Targeted multimodal imaging modalities

Molecular imaging non-invasively visualizes and characterizes the biologic functions and mechanisms in living organisms at a molecular level. In recent years, advances in imaging instruments, imaging probes, assay methods, and quantification techniques have enabled more refined and reliable images for more accurate diagnoses. Multimodal imaging combines two or more imaging modalities into one system to produce details in clinical diagnostic imaging that are more precise than conventional imaging. Multimodal imaging offers complementary advantages: high spatial resolution, soft tissue contrast, and biological information on the molecular level with high sensitivity. However, combining all modalities into a single imaging probe involves problems yet to be solved due to the requirement of high dose contrast agents for a component of imaging modality with low sensitivity. The introduction of targeting moieties into the probes enhances the specific binding of targeted multimodal imaging modalities and selective accumulation of the imaging agents at a disease site to provide more accurate diagnoses. An extensive list of prior reports on the targeted multimodal imaging probes categorized by each modality is presented and discussed. In addition to accurate diagnosis, targeted multimodal imaging agents carrying therapeutic medications make it possible to visualize the theranostic effect and the progress of disease. This will facilitate the development of an imaging-guided therapy, which will widen the application of the targeted multimodal imaging field to experiments in vivo.     

Nucleus classification in histology images using message passing network

Identification of nuclear components in the histology landscape is an important step towards developing computational pathology tools for the profiling of tumor micro-environment. Most existing methods for the identification of such components are limited in scope due to heterogeneous nature of the nuclei. Graph-based methods offer a natural way to formulate the nucleus classification problem to incorporate both appearance and geometric locations of the nuclei. The main challenge is to define models that can handle such an unstructured domain. Current approaches focus on learning better features and then employ well-known classifiers for identifying distinct nuclear phenotypes. In contrast, we propose a message passing network that is a fully learnable framework build on classical network flow formulation. Based on physical interaction of the nuclei, a nearest neighbor graph is constructed such that the nodes represent the nuclei centroids. For each edge and node, appearance and geometric features are computed which are then used for the construction of messages utilized for diffusing contextual information to the neighboring nodes. Such an algorithm can infer global information over an entire network and predict biologically meaningful nuclear communities. We show that learning such communities improves the performance of nucleus classification task in histology images. The proposed algorithm can be used as a component in existing state-of-the-art methods resulting in improved nucleus classification performance across four different publicly available datasets.     

First-line nivolumab plus chemotherapy vs chemotherapy in patients with advanced gastric,gastroesophageal junction and esophageal adenocarcinoma: CheckMate 649 Chinese subgroup analysis

First-line chemotherapy for advanced/metastatic human epidermal growth factor receptor 2 (HER2)-negative gastric/gastroesophageal junction cancer (GC/GEJC) has poor median overall survival (OS; <1 year). We report efficacy and safety results from Chinese patients in the phase III global CheckMate 649 study of nivolumab plus chemotherapy vs chemotherapy for the first-line treatment of GC/GEJC/esophageal adenocarcinoma (EAC). Chinese patients with previously untreated advanced or metastatic GC/GEJC/EAC were randomized to receive nivolumab (360 mg Q3W or 240 mg Q2W) plus chemotherapy (XELOX [capecitabine and oxaliplatin] Q3W or FOLFOX [oxaliplatin, leucovorin and 5-fluorouracil] Q2W), nivolumab plus ipilimumab (not reported) or chemotherapy alone. OS, blinded independent central review-assessed progression-free survival (PFS), objective response rate (ORR), duration of response (DOR) and safety are reported. Of 1581 patients enrolled and randomized, 208 were Chinese. In these patients, nivolumab plus chemotherapy resulted in clinically meaningful improvement in median OS (14.3 vs 10.2 months; HR 0.61 [95% CI: 0.44-0.85]), median PFS (8.3 vs 5.6 months; HR 0.57 [95% CI: 0.40-0.80]), ORR (66% vs 45%) and median DOR (12.2 vs 5.6 months) vs chemotherapy, respectively. The safety profile was acceptable, with no new safety signals observed. Consistent with results from the global primary analysis of CheckMate 649, nivolumab plus chemotherapy demonstrated a clinically meaningful improvement in OS and PFS and higher response rate vs chemotherapy and an acceptable safety profile in Chinese patients. Nivolumab plus chemotherapy represents a new standard first-line treatment for Chinese patients with non-HER2-positive advanced GC/GEJC/EAC.     

Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses

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Feasibility of coronary artery calcium scoring on virtual unenhanced images derived from single-source fast kVp-switching dual-energy coronary CT angiography

BackgroundDual-energy CT technology enables acquisition of virtual unenhanced (VUE) images from contrast-enhanced scans.ObjectiveTo assess the feasibility of coronary artery calcium (CAC) scoring on VUE images derived from fast kVp-switching dual-energy coronary CT angiography.MethodsTwenty-seven patients underwent true noncontrast CAC-scoring CT followed by routine single-energy (120-kVp) and fast kVp-switching dual-energy coronary CT angiography, in a random acquisition order on the same day. We calculated the CAC scores on true noncontrast and VUE images. The image noises and the signal-to-noise and contrast-to-noise ratios of the aorta and coronary arteries were measured on both the single-energy coronary CT angiography images and dual-energy coronary CT angiography images (70 keV virtual monochromatic spectral images). The Pearson correlation coefficient test and paired t test were used for statistical analysis.ResultsExcellent correlation was observed between the CAC scores on the true noncontrast and those on the VUE images (r = 0.88; P < .001). Compared with single-energy coronary CT angiography, dual-energy coronary CT angiography showed significantly reduced image noise and increased signal-to-noise and contrast-to-noise ratios in all regions (all P < .001). The effective dose of dual-energy coronary CT angiography (4.3 ± 0.3 mSv) was significantly lower than that of true noncontrast CAC-scoring CT plus single-energy coronary CT angiography (5.4 ± 0.7 mSv; P < .0001).ConclusionsExcellent correlation was observed between the CAC scores on the VUE images and true noncontrast images. Thus, fast kVp-switching dual-energy coronary CT angiography could allow prediction of the true CAC scores, potentially reducing the total radiation exposure and image acquisition time by obviating the need for true noncontrast CAC-scoring CT.