the network architecture or the information presented to the network. These two design levers are usually studied together, which makes their individual contributions difficult to see. We present an integrated analysis of two completed, independent studies, each of which isolates one lever. Methods. In the architecture-level study (Slice), SIBA-UNet - a 2D U-Net combining depthwise separable convolution, inverted-bottleneck blocks and attention-guided skip fusion - was trained on 128 x 128 multimodal slices from BraTS 2020 with a patient-level split, and a sigmoid decision threshold was selected on validation data. In the representation-level study (Subtract), CMSR-3D augmented the four standard MRI sequences with four voxel-wise cross-modal subtraction maps (T1-T1CE, T2-FLAIR, FLAIR-T1CE, T2-T1CE) and evaluated 4-, 6- and 8-channel inputs with an identical 3D U-Net on 1,251 BraTS-GLI 2023 cases (1,001/125/125 split). Results. SIBA-UNet reached an overall validation Dice of 0.8601 at a validation-selected threshold of 0.51, with reported subregion Dice of 0.8795 (necrotic core), 0.8501 (edema) and 0.9125 (enhancing tumor); the source ablation was only partially reported. With the architecture held fixed, the 8-channel CMSR-3D configuration gave the highest mean Dice among the evaluated inputs (0.8699 vs. 0.8534 for the 4-channel baseline) and the lowest mean HD95 (5.9859 vs. 8.2308), whereas either two-map subset alone changed mean Dice by at most 0.0037. Conclusions. Architecture design and input-representation design are separable levers for brain tumor segmentation. The controlled representation ablation indicates that the complete set of subtraction maps was more informative than either subset under a fixed 3D U-Net. Both studies were evaluated internally on their respective datasets, and their numerical results are not directly compared because the datasets, dimensionality and evaluation protocols differ.

Digital health technology (DHT) enables to detect and quantify charateristics motor dysfunction in Parkinsons disease (PD). However, the understanding of the relationship between dopaminergic nigrostriatal denervation and quantitative composite digital motor scores (DMS) captured by DHT remains largely limited to date. In this hybrid PET-MRI study, we used simultaneously acquired PET with 11C-d-threo-methylphenidate (11C-dMP-PET) - a tracer of presynaptic dopamine transporters - and resting-state-fMRI (rs-fMRI) to investigate this relationship and to explore associated neural network correlates in PD. Seventeen PD patients and 39 healthy controls underwent 11C-dMP-PET and rs-fMRI. Quantitative composite DMS for arm swing, gait and balance were calculated using DHT-derived parameters. Voxel-based correlation analysis of 11C-dMP-PET binding potential (BPND) was performed for each DMS and resulting clusters were used for seed-based functional connectivity analyses. We found that DMS of arm swing, gait and balance exhibit differential striatal representations associated with dopaminergic denervation as assessed by 11C-dMP-PET. Specifically, lower BPND correlated with more pathologic arm swing DMS in the central putamen and higher balance DMS in the right caudate, whereas the most robust correlation was observed for lower BPND with more pathologic gait DMS in the right caudate and anteromedial putamen. Seed-based functional connectivity analysis revealed lower functional connectivity in a frontostriatal network encompassing the basal ganglia and medial frontal regions associated to pathological gait DMS in PD. Taken together, our study demonstrates striatal representations of key domains of PD motor dysfunction captured by DHT. Highlighting the complex interplay of gait motor control, we show that gait dysfunction is associated with dopaminergic denervation in the anteromedial executive subregion of the striatum and frontostriatal connectivity changes in regions involved in planning and initiation of movement sequences. Therefore, our study provides first preliminary insights into the relationship of dopaminergic degeneration and DMS encouraging further investigation in larger cohorts and longitudinal studies to construct and validate digital motor biomarkers obtained from DHT as outcome measures in clinical trials in PD.

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.

