Immunosuppressive tumor microenvironments can restrain antitumor immunity, particularly in pancreatic ductal adenocarcinoma (PDA). Because CD40 activation can reverse immune suppression and drive antitumor T cell responses, we tested the combination of an agonist CD40 antibody with gemcitabine chemotherapy in a small cohort of patients with surgically incurable PDA and observed tumor regressions in some patients. We reproduced this treatment effect in a genetically engineered mouse model of PDA and found unexpectedly that tumor regression required macrophages but not T cells or gemcitabine. CD40-activated macrophages rapidly infiltrated tumors, became tumoricidal, and facilitated the depletion of tumor stroma. Thus, cancer immune surveillance does not necessarily depend on therapy-induced T cells; rather, our findings demonstrate a CD40-dependent mechanism for targeting tumor stroma in the treatment of cancer.

Purpose This phase I study investigated the maximum-tolerated dose (MTD), safety, pharmacodynamics, immunological correlatives, and anti-tumor activity of CP-870,893, an agonist CD40 antibody, when administered in combination with gemcitabine in patients with advanced pancreatic ductal adenocarcinoma (PDA). Experimental Design Twenty-two patients with chemotherapy-naïve advanced PDA were treated with 1000 mg/m2 gemcitabine once weekly for 3 weeks with infusion of CP-870,893 at 0.1 mg/kg or 0.2 mg/kg on day 3 of each 28 day cycle. Results CP-870,893 was well-tolerated; one dose-limiting toxicity (grade 4 cerebrovascular accident) occurred at the 0.2 mg/kg dose level, which was estimated as MTD. The most common adverse event was cytokine release syndrome (grade 1 to 2). CP-870,893 infusion triggered immune activation marked by an increase in inflammatory cytokines, an increase in B cell expression of co-stimulatory molecules, and a transient depletion of B cells. Four patients achieved a partial response (PR). [18F]-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) demonstrated >25% decrease in FDG uptake within primary pancreatic lesions in 6 of 8 patients; however, responses observed in metastatic lesions were heterogeneous with some lesions responding with complete loss of FDG uptake while other lesions in the same patient failed to respond. Improved overall survival correlated with a decrease in FDG uptake in hepatic lesions (R = −0.929; p = 0.007). Conclusions CP-870,893 in combination with gemcitabine was well-tolerated and associated with anti-tumor activity in patients with PDA. Changes in FDG uptake detected on PET/CT imaging provide insight into therapeutic benefit. Phase II studies are warranted.

To evaluate the feasibility of using [18F]fluorodeoxyglucose positron emission tomographycomputed tomography (FDG-PET/CT) to detect and quantify systemic inflammation in patients with psoriasis.Design: Case series with a nested case-control study.Setting: Referral dermatology and preventive cardiology practices.Participants: Six patients with psoriasis affecting more than 10% of their body surface area and 4 controls age and sex matched to 4 of the patients with psoriasis for a nested case-control study. Main Outcome Measures:The FDG uptake in the liver, musculoskeletal structures, and aorta measured by mean standardized uptake value, a measure of FDG tracer uptake by macrophages and other inflammatory cells.Results: FDG-PET/CT identified numerous foci of inflammation in 6 patients with psoriasis within the skin, liver, joints, tendons, and aorta. Inflammation in the joints

To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide automatic anatomy recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions – thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) – involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and testing data sets are divided into equal size in all cases except for the neck. Overall the AAR method achieves a mean accuracy of about 2 voxels in localizing non-sparse blob-like objects and most sparse tubular objects. The delineation accuracy in terms of mean false positive and negative volume fractions is 2% and 8%, respectively, for non-sparse objects, and 5% and 15%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 0.9 and 1.5 voxels, respectively. Some sparse objects – venous system (in the thorax on CT), inferior vena cava (in the abdomen on CT), and mandible and naso-pharynx (in neck on MRI, but not on CT) – pose challenges at all levels, leading to poor recognition and/or delineation results. The AAR method fares quite favorably when compared with methods from the recent literature for liver, kidneys, and spleen on CT images. We conclude that separation of modality-independent from dependent aspects, organization of objects in a hierarchy, encoding of object relationship informati...

BACKGROUND The utility flourodeoxyglucose PET (FDG-PET) imaging in Alzheimer’s Disease (AD) diagnosis is well established. Recently, measurement of cerebral blood flow using arterial spin labeling MRI (ASL-MRI) has shown diagnostic potential in AD, though it has never been directly compared to FDG-PET. METHODS We employed a novel imaging protocol to obtain FDG-PET and ASL-MRI images concurrently in 17 AD patients and 19 age-matched controls. Paired FDG-PET and ASL-MRI images from 19 controls and 15 AD patients were included for qualitative analysis, while paired images 18 controls and 13 AD patients were suitable for quantitative analyses. RESULTS The combined imaging protocol was well tolerated. Both modalities revealed very similar regional abnormalities in AD, as well as comparable sensitivity and specificity for the detection of AD following visual review by two expert readers. Interobserver agreement was better for FDG-PET (kappa 0.75, SE 0.12) than ASL-MRI (kappa 0.51, SE 0.15), intermodality agreement was moderate to strong (kappa 0.45-0.61), and readers were more confident of FDG-PET reads. Simple quantitative analysis of global cerebral FDG uptake (FDG-PET) or whole brain cerebral blood flow (ASL-MRI) showed excellent diagnostic accuracy for both modalities, with area under ROC curves of 0.90 for FDG-PET (95% CI 0.79-0.99) and 0.91 for ASL-MRI (95% CI 0.80-1.00). CONCLUSIONS Our results demonstrate that FDG-PET and ASL-MRI identify similar regional abnormalities and have comparable diagnostic accuracy in a small population of AD patients, and support the further study of ASL-MRI in dementia diagnosis.

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13121038/-/DC1.

IMPORTANCE Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. OBJECTIVE To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set

Nanomedicine is emerging as a promising approach for diagnostic applications. Nanoparticles are structures in the nanometer size range, which can present different shapes, compositions, charges, surface modifications, in vitro and in vivo stabilities, and in vivo performances. Nanoparticles can be made of materials of diverse chemical nature, the most common being metals, metal oxides, silicates, polymers, carbon, lipids, and biomolecules. Nanoparticles exist in various morphologies, such as spheres, cylinders, platelets, and tubes. Radiolabeled nanoparticles represent a new class of agent with great potential for clinical applications. This is partly due to their long blood circulation time and plasma stability. In addition, because of the high sensitivity of imaging with radiolabeled compounds, their use has promise of achieving accurate and early diagnosis. This review article focuses on the application of radiolabeled nanoparticles in detecting diseases such as cancer and cardiovascular diseases and also presents an overview about the formulation, stability, and biological properties of the nanoparticles used for diagnostic purposes.