Selection of examples in case-based computer-aided decision systems

Use of patient decision aids increased younger women's reluctance to begin screening mammography: a systematic review and meta-analysis

BACKGROUND

As breast cancer screening guidelines have changed recently, additional investigation is needed to understand changes in women's behavior after using breast cancer screening patient decision aids (BCS-PtDAs) and the potential effect on mammography utilization. This systematic review and meta-analysis sought to evaluate the effect of BCS-PtDAs on changes in women's intentions to undergo screening mammography and whether women deciding to begin or discontinue screening mammography displayed similar changes in screening intentions after using a BCS-PtDA.

METHODS

We searched Medline, Scopus, PsycINFO, CENTRAL, Health and Psychosocial Instruments, Health Technology Assessment Database, PsycARTICLES, and cited references in eligible papers for randomized controlled trials (RCTs) and observational studies, published through August 24, 2016. The proportions of women who did and not intend to undergo screening and who were uncertain about undergoing screening mammography were pooled, using risk ratios (RR) and random effects. According to the protocol, RCTs or observational studies and any language were considered eligible for systematic review if they included data about women for which shared decision making is recommended.

RESULTS

We ultimately included six studies with screening intention data for 2040 women. Compared to usual care, the use of BCS-PtDAs in three RCTs resulted in significantly more women deciding not to undergo screening mammography (RR 1.48 [95% CI 1.04-2.13]; P = 0.03), particularly for younger (38-50 years) women (1.77 [1.34-2.34]; P < 0.001). The use of BCS-PtDAs had a non-significant effect on the intentions of older women (69-89 years) to discontinue screening.

CONCLUSIONS

The use of BCS-PtDAs increased younger women's reluctance to undergo screening for breast cancer. The implementation of such BCS-PtDAs in clinical practice would be expected to result in a 77% increase in the number of younger women (aged 38-50) who do not intend to be screened, and as a consequence, may reduce utilization of screening mammography.

REGISTRATION

The protocol of this review is registered in the PROSPERO database, #CRD42016036695.

Observer study of a prototype clinical decision support system for breast cancer diagnosis using dynamic contrast-enhanced MRI

OBJECTIVE

The purpose of this article is to evaluate the performance of radiologists using a prototype clinical decision support system to diagnose and manage patients with breast cancer based on dynamic contrast-enhanced MRI studies.

MATERIALS AND METHODS

The study was conducted with three breast radiologists and two breast imaging fellows who gave patient treatment recommendations and confidence ratings, both without and with computer aid. The computer aid presented similar cases from a retrieval database of 192 lesions (96 malignant and 96 benign) for a test set of 97 mass lesions (46 malignant and 51 benign). The performance of each observer was quantified by receiver operating characteristic analysis. The radiologists' confidence in their recommendations was analyzed with respect to the query case pathologic diagnosis, perceived usefulness of the similar cases, and the accuracy of the computer in retrieving cases of the correct diagnosis. The statistical significance in the performance measure differences was determined by using a two-tailed Student t test for paired data.

RESULTS

For each observer, the area under the receiver operating characteristic curve did not change significantly with the use of the computer aid (from a mean of 0.8 to a mean of 0.8; p = 0.61). The average confidence of three of the five observers increased significantly with the computer aid (from 5.9 to 6.3 [p < 0.001], from 7.0 to 7.2 [p = 0.04], and from 4.4 to 5.4 [p < 0.001], respectively). The confidence change of the radiologists was more frequent and larger for malignant lesions where the computer was correct. However, for benign lesions, even when the computer was correct, the confidence of the radiologists did not necessarily change.

CONCLUSION

The presentation of similar cases reinforced radiologists' confidence rating in the diagnosis of malignant lesions; however, it did not change their confidence rating for benign lesions or reduce the number of unnecessary biopsies in managing patients with breast cancer using dynamic contrast-enhanced MRI under the limited study conditions.

