The combination of nanoparticle contrast agents with advanced image analysis techniques like radiomics offers a powerful new approach for disease detection and characterization. This study presents a novel automated pipeline for segmentation and nano-radiomic analysis of nanoprobe-enhanced images. We demonstrate the effectiveness of this pipeline in an Alzheimer’s disease (AD) mouse model, showing improved detection of amyloid pathology compared to conventional methods. The study used the magnetic resonance imaging (MRI) data collected from double transgenic (TG) AD mice, including 13 mice aged 6–8 month old with lower amyloid burden and 36 mice aged 11–18 month old with higher amyloid burden. Wild type (WT) mice served as controls. Three contrast doses were administered to older mice, while one dose was applied to younger mice. A UNet-based model was trained on mouse brain scans to automatically segment two specific regions, and the results were compared to those from semi-automatic segmentation (i.e. manual brain atlas registration to MRIs with automatic region extraction). 89 radiomic features (RFs) per region were computed, followed by genotype classification using machine learning and sensitivity analyses. Image-based conventional metric (percentage change between pre- and post-contrast MRI intensity signal) was compared to radiomics-based approaches. The UNet-based model with SWIN transformer performed the best, achieving Dice coefficients between 0.80 and 0.95. Non-parametric statistical tests showed no significant difference in segmentation performance based on amyloid burden, nanoparticle contrast, or contrast doses. RF-based classification performance was similar between automatic and semi-automatic approaches. In older mice with good-quality segmentation, the automatic approach outperformed the semi-automatic method across all dose-specific test sets (all 1.0) but not the validation set (0.90 vs. 1.0). The top classifier from automatic approach also required 2x less RFs. In younger mice, classification performance varied with segmentation approach and inclusion of fair-quality segmentation. Yet the top classifiers typically required only one or two RFs. Nano-radiomic analysis of targeted nanoparticle contrast-enhanced MRIs outperformed conventional imaging metrics for early detection of pathological amyloid deposition in an AD mouse model. Automated segmentation achieved performance comparable to the semi-automatic method, while improving efficiency.

The reconstruction of physically valid transport fields from subject-specific imaging data is a fundamental challenge in image-based computational modeling due to measurement noise, modeling uncertainties and discretization errors. Without a methodology to construct models that faithfully reflect the underlying physics, mechanistic understanding of complex biological systems is inherently limited. In this work, we address this challenge in the glymphatic system, the brain's waste-clearance network, where cerebrospinal fluid (CSF) is transported through perivascular spaces into the brain parenchyma to facilitate metabolic waste removal. We introduce a computational framework for the high-fidelity reconstruction of subject-specific glymphatic transport fields from spatiotemporal imaging data. The formulation utilizes an advection-diffusion model with a velocity decomposition that imposes mass conservation, enabling the recovery of solenoidal (divergence-free) velocity fields through the solution of a constrained inverse problem. The system is discretized using immersed isogeometric analysis with quadratic B-spline basis functions, providing smooth, high-continuity solutions and inherent regularization of imaging noise. We demonstrate the framework's utility by using contrast-enhanced magnetic resonance imaging of tracer transport in a mouse brain, obtaining spatially varying estimates of CSF velocity, diffusivity, and clearance parameters. Forward simulations using the recovered fields show close agreement with experimental observations, validating the framework's ability to characterize complex transport dynamics while preserving physical integrity. This approach provides a generalizable methodology for the robust inference of physically consistent transport fields from imperfect imaging data, with broad applicability to the image-guided modeling of biological and engineering systems.

