Facilitating Colorectal Cancer Diagnosis with Computed Tomographic Colonography

Computed tomographic colonography (CTC) is a diagnostic technique involving helical volume acquisition of the cleansed, distended colorectum to detect colorectal cancer or potentially premalignant polyps. This Thesis summarises the evidence base, identifies areas in need of further research, quantifies sources of bias and presents novel techniques to facilitate colorectal cancer diagnosis using CTC. CTC literature is reviewed to justify the rationale for current implementation and to identify fruitful areas for research. This confirms excellent diagnostic performance can be attained providing CTC is interpreted by trained, experienced observers employing state-of-the-art implementation. The technique is superior to barium enema and consequently, it has been embraced by radiologists, clinicians and health policy-makers. Factors influencing generalisability of CTC research are investigated, firstly with a survey of European educational workshop participants which revealed limited CTC experience and training, followed by a systematic review exploring bias in research studies of diagnostic test accuracy which established that studies focussing on these aspects were lacking. Experiments to address these sources of bias are presented, using novel methodology: Conjoint analysis is used to ascertain patients‘ and clinicians’ attitudes to false-positive screening diagnoses, showing that both groups overwhelmingly value sensitivity over specificity. The results inform a weighted statistical analysis for CAD which is applied to the results of two previous studies showing the incremental benefit is significantly higher for novices than experienced readers. We have employed eye-tracking technology to establish the visual search patterns of observers reading CTC, demonstrated feasibility and developed metrics for analysis. We also describe development and validation of computer software to register prone and supine endoluminal surface locations demonstrating accurate matching of corresponding points when applied to a phantom and a generalisable, publically available, CTC database. Finally, areas in need of future development are suggested

Colonoscopy and Colorectal Cancer Screening

Colorectal cancer (CRC) represents a major public health problem worldwide. Fortunately most CRCs originate from a precursor lesion, the adenoma, which is accessible and removable. This is the rationale for CRC screening programs, which are aimed to diagnose CRC at an early stage or even better to detect and resect the advanced adenoma before CRC has developed. In this background colonoscopy emerges as the main tool to achieve these goals with recent evidence supporting its role in CRC prevention. This book deals with several topics to be faced when implementing a CRC screening program. The interested reader will learn about the rationale and challenges of implementing such a program, the management of the detected lesions, the prevention of complications of colonoscopy, and finally the use of other screening modalities that are emerging as valuable alternatives. The relevance of the topics covered in it and the updated evidence included by the authors turn this book into a very useful tool to introduce the reader in this amazing and evolving field

Competence and Professional Advancement in Computed Tomography (ComPACT)

Background: Evolving technology, practice boundaries, and models of service provision have changed what the diagnostic radiography workforce in Computed Tomography (CT) need to know and be able to do at different career stages. It is unknown whether existing UK educational and practice frameworks support CT roles, or how organisational role descriptors constitute CT competence. Aims: The study explores multiple stakeholder perspectives of competent practice in diagnostic radiography and CT using this information to generate and gain consensus on novel modality-specific skills for the four-tier radiography structure. Methods: Study 1: Document analysis of published UK educational and practice frameworks using content and framework analysis. Study 2: Document analysis of UK CT role descriptors using a context analytical approach. Study 3: Modified e-Delphi study to gain consensus on novel technical and clinical CT practice competencies. Results: Existing radiography competencies were classified into 3 themes: delivering person-centred care; applying technical principles, quality and efficiency; and ensuring best practice. Generic competencies endorsed by professional and regulatory bodies were not consistently replicated within organisational role descriptors. CT practice expectations are ambiguous and modified from radiographic competencies. Modified e-Delphi panellists provided judgement on 215 practice competencies and advanced capabilities which have been refined and organised into a coherent framework. Conclusions: The ComPACT framework formalises the tacit technical knowledge, clinical competencies and professional capabilities that specifically address the practice area of CT. Further work is required to validate the framework and define educational standards, but there is potential to influence future graduates, workforce development and national standards

Developing a patient reported experience measure for gastrointestinal procedures (ENDOPREM)