Assessment of performance and reliability of computer-aided detection scheme using content-based image retrieval approach and limited reference database

Content-based image retrieval approach was used in our computer-aided detection (CAD) schemes for breast cancer detection with mammography. In this study, we assessed CAD performance and reliability using a reference database including 1500 positive (breast mass) regions of interest (ROIs) and 1500 normal ROIs. To test the relationship between CAD performance and the similarity level between the queried ROI and the retrieved ROIs, we applied a set of similarity thresholds to the retrieved similar ROIs selected by the CAD schemes for all queried suspicious regions, and used only the ROIs that were above the threshold for assessing CAD performance at each threshold level. Using the leave-one-out testing method, we computed areas under receiver operating characteristic (ROC) curves (A(Z)) to assess CAD performance. The experimental results showed that as threshold increase, (1) less true positive ROIs can be referenced in the database than normal ROIs and (2) the A(Z) value was monotonically increased from 0.854 ± 0.004 to 0.932 ± 0.016. This study suggests that (1) in order to more accurately detect and diagnose subtle masses, a large and diverse database is required, and (2) assessing the reliability of the decision scores based on the similarity measurement is important in application of the CBIR-based CAD schemes when the limited database is used.

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis

Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algorithms for analysis of the new images and finally testing of the resulting system. Since new imaging modalities are developed more rapidly than ever before, any effort for decreasing the time and cost of this development process could result in maximizing the benefit of the new imaging modality to patients by making the computer aids quickly available to radiologists that interpret the images. In this paper, we make a step in this direction and investigate the possibility of translating the knowledge about the detection problem from one imaging modality to another. Specifically, we present a computer-aided detection (CAD) system for mammographic masses that uses a mutual information-based template matching scheme with intelligently selected templates. We presented principles of template matching with mutual information for mammography before. In this paper, we present an implementation of those principles in a complete computer-aided detection system. The proposed system, through an automatic optimization process, chooses the most useful templates (mammographic regions of interest) using a large database of previously collected and annotated mammograms. Through this process, the knowledge about the task of detecting masses in mammograms is incorporated in the system. Then, we evaluate whether our system developed for screen-film mammograms can be successfully applied not only to other mammograms but also to digital breast tomosynthesis (DBT) reconstructed slices without adding any DBT cases for training. Our rationale is that since mutual information is known to be a robust inter-modality image similarity measure, it has high potential of transferring knowledge between modalities in the context of the mass detection task. Experimental evaluation of the system on mammograms showed competitive performance compared to other mammography CAD systems recently published in the literature. When the system was applied "as-is" to DBT, its performance was notably worse than that for mammograms. However, with a simple additional preprocessing step, the performance of the system reached levels similar to that obtained for mammograms. In conclusion, the presented CAD system not only performed competitively on screen-film mammograms but it also performed robustly on DBT showing that direct transfer of knowledge across breast imaging modalities for mass detection is in fact possible.

Exploring the potential of context-sensitive CADe in screening mammography

PURPOSE

Conventional computer-assisted detection (CADe) systems in screening mammography provide the same decision support to all users. The aim of this study was to investigate the potential of a context-sensitive CADe system which provides decision support guided by each user's focus of attention during visual search and reporting patterns for a specific case.

METHODS

An observer study for the detection of malignant masses in screening mammograms was conducted in which six radiologists evaluated 20 mammograms while wearing an eye-tracking device. Eye-position data and diagnostic decisions were collected for each radiologist and case they reviewed. These cases were subsequently analyzed with an in-house knowledge-based CADe system using two different modes: Conventional mode with a globally fixed decision threshold and context-sensitive mode with a location-variable decision threshold based on the radiologists' eye dwelling data and reporting information.

RESULTS

The CADe system operating in conventional mode had 85.7% per-image malignant mass sensitivity at 3.15 false positives per image (FPsI). The same system operating in context-sensitive mode provided personalized decision support at 85.7%-100% sensitivity and 0.35-0.40 FPsI to all six radiologists. Furthermore, context-sensitive CADe system could improve the radiologists' sensitivity and reduce their performance gap more effectively than conventional CADe.

CONCLUSIONS

Context-sensitive CADe support shows promise in delineating and reducing the radiologists' perceptual and cognitive errors in the diagnostic interpretation of screening mammograms more effectively than conventional CADe.

