Positron emission tomography/computerised tomography imaging in detecting and managing recurrent cervical cancer: systematic review of evidence, elicitation of subjective probabilities and economic modelling.

© Queen’s Printer and Controller of HMSO 2013. This work was produced by Meads et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising.Cancer of the uterine cervix is a common cause of mortality in women. After initial treatment women may be symptom free, but the cancer may recur within a few years. It is uncertain whether it is more clinically effective to survey asymptomatic women for signs of recurrence or to await symptoms or signs before using imaging.National Institute for Health Research Health Technology Assessment programm

Evaluating PET-CT in the detection and management of recurrent cervical cancer: Systematic reviews of diagnostic accuracy and subjective elicitation

Background: Positron emission tomography-computed tomography (PET-CT) is recommended to triage women for exenterative surgery and surveillance after treatment for advanced cervical cancer. Objective: To evaluate diagnostic accuracy of additional whole body PET-CT compared with CT/magnetic resonance imaging (MRI) alone in women with suspected recurrent/persistent cervical cancer and in asymptomatic women as surveillance. Design: Systematic reviews. Subjective elicitation to supplement diagnostic information. Search strategy/Selection criteria/Data collection and analysis: Searches of electronic databases were performed to June 2013. Studies in women with suspected recurrent/persistent cervical cancer and in asymptomatic women undergoing follow up with sufficient numeric data were included. We calculated sensitivity, specificity and corresponding 95% confidence intervals. Meta-analyses employed a bivariate model that included a random-effects term for between-study variations (CT studies) and univariate random effects meta-analyses (PET-CT studies) for sensitivity and specificity separately. Subjective elicitation: Prevalence of recurrence and the accuracy of imaging elicited using the allocation of points technique. Coherence of elicited subjective probabilities with estimates in the literature examined. Results: We identified 15 relevant studies; none directly compared additional PET-CT with MRI or CT separately. Most CT and MRI studies used older protocols and the majority did not distinguish between asymptomatic and symptomatic women. Meta-analysis of nine PET-CT studies in mostly symptomatic women showed sensitivity of 94.8 (95% CI 91.2-96.9), and specificity of 86.9% (95% CI 82.2-90.5). The summary estimate of the sensitivity of CT for detection of recurrence was 89.64% (95% CI 81.59-94.41) and specificity was 76% (95% CI 43.68-92.82). Meta-analysis for MRI test accuracy studies was not possible because of clinical heterogeneity. The sensitivity and specificity of MRI in pelvic recurrence varied between 82 and 100% and between 78 and 100%, respectively. Formal statistical comparisons of the accuracy of index tests were not possible. Subjective elicitation provided estimates comparable to the literature. Subjective estimates of the increase in accuracy from the addition of PET-CT were less than elicited increases required to justify the use in PET-CT for surveillance. Conclusion: Evidence to support additional PET-CT is scarce, of average quality and does not distinguish between application for surveillance and diagnosis. Guidelines recommending PET-CT in recurrent cervical cancer need to be reconsidered in the light of the existing evidence base

Nondestructive detection method for the calcium and nitrogen content of living plants based on Convolutional Neural Networks (CNN) using multispectral images

Herein, we present the novel method targeted for determination of plant nutritional state with the use of computer vision and Neural Networks. The method is based on multispectral imaging performed by an exclusively designed Agroscanner and a dedicated analytical system for further data analysis with Neural Networks. An Agroscanner is a low-cost mobile construction intended for multispectral measurements at macro-scale, operating at four wavelengths: 470, 550, 640 and 850 nm. Together with developed software and implementation of a Neural Network it was possible to design a unique approach to process acquired plant images and assess information about plant physiological state. The novelty of the developed technology is focused on the multispectral, macro-scale analysis of individual plant leaves, rather than entire fields. Such an approach makes the method highly sensitive and precise. The method presented herein determines the basic physiological deficiencies of crops with around 80% efficiency

A method for matching sequences of protein secondary structures

Alignment of specific regions of two biological molecules is a basic method for determination how similar these two molecules are. There are several methods of optimal alignment that were developed through many years. However, they are dedicated for nucleotide sequences of DNA⁄RNA or amino acid sequences of proteins. Since the construction of proteins can also be analyzed at the level of secondary structure (and higher), we need a comparative method, which would allow us to determine the similarity between biological particles at this level and express it through the appropriate similarity measure. For this reason, we have modified an existing Smith–Waterman method towards matching sequences of secondary structures elements (SSEs). In the paper, we present our modification to the method. We also describe how we find several alternative and equally optimal alignment paths on the basis of the characteristics of compared sequences. Presented alignment method is used in the PSS–SQL language, which allows searching a database in order to find proteins having secondary structures similar to the structural pattern specified by a user

A declarative query language for protein secondary structures

Searching proteins on their secondary structures provides a rough and fast method of identification of molecules having a similar fold. Since existing database management systems do not offer integrated exploration methods for querying protein structures, the structural similarity searching is usually performed by external tools. This often lengthens the processing time and requires additional processing steps, like adaptation of input and output data formats. In the paper, we present the extended SQL language, which allows searching a database in order to find proteins having secondary structures similar to the structural pattern specified by a user. Presented query language is integrated with the relational database management system and it simplifies the manipulation of biological data

The poll tax and property prices

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