Yoko Sawamura

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Correlations between mammalian target of rapamycin (mTOR) expression, and clinicopathological features, outcome and Akt expression in endometrial endometrioid adenocarcinoma (EEC) were investigated. Tumour samples were obtained from 82 patients with EEC who had undergone hysterectomy, and phosphorylated mTOR (p-mTOR) and Akt (p-Akt) expression in the(More)
Cisplatin is one of the most potent antitumor agents for ovarian cancer, but has also been implicated in normal tissue cytotoxicity. We examined the effect of cisplatin alone and in combination with C16Y, a newly-identified anti-angiogenic peptide from the NH2-terminal domains of the γ-chain of laminin-1, on the modulation of Bcl-2/Bax expression and(More)
UNLABELLED The purpose of this study was to prospectively determine whether combined MRI and (18)F-FDG PET is more accurate than MRI in assessing nonbenign uterine smooth muscle tumors (USMTs). METHODS Seventy patients (mean age, 49+/-10 y; range, 28-77 y) suspected of having nonbenign USMTs underwent both MRI and (18)F-FDG PET before surgery. Results(More)
BACKGROUND The positron emission tomography (PET) with F18 17beta-estradiol (FES) has good imaging for assessment of estrogen receptor in breast cancer. CASE We report on a 30-year-old woman who desired to preserve her fertility with well-differentiated endometrial adenocarcinoma. Before hormone treatment was started, FES-PET showed increased uptake of(More)
OBJECTIVE To report 2 cases of a probable interaction between cisplatin and warfarin. CASE SUMMARY Two cases of transient elevation of international normalized ratio (INR) during irinotecan (60 mg/m2 on days 1, 8, and 15) plus cisplatin (60 mg/m2 on day 1) chemotherapy with concomitant warfarin are presented. In both cases, warfarin dosages were stable at(More)
This paper considers image unmixing of hyperspectral data with a small training data set. We propose a semi-supervised contextual unmixing method for hyperspectral data. Gaussian mixture models and a novel MRF (Markov random field) are assumed for distributions of feature vectors and category fraction vectors, respectively. Then, we derive a semi-supervised(More)
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