Abstract Despite advances in the treatment of head and neck cancer, squamous cell carcinoma of the oral cavity remains primarily a surgical disease with few effective systemic therapies. Oral cavity tumors harbor both innate and acquired resistance mechanisms to cytotoxic chemotherapy. Additionally, the tumor microenvironment (TME) has limited immune cell infiltration, resulting in low response rates to immune checkpoint inhibitors. Given the need for novel systemic therapies for oral cavity squamous cell carcinoma, we investigated the role of liposomal doxorubicin (Doxil®) as both a cytotoxic agent and an immunomodulatory agent. We utilized the MOC2 syngeneic murine model of oral cavity squamous cell carcinoma, an aggressive tumor model with resistance to immune checkpoint blockade. We demonstrated that Doxil has moderate activity as a single agent in vivo for C57BL/6J mice harboring MOC2 flank tumors. We next performed flow cytometric analysis to characterize the changes in immune cell populations in the TME after treatment with Doxil. We found significantly increased numbers of innate immune cells including NK cells and myeloid cells. Multiplex immunofluorescence was also used to confirm the increase in myeloid cell tumor infiltration upon Doxil treatment. Given the changes seen in the tumor immune microenvironment, we hypothesized Doxil may improve response to immune checkpoint blockade. Therefore, we treated C57BL/6J mice inoculated with MOC2 flank tumors with Doxil alone or in combination with radiation therapy (RT) and/or anti-CTLA-4 therapy. While RT or anti-CTLA-4 alone had modest anti-tumor activity, combining either RT or anti-CTLA-4 with Doxil significantly reduced tumor growth. Moreover, the triple combination therapy of Doxil, RT, and anti-CTLA-4 had an increased effect compared to the dual therapy of Doxil and anti-CTLA-4. This included several complete responses, which resulted in a significant improvement in survival. Triple combination therapy also reduced both local and distant metastatic burden compared to single agent and dual combination therapy. Next, we selectively inhibited CD8+ T cells, myeloid cells, or NK cells to determine which immune cell populations contribute to the immunomodulatory activity of Doxil. We observed that inhibition of NK cells resulted in increased tumor growth as well as decreased survival, suggesting that Doxil-mediated infiltration of NK cells into the TME contributes to response to anti-CTLA-4 therapy. Taken together, these data provide a rationale for combining liposomal doxorubicin with anti-CTLA-4 therapy for the treatment of oral cavity squamous cell carcinoma. Citation Format: Jennifer L Anderson, Fabio H Brasil Da Costa, Allison Nipper, Laxman Devkota, Rohan Bhavane, Ratna Veeramachaneni, Sofia Cortes, Neeraja Dharmaraj, Sarah Latka, Andrew Badachhape, Renuka TR Menon, Prajwal Bhandari, Ketankumar Ghaghada, Simon Young, Ananth Annapragada, Andrew Sikora. Liposomal doxorubicin improves response to immune checkpoint blockade by enhancing innate immunity in a murine model of oral cavity squamous cell carcinoma [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A119.

Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods. Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor sampling, and bone/soft tissue) and used for MRI-based radiomics. These were then combined with clinical features to predict OS clinical outcomes. Results: The mean age of OS patients was 11.8 ± 3.5 years. Most tumors were located in the femur (65%). Osteoblastic subtype was the most common histological classification (79%). The majority of OS patients (79%) did not have evidence of metastasis at diagnosis. Progressive disease occurred in 27% of patients, 59% of patients showed adequate therapy response, 25% experienced relapse after therapy, and 30% died from OS. Classification models based on bone/soft tissue segmentation generally performed the best, with certain clinical features improving performance, especially for therapy response and mortality. The top performing classifier in each outcome achieved 0.94–1.0 validation ROC AUC and 0.63–1.0 testing ROC AUC, while those without radiomic features (RFs) generally performed suboptimally. Conclusions: This study demonstrates the strong predictive capabilities of MRI-based radiomics and multi-region segmentations for predicting clinical outcomes in pediatric OS.

Introduction Placenta accreta spectrum (PAS) occurs when the placenta is pathologically adherent to the myometrium. An intact retroplacental clear space (RPCS) is a marker of normal placentation. In this study, we investigate use of the FDA-approved iron supplement ferumoxytol for contrast-enhanced MRI of the RPCS in mouse models of normal pregnancy and PAS. We then demonstrate the translational potential of this technique in human patients (n=6) presenting with severe PAS (FIGO Grade 3C), moderate PAS (FIGO Grade 1), and no PAS. Methods T1-weighted sequences were used to determine the optimal dose of ferumoxytol in pregnant mice. Pregnant Gab3-/- mice which demonstrate adherent placentation were imaged alongside wild-type (WT) pregnant mice with non-adherent placentation. Fe-MRI was also performed in 6 pregnant subjects using standard T1 and T2 weighted sequences and a 3D magnetic resonance angiography (MRA) sequence. Results Ferumoxytol administered at 5 mg/kg led to strong placental enhancement in Fe-MRI images. Gab3-/- mice demonstrated loss of the hypointense region characteristic of the RPCS relative to WT mice. In human patients, Fe-MRI enabled high uteroplacental vasculature signal and quantification of the volume and signal profile in severe and moderate invasion of the placenta relative to non-PAS cases. Discussion Ferumoxytol, an FDA-approved iron oxide nanoparticle formulation, enabled T1w MRI visualization of abnormal vascularization and loss of uteroplacental interface in a murine model of PAS. The potential of this non-invasive visualization technique was then further demonstrated in human subjects and suggests the possibility of PAS diagnosis using contrast enhanced MRI.