M.D. ThesisBackground: Gastrointestinal (GI) endoscopy and computed tomography colonoscopy (CTC) are important diagnostic and therapeutic tools in the investigation and management of gastrointestinal diseases. Current measures of patient satisfaction and experience within GI endoscopy are clinician derived and measured. This study aims to develop a patient reported experience measure (PREM) specific to GI procedures, derived from the patient’s perspective. Methods: The study comprised three phases. Phase 1: This qualitative phase involved semi-structured interviews with patients who had recently undergone endoscopy/CTC. Thematic analysis identified important aspects of the patient experience. Phase 2: A questionnaire bank was developed from the thematic analysis. An iterative process of review and revision within the wider study team refined the questions. Rounds of cognitive interviews with patients who had undergone GI procedures were used to further refine the questionnaire. Phase 3: The resultant PREM was prospectively administered, for self-completion, to 1652 patients following a GI procedure. IBM® SPSS® 24 was used to investigate the psychometric properties of the instrument. Results: Phase 1: 35 participants participated in semi-structured interviews. Six over-arching themes were identified: anxiety, expectations, information & communication, embarrassment & dignity, choice &control and comfort. Phase 2: Areas related to these themes were structured by procedural stage. Ten rounds of review and revision within the study team were conducted, followed by five rounds of cognitive interviews (total n=15). Phase 3: 799 participants completed the questionnaire (response rate= 48.4%). Of the 59 questionnaire items, a ‘ceiling’ effect was present in 24. No questions demonstrated ‘floor’ effects. Individual item completion rates were high, with only three items having >5% missing. Exploratory factor analysis identified potential scales within the questionnaire. Conclusion: The ENDOPREM™ is a tool which assesses all aspects of the GI procedure experience. Potential future uses include assessing patient experience in routine care or comparing experience associated with different endoscopic interventions in trials

Pixel N-grams for Mammographic Image Classification

X-ray screening for breast cancer is an important public health initiative in the management of a leading cause of death for women. However, screening is expensive if mammograms are required to be manually assessed by radiologists. Moreover, manual screening is subject to perception and interpretation errors. Computer aided detection/diagnosis (CAD) systems can help radiologists as computer algorithms are good at performing image analysis consistently and repetitively. However, image features that enhance CAD classification accuracies are necessary for CAD systems to be deployed. Many CAD systems have been developed but the specificity and sensitivity is not high; in part because of challenges inherent in identifying effective features to be initially extracted from raw images. Existing feature extraction techniques can be grouped under three main approaches; statistical, spectral and structural. Statistical and spectral techniques provide global image features but often fail to distinguish between local pattern variations within an image. On the other hand, structural approach have given rise to the Bag-of-Visual-Words (BoVW) model, which captures local variations in an image, but typically do not consider spatial relationships between the visual “words”. Moreover, statistical features and features based on BoVW models are computationally very expensive. Similarly, structural feature computation methods other than BoVW are also computationally expensive and strongly dependent upon algorithms that can segment an image to localize a region of interest likely to contain the tumour. Thus, classification algorithms using structural features require high resource computers. In order for a radiologist to classify the lesions on low resource computers such as Ipads, Tablets, and Mobile phones, in a remote location, it is necessary to develop computationally inexpensive classification algorithms. Therefore, the overarching aim of this research is to discover a feature extraction/image representation model which can be used to classify mammographic lesions with high accuracy, sensitivity and specificity along with low computational cost. For this purpose a novel feature extraction technique called ‘Pixel N-grams’ is proposed. The Pixel N-grams approach is inspired from the character N-gram concept in text categorization. Here, N number of consecutive pixel intensities are considered in a particular direction. The image is then represented with the help of histogram of occurrences of the Pixel N-grams in an image. Shape and texture of mammographic lesions play an important role in determining the malignancy of the lesion. It was hypothesized that the Pixel N-grams would be able to distinguish between various textures and shapes. Experiments carried out on benchmark texture databases and binary basic shapes database have demonstrated that the hypothesis was correct. Moreover, the Pixel N-grams were able to distinguish between various shapes irrespective of size and location of shape in an image. The efficacy of the Pixel N-gram technique was tested on mammographic database of primary digital mammograms sourced from a radiological facility in Australia (LakeImaging Pty Ltd) and secondary digital mammograms (benchmark miniMIAS database). A senior radiologist from LakeImaging provided real time de-identified high resolution mammogram images with annotated regions of interests (which were used as groundtruth), and valuable radiological diagnostic knowledge. Two types of classifications were observed on these two datasets. Normal/abnormal classification useful for automated screening and circumscribed/speculation/normal classification useful for automated diagnosis of breast cancer. The classification results on both the mammography datasets using Pixel N-grams were promising. Classification performance (Fscore, sensitivity and specificity) using Pixel N-gram technique was observed to be significantly better than the existing techniques such as intensity histogram, co-occurrence matrix based features and comparable with the BoVW features. Further, Pixel N-gram features are found to be computationally less complex than the co-occurrence matrix based features as well as BoVW features paving the way for mammogram classification on low resource computers. Although, the Pixel N-gram technique was designed for mammographic classification, it could be applied to other image classification applications such as diabetic retinopathy, histopathological image classification, lung tumour detection using CT images, brain tumour detection using MRI images, wound image classification and tooth decay classification using dentistry x-ray images. Further, texture and shape classification is also useful for classification of real world images outside the medical domain. Therefore, the pixel N-gram technique could be extended for applications such as classification of satellite imagery and other object detection tasks.Doctor of Philosoph

