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Zhan Feng,1 Shuangshuang Lou,1 Lixia Zhang,1 Liang Zhang,2 Wenting Lan,3 Minhong Wang,4 Qijun Shen,5 Zhengyu Hu,6 Feng Chen1 1Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, People’s Republic of China; 2Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310003, People’s Republic of China; 3Department of Radiology, Ningbo First Hospital, Ningbo 315000, People’s Republic of China; 4Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241000, People’s Republic of China; 5Department of Radiology, Hangzhou First People’s Hospital, Hangzhou 310003, People’s Republic of China; 6Department of Radiology, Second People’s Hospital of Yuhang District, Hangzhou 310003, People’s Republic of ChinaCorrespondence: Feng ChenDepartment of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou 310003, People’s Republic of ChinaEmail chenfenghz@zju.edu.cnObjective: Nuclear grading is an independent prognosis factor of clear-cell renal cell carcinoma (ccRCC). A non-invasive preoperative predictive WHO/International Society of Urologic Pathology (WHO/ISUP) grading of ccRCC model is needed for clinical use. The anatomical complexity scoring system can span a variety of image modalities. The Centrality index (CI) is a quantitatively anatomical score commonly used for renal tumors. The purpose of this study was to develop a simple model to predict WHO/ISUP grading based on CI.Materials and methods: The data in this study were from 248 ccRCC patients from five hospitals. We developed three predictive models using training data from 167 patients: a CI-only model, a valuable clinical parameter model and a fusion model of CI with valuable clinical parameters. We compared and evaluated the three models by discrimination, clinical usefulness and calibration, then tested them in a set of validation data from 81 patients.Results: The fusion model consisting of CI and tumor size (valuable clinical parameter) had an area under the curve (AUC) of 0.82. In the validation set, the AUC was 0.85. The decision curve showed that the model had a good net benefit between the threshold probabilities of 5–80%. And the calibration curve showed good calibration in the training set and validation set.Conclusion: This study confirms that CI is associated with the WHO/ISUP grade of ccRCC, and the possibility that a bivariate model incorporating tumor size may help urologist’s evaluation patients’ prognostic.Keywords: kidney, carcinoma, renal cell, nomograms, validation studies, decision support techniques, anatomy, nephrectomy

Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: Accurately estimating the risks of thymic epithelial tumors (TETs) is of great guiding significance to choice of treatment approaches. However, TET’s WHO histological classification is very complex[1], and its correlation with CT findings is still...

Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: Hematoma expansion occurs in up to 73% of patients with primary intracerebral hemorrhage (ICH) and is independently associated with early neurological deterioration, disability and death[1,2].Prevention of expansion is the only intervention and...

As modern computer systems become increasingly complex in infrastructure and usage, the demand for capabilities of detecting anomalous behavior has grown urgent. Although techniques for point anomaly detection have been proposed and adopted in practice, behavior anomaly detection still lacks effective approaches due to its inherent complexities. We present a new anomalous behavior detection model based on similarity network. Instead of learning behaviors according to frequencies of occurrences as current approaches do, our model exploits the similarity relationship between emerging behavior patterns. Specifically, a Markov model rank algorithm is performed on the similarity network to discover behavior anomalies. Our model is able to distinguish normal behavior patterns and anomalous ones in changing environments without training phase. We implemented this model and conducted extensive experiments on a range of data sets. Results show that our model can detect behavior anomalies in computer systems with high accuracy.

PI-103, the first synthetic multitargeted compound which simultaneously inhibits PI3Kalpha and mammalian target of rapamycin (mTOR) shows high antitumor activity in glioma xenografts. In the present study, clear antitumor activity was observed with PI-103 treatment in two gefitinib-resistant non-small cell lung cancer (NSCLC) cell lines, A549 and H460, by simultaneously inhibiting p70s6k phosporylation and Akt phosphorylation in response to mTOR inhibition. In addition, H460 cells with activating mutations of PIK3CA were more sensitive to PI-103 than A549 cells with wild-type PIK3CA. PI-103 was found to inhibit growth by causing G0-G1 arrest in A549 and H460 cells. Western blotting showed that PI-103 induced down-regulation of cyclin D1 and E1 and simultaneously up-regulated p21 and p27, associated with arrest in the G0-G1 phase of the cell cycle. Furthermore, p53, the tumor suppressor which transcriptionally regulates p21, was also upregulated with PI-103 treatment. Collectively, our results suggest that multitargeted intervention is the most effective tumor therapy, and the cooperative blockade of PI3Kalpha and mTOR with PI-103 shows promise for treating gefitinib-resistant NSCLC.