A hazard model of the probability of medical school dropout in the United Kingdom

From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons

Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer

Background: Radiomics allows information not readily available to the naked eye to be extracted from high resolution imaging modalities such as CT. Identifying that a cancer has already metastasised at the time of presentation through a radiomic signature will affect the treatment pathway. The ability to recognise the existence of metastases earlier will have a significant impact on the survival outcomes. / Aim: To create a novel radiomic signature using textural analysis in the evaluation of synchronous liver metastases in colorectal cancer. / Methods: CT images at baseline and subsequent surveillance over a 5-year period of patients with colorectal cancer were processed using textural analysis software. Comparison was made between those patients who developed liver metastases and those that remained disease free to detect differences in the ‘texture’ of the liver. / Results: A total of 24 patients were divided into two matched groups for comparison. Significant differences between the two groups scores when using the textural analysis programme were found on coarse filtration (p = 0.044). Patients that went on to develop metastases an average of 18 months after presentation had higher levels of hepatic heterogeneity on CT. / Conclusion: This initial study demonstrates the potential of using a textural analysis programme to build a radiomic signature to predict the development of hepatic metastases in rectal cancer patients otherwise thought to have clear staging CT scans at time of presentation

Magnetic resonance-based texture parameters as potential imaging biomarkers for predicting long term survival in locally advanced rectal cancer treated by chemoradiotherapy

AIM: The study aimed to investigate whether textural features of rectal cancer on magnetic resonance imaging (MRI) can predict long term survival in patients treated with long-course chemoradiotherapy. METHOD: Textural analysis (TA) using a filtration-histogram technique of T2-weighted pre- and six-week post chemoradiotherapy MRI was undertaken using TexRAD, a proprietary software algorithm. Regions of interest enclosing the largest cross-sectional area of the tumour were manually delineated on the axial images and filtration-step extracted features at different anatomical scales (fine, medium, and coarse) followed by quantification of statistical features (mean intensity, standard-deviation, entropy, skewness, kurtosis and mean of positive pixels [MPP]) using histogram analysis. Cox multiple regression analysis determined which univariate features including textural, radiological and histological, independently predicted overall survival (OS), disease free survival (DFS) and recurrence-free survival (RFS). RESULTS: MPP (fine-texture, HR: 6.9, 95% CI [2.43-19.55], p= <0.001), mean (medium-texture, HR: 5.6 [1.4-21.7], p=0.007) and extramural venous invasion (EMVI) on MRI (HR: 2.96, [1.04-8.37], p=0.041) independently predicted OS while mean (medium texture, HR: 4.53, [1.58-12.94], p=0.003), MPP (fine texture, HR: 3.36 [1.36-8.31], p=0.008) and threatened circumferential resection margin (CRM) on MRI (HR: 3.1 [1.01-9.46], p=0.046) predicted DFS. For OS; EMVI on MRI (HR: 4.23 [1.41-12.69], p=0.01) and for DFS; kurtosis (medium-texture, HR: 3.97 [1.44-10.94], p=0.007) and CRM involvement on MRI (HR: 3.36 [1.21-9.32], p=0.02) were the independent post-treatment factors. Only TA independently predicted RFS on pre- or post-treatment analyses. CONCLUSION: MR based TA of rectal cancers can predict outcome before undergoing surgery and could potentially select patients for individualized therapy. This article is protected by copyright. All rights reserved

Image-Based Monte-Carlo Localisation without a Map

In this paper, we propose a way to fuse the image-based localisation approach with the Monte-Carlo localisation approach. The method we propose does not suffer of the major limitation of the two separated methods: the need of a metric map of the environment for the Monte-Carlo localisation and the failure of the image-based approach in environments with spatial periodicity (perceptual aliasing). The approach we developed exploits the properties of the Fourier Transform of the omnidirectional images and uses the similarity between the images to weights the beliefs about the robot position. Successful experiments in large indoor environment are presented in which we do not used a priory information on the metrical map of the environment

Legacy of COVID‐19 – the opportunity to enhance surgical services for patients with colorectal disease

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An advanced Bayesian model for the visual tracking of multiple interacting objects

Visual tracking of multiple objects is a key component of many visual-based systems. While there are reliable algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple interacting objects that have a complex dynamics. In this article, a novel Bayesian model for tracking multiple interacting objects in unrestricted situations is proposed. This is accomplished by means of an advanced object dynamic model that predicts possible interactive behaviors, which in turn depend on the inference of potential events of object occlusion. The proposed tracking model can also handle false and missing detections that are typical from visual object detectors operating in uncontrolled scenarios. On the other hand, a Rao-Blackwellization technique has been used to improve the accuracy of the estimated object trajectories, which is a fundamental aspect in the tracking of multiple objects due to its high dimensionality. Excellent results have been obtained using a publicly available database, proving the efficiency of the proposed approach