Precision Imaging Ultrasound Technology - Does It Improve Accuracy And Increase Confidence In Diagnosing Breast Tumours?

Objective To determine the effect of Precision Imaging (PI), an innovative speckle reduction algorithm, on the diagnostic efficacy in breast ultrasound Material and methods Patients aged from 20 to 84 years screened by the breast clinic from October 2010 to June 2011 were included in this research. The commercial ultrasound scanner Toshiba AplioMX, with compact linear transducers 15-7MHz and 12-5 MHz was used for image acquisition. A single projection image that was considered to best represent the lesion was recorded without PI (L0), then with all other 3 levels of PI, namely Precision 1 (L1), Precision 2 (L2) and Precision 3 (L3), with higher numbers signifying greater speckle reduction. Fifty one breast lesions (20 malignant and 31 benign) were selected from over 200 collected lesions, with selection criteria based on the 1- 5 classification system developed by National Breast Cancer Centre in collaboration with the Royal Australian and New Zealand College of Radiologists. These selected images were cropped to remove the technical details, which included patient information as well as PI level. These processed images were then organised into four sets (A,B,C,D) with images in same PI level. These four sets of images were evaluated by six radiologists and six sonographers dedicated to breast imaging, scoring each lesion between 1 and 6.These scores were subjected to Q-Perform software, DBMMRMC, Mann-Whitney U-test, Wilcoxon Signed rank test and IBM SPSS statistics for statistical analyses. Results The overall means ROCAUC for L0 was 0.79, L1 was 0.80, L2 was 0.81, and L3 was 0.81. The overall means sensitivity for L0 was 0.75, L1 was 0.79, L2 was 0.80, and L3 was 0.78.Overall means specificity for L0 was 0.74, L1 was 0.72, L2 was 0.73, and L3 was 0.71. Conclusion The data analysis on ROC, sensitivity, and specificity did not demonstrate any significant improvement in diagnostic efficacy amongst expert observers in this study

Recall Rates in Screening Mammography: Variability in Performance and Decisions

Having a high recall rate may increase the probability of cancer being detected earlier, however it also has been related to increased false positive decisions, causing significant psychological and economical costs for both screened women and the mammography screening service. Therefore, the purpose of this thesis is to explore the impact of various recall rates on breast radiologists’ performance in a laboratory setting. Methods This study was designed to encompass two aspects 1) the effect of setting varying recall rates on the performance of breast radiologists in screening mammography 2) types of mammographic appearances of breast cancer are more likely to be missed at different recall rates. Five Australian breast radiologists were recruited to read one single test set of 200 mammographic cases (180 normal and 20 abnormal cases) over three different recall rate conditions: free recall, 15% and 10%. These radiologists were tasked with marking the location of suspicious lesions and providing a confidence. Results A significant decrease in radiologists’ performance was observed when reading at lower recall rates, with lower sensitivity (P=0.002), case location sensitivity (P=0.002) and ROC AUC (P=0.003). Reading at a lower recall rate had a significant increase in specificity (P=0.002). The second study of this thesis showed that breast radiologists demonstrated lower sensitivity and receiver ROC AUC for non-specific density (NSD) (P=0.04 and P=0.03 respectively) and mixed features (P=0.01 and P=0.04 respectively) when reading at 15% and 10% recall rates. No significant change was observed on cancer characterized with stellate masses (P=0.18 and P=0.54 respectively) and architectural distortion (P=1.00 and P=0.37 respectively). Conclusion Reducing the number of recalled cases to 10% significantly reduced breast radiologists’ performance. Stellate masses were likely to be recalled (90.0%) while NSDs were likely to be missed (45.6%) at reduced recall rates

Methodology for taking a computer-aided breast cancer screening system from the laboratory to the marketplace

