Predicting the survival probability of functional neuroendocrine tumors treated with peptide receptor radionuclide therapy: Serbian experience

IntroductionPeptide receptor radionuclide therapy (PRRT) is a treatment option for well-differentiated, somatostatin receptor positive, unresectable or/and metastatic neuroendocrine tumors (NETs). Although high disease control rates seen with PRRT a significant number NET patients have a short progression-free interval, and currently, there is a deficiency of effective biomarkers to pre-identify these patients. This study is aimed at determining the prognostic significance of biomarkers on survival of patients with NETs in initial PRRT treatment.MethodologyWe retrospectively analyzed 51 patients with NETs treated with PRRT at the Department for nuclear medicine, University Clinical Center Kragujevac, Serbia, with a five-year follow-up. Eligible patients with confirmed inoperable NETs, were retrospectively evaluated hematological, blood-based inflammatory markers, biochemical markers and clinical characteristics on disease progression. In accordance with the progression og the disease, the patients were divided into two groups: progression group (n=18) and a non-progression group (n=33). Clinical data were compared between the two groups.ResultsA total of 51 patients (Md=60, age 25-75 years) were treated with PRRT, of whom 29 (56.86%) demonstrated stable disease, 4 (7.84%) demonstrated a partial response, and 14 (27.46%) demonstrated progressive disease and death was recorded in 4 (7.84%) patients. The mean PFS was a 36.22 months (95% CI 30.14-42.29) and the mean OS was 44.68 months (95% CI 37.40-51.97). Univariate logistic regression analysis displayed that age (p<0.05), functional tumors (p<0.05), absolute neutrophil count (p<0.05), neutrophil-lymphocyte ratio-NLR (p<0.05), C-reactive protein-CRP (p<0.05), CRP/Albumin (p<0.05), alanine aminotransferase-ALT (p<0.05), were risk factors for disease progression. Multivariate logistic regression analysis exhibited that functional tumors (p<0.001), age (p<0.05), CRP (p<0.05), and ALT (p<0.05), were independent risk factors for the disease progression in patients with NETs. Tumor functionality was the most powerful prognostic factor. The median PFS (11.86 ± 1.41 vs. 43.38 ± 3.16 months; p=0.001) and OS (21.81 ± 2.70 vs 53.86 ± 3.70, p=0.001) were significantly shorter in patients with functional than non-functional NETs respectively.ConclusionThe study’s results suggest that tumor functionality, and certain biomarkers may serve as prognostic survival indicators for patients with NETs undergoing PRRT. The findings can potentially help to identify patients who are at higher risk of disease progression and tailor treatment strategies accordingly

An organizational capability perspective on multi-unit franchising

International audiencePrevious research on multi-unit franchising (MUF) has primarily focused on agency and transaction cost perspectives. The present study develops and tests an organizational capability (OC) model of the franchisor’s choice of MUF. According to the OC view, the franchisor gains a competitive advantage by exploration and exploitation of firm-specific resources and capabilities. We hypothesize that, if the franchisor expects to obtain a competitive advantage resulting from higher exploration and exploitation capabilities when using MUF as opposed to single-unit franchising, the franchisor will more likely choose MUF as a governance mode of the franchise system. Based on empirical data from the German and Swiss franchise sectors, the results of the regression analysis support these hypotheses. Our main contribution to the franchise literature is the development of an OC model of the franchisor’s choice of MUF that complements the existing organizational economics explanation of MUF

Design and Management of Interfirm Networks

International audienceOffers an interdisciplinary understanding of the economics and management of interfirm networks Presents new research perspectives on the relationship between networks and innovation Highlights a network-centric view of the new digital economyInterfirm networks include franchising, retail and service chains, cooperatives, financial networks, joint ventures, strategic alliances, licensing, public-private partnerships and new network forms in the digital economy. This book gathers the latest research studies that approach these networks – and the creation of innovation under the conditions of a complex, dynamic, knowledge-intensive and digital economy – from an interdisciplinary perspective. The studies, all of which were written by respected experts, explore how firms can improve their competitiveness by securing access to innovation, knowledge, complementary resources and capabilities otherwise not available to them. In addition, they highlight how, driven by an unpredictable environment, firms embedded in inter-organizational networks are increasingly transforming from co-operators to collaborators and valuable co-creators of innovation

Network innovation versus innovation through networks

International audienceIn today's dynamic, complex and interconnected environments, interfirm networks in its various forms (e.g. franchising, retail and service chains, cooperatives, financial networks, joint ventures, strategic alliances, clusters, public-private partnerships, digital platforms) are becoming increasingly important in helping firms improve their competitive position through an enhanced access to innovation, complementary resources and capabilities otherwise not available to them. Driven by increased performance pressures in unpredictable environments, firms embedded in networks are increasingly moving from cooperators to collaborators as value co-creators. The aim of this introductory article is to discuss the role of innovation in business networks by focusing on two major topics: Network innovation versus innovation through networks. In addition, we provide an overview of the articles included in the special issue on Networks and Innovation focusing on the questions: (1) what is the impact of network characteristics on a firm's innovation?; and (2) what are the determinants of innovation in interfirm networks? © 2020 Elsevier Inc