Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

We summarise a set of analyses and studies conducted to assess the effects of the use of a computer-aided detection (CAD) tool in breast screening. We have used an interdisciplinary approach that combines: (a) statistical analyses inspired by reliability modelling in engineering; (b) experimental studies of decisions of mammography experts using the tool, interpreted in the light of human factors psychology; and (c) ethnographic observations of the use of the tool both in trial conditions and in everyday screening practice. Our investigations have shown patterns of human behaviour and effects of computer-based advice that would not have been revealed by a standard clinical trial approach. For example, we found that the negligible measured effect of CAD could be explained by a range of effects on experts' decisions, beneficial in some cases and detrimental in others. There is some evidence of the latter effects being due to the experts using the computer tool differently from the intentions of the developers. We integrate insights from the different pieces of evidence and highlight their implications for the design, evaluation and deployment of this sort of computer tool

How to discriminate between computer-aided and computer-hindered decisions: a case study in mammography

Background. Computer aids can affect decisions in complex ways, potentially even making them worse; common assessment methods may miss these effects. We developed a method for estimating the quality of decisions, as well as how computer aids affect it, and applied it to computer-aided detection (CAD) of cancer, reanalyzing data from a published study where 50 professionals (“readers”) interpreted 180 mammograms, both with and without computer support. Method. We used stepwise regression to estimate how CAD affected the probability of a reader making a correct screening decision on a patient with cancer (sensitivity), thereby taking into account the effects of the difficulty of the cancer (proportion of readers who missed it) and the reader’s discriminating ability (Youden’s determinant). Using regression estimates, we obtained thresholds for classifying a posteriori the cases (by difficulty) and the readers (by discriminating ability). Results. Use of CAD was associated with a 0.016 increase in sensitivity (95% confidence interval [CI], 0.003–0.028) for the 44 least discriminating radiologists for 45 relatively easy, mostly CAD-detected cancers. However, for the 6 most discriminating radiologists, with CAD, sensitivity decreased by 0.145 (95% CI, 0.034–0.257) for the 15 relatively difficult cancers. Conclusions. Our exploratory analysis method reveals unexpected effects. It indicates that, despite the original study detecting no significant average effect, CAD helped the less discriminating readers but hindered the more discriminating readers. Such differential effects, although subtle, may be clinically significant and important for improving both computer algorithms and protocols for their use. They should be assessed when evaluating CAD and similar warning systems

Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography

To investigate the effects of incorrect computer output on the reliability of the decisions of human users. This work followed an independent UK clinical trial that evaluated the impact of computer-aided detection(CAD) in breast screening. The aim was to use data from this trial to feed into probabilistic models (similar to those used in "reliability engineering") which would detect and assess possible ways of improving the human-CAD interaction. Some analyses required extra data; therefore, two supplementary studies were conducted. Study 1 was designed to elucidate the effects of computer failure on human performance. Study 2 was conducted to clarify unexpected findings from Study 1

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

Computer-assisted mammographic imaging

Computer-assisted mammography imaging comprises computer-based analysis of digitized images resulting in prompts aiding mammographic interpretation and computerized stereotactic localization devices which improve location accuracy. The commercial prompting systems available are designed to draw attention to mammographic abnormalities detected by algorithms based on symptomatic practise in North America. High sensitivity rates are important commercially but result in increased false prompt rates, which are known to distract radiologists. A national shortage of breast radiologists in the UK necessitates evaluation of such systems in a population breast screening programme to determine effectiveness in increasing cancer detection and feasibility of implementation

Using computer-aided detection in mammography as a decision support

Contains fulltext : 87548.pdf (publisher's version ) (Closed access)OBJECTIVE: To evaluate an interactive computer-aided detection (CAD) system for reading mammograms to improve decision making. METHODS: A dedicated mammographic workstation has been developed in which readers can probe image locations for the presence of CAD information. If present, CAD findings are displayed with the computed malignancy rating. A reader study was conducted in which four screening radiologists and five non-radiologists participated to study the effect of this system on detection performance. The participants read 120 cases of which 40 cases had a malignant mass that was missed at the original screening. The readers read each mammogram both with and without CAD in separate sessions. Each reader reported localized findings and assigned a malignancy score per finding. Mean sensitivity was computed in an interval of false-positive fractions less than 10%. RESULTS: Mean sensitivity was 25.1% in the sessions without CAD and 34.8% in the CAD-assisted sessions. The increase in detection performance was significant (p = 0.012). Average reading time was 84.7 +/- 61.5 s/case in the unaided sessions and was not significantly higher when interactive CAD was used (85.9 +/- 57.8 s/case). CONCLUSION: Interactive use of CAD in mammography may be more effective than traditional CAD for improving mass detection without affecting reading time.1 oktober 201

Microsatellite instability, KRAS mutations and cellular distribution of TRAIL-receptors in early stage colorectal cancer.

Thus, we evaluated the immunofluorescence pattern of TRAIL-receptors and E-cadherin to assess the fraction of membrane-bound TRAIL-receptors in 231 selected patients with early-stage CRC undergoing surgical treatment only. Moreover, we investigated whether membrane staining for TRAIL-receptors as well as the presence of KRAS mutations or of microsatellite instability (MSI) had an effect on survival and thus a prognostic effect. The fact that the receptors for the TNF-related apoptosis inducing ligand (TRAIL) are almost invariably expressed in colorectal cancer (CRC) represents the rationale for the employment of TRAIL-receptors targeting compounds for the therapy of patients affected by this tumor. Yet, first reports on the use of these bioactive agents provided disappointing results. We therefore hypothesized that loss of membrane-bound TRAIL-R might be a feature of some CRC and that the evaluation of membrane staining rather than that of the overall expression of TRAIL-R might predict the response to TRAIL-R targeting compounds in this tumor. As expected, almost all CRC samples stained positive for TRAIL-R1 and 2. Instead, membrane staining for these receptors was positive in only 71% and 16% of samples respectively. No correlation between KRAS mutation status or MSI-phenotype and prognosis could be detected. TRAIL-R1 staining intensity correlated with survival in univariate analysis, but only membranous staining of TRAIL-R1 and TRAIL-R2 on cell membranes was an independent predictor of survival (cox multivariate analysis: TRAIL-R1: p = 0.019, RR 2.06[1.12-3.77]; TRAIL-R2: p = 0.033, RR 3.63[1.11-11.84]). In contrast to the current assumptions, loss of membrane staining for TRAIL-receptors is a common feature of early stage CRC which supersedes the prognostic significance of their staining intensity. Failure to achieve therapeutic effects in recent clinical trials using TRAIL-receptors targeting compounds might be due to insufficient selection of patients bearing tumors with membrane-bound TRAIL-receptors