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Sasaki, Yusuke,Hamaguchi, Tetsuya,Yamada, Yasuhide,Takahashi, Naoki,Shoji, Hirokazu,Honma, Yoshitaka,Iwasa, Satoru,Okita, Natsuko,Takashima, Atsuo,Kato, Ken,Nagai, Yushi,Taniguchi, Hirokazu,Boku, Nari Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.2
Background: It is well known that peritoneal carcinomatosis (PC) from colorectal cancer (CRC) is associated with a poor prognosis. However, data on the prognostic significance of modern chemotherapy containing bevacizumab, cetuximab or panitumumab are not available. Materials and Methods: This retrospective review concerned 526 patients with metastatic CRC who were classified into two groups according to the presence or absence of PC, and were treated with systemic chemotherapy, with or without bevacizumab or anti-EGFR antibodies. The genetic background, in particular KRAS, BRAF, and PIK3CA gene mutations, and overall survival (OS) were compared between the two groups. Results: The median OS values were 23.3 and 29.1 months for PC and non-PC patients, respectively (hazard ratio [HR]=1.20; p=0.17). Among all patients, tumor location, number of metastatic sites and BRAF mutation status were significant prognostic factors, whereas the presence of PC was not. In the PC group, chemotherapy with bevacizumab resulted in a significantly longer OS than forchemotherapy without bevacizumab (HR=0.38, p<0.01), but this was not the case in the non-PC group (HR=0.80, p=0.10). Furthermore, the incidence of the BRAF V600E mutation was significantly higher in PC than in non-PC patients (27.7% versus 7.3%, p<0.01). BRAF mutations displayed a strong correlation with shorter OS in non-PC (HR=2.26), but not PC patients (HR=1.04). Conclusions: Systemic chemotherapy, especially when combined with bevacizumab, improved survival in patients with PC from CRC as well as non-PC patients. While BRAF mutation demonstrated a high frequency in PC patients, but it was not associated with prognosis.
Nguyen Ho Minh Duy,Tran Anh Tuan,Nguyen Hai Duong,Tran Anh Tuan,Nguyen Kim Dao,Atsuo Yoshitaka,Jin Young Kim,Seung Ho Choi,Pham The Bao 한국정보기술학회 2016 한국정보기술학회논문지 Vol.14 No.5
MRI and CT images are the most popular formats assisting a doctor in diagnosis and treatment, but highly accurate segmentation is a challenging problem due to intensity inhomogeneity and environmental noises. In this paper, we introduce an appropriate and effective automatic approach to facilitate this problem in two stages. In the first stage, skull region is removed from the brain by morphological active contour and level set process. Moreover, in level set process, some AI rules are defined on slice positions of brain to increase the accuracy. In the second stage, a modified EM method is performed on the resultant skull-stripping image to identify some candidate main regions of CSF (cerebro-spinal fluid), GM (gray matter), and WM (white matter). The candidate regions are then re-estimated into the proper CSF, GM, and WM through a Bayesian Estimation Process. The experimental results show that the proposed approach obtains a robust segmentation for IBSR, OASIS and Korean Hospital database. With the proposed AI-rules, the level set method gets good skull-stripping images regardless of MRI slice position in bran. Also, Bayesian postprocessing can improve the segmentation performance by 10~15% in CSF, GM and WM ratios compared the basic EM algorithm.