Clinical studies in COVID-19 patients have demonstrated evidence of neuroinflammation in patients infected with SARS-CoV-2. In this pre-clinical work, we investigate neuroinflammation in vivo in a mouse model of Alzheimer’s disease (AD) that exhibits both amyloid and tau pathology, the 3xTg-AD mice crossed with K18 hAce2 and infected with SARS-CoV-2 using a folate receptor-targeted nanoprobe for molecular MRI (mMRI) followed by ex vivo histopathology. In vivo studies with SARS-CoV-2 were performed in an animal biosafety level 3 (ABSL3) facility. 3xTg transgenic mice, a model of amyloid and tau pathology, were bred with k18-hACE2 transgenic mice to generate double transgenic k18-hACE2/3xTg transgenic mice that express hACE2 receptors (critical for viral infection) and develop amyloid and tau pathology. 11–14-month-old transgenic k18-hACE2/3xTg mice (n=6) were infected with 10 3 PFU of SARS-CoV-2 Delta variant and underwent mMRI studies 10 days post-infection. Age-matched non-infected k18-hACE2/3xTg mice (n=6) were used as controls. All mice underwent pre-contrast and post-contrast T1-weighted MRI (T1w-MRI). Post-contrast T1w-MRI was performed 4 days after systemic administration of a high T1 relaxivity folate targeted liposomal-Gd contrast agent. Signal changes between pre-contrast and post-contrast MRI were determined in target regions of the brain. Animals were euthanized after final imaging session for microscopic analysis of the brain. Non-infected K18-hACE2/3xTg mice showed relatively low brain signal enhancement in post-contrast MRI (3.3 %) consistent with the absence of frank neuroinflammation. In contrast, K18-hACE2/3xTg transgenic mice infected with SARS-CoV-2 demonstrated significantly higher brain signal enhancement (+10.9%) in post-contrast T1w-MRI compared to pre-contrast MRI (Figure 1) suggesting infection-induced neuroinflammation. Signal enhancement was seen in cortical and hippocampal regions of the brain. Microscopic analysis of mice brain sections depicting the hippocampus region show the folate nanoparticles in the vicinity of amyloid deposits. Quantitative analysis shows increasing amyloid deposition in infected mice. Activated microglia in infected mice staining is also observed at higher levels. Data suggests that the hybrid hACE2-3xTg-AD mice exhibit neuroinflammation and increased amyloid deposition following SARS-CoV2 infection indicating acceleration of AD pathology in these mice.

The clinical availability of photon-counting computed tomography (PCCT) has ushered in a new era of CT imaging. Spectral imaging coupled with superior contrast resolution, and ultrahigh spatial resolution (200 μm) offered by PCCT has the potential to revolutionize value-driven imaging. The potential of multicolor PCCT has generated excitement, and renewed interest, in novel contrast agent development for comprehensive disease interrogation, prediction and monitoring of treatment outcomes. Nanoparticles provide a versatile and powerful platform for the development of next generation contrast agents for spectral PCCT. In this article, we review recent developments and use of nanoparticle contrast agents for PCCT. We also discuss future research and translational opportunities for nanoparticle-based CT contrast agents enabled by the advent of PCCT and describe key considerations for their clinical translation.