TROP2-targeted antibody-drug conjugates (ADCs) have demonstrated promising clinical activity in triple-negative breast cancer (TNBC) as monotherapies; however, therapeutic benefit varies among patients. Combination strategies pairing TROP2-targeted ADCs with immune checkpoint inhibitors are also being investigated. Elucidating the mechanistic drivers of ADC monotherapy variability and enabling the rational development of combination regimens require computational frameworks that integrate ADC pharmacology with tumor-immune interactions. A quantitative systems pharmacology (QSP) model is presented that incorporates an ADC module into our established immuno-oncology model for TNBC. The module captures ADC and payload pharmacokinetics and pharmacodynamics. TNBC heterogeneity is represented by two tumor cell clones with high and low TROP2 expression, informed by prior characterizations, and differential sensitivity to the ADC payload is incorporated as an intrinsic property of each clone. Although generalizable, the model was applied to the TROP2-targeted ADC sacituzumab govitecan (SG, TRODELVY). A virtual patient cohort was generated using Latin hypercube sampling and calibrated against objective response rate (ORR) data from SG Phase I/II TNBC basket trial. The model predicted an ORR of 33.2% consistent with ASCENT study (NCT02574455). Simulations suggest TROP2-mediated delivery contributes modestly to SG efficacy with tumor exposure driven largely by systemically released SN-38 payload being sufficient to induce cytotoxicity. Tumor heterogeneity emerged as a key determinant of response with ORR increasing as the fraction of payload-sensitive clones increased. Overall, this QSP framework for TROP2-targeted ADCs accounts for TNBC heterogeneity and is extendable to other ADCs and targets enabling interrogation of ADC mechanisms of action in conjunction with tumor-immune interactions.

Therapeutic strategies targeting amyloid-{beta} (A{beta}) in Alzheimer disease (AD) include antibodies directed against fibrillar amyloid plaques or soluble amyloid-{beta} (A{beta}) species, yet how these approaches reshape brain pathology remains poorly understood. Postmortem neuropathological assessment is crucial for evaluating the degree of A{beta} removal, providing information and context that fluid biomarkers and positron emission tomography (PET) alone cannot, and guiding development of more effective disease modifying therapies for AD. In this study, using high resolution mass spectrometry we quantitatively measured A{beta} and tau proteoforms in postmortem brain tissue homogenates to evaluate the impact of long-term treatment with anti-A{beta} monoclonal antibodies gantenerumab or solanezumab in a clinical trial of dominantly inherited Alzheimer disease (DIAD). Symptomatic DIAD participants who had been treated for ~ 4 years with anti-A{beta} monoclonal antibodies (gantenerumab, n=4 or solanezumab, n=4) were compared with untreated, symptomatic DIAD mutation carriers (n=40) and with age-relevant non-DIAD controls with modest or no plaque pathology (n=18). Consistent with antemortem A{beta}-PET findings in the clinical trial, gantenerumab-treated participant samples showed significantly lower levels of specific A{beta} proteoforms (A{beta} 42, A{beta}40) in the insoluble plaque associated pool (range: 60-80 % reduction); they also showed lower levels of post-translationally modified A{beta} proteoform A{beta}3pGlu in both the insoluble and soluble pool, compared to untreated DIAD participants, but without reaching the levels found in non-DIAD controls. Samples from gantenerumab-treated participants also showed regionally lower amounts of insoluble phosphorylated tau species (pT181, pT205, pT208) and MTBR tau species (354-369, 306-317, 299-317) without apparent differences in insoluble total tau levels. In contrast, brain tissue samples from solanezumab-treated participants did not show significant differences overall in soluble or insoluble levels of A{beta} and tau proteoforms in comparison to those from non-treated DIAD individuals. These findings support that amyloid targeting treatment (ATT) with gantenerumab, an antibody that targets fibrillar A{beta}, can remove insoluble A{beta} from the DIAD brain and also mitigate downstream biochemical and pathophysiological changes associated with AD.

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.

Solid tumors are known to be refractory to treatment due to a poorly perfused microenvironment that blocks immune cell infiltration and effective drug delivery. Histotripsy is a non-invasive focused ultrasound modality that generates cavitation microbubbles to mechanically disrupt solid tumors, resulting in decreased hypoxia and stimulation of tumor-targeted adaptive immune responses. In this study, we aim to investigate whether post-histotripsy hypoxia reduction and cytotoxic immune cell infiltration are associated with normalization of tumor vasculature and enhanced perfusion. Structural analysis of both treated and contralateral off-target tumors following partial histotripsy revealed significant vascular remodeling, enhanced vascular integrity, and decreased vascular leakage indicative of vascular normalization in multiple in vivo models of melanoma, hepatocellular carcinoma, and pancreatic adenocarcinoma. These effects appeared antigen-specific, as no remodeling was observed in contralateral off-target tumors of discordant pathology. Mechanistically, CXCR3+CD8+ T cells played a role in mediating these vascular changes, as genetic knockout of CD8 and pharmacological antagonism of CXCR3 impaired both remodeling and normalization. Additionally, significant changes in endothelial CXCR4 and Angiopoietin-1 and -2 expressions were observed following histotripsy, indicating their potential involvement in promoting vascular normalization and remodeling. Significant vascular remodeling was accompanied by enhanced tumor perfusion and intratumoral delivery of chemotherapeutic agents and therapeutic monoclonal antibodies. These findings suggest that histotripsy not only creates a more favorable microenvironment for immunotherapy, but also optimizes drug delivery into solid tumors. Combinatorial approaches using histotripsy with chemotherapy or immunotherapy may be a novel therapeutic strategy to overcome current limitations of conventional solid tumor therapies.