Colorectal Cancer Screening among Chamoru on Guahan: Barriers and Access to Care.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

A comparison of fixed tube current (FTC) and automatic tube current modulation (ATCM) CT methods for abdominal scanning : implications on radiation dose and image quality

PURPOSE: There has been a huge increase in the use of abdominal CT scanning in recent years. This has contributed to an increase in radiation dose administered to patients. Abdominal CT scans generally require higher exposure factors when compared to other anatomical regions. This drives a need for urgent optimisation of the radiation dose and image quality for abdominal CT examinations. The aim of this thesis is to evaluate Fixed Tube Current (FTC) and Automatic Tube Current Modulation (ATCM) on image quality and radiation dose during abdominal CT examinations across a range of scanning parameters. MATERIALS AND METHODS: Using a Toshiba Aquilion 16 CT scanner (120 kVp, 0.5 seconds tube rotation), an adult ATOM dosimetry and abdominal anthropomorphic phantom were exposed to a series of FTC and ATCM CT protocols with variations in tube current as follows: FTC - 100, 200, 250, 300 and 400mA; ATCM - low dose+, low dose, standard, quality and high quality. The pitch factors evaluated included were 0.688, 0.938 & 1.438 and the detector configurations included were 0.5×16 mm, 1.0×16 mm and 2.0×16 mm. Radiation doses for nine abdominal organs were directly measured using the Metal Oxide Semiconductor Field Effect Transistors (MOSFET). Effective dose (ED) was measured and estimation comprised of three methods: mathematical modelling with k-factors and dose length product DLP, direct with MOSFET and indirectly with Monte Carlo simulation (ImPACT). Effective risk (ER) was estimated using MOSFET data and Brenner’s equations / BEIR VII 2006 report. The raw data for ATCM radiation dose was corrected using an equivalence equation. The ATCM corrected and uncorrected data were compared against FTC. Image quality was assessed using SNR (five abdominal organs) and a relative visual grading analysis (VGA) method (five different axial images). Image quality evaluation was performed by the researcher after testing agreement between against five different observers. RESULTS: There were no significant differences in the mean radiation doses between FTC and corrected ATCM across a range of acquisition protocols (P>0.05). This was with the exception of the 300mA/quality protocols, and for a fast pitch factor with 0.5×16mm detector configurations. These had significantly lower doses for FTC (P<0.05). These differences were up to 13% for the mean abdominal organ doses, effective doses and the effective risk. In addition, for all acquisition parameters, the mean radiation dose was significantly higher (P<0.05; 17%-23%) for uncorrected ATCM when compared to FTC. In terms of image quality, there were no differences in SNR values between FTC and ATCM for the majority of acquisition protocols, excepting the higher mean SNR value (P<0.05) for the FTC at 100mA/low dose + and 200 mA/ low dose (pancreas, left and right kidneys). Conversely, the mean SNR values were significantly higher (P<0.05) for the ATCM scans for 300mA/quality and fast pitch factor (1.438) (liver, spleen and pancreas) than FTC. Finally, relative VGA scores for both FTC and ATCM demonstrated no significant difference, except for ‘quality’ ATCM scans (image # 1, image # 2) and a fast pitch factor (1.438) for image #2 and #3. CONCLUSION: FTC and corrected ATCM were generally similar in terms of radiation dose and image quality except for some acquisition parameters; 300mA/quality tube current and fast (1.483) pitch factor FTC was lower than the corrected ATCM. However, the uncorrected ATCM produced higher radiation dose when compared with FTC techniques. In addition, FTC and ATCM generally produced similar SNR, again with the exception of some protocols. The SNR was higher for FTC than ATCM at lower tube current (pancreas, left and right kidneys), at 300mA/quality and fast pitch factor (1.438) SNR values for ATCM higher than FTC (liver and spleen). However, the ATCM technique is able to produce higher mean relative VGA scores for upper and middle abdominal organs. Further investigation of image quality and radiation dose difference between FTC and ATCM is required