We performed virus whole-genome sequencing of 6916 upper respiratory swabs from adults and children from March 2020 to May 2023 and collected clinical metadata to assess differences in SARS-CoV-2 variant severity and symptomatology. Multivariable logistic regression showed a severity peak with Delta, which had the highest likelihood of severe infection. In children, another peak was observed with BA.4/BA.5, which was associated with more severe infection than both prior (BA.1) and later (BQ.1, BF.7, and XBB) Omicron variants. In contrast, BA.4/BA.5 in adults was associated with less severe infection than BA.1. Genome-wide association studies revealed that nonstructural protein 5 (nsp5, also called 3C-chymotrypsin-like protease), the Paxlovid target, and the spike N-terminal domain were strongly associated with severity. Kmers (contiguous nucleotide sequences of a fixed length k) from these regions matched the prototype Wuhan sequence exactly, corroborating decreases in severity over time. One kmer in the spike gene region was conserved in Delta genomes, with the kmer retained in higher proportions in patients with more severe infection. Our results show, with the exception of Delta, decreases in severity associated with SARS-CoV-2 variant infection over time and underscore the potential utility of kmer monitoring to assess variant severity.

Alzheimer's disease is the most common cause of dementia and a leading cause of mortality in the elderly population. Diagnosis of Alzheimer's disease has traditionally relied on evaluation of clinical symptoms for cognitive impairment with a definitive diagnosis requiring post-mortem demonstration of neuropathology. However, advances in disease pathogenesis have revealed that patients exhibit Alzheimer's disease pathology several decades before the manifestation of clinical symptoms. Magnetic resonance imaging (MRI) plays an important role in the management of patients with Alzheimer's disease. The clinical availability of molecular MRI (mMRI) contrast agents can revolutionize the diagnosis of Alzheimer's disease. In this article, we review advances in nanoparticle contrast agents, also referred to as nanoprobes, for mMRI of Alzheimer's disease. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease

Background:Coronavirus disease 2019 (COVID-19) continues to cause hospitalizations and severe disease in children and adults. Methods:This study compared the risk factors, symptoms, and outcomes of children and adults hospitalized for COVID-19 from March 2020 to May 2023 across age strata at 5 US sites participating in the Predicting Viral-Associated Inflammatory Disease Severity in Children with Laboratory Diagnostics and Artificial Intelligence consortium. Eligible patients had an upper respiratory swab that tested positive for severe acute respiratory syndrome coronavirus 2 by nucleic acid amplification. Adjusted odds ratios (aOR) of clinical outcomes were determined for children versus adults, for pediatric age strata compared to adolescents (12-17 years), and for adult age strata compared to young adults (22-49 years). Results:Of 9101 patients in the Predicting Viral-Associated Inflammatory Disease Severity in Children with Laboratory Diagnostics and Artificial Intelligence cohort, 1560 were hospitalized for COVID-19 as the primary reason. Compared to adults (22-105 years, n = 675), children (0-21 years, n = 885) were less commonly vaccinated (14.3% vs 34.5%), more commonly infected with the Omicron variant (49.5% vs 26.1%) and had fewer comorbidities (P < .001 for most comparisons), except for lung disease (P = .24). After adjusting for confounding variables, children had significantly lower odds of receiving supplemental oxygen (aOR, 0.57; 95% confidence interval, .35-.92) and death (aOR, 0.011; 95% confidence interval, <.01-.58) compa--red to adults. Among pediatric age strata, adolescents 12-17 years had the highest odds of receiving supplemental oxygen, high-flow oxygen, and ICU admission. Among adults, those 50-64 years had the highest odds of mechanical ventilation and ICU admission. Conclusions:Clinical outcomes of COVID-19 differed across pediatric and adult age strata. Adolescents experienced the most severe disease among children, whereas adults 50-64 years experienced the most severe disease among adults.

Revascularization plays a critical role in the successful engraftment of transplanted pancreatic islets, which are inherently rich in capillaries to meet their high metabolic demands. Innovative islet encapsulation strategies such as the NICHE (neovascularized implantable cell homing and encapsulation), generate a prevascularized transplantation site that allows for direct integration of the graft with the systemic circulation. Timing the transplantation is key to maximizing islet engraftment and survival, especially in diabetic individuals, who exhibit impaired wound healing. Therefore, in this study, we explored different methods to assess vascular development within NICHE in vivo in a non-invasive fashion. We effectively tracked neoangiogenesis using nanoparticle contrast-enhanced computed tomography (nCECT), observing a steady increase in vascularization over an 8-week period, which was confirmed histologically. Next, we estimated relative vascularization changes via T2 mapping with magnetic resonance imaging (MRI) before and after islet transplantation. On the first day post-transplantation, we measured a slight decrease in T2 values followed by a significant increase by day 14 attributable to islet revascularization. Our findings underscore the potential of non-invasive imaging techniques to provide insightful information on the readiness of the transplant site within cell encapsulation systems to support cell graft transplantation.

