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Munetoshi Akazawa,Kazunori Hashimoto,Katsuhiko Noda,Kaname Yoshida 대한산부인과학회 2021 Obstetrics & Gynecology Science Vol.64 No.3
ObjectiveMost women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patientswho develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probabilityof recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinicaldata. MethodsWe enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetricsstage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used,including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boostedtree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic,stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracyand the area under the curve (AUC). ResultsThe highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the bestpredictive model for this analysis was LR. ConclusionThe performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The useof a machine learning model made it possible to predict recurrence in early stage endometrial cancer.
Southeast Asian Foot-and-Mouth Disease Viruses in Eastern Asia
Knowles, Nick J.,He, JiJun,Shang, Youjun,Wadsworth, Jemma,Valdazo-Gonzá,lez, Begoñ,a,Onosato, Hiroyuki,Fukai, Katsuhiko,Morioka, Kazuki,Yoshida, Kazuo,Cho, In-Soo,Kim, Su-Mi,Park, Jong-Hye Centers for Disease Control and Prevention 2012 Emerging infectious diseases Vol.18 No.3
<P>Foot-and-mouth disease (FMD) outbreaks recently affected 2 countries (Japan and South Korea) in eastern Asia that were free of FMD without vaccination. Analysis of viral protein 1 nucleotide sequences indicated that FMD serotype A and O viruses that caused these outbreaks originated in mainland Southeast Asia to which these viruses are endemic.</P>