Dr Iain Phillips

PhD Student
MBBS, BSc (hons), MRCP, FRCR, PGDipPallMed

Academic and research departments

School of Biosciences and Medicine.

Publications

Phillips Iain, Ajaz Mazhar, Ezhil Veni, Prakash Vineet, Alobaidli Sheaka, McQuaid Sarah J., South Christopher, Scuffham James, Nisbet Andrew, Evans Philip (2017) Clinical Applications of textural analysis in Non-Small Cell Lung cancer,British Journal of Radiology91(1081) British Institute of Radiology
DOI: 10.1259/bjr.20170267
Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed ?radiomics? and includes semantic and agnostic approaches. Texture Analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue sub-types of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarise the current published data and understand the potential future role of TA in managing lung cancer.
Phillips Iain, Ezhil Veni, Hussein Mohammad, South Christopher, Alobaidli Sheaka, Nisbet Andrew, Ajaz Mazhar, Prakash Vineet, Wang Helen, Evans Philip (2018) Textural Analysis and Lung Function study: Predicting lung fitness for radiotherapy from a CT scan,BJR Open British Institute of Radiology

Objectives

This study tested the hypothesis that shows advanced image analysis can differentiate fit and unfit patients for radical radiotherapy from standard radiotherapy planning imaging, when compared to formal lung function tests (FEV1, Forced Expiratory Volume in 1 second) and TLCO (Transfer Factor of Carbon Monoxide).

Methods

An apical region of interest (ROI) of lung parenchyma was extracted from a standard radiotherapy planning CT scan. Software using a grey level co-occurrence matrix (GLCM) assigned an entropy score to each voxel, based on its similarity to the voxels around it. Density and entropy scores were compared between a cohort of fit patients (defined as FEV1 and TLCO above 50% predicted value) and unfit patients (FEV1 or TLCO below 50% predicted).

Results

29 fit and 32 unfit patients were included. Mean and median density and mean and median entropy were significantly different between fit and unfit patients (p= 0.0021, 0.0019, 0.0357 and 0.0363 respectively, 2 sided t-test).

Conclusions

Density and entropy assessment can differentiate between fit and unfit patients for radical radiotherapy, using standard CT imaging.

Advances in knowledge

This study shows that a novel intervention can generate further data from standard CT imaging. This data could be combined with existing studies to form a multi-organ patient fitness assessment from a single CT scan.

Wang Helen Yu Chi, Donovan Ellen M, Nisbet Andrew, South Christopher P, Alobaidli Sheaka, Ezhil Veni, Phillips Iain, Prakash Vineet, Ferreira Mark, Webster Philip, Evans Philip M (2019) The stability of imaging biomarkers in radiomics: a framework for evaluation,Physics in Medicine and Biology64(16)165012pp. 1-12 IOP Publishing
DOI: 10.1088/1361-6560/ab23a7
This paper studies the sensitivity of a range of image texture parameters used in radiomics to: i) the number of intensity levels, ii) the method of quantisation to select the intensity levels and iii) the use of an intensity threshold. 43 commonly used texture features were studied for the gross target volume outlined on the CT component of PET/CT scans of 50 patients with non-small cell lung carcinoma (NSCLC). All cases were quantised for all values between 4 and 128 intensity levels using four commonly used quantisation methods. All results were analysed with and without a threshold range of -200 HU to 300 HU. Cases were ranked for each texture feature and for all quantisation methods with the Spearman's rank correlation coefficient determined to evaluate stability. Results showed large fluctuations in ranking, particularly for low numbers of levels, differences between quantisation methods and with the use of a threshold, with values Spearman's Rank Correlation for many parameters below 0.2. Our results demonstrated the sensitivity of radiomics features to the parameters used during analysis and highlight the risk of low reproducibility comparing studies with slightly different parameters. In terms of the lung cancer CT datasets, this study supports the use of 128 intensity levels, the same uniform quantiser applied to all scans and thresholding of the data. It also supports several of the features recommended in the literature for such studies such as skewness and kurtosis. A recommended framework is presented for curation of the data analysis process to ensure stability of results.

Additional publications