Identifying factors controlling protein release from combinatorial biomaterial libraries via hybrid data mining methods

Polyanhydrides are a class of degradable biomaterials that have shown much promise for applications in drug and vaccine delivery. Their properties can be tailored for controlled drug release, drug/protein stability, and immune regulation (adjuvant effect). Identifying the relationship between the molecular structures of the polymers and the drug release kinetics profiles would help understand the release mechanism and aid in the accurate prediction of drug release and the rational design of polymer-based drug carrier systems. The molecular structure descriptors that had the most impact on the release kinetics were identified using a prediction/optimization data mining approach. Using this new approach for modeling nonlinear release kinetics behavior, we determined that the descriptors which had the greatest effect on the release kinetics were the number of backbone -COO- nonconjugated bonds, the number of aromatic rings, and the number of -CH₂- bonds.

Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support

When constructing a pattern classifier, it is important to make best use of the instances (a.k.a. cases, examples, patterns or prototypes) available for its development. In this paper we present an extensive comparative analysis of algorithms that, given a pool of previously acquired instances, attempt to select those that will be the most effective to construct an instance-based classifier in terms of classification performance, time efficiency and storage requirements. We evaluate seven previously proposed instance selection algorithms and compare their performance to simple random selection of instances. We perform the evaluation using k-nearest neighbor classifier and three classification problems: one with simulated Gaussian data and two based on clinical databases for breast cancer detection and diagnosis, respectively. Finally, we evaluate the impact of the number of instances available for selection on the performance of the selection algorithms and conduct initial analysis of the selected instances. The experiments show that for all investigated classification problems, it was possible to reduce the size of the original development dataset to less than 3% of its initial size while maintaining or improving the classification performance. Random mutation hill climbing emerges as the superior selection algorithm. Furthermore, we show that some previously proposed algorithms perform worse than random selection. Regarding the impact of the number of instances available for the classifier development on the performance of the selection algorithms, we confirm that the selection algorithms are generally more effective as the pool of available instances increases. In conclusion, instance selection is generally beneficial for instance-based classifiers as it can improve their performance, reduce their storage requirements and improve their response time. However, choosing the right selection algorithm is crucial.

An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms

Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two general ways of constructing subclassifiers by resampling of the available development dataset: Random division and random selection. Furthermore, they discuss the problem of selecting the ensemble size and propose two adaptive incremental techniques that automatically select the size for the problem at hand. All the techniques are evaluated with respect to a previously proposed information-theoretic CAD system (IT-CAD). The experimental results show that the examined ensemble techniques provide a statistically significant improvement (AUC = 0.905 +/- 0.024) in performance as compared to the original IT-CAD system (AUC = 0.865 +/- 0.029). Some of the techniques allow for a notable reduction in the total number of examples stored in the case base (to 1.3% of the original size), which, in turn, results in lower storage requirements and a shorter response time of the system. Among the methods examined in this article, the two proposed adaptive techniques are by far the most effective for this purpose. Furthermore, the authors provide some discussion and guidance for choosing the ensemble parameters.

Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives

As the rapid advance of digital imaging technologies, the content-based image retrieval (CBIR) has became one of the most vivid research areas in computer vision. In the last several years, developing computer-aided detection and/or diagnosis (CAD) schemes that use CBIR to search for the clinically relevant and visually similar medical images (or regions) depicting suspicious lesions has also been attracting research interest. CBIR-based CAD schemes have potential to provide radiologists with "visual aid" and increase their confidence in accepting CAD-cued results in the decision making. The CAD performance and reliability depends on a number of factors including the optimization of lesion segmentation, feature selection, reference database size, computational efficiency, and relationship between the clinical relevance and visual similarity of the CAD results. By presenting and comparing a number of approaches commonly used in previous studies, this article identifies and discusses the optimal approaches in developing CBIR-based CAD schemes and assessing their performance. Although preliminary studies have suggested that using CBIR-based CAD schemes might improve radiologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage. Much research work is needed before the CBIR-based CAD schemes can be accepted in the clinical practice.