Does pre-operative estimation of oesophageal tumour metabolic length using F-18-fluorodeoxyglucose PET/CT images compare with surgical pathology length?

PURPOSE:
The aim of the study was to compare the pre-operative metabolic tumour length on FDG PET/CT with the resected pathological specimen in patients with oesophageal cancer.

METHODS:
All patients diagnosed with oesophageal carcinoma who had undergone staging PET/CT imaging between the period of June 2002 and May 2008 who were then suitable for curative surgery, either with or without neo-adjuvant chemotherapy, were included in this study. Metabolic tumour length was assessed using both visual analysis and a maximum standardised uptake value (SUV(max)) cutoff of 2.5.

RESULTS:
Thirty-nine patients proceeded directly to curative surgical resection, whereas 48 patients received neo-adjuvant chemotherapy, followed by curative surgery. The 95% limits of agreement in the surgical arm were more accurate when the metabolic tumour length was visually assessed with a mean difference of -0.05 cm (SD 2.16 cm) compared to a mean difference of +2.42 cm (SD 3.46 cm) when assessed with an SUV(max) cutoff of 2.5. In the neo-adjuvant group, the 95% limits of agreement were once again more accurate when assessed visually with a mean difference of -0.6 cm (SD 1.84 cm) compared to a mean difference of +1.58 cm (SD 3.1 cm) when assessed with an SUV(max) cutoff of 2.5.

CONCLUSION:
This study confirms the high accuracy of PET/CT in measuring gross target volume (GTV) length. A visual method for GTV length measurement was demonstrated to be superior and more accurate than when using an SUV(max) cutoff of 2.5. This has the potential of reducing the planning target volume with dose escalation to the tumour with a corresponding reduction in normal tissue complication probability.

MicroRNA-130b regulates the tumour suppressor RUNX3 in gastric cancer

Aim: Accumulating evidence indicates that RUNX3 is an important tumour suppressor that is inactivated in many cancer types. This study aimed to assess the role of microRNA (miRNA) in the regulation of RUNX3.

An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano-circuit simulation. The FinFET used in this work is designed using careful engineering of source-drain extension, which simultaneously improves maximum frequency of oscillation f(max) because of lower gate to drain capacitance, and intrinsic gain A(V0) = g(m)/g(ds), due to lower output conductance g(ds). The framework for the ANN-based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current I-d on drain-source V-ds and gate-source V-gs is derived by a simple two-layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low-noise amplifier. At low power (J(ds) similar to 10 mu A/mu m) improvement was observed in both third-order-intercept IIP3 (similar to 10 dBm) and intrinsic gain A(V0) (similar to 20 dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first-order to third-order derivative of I-d with respect to gate voltage and lower g(ds), in FinFET compared to bulk MOSFET. Copyright (C) 2009 John Wiley & Sons, Ltd.

Image segmentation based on multi-scan constraint satisfaction neural network

A novel image segmentation method based on a constraint satisfaction neural network (CSNN) is presented. The new method uses CSNN-based relaxation but with a modified scanning scheme of the image. The pixels are visited with more distant intervals and wider neighborhoods in the first level of the algorithm. The intervals between pixels and their neighborhoods are reduced in the following stages of the algorithm. This method contributes to the formation of more regular segments rapidly and consistently. A cluster validity index to determine the number of segments is also added to complete the proposed method into a fully automatic unsupervised segmentation scheme. The results are compared quantitatively by means of a novel segmentation evaluation criterion. The results are promising.