Extranodal NK/T-cell lymphoma: Updates in biology and management strategies

Extranodal NK/T-cell lymphoma, nasal type (ENKL), is a rare lymphoma subtype of peripheral T/NK-cell lymphoma that is very common in East Asia and Latin America. Two-thirds of patients have localized disease in the nasal cavity or adjacent sites. Large retrospective studies have revealed the clinicopathologic features of ENKL patients, identified risk factors for short survival time, and developed prognostic models. Next-generation sequencing studies have provided a comprehensive list of recurrent mutations in ENKL. Since the early 2000s, disease-specific therapeutic approaches have been developed, and the standard of care for ENKL has markedly changed. Non-anthracycline-containing chemotherapy with or without radiotherapy is the current standard approach for ENKL treatment. Emerging therapies, including the use of immune checkpoint inhibitors, are being investigated.     

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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Who will become my co-residents? The role of attractiveness of institutional care in the changing demand for long-term care institutions

BackgroundIn several OECD countries the percentage of people over 80 in LTC institutions has been declining for more than a decade, despite population ageing. The standard model to explain healthcare utilization, the Andersen model, cannot explain this trend. We extend the Andersen model by including proxies for the relative attractiveness of community living compared to institutional care. Using longitudinal data on long-term care use in the Netherlands from 1996 to 2012, we examine to what extent a decline in institutional care is associated with changes in perceived attractiveness of institutional LTC care compared to community living.MethodsWith a Blinder–Oaxaca decomposition regression, we decomposed the difference in admission to LTC institutions between the period 1996–1999 and 2009–2012 into a part that accounts for differences in predictors of the Andersen model and an “unexplained” part, and investigate whether the perceived attractiveness of institutional care reduces the size of the unexplained part.ResultsWe find that factors related to the perceived attractiveness of institutional care compared to community living explains 12.8% of the unexplained negative time trend in admission rates over the total period (1996–2012), and 19.1–19.2% over shorter time frames.DiscussionOur results show that changes in the perceived attractiveness of institutional LTC may explain part of the decline in demand for institutional care. Our findings imply that policies to encourage community living may have a self-reinforcing effect.