Abstract: Purpose: Superparamagnetic iron oxide nanoparticles (SPION) are widely used as MRI contrast agents for cell tracking and molecular imaging. However, their detection using conventional T2/T2* weighted and susceptibility-weighted MRI relies on negative contrast (signal voids), which lacks specificity because similar hypointense signals can arise from other sources of magnetic susceptibility. This study introduces Frequency Shift Imaging (FSI), a positive-contrast technique based on an adiabatic Shinnar-Le Roux (SLR) self-refocused pulse that addresses the limited specificity of conventional SPION imaging. Methods: An adiabatic self-refocused RF pulse was designed using the SLR algorithm for frequency-selective positive-contrast imaging. The optimized pulse had a spectral bandwidth of 370 Hz, a peak RF amplitude of 14 T, and a duration of 15 ms. FSI data were acquired on a 7T MRI scanner. Phantom experiments used agarose-embedded 30 nm cell-labeled SPION at 0%, 25%, 75%, and 100% concentrations (pure). In vivo validation was performed following intramuscular SPION injection into the hind limb of a mouse. FSI images were compared with conventional acquisitions. Results: Quantitative phantom analysis showed normalized FSI signal intensities of 25.02% and 75.16% for samples containing 25% and 75% labeled cells, respectively, demonstrating an approximately proportional relationship between FSI signal and labeled-cell concentration. In vivo, FSI generated localized hyperintense positive contrast at the SPIO injection site, providing improved conspicuity and localization compared with the corresponding negative-contrast signal void. Discussion and Conclusion: FSI enables robust, B1-insensitive, positive-contrast SPION imaging at 7T with reduced echo time and SAR compared to paired adiabatic approaches, supporting its potential for quantitative cell tracking and molecular imaging applications.

Background. Nigrostriatal dopaminergic dysfunction, a biological anchor of Parkinson's disease (PD), is routinely assessed using single-photon emission computed tomography of the dopamine transporter (DaT-SPECT). Dopa decarboxylase (DDC) in cerebrospinal fluid (CSF) is a promising biomarker of dopaminergic dysfunction, but its association with DaT-SPECT has to be demonstrated. Objectives. To investigate the association of CSF DDC with DaT-SPECT. Methods. We first analysed proteomic and DaT-SPECT data from the Parkinson's Progression Markers Initiative (PPMI), including PD (n=891), prodromal PD (n=867) and healthy controls (n=161). We then verified this association in a single-center clinical cohort (UNIPG), including PD/Dementia with Lewy Bodies (DLB) (n=78), atypical parkinsonisms (AP, n=11), other neurodegenerative diseases (OND, n=25) and controls (CTRL, n=40). Here, CSF DDC was measured by a novel fully automated quantitative immunoassay. Results. In PPMI, among 129 proteins assessed, DDC showed the strongest association with DaT-SPECT ({beta} = -0.132, FDR = 6.92 x 10-33). Across PPMI and UNIPG cohorts, DDC correlated with striatal binding ratio (SBR) ({rho} = -0.377, P < 0.001; {rho} = -0.466, P < 0.001, respectively) and accurately discriminated positive from negative scans (AUC 0.811, 95% CI 0.762-0.859; AUC 0.870, 95% CI 0.777-0.963, respectively). In PPMI prodromal cases, higher DDC predicted faster motor progression. In UNIPG, DDC distinguished PD/DLB and AP from OND and CTRL groups (AUC 0.830, 95% CI 0.764-0.896), independently of dopaminergic treatment. Conclusion. CSF DDC reflects nigrostriatal dopaminergic dysfunction, representing a potential quantitative biochemical alternative to DaT-SPECT that might be readily implemented via automated immunoassays.