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

How does image quality affect radiologists' perceived ability for image interpretation and lesion detection in digital mammography?

OBJECTIVES: To study how radiologists' perceived ability to interpret digital mammography (DM) images is affected by decreases in image quality. METHODS: One view from 45 DM cases (including 30 cancers) was degraded to six levels each of two acquisition-related issues (lower spatial resolution and increased quantum noise) and three post-processing-related issues (lower and higher contrast and increased correlated noise) seen during clinical evaluation of DM systems. The images were shown to fifteen breast screening radiologists from five countries. Aware of lesion location, the radiologists selected the most-degraded mammogram (indexed from 1 (reference) to 7 (most degraded)) they still felt was acceptable for interpretation. The median selected index, per degradation type, was calculated separately for calcification and soft tissue (including normal) cases. Using the two-sided, non-parametric Mann-Whitney test, the median indices for each case and degradation type were compared. RESULTS: Radiologists were not tolerant to increases (medians: 1.5 (calcifications) and 2 (soft tissue)) or decreases (median: 2, for both types) in contrast, but were more tolerant to correlated noise (median: 3, for both types). Increases in quantum noise were tolerated more for calcifications than for soft tissue cases (medians: 3 vs. 4, p = 0.02). Spatial resolution losses were considered less acceptable for calcification detection than for soft tissue cases (medians: 3.5 vs. 5, p = 0.001). CONCLUSIONS: Perceived ability of radiologists for image interpretation in DM was affected not only by image acquisition-related issues but also by image post-processing issues, and some of those issues affected calcification cases more than soft tissue cases. KEY POINTS: • Lower spatial resolution and increased quantum noise affected the radiologists' perceived ability to interpret calcification cases more than soft tissue lesion or normal cases. • Post-acquisition image processing-related effects, not only image acquisition-related effects, also impact the perceived ability of radiologists to interpret images and detect lesions. • In addition to current practices, post-acquisition image processing-related effects need to also be considered during the testing and evaluation of digital mammography systems

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

The impact of simulated motion blur on breast cancer detection performance in full field digital mammography (FFDM)

Objective: Full-field Digital Mammography (FFDM) is employed in breast screening for the early detection of breast cancer. High quality, artefact free, diagnostic images are crucial to the accuracy of this process. Unwanted motion during the image acquisition phase and subsequent image blurring is an unfortunate occurrence in some FFDM images. The research detailed in this thesis seeks to understand the impact of motion blur on cancer detection performance in FFDM images using novel software to perform simulation of motion, an observer study to measure the lesion detection performance and physical measures to assess the impact of simulated motion blur on image characteristics of the lesions. Method: Seven observers (15±5 years’ reporting experience) evaluated 248 cases (62 containing malignant masses, 62 containing malignant microcalcifications and 124 normal cases) for three conditions: no motion blur (0.0 mm) and two magnitudes of simulated motion blur (0.7 mm and 1.5 mm). Abnormal cases were biopsy proven. A free-response observer study was conducted to compare lesion detection performance for the three conditions. Equally weighted jackknife alternative free-response receiver operating characteristic (wJAFROC) was used as the figure of merit. A secondary analysis of data was deemed important to simulate ‘double reporting’. In this secondary analysis, six of the observers are combined with the seventh observer to evaluate the impact of combined free-response data for lesion detection and to assess if combined two observers data could reduce the impact of simulated motion blur on detection performance. To compliment this, the physical characteristics of the lesions were obtained under the three conditions in order to assess any change in characteristics of the lesions when blur is present in the image. The impact of simulated motion blur on physical characteristics of malignant masses was assessed using a conspicuity index; for microcalcifications, a new novel metric, known as dispersion index, was used. Results: wJAFROC analysis found a statistically significant difference in lesion detection performance for both masses (F (2,22) = 6.01, P=0.0084) and microcalcifications (F(2,49) = 23.14, P<0.0001). For both lesion types, the figure of merit reduced as the magnitude of simulated motion blur increased. Statistical differences were found between some of the pairs investigated for the detection of masses (0.0mm v 0.7mm, and 0.0mm v 1.5mm) and all pairs for microcalcifications (0.0 mm v 0.7 mm, 0.0 mm v 1.5 mm, and 0.7 mm v 1.5 mm). No difference was detected between 0.7 mm and 1.5 mm for masses. For combined two observers’ data of masses, there was no statistically significant difference between single and combined free-response data for masses (F(1,6) = 4.04, p=0.1001, -0.031 (-0.070, 0.008) [treatment difference (95% CI)]. For combined data of microcalcifications, there was a statistically significant difference between single and combined free-response data (F(1,6) = 12.28, p=0.0122, -0.056 (-0.095, -0.017) [treatment difference (95% CI)]. Regarding the physical measures of masses, conspicuity index increases as the magnitude of simulated motion blur increases. Statistically significant differences were demonstrated for 0.0–0.7 mm t(22)=-6.158 (p<0.000); 0.0–1.5 mm t(22)=-6.273 (p<0.000); and 0.7–1.5 mm (t(22)=-6.231 (p<0.000). Lesion edge angle decreases as the magnitude of simulated motion blur increases. Statistically significant differences were demonstrated for 0.0–0.7 mm t(22)=3.232 (p<0.004); for 0.0–1.5 mm t(22)=6.592 (p<0.000); and 0.7–1.5mm t(22)=2.234 (p<0.036). For the grey level change there was no statistically significant difference as simulated motion blur increases to 0.7 and then to 1.5mm. For image noise there was a statistically significant difference, where noise reduced as simulated motion blur increased: 0.0–0.7 mm t(22)=22.95 (p<0.000); 0.0–1.5mm t(22)=24.66 (p<0.000); 0.7–1.5 mm t(22)=18.11 (p<0.000). For microcalcifications, simulated motion blur had a negative impact on the ‘dispersion index’. Conclusion: Mathematical simulations of motion blur resulted in a statistically significant reduction in lesion detection performance. This reduction in performance could have implications for clinical practice. Simulated motion blur has a negative impact on the edge angle of breast masses and a negative impact on the image characteristics of microcalcifications. These changes in the image lesion characteristics appear to have a negative effect on the visual identification of breast cancer