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Introduction: The prognosis of medullary thyroid carcinoma (MTC) is poor using common chemotherapeutic approaches. However, during the last years encouraging results of recently introduced tyrosine kinase inhibitors (TKI) such as vandetanib have been published. In this study we aimed to correlate the results of \(^{18}\)F-fluorodeoxyglucose ([\(^{18}\)F]FDG) positron emission tomography (PET) imaging with treatment outcome.
Methods: Eighteen patients after thyroidectomy with recurrent/advanced MTC lesions receiving vandetanib (300 mg orally/day) could be analysed. A baseline \(^{18}\)F-FDG PET prior to and a follow-up \(^{18}\)F-FDG PET 3 months after TKI initiation were performed. During follow-up, tumor progression was assessed every 3 months including computed tomography according to RECIST. Progression-free survival (PFS) was correlated with the maximum standardized uptake value of \(^{18}\)F-FDG in lymph nodes (SUV(LN)max) or visceral metastases (SUV(MTS)max) as well as with clinical parameters using ROC analysis.
Results: Within median 3.6 years of follow-up, 9 patients showed disease progression at median 8.5 months after TKI initiation. An elevated glucose consumption assessed by baseline \(^{18}\)F-FDG PET (SUV(LN)max > 7.25) could predict a shorter PFS (2 y) with an accuracy of 76.5% (SUV(LN)max <7.25, 4.3 y; p=0.03). Accordingly, preserved tumor metabolism in the follow-up PET (SUV(MTS)max >2.7) also demonstrated an unfavorable prognosis (accuracy, 85.7%). On the other hand, none of the clinical parameters reached significance in response prediction.
Conclusions: In patients with advanced and progressive MTC, tumors with higher metabolic activity at baseline are more aggressive and more prone to progression as reflected by a shorter PFS; they should be monitored more closely. Preserved glucose consumption 3 months after treatment initiation was also related to poorer prognosis.
T-pattern analysis supports studies of various aspects of human or animal behavior as well as interaction between human subjects and animal or artificial agents. The following proceedings give an overiew on the application of T-pattern analysis in different research fields like media, gaming, human behaviour, social and organisational interaction as well as sports and health.
The International Symposium on Phytochemicals in Medicine and Food (ISPMF2015), organized by the Phytochemical Society of Europe (PSE) and the Phytochemical Society of Asia (PSA), was held June 26-29, 2015, in Shanghai of China. This was the first time that a PSE meeting has been held in Asia and a PSE-PSA joint symposium provided an opportunity for communication between scientists from Europe and Asia and other continents. ISPMF2015 has been jointly sponsored by Fujian Agriculture and Forestry University, Guizhou Medical University, Shanghai Normal University, Yancheng Institute of Technology, Beijing Normal University, and Fudan University. More than 270 scientists from 48 countries attended this meeting and presented their research and opinions on phytochemistry, phytomedicine and phytoneering. The international organizing committee and scientific advisory board of ISPMF 2015 comprised of outstanding scientists from around the globe. Dr. Jianbo Xiao was the chairman of the International Organizing Committee of ISPMF2015 and moderated the open address on June 26.
The organizing committee of ISPMF2015 assembled an exciting and diverse program, featuring 16 sessions including 12 plenary lectures, 20 invited talks, 55 short oral presentations, and more than 130 posters, which were dedicated to creating a podium for exchanging the latest research results in the phytochemicals for food and human health.
Based on the results of a 3-day workshop at the Brown University (2012) this white paper tries to sum up important topics and problems which came up in the presentations and discussions and to outline some general aspects of data modeling in digital humanities. Starting with an attempt to define data modeling it introduces distinctions like curation-driven vs. research-driven for a more general description of data modeling. The second part discusses specific problems and challenges of data modeling in the Humanities, while the third part outlines practical aspects, like the creation of data models or their evaluation.