The pathogenesis of Parkinson’s disease (PD) is characterized by progressive deposition of alpha-synuclein (α-syn) aggregates in dopaminergic neurons and neuroinflammation. Noninvasive in vivo imaging of α-syn aggregate accumulation and neuroinflammation can elicit the underlying mechanisms involved in disease progression and facilitate the development of effective treatment as well as disease diagnosis and prognosis. Here we present a novel approach to simultaneously profile α-syn aggregation and reactive microgliosis in vivo, by targeting oligomeric α-syn in cerebrospinal fluid with nanoparticle bearing a magnetic resonance imaging (MRI), contrast payload. In this proof-of-concept report we demonstrate, in vitro, that microglia and neuroblastoma cell lines internalize agglomerates formed by cross-linking the nanoparticles with oligomeric α-syn. Delayed in vivo MRI scans following intravenous administration of the nanoparticles in the M83 α-syn transgenic mouse line show statistically significant MR signal enhancement in test mice versus controls. The in vivo data were validated by ex-vivo immunohistochemical analysis which show strong correlation between in vivo MRI signal enhancement, Lewy pathology distribution, and microglia activity in the treated brain tissue. Furthermore, neuronal and microglial cells in brain tissue from treated mice display strong cytosolic signal originating from the nanoparticles, attributed to in vivo cell uptake of nanoparticle/oligomeric α-syn agglomerates.

Extracellular deposits of amyloid-β (Aβ) aggregates are pathological hallmarks of Alzheimer's disease (AD). In our previous work, we showed that an amyloid-targeted liposomal gadolinium (Gd) contrast agent, ADx-001, demonstrated dose-related varying performance (accuracy 50% - 100%) for in vivo MRI-based detection of amyloid plaques in a mouse model of AD. The goal of this study was to determine if nano-radiomics (radiomic analysis of nanoparticle contrast-enhanced images) could improve performance in differentiating amyloid-positive transgenic (TG) APP/PSEN1 mice and age-matched amyloid-negative Wild Type (WT) mice. Nanoparticle contrast-enhanced MRI (nCE-MRI) was performed using a T1w-SE sequence in wild type (amyloid negative) and transgenic APP/PSEN1 mice (amyloid positive). The effect of ADx-001 dose and plaque burden on the performance of radiomics was determined. nCE-MRI was performed at three ADx-001 dose levels (0.10, 0.15, 0.20 mmol Gd/kg) in mice with high plaque burden and single ADx-001 dose level (0.20 mmol Gd/kg) in mice with low plaque burden. Following semi-automatic registration and segmentation of brain atlas on mouse MR images, two sets of radiomic features (RFs), including the RFs recommended by Image Biomarker Standardization Initiative, were calculated and evaluated for their performance in classifying TG and WT mice. Linear and nonlinear classifiers using RFs were examined to improve the model performance. 5-fold cross-validation was performed to confirm the accuracy of group separation. Nano-radiomic analysis in mice with high plaque burden achieved superb classification performance in terms of accuracy, sensitivity, and specificity, with one universal classifier for all dose levels of ADx-001. In comparison, conventional MR metric of signal enhancement demonstrated dose-related varying performance with suboptimal accuracy (⪅0.7) at lower dose levels. In mice with low plaque burden, radiomic analysis outperformed conventional MR metric for detection of amyloid pathology. In conclusion, nano-radiomics exhibited excellent performance for early detection and amyloid burden classification in a mouse model of Alzheimer's disease.

