Portal vein thrombosis may alter the correct evaluation of hepatocellular carcinoma with the sonographic contrast pulse sequence technique

[No abstract available

Olympic legacy and cultural tourism: Exploring the facets of Athens' Olympic heritage

This study examines the effects of the Olympic Games on Athens’ cultural tourism and the city’s potential to leverage the Olympic legacy in synergy with its rich heritage in order to enhance its tourism product during the post-Games period. In doing so, a qualitative and interpretive approach was employed. This includes a literature review on Athens’ 2004 Olympics to identify the sport facilities and regeneration projects, which constitute the Olympic legacy and heritage. Based on that, an empirical analysis was undertaken, by collecting official documents about the 2004 Olympics, and conducting five semi-structured interviews with tourism/administrative officials. The findings indicate that the Olympiad contributed significantly to Athens’ built and human heritage, revealing the dimensions of new venues/facilities, infrastructure, transportation and aesthetic image of the city, and human capital enhancement. Hence, the Games affected to the multifaceted representation and reconstruction of the city’s identity and cultural heritage. However, the potential afforded from the post-Olympic Athens remains unrealised due to lack of strategic planning/management. The study concludes that there is a need to develop cross-leveraging synergies between the Olympic legacy and cultural tourism for the host city. Finally, a strategic planning framework for leveraging post-Games Olympic tourism is suggested in order to maximise the benefits of Olympic legacy and heritage in a host city’s tourism development

Experience with rituximab in scleroderma: results from a 1-year, proof-of-principle study

Objective. To assess the efficacy of rituximab (RTX) in SSc

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing