Purpose: Several scoring systems have been developed to distinguish between benign and malignant adnexal tumors. However, few of them have been externally validated in new populations. Our aim was to compare their performance on a prospectively collected large multicenter data set. Experimental Design: In phase I of the International OvarianTumor Analysis multicenter study, patients with a persistent adnexal mass were examined with transvaginal ultrasound and color Doppler imaging. More than 50 end point variables were prospectively recorded for analysis. The outcome measure was the histologic classification of excised tissue as malignant or benign. We used the International Ovarian Tumor Analysis data to test the accuracy of previously published scoring systems. Receiver operating characteristic curves were constructed to compare the performance of the models. Results: Data from 1,066 patients were included; 800 patients (75%) had benign tumors and 266 patients (25%) had malignant tumors. The morphologic scoring system used by Lerner gave an area under the receiver operating characteristic curve (AUC) of 0.68, whereas the multimodal risk of malignancy index used by Jacobs gave an AUC of 0.88. The corresponding values for logistic regression and artificial neural network models varied between 0.76 and 0.91 and between 0.87 and 0.90, respectively. Advanced kernel-based classifiers gave an AUC of up to 0.92. Conclusion: The performance of the risk of malignancy index was similar to that of most logistic regression and artificial neural network models. The best result was obtained with a relevance vector machine with radial basis function kernel. Because the models were tested on a large multicenter data set, results are likely to be generally applicable.

Radiotherapy is an important treatment modality against cancer resulting in apoptosis and inhibition of cell growth. Survivin is an important cancer biomarker conferring to tumour cells increased survival potential by inhibiting apoptosis. In the present study, we investigated the implication of breast cancer cells features, as hormone receptors and p53 status, in the radio-resistance of breast cancer cells and in the regulation of survivin’s expression by nuclear factor (NF)-κB and c-myc. Six breast cancer cell lines Michigan Cancer Foundation (MCF-7), MCF-7/Human Epidermal Growth Factor Receptor (HER)2, M. D. Anderson – Metastatic Breast (MDA-MB-231), SK-BR-3, BT-474 and Human Breast Lactating (HBL-100) were irradiated and cell viability as well as cell cycle distribution were evaluated by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and flow cytometry, respectively. Survivin mRNA and protein levels were evaluated by real time PCR and Western blot analysis. Survivin and HER2 gene knockdown was performed with siRNA technology and investigation of transcription factors binding to survivin and c-myc gene promoters was assessed by chromatin immunoprecipitation. Student’s t-test and F-statistics were used for statistical evaluation. Our results demonstrated that only HER2+ breast cancer cells up-regulated survivin upon irradiation, whereas HER2 knockdown in HER2+ cells led to survivin’s down-regulation. Survivin and especially HER2 knockdown abolished the observed G2/M cell cycle checkpoint and reduced the radio-resistance of HER2 overexpressing breast cancer cells. Additionally, HER2 was found to regulate survivin’s expression through NF-κB and c-myc transcription factors. This study revealed the significance of HER2 in the radio-resistance of HER2+ breast cancer cells through induction of transcription factors NF-κB and c-myc, leading to activation of survivin, a downstream target oncogene preventing apoptosis.

The present study suggests that following irradiation, HER2 receptor activates hTERT/telomerase, increasing the breast cancer cells' survival potential, through sequential induction of transcription factors NF-κΒ and c-myc.

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Abstract. In this work, an artificial neural network (ANN) model is developed and used to predict the presence of ducting phenomena for a specific time, taking into account ground values of atmospheric pressure, relative humidity and temperature. A feed forward backpropagation ANN is implemented, which is trained, validated and tested using atmospheric radiosonde data from the Helliniko airport, for the period from 1991 to 2004. The network's quality and generality is assessed using the Area Under the Receiver Operating Characteristics (ROC) Curves (AUC), which resulted to a mean value of about 0.86 to 0.90, depending on the observation time. In order to validate the ANN results and to evaluate any further improvement options of the proposed method, the problem was additionally treated using Least Squares Support Vector Machine (LS-SVM) classifiers, trained and tested with identical data sets for direct performance comparison with the ANN. Furthermore, time series prediction and the effect of surface wind to the presence of tropospheric ducts appearance are discussed. The results show that the ANN model presented here performs efficiently and gives successful tropospheric ducts predictions.