Breast cancer is one of the most common causes of death in women, and yet is one of the more 'curable' cancers if caught early. Since its inception in 1987, the Breast Screening Programme has been the principal tool in the National Health Service's fight to reduce the number of cancer related deaths in the UK. Breast screening using mammography is widely viewed as the most effective way of detecting early breast cancer, with the UK population of women over the age of 50 being invited to a screening session every three years. However, national shortages of clinical staff willing to enter and remain in this field mean that the NHS Breast Screening Programme is severely understaffed. This thesis discusses one way in which technology can assist in the screening programme; specifically, the use of a computer-aided cancer detection system. Here, we will present the design and analysis of a sequence of experiments used to develop and evaluate such a system. PROMAM (PROmpting for MAMmography) involved the scanning and digitising of mammograms, and the subsequent analysis of the digital image by a series of algorithms. Initial evaluation was done to ensure that the algorithms were performing satisfactorily at a technical level before being introduced into a clinical setting. Two large experiments with the algorithms were designed and evaluated: 1. offering radiologists three levels of algorithm prompting and, as a control, an unprompted level, on samples of mammographic films, with outcomes being their recall rate and subjective views at each prompting level, 2. a pre-clinical experiment, conducted under semi-clinical conditions, where two readers would see a batch of films seeded with higher than normal numbers of cancers, with readers allocated randomly to prompted and unprompted views of films. The first experiment was designed using a Graeco-Latin Square, with three 'nuisance' variables and the treatment factor of prompting levels (no prompts, low level of prompt¬ ing, medium and high). Four radiologists read at each level of prompting once, on dif¬ ferent sets of films. One of the more interesting results was that the recall rate did not increase as the prompting rate rose - contrary to prior expectations. Most of the differ¬ ences seen between the prompting rates could be explained as radiologist differences. Once these were taken into account, the level of prompting had little effect. Addition¬ ally, although the time taken to read a set of films increased as the prompting rate increased (as would be expected), it was only an increase of 26% from the unprompted set to the set with the highest number of prompts. Observational data suggested that the lowest level of prompting was not maintaining the interest of the radiologist, thus leading them to neglect the prompts. The following experiment moved the system a step closer to a true clinical demonstra¬ tion of the efficacy of PROMAM, being conducted under semi-clinical conditions. Using a method of minimisation, the number of cancers each radiologist viewed as first reader, second reader, prompted or unprompted were balanced. Preliminary exploratory anal¬ ysis indicated that the recall rate declined with the introduction of the prompting system, but more detailed, analysis indicated that much of this difference was due to a radiologist effect. Although cancer detection was slightly lower with the prompting system, examination of the 11 cancers missed by the prompted radiologist showed that six of these had been correctly prompted by the algorithms. This demonstrated scope to improve the cancer detection rate by nearly 5%. These experiments determined the 'production' version of the prompting system. A design to evaluate the system in a sample of 100,000 women in six centres was produced, but due to circumstances beyond the project team's control, it was not possible to take this work to the stage of a full 'trial' of the system. The design concept can, however, apply to the evaluation of any similar prompting system. The recommended design is therefore presented, together with an analysis of data from a simulated application of this design. This simulation has allowed recommendations to be made on the most appropriate ways to analyse the extensive and complicated dataset that will be obtained. In particular, it identified technical problems that can arise from the application on one candidate analytical method, and an explanation for the failure obtained It is quite clear from the evidence presented in this thesis that there is much scope for improvement in the cancer detection rate by the use of a prompting system, with¬ out a corresponding loss in the specificity. With the shortage of radiologists and ra¬ diographers, and the increasing demand placed on the Breast Screening Programme, technology could play a beneficial role in screening for breast cancer in the coming year

Doctor of Philosophy

dissertationUsing eye-tracking technology to capture the visual scanpaths of a sample of laypersons (N = 92), the current study employed a 2 (training condition: ABCDE vs. Ugly Duckling Sign) Ã- 2 (visual condition: photorealistic images vs. illustrations) factorial design to assess whether SSE training succeeds or fails in facilitating increases in sensitivity and specificity. Self-efficacy and perceived importance were tested as moderators, and eye-tracking fixation metrics as mediators, within the framework of Visual Skill Acquisition Theory (VSAT). For sensitivity, results indicated a significant main effect for visual condition, F(1,88) = 7.102, p = .009, wherein illustrations (M = .524, SD = .197) resulted in greater sensitivity than photos (M = .425, SD = .159, d = .55). For specificity, the main effect for training was not significant, F(1,88) = 2.120, p = .149; however, results indicated a significant main effect for visual condition, F(1,88) = 4.079, p = .046, wherein photos (M = .821, SD = .108) resulted in greater specificity than illustrations (M = .770, SD = .137, d = .41). The interaction for training Ã- visual condition, F(1,88) = 3.554, p = .063, was significant within a 90% confidence interval, such that those within the UDS Photo condition displayed greater specificity than all other combinations of training and visual condition. No significant moderated mediation manifested for sensitivity, but for specificity, the model was significant, r = .59, R2 = .34, F(9,82) = 4.7783, p =.001, with Percent of Time in Lookzone serving as a significant mediator, and both self-efficacy and visual condition significantly moderating the mediation. For those in the photo condition with very high self-efficacy, UDS increased specificity directly. For those in the photo condition with self-efficacy levels at the mean or lower, there was a conditional indirect effect through Percent of Time in Lookzoneâ€"which is to say that these individuals spent a larger amount of their viewing time on target (observing the atypical nevi)â€"and time on target is positively related to specificity. Findings suggest that existing SSE training techniques may be enhanced by maximizing visual processing efficiency

Getting the gist of it: An investigation of gist processing and the learning of novel gist categories