In recent years, several diagnostic challenges have developed due to the COVID-19 pandemic, including the post-infectious sequelae multisystem inflammatory syndrome in chil-dren (MIS-C). This syndrome shares several clinical features with other entities, such as Kawasaki disease (KD) and endemic typhus, among other febrile diseases. Endemic typhus, or murine typhus, is an acute infection treated much differently than MIS-C and KD. Early diagnosis and appropriate treatment are crucial to a favorable outcome for patients with these disorders. To address these challenges, a Clinical Decision Support System (CDSS) designed to support the decision-making of medical teams can be implemented to differentiate between these disorders. We developed and evaluated a CDSS based on a Triplet Loss Siamese Network to distinguish between patients presenting with clinically similar febrile illnesses, KD, MIS-C, or typhus. We used eight clinical and laboratory features typically available within six hours of presentation. The performance assessment for AI-HEAT, Logistic Regression, Support Vector Machine, XGBoost, and the TabPFN machine learning models was performed by computing Balanced Accuracy. AI-HEAT is a CDSS capable of obtaining performance similar to a state-of-the-art Transformer-type deep learning model such as TabPFN, with advantages such as being almost a thousand times smaller.

Alzheimer's disease is the most common cause of dementia and a leading cause of mortality in the elderly population. Diagnosis of Alzheimer's disease has traditionally relied on evaluation of clinical symptoms for cognitive impairment with a definitive diagnosis requiring post-mortem demonstration of neuropathology. However, advances in disease pathogenesis have revealed that patients exhibit Alzheimer's disease pathology several decades before the manifestation of clinical symptoms. Magnetic resonance imaging (MRI) plays an important role in the management of patients with Alzheimer's disease. The clinical availability of molecular MRI (mMRI) contrast agents can revolutionize the diagnosis of Alzheimer's disease. In this article, we review advances in nanoparticle contrast agents, also referred to as nanoprobes, for mMRI of Alzheimer's disease. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Familial Alzheimer's disease (AD) involving known AD causing genes accounts for a small fraction of cases, the vast majority are sporadic. Neuroinflammation, secondary to viral infection, has been suggested as an initiating or accelerating factor. In this work we tested the hypothesis that SARS-CoV-2 (SCV2) viral infection accelerates the development of AD pathology in mouse models of AD. We profiled transcriptomic changes using transgenic APP/PSEN1 and P301S mouse models that develop AD pathology and k18hACE2 mice that express the humanized ACE2 receptor used by SCV2 to enter cells. This study identified the interferon and chemokine responses constituting key shared pathways between SCV2 infection and the development of AD pathology. Two transgenic mouse models of AD: APP/PSEN1 (develops amyloid pathology) and 3xTg AD (develops both amyloid and tau pathology) were crossed with k18-hACE2 mice to generate hybrid hACE2-3xTg and hACE2-APP/PSEN1 mice. Neuroinflammation and amyloid deposition in the brain of infected mice were imaged in vivo using molecular MRI (mMRI) probes and confirmed postmortem by histopathology. Results show that 11-14-month-old SCV2 infected hACE2-3xTg mice exhibit neuroinflammation 10 days post infection and 4-5-month-old hACE2-APP/PS1 hybrid mice develop amyloid deposits, while age-matched uninfected mice exhibit neither phenotype. This suggests that SCV2 infection could induce or accelerate AD when risk factors are present. ### Competing Interest Statement AA, MS, ET hold stock in Alzeca Inc. AA, KG, ET have received consulting fees from Alzeca Inc.

