FREE PhD study and funding virtual fair REGISTER NOW FREE PhD study and funding virtual fair REGISTER NOW

Detection of interval cancers of Upper GI Tract using advanced text mining methods

   School of Computing Sciences

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Dr B de la Iglesia  No more applications being accepted  Self-Funded PhD Students Only

About the Project

According to various studies [1,2,3], upper gastrointestinal (UGI) cancers are frequently missed at endoscopy. Also the percentage of early diagnosis of such cancers is relatively low around 12% and has remained unchanged for an number of years. The study hypothesis is that demographic and clinical variables extracted from routinely collected data in written endoscopy and histology reports, at the time of a "cancer-negative" endoscopy, serve as risk factors for interval cancers of the UGI tract.

While the combination of state-of-the art machine learning approaches and routinely collected health data for the development and validation of risk prediction models are established in a number of diseases including colorectal cancer [4], evidence of their application for the prediction of interval UGI cancers from such sources are lacking. This study aims to establish the feasibility of applying advanced text mining to routinely collected endoscopy reports to predict missed UGI tract cancers.

Text mining methods have advanced rapidly in recent years and deep learning in particular offer much promise [5]. Deep learning algorithms such as Recurrent Neural Networks or Convolutional Neural Networks may discover new features associated with missed cancers in medical reports. We will compare different feature extraction methods (e.g. n-grams, word embeddings, etc.) and also different algorithms (classical text mining algorithms, deep learning, etc.) in order to find a most efficient approach. This project will be in collaboration with Norwich Medical School and the Department of Gastroenterology, Norfolk and Norwich University Hospital, so it will be interdisciplinary.  They will provide us with access to the necessary data for the study. We will offer the student training in machine learning, and algorithm development as well as in aspects of the medical domain necessary to analyse the data successfully. 

Funding Notes

This PhD project is offered on a self-funding basis. It is open to applicants with funding or those applying to funding sources. Details of tuition fees can be found at
A bench fee is also payable on top of the tuition fee to cover specialist equipment or laboratory costs required for the research. Applicants should contact the primary supervisor for further information about the fee associated with the project.


[1] Chadwick G, Groene O, Hoare J, Hardwick RH, Riley S, Crosby TD, et al. A population-based, retrospective, cohort study of esophageal cancer missed at endoscopy. Endoscopy. 2014;46(7):553-60.
[2] Menon S, Trudgill N. How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis. Endoscopy international open. 2014;2(2):E46-50.
[3] Chadwick G, Groene O, Riley S, Hardwick R, Crosby T, Hoare J, et al. Gastric Cancers Missed During Endoscopy in England. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2015;13(7):1264-70 e1.
[4] Hoogendoorn M, Szolovits P, Moons LMG, Numans ME. Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer. Artificial intelligence in medicine. 2016;69:53-61.
[5] O. Edo-Osagie, B. De La Iglesia, a. Lake and Edeghere, "Deep Learning for Relevance Filtering in Syndromic Surveillance: A Case Study in Asthma/Difficulty Breathing," in 8th International Conference on Pattern Recognition Applications and Methods (ICPRAM 8th International Conference on Pattern Recognition Applications and Methods), 2018.
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs