Computer-based detection and prompting of mammographic abnormalities

Potential plasma lipid biomarkers in early-stage breast cancer

OBJECTIVE

To find new biomarkers for early diagnosis of breast cancer.

RESULTS

847 lipid species were identified from 78 plasma samples (37 breast cancer samples and 41 healthy controls) by ultra HPLC coupled with quadrupole time-of-flight tandem mass spectrometry. These include 321 glycerophospholipids (GPs), 265 glycerolipids (GLs), 91 sphingolipids (SPs), 77 fatty acyls (FAs), 68 sterol lipids (STs), 18 prenol lipids (PRs), 6 polyketides (PKs), and 1 saccharolipid (SL). Separation was observed from an orthogonal signal correction Partial Least Square Discrimination Analysis model. Based on this analysis, six differentiating lipids were identified: PC (20:2/20:5), PC (22:0/24:1), TG (12:0/14:1), and DG (18:1/18:2) had high levels, whereas PE (15:0/19:1) and N-palmitoyl proline had low levels in the breast cancer samples compared with the healthy controls. Furthermore, significant differences in metabolites were found among some clinical characteristics.

CONCLUSIONS

Our results reveal that six specific lipids could serve as potential biomarkers for early diagnosis of breast cancer.

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.

Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: A comparative analysis

Female breast cancer is the second most common cancer in the world. Several efforts in artificial intelligence have been made to help improving the diagnostic accuracy at earlier stages. However, the identification of breast abnormalities, like masses, on mammographic images is not a trivial task, especially for dense breasts. In this paper we describe our novel mass detection process that includes three successive steps of enhancement, characterization and classification. The proposed enhancement system is based mainly on the analysis of the breast texture. First of all, a filtering step with morphological operators and soft thresholding is achieved. Then, we remove from the filtered breast region, all the details that may interfere with the eventual masses, including pectoral muscle and galactophorous tree. The pixels belonging to this tree will be interpolated and replaced by the average of the neighborhood. In the characterization process, measurement of the Gaussian density in the wavelet domain allows the segmentation of the masses. Finally, a comparative classification mechanism based on the Bayesian regularization back-propagation networks and ANFIS techniques is proposed. The tests were conducted on the MIAS database. The results showed the robustness of the proposed enhancement method.

Mass spectrometry-based quantitative metabolomics revealed a distinct lipid profile in breast cancer patients

Breast cancer accounts for the largest number of newly diagnosed cases in female cancer patients. Although mammography is a powerful screening tool, about 20% of breast cancer cases cannot be detected by this method. New diagnostic biomarkers for breast cancer are necessary. Here, we used a mass spectrometry-based quantitative metabolomics method to analyze plasma samples from 55 breast cancer patients and 25 healthy controls. A number of 30 patients and 20 age-matched healthy controls were used as a training dataset to establish a diagnostic model and to identify potential biomarkers. The remaining samples were used as a validation dataset to evaluate the predictive accuracy for the established model. Distinct separation was obtained from an orthogonal partial least squares-discriminant analysis (OPLS-DA) model with good prediction accuracy. Based on this analysis, 39 differentiating metabolites were identified, including significantly lower levels of lysophosphatidylcholines and higher levels of sphingomyelins in the plasma samples obtained from breast cancer patients compared with healthy controls. Using logical regression, a diagnostic equation based on three metabolites (lysoPC a C16:0, PC ae C42:5 and PC aa C34:2) successfully differentiated breast cancer patients from healthy controls, with a sensitivity of 98.1% and a specificity of 96.0%.

Improved classification of breast cancer peptide and protein profiles by combining two serum workup procedures

Purpose

Detection of breast cancer at early stage increases patient’s survival. Mass spectrometry-based protein analysis of serum samples is a promising approach to obtain biomarker profiles for early detection. A combination of commonly applied solid-phase extraction procedures for clean-up may increase the number of detectable peptides and proteins. In this study, we have evaluated whether the classification performance of breast cancer profiles improves by using two serum workup procedures.

Methods

Serum samples from 105 breast cancer patients and 202 healthy volunteers were processed according to a standardized protocol implemented on a high-end liquid-handling robot. Peptide and protein enrichments were carried out using weak-cation exchange (WCX) and reversed-phase (RP) C18 magnetic beads. Profiles were acquired on a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometer. In this way, two different biomarker profiles were obtained for each serum sample, yielding a WCX- and RPC18-dataset.

Results

The profiles were statistically evaluated with double cross-validation. Classification results of WCX- and RPC18-datasets were determined for each set separately and for the combination of both sets. Sensitivity and specificity were 82 and 87 % (WCX) and 73 and 93 % (RPC18) for the individual workup procedures. These values increased up to 84 and 95 %, respectively, upon combining the data.

Conclusion

It was found that MALDI-TOF peptide and protein profiles can be used for classification of breast cancer with high sensitivity and specificity. The classification performance even improved when two workup procedures were applied, since these provide a greater number of features (proteins).

Electronic supplementary material

The online version of this article (doi:10.1007/s00432-012-1273-4) contains supplementary material, which is available to authorized users.

Detection of clustered microcalcifications using spatial point process modeling

In this work we propose a spatial point process (SPP) approach to improve the detection accuracy of clustered microcalcifications (MCs) in mammogram images. The conventional approach to MC detection has been to first detect the individual MCs in an image independently, which are subsequently grouped into clusters. Our proposed approach aims to exploit the spatial distribution among the different MCs in a mammogram image (i.e. MCs tend to appear in small clusters) directly during the detection process. We model the MCs by a marked point process (MPP) in which spatially neighboring MCs interact with each other. The MCs are then simultaneously detected through maximum a posteriori (MAP) estimation of the model parameters associated with the MPP process. The proposed approach was evaluated with a dataset of 141 clinical mammograms from 66 cases, and the results show that it could yield improved detection performance compared to a recently proposed support vector machine (SVM) detector. In particular, the proposed approach achieved a sensitivity of about 90% with the FP rate at around 0.5 clusters per image, compared to about 83% for the SVM; the performance of the proposed approach was also demonstrated to be more stable over different compositions of the test images.

Proteomic serum biomarkers and their potential application in cancer screening programs

Early diagnosis of cancer is of pivotal importance to reduce disease-related mortality. There is great need for non-invasive screening methods, yet current screening protocols have limited sensitivity and specificity. The use of serum biomarkers to discriminate cancer patients from healthy persons might be a tool to improve screening programs. Mass spectrometry based proteomics is widely applied as a technology for mapping and identifying peptides and proteins in body fluids. One commonly used approach in proteomics is peptide and protein profiling. Here, we present an overview of profiling methods that have the potential for implementation in a clinical setting and in national screening programs.

Investigation of optimal use of computer-aided detection systems: the role of the "machine" in decision making process

RATIONALE AND OBJECTIVES

The aim of this study was to explore different computerized models (the "machine") as a means to achieve optimal use of computer-aided detection (CAD) systems and to investigate whether these models can play a primary role in clinical decision making and possibly replace a clinician's subjective decision for combining his or her own assessment with that provided by a CAD system.

MATERIALS AND METHODS

Data previously collected from a fully crossed, multiple-reader, multiple-case observer study with and without CAD by seven observers asked to identify simulated small masses on two separate sets of 100 mammographic images (low-contrast and high-contrast sets; ie, low-contrast and high-contrast simulated masses added to random locations on normal mammograms) were used. This allowed testing two relative sensitivities between the observers and CAD. Seven models that combined detection assessments from CAD standalone, unaided read, and CAD-aided read (second read and concurrent read) were developed using the leave-one-out technique for training and testing. These models were personalized for each observer. Detection performance accuracies were analyzed using the area under a portion of the free-response receiver-operating characteristic curve (AUFC), sensitivity, and number of false-positives per image.

RESULTS

For the low-contrast set, the use of computerized models resulted in significantly higher AUFCs compared to the unaided read mode for all readers, whereas the increased AUFCs between CAD-aided (second and concurrent reads; ie, decisions made by the readers) and unaided read modes were statistically significant for a majority, but not all, of the readers (four and five of the seven readers, respectively). For the high-contrast set, there were no significant trends in the AUFCs whether or not a model was used to combine the original reading modes. Similar results were observed when using sensitivity as the figure of merit. However, the average number of false-positives per image resulting from the computerized models remained the same as that obtained from the unaided read modes.

CONCLUSIONS

Individual computerized models (the machine) that combine image assessments from CAD standalone, unaided read, and CAD-aided read can increase detection performance compared to the reading done by the observer. However, relative sensitivity (ie, the difference in sensitivity between CAD standalone and unaided read) was a critical factor that determined incremental improvement in decision making, whether made by the observer or using computerized models.

Rapid image stitching and computer-aided detection for multipass automated breast ultrasound

PURPOSE

Breast ultrasound (US) is recently becoming more and more popular for detecting breast lesions. However, screening results in hundreds of US images for each subject. This magnitude of images can lead to fatigue in radiologist, causing failure in the detection of lesions of a subtle nature. In this study, an image stitching technique is proposed for combining multipass images of the whole breast into a series of full-view images, and a fully automatic screening system that works off these images is also presented.

METHODS

Using the registration technique based on the simple sum of absolute block-mean difference (SBMD) measure, three-pass images were merged into full-view US images. An automatic screening system was then developed for detecting tumors from these full-view images. The preprocessing step was used to reduce the tumor detection time of the system and to improve image quality. The gray-level slicing method was then used to divide images into numerous regions. Finally, seven computerized features--darkness, uniformity, width-height ratio, area size, nonpersistence, coronal area size, and region continuity--were defined and used to determine whether or not each region was a part of a tumor.

RESULTS

In the experiment, there was a total of 25 experimental cases with 26 lesions, and each case was composed of 252 images (three passes, 84 images/pass). The processing time of the proposed stitching procedure for each case was within 30 s with a Pentium IV 2.0 processor, and the detection sensitivity of the proposed CAD system was 92.3% with 1.76 false positives per case.

CONCLUSIONS

The proposed automatic screening system can be applied to the whole breast images stitched together via SBMD-based registration in order to detect tumors.

Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer

Background

Mass spectrometric analysis of the blood proteome is an emerging method of clinical proteomics. The approach exploiting multi-protein/peptide sets (fingerprints) detected by mass spectrometry that reflect overall features of a specimen's proteome, termed proteome pattern analysis, have been already shown in several studies to have applicability in cancer diagnostics. We aimed to identify serum proteome patterns specific for early stage breast cancer patients using MALDI-ToF mass spectrometry.

Methods

Blood samples were collected before the start of therapy in a group of 92 patients diagnosed at stages I and II of the disease, and in a group of age-matched healthy controls (104 women). Serum specimens were purified and the low-molecular-weight proteome fraction was examined using MALDI-ToF mass spectrometry after removal of albumin and other high-molecular-weight serum proteins. Protein ions registered in a mass range between 2,000 and 10,000 Da were analyzed using a new bioinformatic tool created in our group, which included modeling spectra as a sum of Gaussian bell-shaped curves.

Results

We have identified features of serum proteome patterns that were significantly different between blood samples of healthy individuals and early stage breast cancer patients. The classifier built of three spectral components that differentiated controls and cancer patients had 83% sensitivity and 85% specificity. Spectral components (i.e., protein ions) that were the most frequent in such classifiers had approximate m/z values of 2303, 2866 and 3579 Da (a biomarker built from these three components showed 88% sensitivity and 78% specificity). Of note, we did not find a significant correlation between features of serum proteome patterns and established prognostic or predictive factors like tumor size, nodal involvement, histopathological grade, estrogen and progesterone receptor expression. In addition, we observed a significantly (p = 0.0003) increased level of osteopontin in blood of the group of cancer patients studied (however, the plasma level of osteopontin classified cancer samples with 88% sensitivity but only 28% specificity).

Conclusion

MALDI-ToF spectrometry of serum has an obvious potential to differentiate samples between early breast cancer patients and healthy controls. Importantly, a classifier built on MS-based serum proteome patterns outperforms available protein biomarkers analyzed in blood by immunoassays.

Variable size computer-aided detection prompts and mammography film reader decisions

Introduction

The purpose of the present study was to investigate the effect of computer-aided detection (CAD) prompts on reader behaviour in a large sample of breast screening mammograms by analysing the relationship of the presence and size of prompts to the recall decision.

Methods

Local research ethics committee approval was obtained; informed consent was not required. Mammograms were obtained from women attending routine mammography at two breast screening centres in 1996. Films, previously double read, were re-read by a different reader using CAD. The study material included 315 cancer cases comprising all screen-detected cancer cases, all subsequent interval cancers and 861 normal cases randomly selected from 10,267 cases. Ground truth data were used to assess the efficacy of CAD prompting. Associations between prompt attributes and tumour features or reader recall decisions were assessed by chi-squared tests.

Results

There was a highly significant relationship between prompting and a decision to recall for cancer cases and for a random sample of normal cases (P < 0.001). Sixty-four per cent of all cases contained at least one CAD prompt. In cancer cases, larger prompts were more likely to be recalled (P = 0.02) for masses but there was no such association for calcifications (P = 0.9). In a random sample of 861 normal cases, larger prompts were more likely to be recalled (P = 0.02) for both mass and calcification prompts. Significant associations were observed with prompting and breast density (p = 0.009) for cancer cases but not for normal cases (P = 0.05).

Conclusions

For both normal cases and cancer cases, prompted mammograms were more likely to be recalled and the prompt size was also associated with a recall decision.

Blinded comparison of computer-aided detection with human second reading in screening mammography

OBJECTIVE

The purpose of this study was to compare a human second reader with computer-aided detection (CAD) for the reduction of false-negative cases by a primary radiologist. We retrospectively reviewed our clinical practice.

MATERIALS AND METHODS

We found that 6,381 consecutive screening mammograms were interpreted by a primary reader. This radiologist then reinterpreted the studies using CAD ("CAD reader"). A second human reader who was blinded to the CAD results but knowledgeable of the primary reader's findings reviewed the studies, looking for abnormalities not seen by the first reader.

RESULTS

Two cancers were called back by the second human reader that were not called back by the CAD reader; however, the CAD system had marked the findings, but they were dismissed by the primary reader. Because of the small numbers, the difference between the CAD and second human reader was not statistically significant. The CAD and human second readers increased the recall rates 6.4% and 7.2% (p = 0.70), respectively, and the biopsy rates 10% and 14.7%. The positive predictive value was 0% (0/3) for the CAD reader and was 40% (2/5) for the human second reader. The relative increases in the cancer detection rate compared with the primary reader's detection rate were 0% for the CAD reader and 15.4% (2/13) for the human second reader (p = 0.50).

CONCLUSION

A human second reader or the use of a CAD system can increase the cancer detection rate, but we found no statistical difference between the two because of the small sample size. A possible benefit from a human second reader is that CAD systems can only point to possible abnormalities, whereas a human must determine the significance of the finding. Having two humans review a study may increase detection rates due to interpreter--hence, perceptual--variability and not just increased detection.

A concentric morphology model for the detection of masses in mammography

We propose a technique for the automated detection of malignant masses in screening mammography. The technique is based on the presence of concentric layers surrounding a focal area with suspicious morphological characteristics and low relative incidence in the breast region. Mammographic locations with high concentration of concentric layers with progressively lower average intensity are considered suspicious deviations from normal parenchyma. The multiple concentric layers (MCLs) technique was trained and tested using the craniocaudal views of 270 mammographic cases with biopsy proven malignant masses from the digital database of screening mammography. One-half of the available cases were used for optimizing the parameters of the detection algorithm. The remaining cases were used for testing. During testing, malignant masses were detected with 92%, 88%, and 81% sensitivity at 5.4, 2.4, and 0.6 false positive marks per image. Testing on 82 normal screening mammograms showed a false positive rate of 5.0, 1.7, and 0.2 marks per image at the previously reported operating points. Furthermore, additional evaluation on 135 benign cases produced a significantly lower detection rate for benign masses (61.6%, 58.3%, and 43.7% at 5.1, 2.8, and 1.2 false positives per image, respectively). Overall, MCL is a promising computer-assisted detection strategy for screening mammograms to identify malignant masses while maintaining the detection rate of benign masses considerably lower.

Computer aided detection of masses in mammograms as decision support

Performance of a computer aided detection (CAD) system for masses in mammograms was investigated. Using data collected in an observer study, in which experienced screening radiologists read a series of 500 screening mammograms without CAD, performance of radiologists was compared to the standalone performance of the CAD system. Due to a larger number of FPs (false positives), the performance of CAD was lower than that of the readers. However, when analysis was restricted to mammographic regions identified by the radiologists, it was found that the CAD system was comparable to the readers in discriminating these regions in cancer and non-cancer. In a retrospective analysis, the effect of independent combination of reader scores with CAD was compared to independent combination of scores of two radiologists. No significant difference was found between the results of these two methods. Both methods improved single reading results significantly.

MALDI-TOF serum protein profiling for the detection of breast cancer

BACKGROUND

Proteomic expression profiling has been suggested as a potential tool for the early diagnosis of cancer and other diseases. The objective of our study was to assess the feasibility of this approach for the detection of breast cancer.

MATERIALS AND METHODS

In a randomized block design pre-operative serum samples obtained from 78 breast cancer patients and 29 controls were used to generate high-resolution MALDI-TOF protein profiles. The spectra generated using C8 magnetic beads assisted mass spectrometry were smoothed, binned and normalized after baseline correction. Linear discriminant analysis with double cross-validation, based on principal component analysis, was used to classify the protein profiles.

RESULTS

A total recognition rate of 99%, a sensitivity of 100%, and a specificity of 97.0% for the detection of breast cancer were shown. The area under the curve of the classifier was 98.3%, which demonstrates the separation power of the classifier. The first 2 principal components account for most of the between- group separation.

CONCLUSIONS

Double cross-validation showed that classification could be attributed to actual information in the protein profiles rather than to chance. Although preliminary, the high sensitivity and specificity indicate the potential usefulness of serum protein profiles for the detection of breast cancer.