Dynamics of Gompertzian tumour growth under environmental fluctuations

We investigate the effect of correlated additive and multiplicative Gaussian white noise oil the Gompertzian growth of tumours. Our results are obtained by Solving numerically the time-dependent Fokker-Planck equation (FPE) associated with the stochastic dynamics. In Our numerical approach we have adopted B-spline functions as a truncated basis to expand the approximated eigenfunctions. The eigenfunctions and eigenvalues obtained using this method are used to derive approximate solutions of the dynamics under Study. We perform simulations to analyze various aspects, of the probability distribution. of the tumour cell populations in the transient- and steady-state regimes. More precisely, we are concerned mainly with the behaviour of the relaxation time (tau) to the steady-state distribution as a function of (i) of the correlation strength (lambda) between the additive noise and the multiplicative noise and (ii) as a function of the multiplicative noise intensity (D) and additive noise intensity (alpha). It is observed that both the correlation strength and the intensities of additive and multiplicative noise, affect the relaxation time.

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.

Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.