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- 저자 : Youwen Zhu (검색결과 2 건)
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Youwen Zhu,Qiuping Yang,Kun Liu,Hui Cao,Hong Zhu 대한부인종양학회 2024 Journal of Gynecologic Oncology Vol.35 No.1
Objective: The PAOLA-1 trial (NCT02477644) reported final sur vival benefit associated witholaparib plus bevacizumab maintenance treatment of patients with advanced ovarian cancer(AOC) based on molecular status. Our aimed to compare the cost-effectiveness of olaparibplus bevacizumab for overall patients, patients with a breast cancer susceptibility genes(BRCA) mutation, homologous recombination deficiency (HRD), or HRD without BRCAmutations AOC from the context of the American healthcare system. Methods: Analysis of health outcomes in life-years (LYs), quality-adjusted life-years (QALYs),and the incremental cost-effectiveness ratio (ICER) in various molecular status-based AOCpatient at a $150,000/QALY of willingness-to-pay was performed using a state-transitionedMarkov model with a 20-year time horizon. Meanwhile, sensitivity analyses assessments werealso used to gauge the model’s stability. Results: The ICERs of olaparib plus bevacizumab versus bevacizumab alone were $487,428($374,758), $249,579 ($191,649), $258,859 ($198,739), and $270,736 ($206,640) per QALY(LY) in the overall patients, patients with BRCA mutations, patients with HRD, and patientswith HRD without BRCA mutations AOC, respectively, which indicated that The ICERs washigher than $150,000/QALY in the US. Progression-free sur vival (PFS) value and olaparib costemerged as the primar y influencing factors of these findings in the sensitivity analysis. Conclusion: At current cost levels, olaparib plus bevacizumab treatment is not a cost-effectivetreatment for patients with AOC regardless of their molecular status in the US. However, thismaintenance treatment may be more favorable health advantages for patients with BRACmutations AOC.
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Steganalysis of Synonym-Substitution Based Natural Language Watermarking
Zhenshan Yu,Liusheng Huang,Zhili Chen,Lingjun Li,Xinxin Zhao,Youwen Zhu 보안공학연구지원센터 2009 International Journal of Multimedia and Ubiquitous Vol.4 No.2
Natural language watermarking (NLW) is a kind of digital rights management (DRM) techniques specially designed for natural language documents. Watermarking algorithms based on synonym substitution are the most popular kind, they embeds watermark into documents in linguistic meaning-preserving ways. A lot of work has been done on embedding, but only a little on steganalysis such as detecting, destroying, and extracting the watermark. In this paper, we try to distinguish between watermarked articles and unwatermarked articles using context information. We evaluate the suitability of words for their context, and then the suitability sequence of words leads to the final judgment made by a SVM (support vector machine) classifier. IDF (inverse document frequency) is used to weight words’ suitability in order to balance common words and rare ones. This scheme is evaluated on internet instead of in a specific corpus, with the help of Google. Experimental results show that classification accuracy achieves 90.0%. And further analysis of several influencing factors affecting detection effects is also presented.
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