Gist extraction rapidly processes global structural regularities to provide access to the general meaning and global categorizations of our visual environment – the gist. Medical experts can also extract gist information from mammograms to categorize them as normal or abnormal. However, the visual properties influencing the gist of medical abnormality are largely unknown. It is also not known how medical experts, or any observer for that matter, learned to recognise the gist of new categories. This thesis investigated the processing and acquisition of the gist of abnormality. Chapter 2 observed no significant differences in performance between 500 ms and unlimited viewing time, suggesting that the gist of abnormality is fully accessible after 500 ms and remains available during further visual processing. Next, chapter 3 demonstrated that certain high-pass filters enhanced gist signals in mammograms at risk of future cancer, without affecting overall performance. These filters could be used to enhance mammograms for gist risk-factor scoring. Chapter 4’s multi-session training showed that perceptual exposure with global feedback is sufficient to induce learning of a new gist categorisation. However, learning was affected by individual differences and was not significantly retained after 7-10 days, suggesting that prolonged perceptual exposure might be needed for consolidation. Chapter 5 observed evidence for the neural signature of gist extraction in medical experts across a network of regions, where neural activity patterns showed clear individual differences. Overall, the findings of this thesis confirm the gist extraction of medical abnormality as a rapid, global process that is sensitive to spatial structural regularities. Additionally, it was shown that a gist category can be learned via global feedback, but this learning is hard to retain and is affected by individual differences. Similarly, individual differences were observed in the neural signature of gist extraction by medical experts

Understanding and Mitigating Search Errors in 3D Volumetric Images

In the field of oncology, three-dimensional volumetric medical images provide radiologists with a detailed visual representation of various anatomical structures that facilitate the early detection and characterization of malignant lesions but at the cost of an increased search space. Recent work (Lago et al., 2021) establishes that human observers rely heavily on peripheral visual processing away from the point of fixation when searching for signals in 3D volumetric images. The searcher’s over-reliance on peripheral vision interacts strongly with how much of the volume they explore and with how much they report they have explored. Specifically, observers under-explore—as determined by the percentage of the volume covered by the Useful Field of View (UFOV)—and overestimate the percentage of volume they explored through self-report measures. Consequently, they miss small signals during the search. This thesis aims to elucidate the psychological factors mediating human under-exploration of 3D volumetric image data. The second thrust of this thesis is to investigate three solutions to mitigate the detrimental impact of under-exploration in 3D images. The first method is a 2D synthetic view of the 3D data that observers can utilize as additional information when performing the 3D search. I establish through behavioral measurements and a computational model simulating foveated vision how the 2D-S guides eye movements to suspicious regions in the 3D volume. In turn, this guidance allows observers to find the small signal that would otherwise be missed without the 2D-S adjunct. The second method involves a different type of search aid, a convolutional neural network, which acts as a computer-aided detection system to assist human observers during the 3D search. Like the 2D-S, it guides eye movements to suspicious regions in a 3D volumetric image that observers would have otherwise not looked at. The last method is inspired by the power of group decision-making. It investigates how combining multiple independent judgments from a group of searchers can lead to more exploration of the search space and a higher chance of detecting the small signal. Together, the body of work herein provides empirical results from laboratory studies to further our understanding of how humans interact with 3D imaging modalities with the goal of improving healthcare services relating to early cancer screenings

Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides

This thesis aims at determining if computer-assisted analysis can be used to better understand pathologists’ perception of mitotic figures on Hematoxylin-Eosin (HE) stained breast histopathological digital slides. It also explores the feasibility of reproducible histologic nuclear atypia scoring by incorporating computer-assisted analysis to cytological scores given by a pathologist. In addition, this thesis investigates the possibility of computer-assisted diagnosis for categorizing HE breast images into different subtypes of cancer or benign masses. In the first study, a data set of 453 mitoses and 265 miscounted non-mitoses within breast cancer digital slides were considered. Different features were extracted from the objects in different channels of eight colour spaces. The findings from the first research study suggested that computer-aided image analysis can provide a better understanding of image-related features related to discrepancies among pathologists in recognition of mitoses. Two tasks done routinely by the pathologists are making diagnosis and grading the breast cancer. In the second study, a new tool for reproducible nuclear atypia scoring in breast cancer histological images was proposed. The third study proposed and tested MuDeRN (MUlti-category classification of breast histopathological image using DEep Residual Networks), which is a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each. The studies indicated that computer-assisted analysis can aid in both nuclear grading (COMPASS) and breast cancer diagnosis (MuDeRN). The results could be used to improve current status of breast cancer prognosis estimation through reducing the inter-pathologist disagreement in counting mitotic figures and reproducible nuclear grading. It can also improve providing a second opinion to the pathologist for making a diagnosis