Background Hormone receptor (HR)+ breast cancer (BC) causes most BC-related deaths in the US.1 Standard treatment for non-metastatic disease involves surgery plus adjuvant hormonotherapy. However, approximately 50% of patients ultimately relapse and require additional lines of treatment including chemotherapy, which is unfortunately associated with limited clinical benefits and severe toxicity. In HR+ BC patients, the efficacy of immunotherapy has also been disappointing so far. Indeed, objective responses to PD-1 blockade with pembrolizumab in women with HR+ BC have been in the range of 5–10%, with no clear advantage on survival. Thus, resistance to PD-1 blockers constitutes a major obstacle towards the implementation of immunotherapy in HR+ BC patients. Methods To obtain insights into the immunological alterations accompanying disease relapse in HR+ BC exposed to PD-1 blockade, we harnessed a unique endogenous model of BC driven in immunocompetent mice by progesterone and a carcinogen. This model recapitulates key aspects of human luminal B BC, including a relatively ´cold´ microenvironment, hence limited sensitivity to PD-1 blockage.2 To overcome PD-1 resistance we treated mice with Flt3-L to stimulate the maturation of cross-presenting dendritic cells, and radiation therapy that can act as an adjuvant to inflame tumor micro-environment.3 In parallel, we tried to achieve complete control of the primary tumor by identifying ablative doses of fractionated radiation therapy (RT). Results Immunotherapy (PD-1 + Flt3L), despite slowing down the tumor growth, failed to improve the overall survival (OS) of mice elicited by RT alone in a unique mouse model of HR+ BC, potentially linked to an accrued systemic immunosuppression (T cells exhaustion, and increase of Tregs). The addition of CTLA-4, even though providing an initial response, failed to improve the tumor growth and OS, due to immunoresistance (immature macrophages, and decrease in T cells and NK cells at the systemic level). Partially ablative RT doses were able to improve the OS and will be combined with PD-1 + Flt3L in the near future. Conclusions Breaking through resistance of HR+ tumors to PD-1 blockers can direct strategies to overcome resistance in HR+ BC patients, the majority of BC patients. If successful, this can inform therapeutic approaches to enable superior therapeutic responses in patients with HR+ BC, hence significantly reducing BC-related deaths. References Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70:7–30. Buque A, Bloy N, Perez-Lanzon M, Iribarren K, Humeau J, Pol JG, Levesque S, Mondragon L, Yamazaki T, Sato A, Aranda F, Durand S, Boissonnas A, Fucikova J, Senovilla L, Enot D, Hensler M, Kremer M, Stoll G, Hu Y, Massa C, Formenti SC, Seliger B, Elemento O, Spisek R, Andre F, Zitvogel L, Delaloge S, Kroemer G, Galluzzi L. Immunoprophylactic and immunotherapeutic control of hormone receptor-positive breast cancer. Nat Commun. 2020;11:3819. Deng L, et al. STING-Dependent Cytosolic DNA Sensing Promotes Radiation- Induced Type I Interferon-Dependent Antitumor Immunity in Immunogenic Tumors. Immunity. 2014;41:843–852

Amyloid plaques are a pathological hallmark of Alzheimer’s disease (AD). In a recent pre-clinical study, we showed an amyloid-targeted liposomal macrocyclic gadolinium (Gd) contrast agent, ADx-001, for in vivo MRI-based detection of amyloid plaques in mouse. Although ADx-001 showed high sensitivity at a high dose (0.2 mmol Gd/kg), the performance was sub-optimal (<70%) at lower doses (0.1 and 0.15 mmol Gd/kg). In this work, we investigated if nano-radiomics (radiomic analysis of nanoparticle contrast-enhanced images) would increase sensitivity of ADx-001 at lower dose levels. In vivo studies were performed in the APP/PSEN1 mouse model of amyloid pathology. The efficacy of ADx-001- enhanced MRI was studied at three dose-levels: 0.10, 0.15, and 0.20 (mmol Gd/kg). Pre- and post-contrast MRI was performed in transgenic (n = 6/dose) and wild-type mice (n = 6/dose) using a T1-weighted spin-echo sequence. Semi-automatic 3D segmentation of hippocampal and cortical regions was performed using a mouse brain MR atlas (Fig.1). Radiomic analysis was executed on the hippocampus and cortex regions of ADx-001-enhanced MR images 900 radiomic features (RFs). RF selection was completed using a non-parametric neighborhood component method. 5-fold cross-validation was performed using a set of linear and non-linear classifiers to confirm the accuracy of group separation. Seven RFs (three RFs for cortex and four RFs for hippocampus) were identified that differentiated amyloid-positive transgenic mice from amyloid-negative wild-type mice based on ADx-001-enhanced MRI. The best performing nearest-neighbor classification model was trained simultaneously on all ADx-001 dose groups. Nano-radiomic analysis of ADx-001-enhanced MRI demonstrated 100% accuracy, specificity, and sensitivity for dose levels of 0.2 and 0.15 mmol Gd/kg. For low dose level (0.1 mmol Gd/kg), radiomics achieved accuracy of 91.6% and sensitivity of 83.3%, while maintaining specificity at 100%. The new results were superior when compared to previously reported results based on global signal enhancement analysis. Nano-radiomic analysis of ADx-001-enhanced MRI improved sensitivity of ADx-001 at lower dose levels for the detection of amyloid pathology and demonstrated excellent for intermediate and high dose. Our study demonstrates that radiomic analysis of contrast-enhanced MR images could boost the performance of targeted molecular imaging agents for early detection of AD.