Use of border information in the classification of mammographic masses

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems (CADs) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel end-to-end deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predicts diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional consideration of important shape and boundary knowledge. One path, called the Locality Preserving Learner (LPL), is devoted to hierarchically extracting and exploiting intrinsic features of the input. Whereas the other path, called the Conditional Graph Learner (CGL), focuses on generating geometrical features via modeling pixel-wise image to mask correlations. By integrating the two learners, both the cancer semantics and cancer representations are well learned, and the component learning paths in return complement each other, contributing an improvement to the mass segmentation and cancer classification problem at the same time. In addition, by integrating an automatic detection set-up, the DualCoreNet achieves fully automatic breast cancer diagnosis practically. Experimental results show that in benchmark DDSM dataset, DualCoreNet has outperformed other related works in both segmentation and classification tasks, achieving 92.27% DI coefficient and 0.85 AUC score. In another benchmark INbreast dataset, DualCoreNet achieves the best mammography segmentation (93.69% DI coefficient) and competitive classification performance (0.93 AUC score).

Mass Detection and Segmentation in Digital Breast Tomosynthesis Using 3D-Mask Region-Based Convolutional Neural Network: A Comparative Analysis

Digital breast tomosynthesis (DBT) is an emerging breast cancer screening and diagnostic modality that uses quasi-three-dimensional breast images to provide detailed assessments of the dense tissue within the breast. In this study, a framework of a 3D-Mask region-based convolutional neural network (3D-Mask RCNN) computer-aided diagnosis (CAD) system was developed for mass detection and segmentation with a comparative analysis of performance on patient subgroups with different clinicopathological characteristics. To this end, 364 samples of DBT data were used and separated into a training dataset (n = 201) and a testing dataset (n = 163). The detection and segmentation results were evaluated on the testing set and on subgroups of patients with different characteristics, including different age ranges, lesion sizes, histological types, lesion shapes and breast densities. The results of our 3D-Mask RCNN framework were compared with those of the 2D-Mask RCNN and Faster RCNN methods. For lesion-based mass detection, the sensitivity of 3D-Mask RCNN-based CAD was 90% with 0.8 false positives (FPs) per lesion, whereas the sensitivity of the 2D-Mask RCNN- and Faster RCNN-based CAD was 90% at 1.3 and 2.37 FPs/lesion, respectively. For breast-based mass detection, the 3D-Mask RCNN generated a sensitivity of 90% at 0.83 FPs/breast, and this framework is better than the 2D-Mask RCNN and Faster RCNN, which generated a sensitivity of 90% with 1.24 and 2.38 FPs/breast, respectively. Additionally, the 3D-Mask RCNN achieved significantly (p < 0.05) better performance than the 2D methods on subgroups of samples with characteristics of ages ranged from 40 to 49 years, malignant tumors, spiculate and irregular masses and dense breast, respectively. Lesion segmentation using the 3D-Mask RCNN achieved an average precision (AP) of 0.934 and a false negative rate (FNR) of 0.053, which are better than those achieved by the 2D methods. The results suggest that the 3D-Mask RCNN CAD framework has advantages over 2D-based mass detection on both the whole data and subgroups with different characteristics.

Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma

Background Previous studies have suggested that breast parenchymal texture features may reflect the biologic risk factors associated with breast cancer development. Therefore, combining the characteristics of normal parenchyma from the contralateral breast with radiomic features of breast tumors may improve the accuracy of digital mammography in the diagnosis of breast cancer. Purpose To determine whether the addition of radiomic analysis of contralateral breast parenchyma to the characterization of breast lesions with digital mammography improves lesion classification over that with radiomic tumor features alone. Materials and Methods This HIPAA-compliant, retrospective study included 182 patients (age range, 25-90 years; mean age, 55.9 years ± 14.9) who underwent mammography between June 2002 and July 2009. There were 106 malignant and 76 benign lesions. Automatic lesion segmentation and radiomic analysis were performed for each breast lesion. Radiomic texture analysis was applied in the normal regions of interest in the contralateral breast parenchyma to assess the mammographic parenchymal patterns. The classification performance of both individual features and the output from a Bayesian artificial neural network classifier was evaluated with the leave-one-patient-out method by using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of differentiating between malignant and benign lesions. Results The performance of the combined lesion and parenchyma classifier in the differentiation between malignant and benign mammographic lesions was better than that with the lesion features alone (AUC = 0.84 ± 0.03 vs 0.79 ± 0.03, respectively; P = .047). Overall, six radiomic features-spiculation, margin sharpness, size, circularity from the tumor feature set, and skewness and power law beta from the parenchymal feature set-were selected more than 50% of the time during the feature selection process on the combined feature set. Conclusion Combining quantitative radiomic data from tumors with contralateral parenchyma characterizations may improve diagnostic accuracy for breast cancer. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Shaffer in this issue.

Visually interpretable deep network for diagnosis of breast masses on mammograms

Recently, deep learning technology has achieved various successes in medical image analysis studies including computer-aided diagnosis (CADx). However, current CADx approaches based on deep learning have a limitation in interpreting diagnostic decisions. The limited interpretability is a major challenge for practical use of current deep learning approaches. In this paper, a novel visually interpretable deep network framework is proposed to provide diagnostic decisions with visual interpretation. The proposed method is motivated by the fact that the radiologists characterize breast masses according to the breast imaging reporting and data system (BIRADS). The proposed deep network framework consists of a BIRADS guided diagnosis network and a BIRADS critic network. A 2D map, named BIRADS guide map, is generated in the inference process of the deep network. The visual features extracted from the breast masses could be refined by the BIRADS guide map, which helps the deep network to focus on more informative areas. The BIRADS critic network makes the BIRADS guide map to be relevant to the characterization of masses in terms of BIRADS description. To verify the proposed method, comparative experiments have been conducted on public mammogram database. On the independent test set (170 malignant masses and 170 benign masses), the proposed method was found to have significantly higher performance compared to the deep network approach without using the BIRADS guide map (p  <  0.05). Moreover, the visualization was conducted to show the location where the deep network exploited more information. This study demonstrated that the proposed visually interpretable CADx framework could be a promising approach for visually interpreting the diagnostic decision of the deep network.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs.

Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning

We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodology lies in the use of deep learning models for the problem of jointly classifying unregistered mammogram views and respective segmentation maps of breast lesions (i.e., masses and micro-calcifications). This is a holistic methodology that can classify a whole mammographic exam, containing the CC and MLO views and the segmentation maps, as opposed to the classification of individual lesions, which is the dominant approach in the field. We also demonstrate that the proposed system is capable of using the segmentation maps generated by automated mass and micro-calcification detection systems, and still producing accurate results. The semi-automated approach (using manually defined mass and micro-calcification segmentation maps) is tested on two publicly available data sets (INbreast and DDSM), and results show that the volume under ROC surface (VUS) for a 3-class problem (normal tissue, benign, and malignant) is over 0.9, the area under ROC curve (AUC) for the 2-class "benign versus malignant" problem is over 0.9, and for the 2-class breast screening problem (malignancy versus normal/benign) is also over 0.9. For the fully automated approach, the VUS results on INbreast is over 0.7, and the AUC for the 2-class "benign versus malignant" problem is over 0.78, and the AUC for the 2-class breast screening is 0.86.

A deep learning approach for the analysis of masses in mammograms with minimal user intervention

We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification. For the detection, we propose a cascade of deep learning methods to select hypotheses that are refined based on Bayesian optimisation. For the segmentation, we propose the use of deep structured output learning that is subsequently refined by a level set method. Finally, for the classification, we propose the use of a deep learning classifier, which is pre-trained with a regression to hand-crafted feature values and fine-tuned based on the annotations of the breast mass classification dataset. We test our proposed system on the publicly available INbreast dataset and compare the results with the current state-of-the-art methodologies. This evaluation shows that our system detects 90% of masses at 1 false positive per image, has a segmentation accuracy of around 0.85 (Dice index) on the correctly detected masses, and overall classifies masses as malignant or benign with sensitivity (Se) of 0.98 and specificity (Sp) of 0.7.

Computer-aided classification of mammographic masses using visually sensitive image features

PURPOSE

To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms.

METHODS

An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score.

RESULTS

Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025.

CONCLUSION

This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a "visual aid" interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features.

Is the false-positive rate in mammography in North America too high?

The practice of investigating pathological abnormalities in the breasts of females who are asymptomatic is primarily employed using X-ray mammography. The importance of breast screening is reflected in the mortality-based benefits observed among females who are found to possess invasive breast carcinoma prior to the manifestation of clinical symptoms. It is estimated that population-based screening constitutes a 17% reduction in the breast cancer mortality rate among females affected by invasive breast carcinoma. In spite of the significant utility that screening confers in those affected by invasive cancer, limitations associated with screening manifest as potential harms affecting individuals who are free of invasive disease. Disease-free and benign tumour-bearing individuals who are subjected to diagnostic work-up following a screening examination constitute a population of cases referred to as false positives (FPs). This article discusses factors contributing to the FP rate in mammography and extends the discussion to an assessment of the consequences associated with FP reporting. We conclude that the mammography FP rate in North America is in excess based upon the observation of overtreatment of in situ lesions and the disproportionate distribution of detriment and benefit among the population of individuals recalled for diagnostic work-up subsequent to screening. To address the excessive incidence of FPs in mammography, we investigate solutions that may be employed to remediate the current status of the FP rate. Subsequently, it can be suggested that improvements in the breast-screening protocol, medical litigation risk, image interpretation software and the implementation of image acquisition modalities that overcome superimposition effects are promising solutions.

Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions

PURPOSE

To develop a new computer-aided diagnosis (CAD) scheme using a content-based image retrieval (CBIR) approach for classification between the malignant and benign breast lesions depicted on the digital mammograms and assess CAD performance and reproducibility.

METHODS

An image dataset including 820 regions of interest (ROIs) was used. Among them, 431 ROIs depict malignant lesions and 389 depict benign lesions. After applying an image preprocessing process to define the lesion center, two image features were computed from each ROI. The first feature is an average pixel value of a mapped region generated using a watershed algorithm. The second feature is an average pixel value difference between a ROI's center region and the rest of the image. A two-step CBIR approach uses these two features sequentially to search for ten most similar reference ROIs for each queried ROI. A similarity based classification score was then computed to predict the likelihood of the queried ROI depicting a malignant lesion. To assess the reproducibility of the CAD scheme, we selected another independent testing dataset of 100 ROIs. For each ROI in the testing dataset, we added four randomly queried lesion center pixels and examined the variation of the classification scores.

RESULTS

The area under the ROC curve (AUC) = 0.962 ± 0.006 was obtained when applying a leave-one-out validation method to 820 ROIs. Using the independent testing dataset, the initial AUC value was 0.832 ± 0.040, and using the median classification score of each ROI with five queried seeds, AUC value increased to 0.878 ± 0.035.

CONCLUSIONS

The authors demonstrated that (1) a simple and efficient CBIR scheme using two lesion density distribution related features achieved high performance in classifying breast lesions without actual lesion segmentation and (2) similar to the conventional CAD schemes using global optimization approaches, improving reproducibility is also one of the challenges in developing CAD schemes using a CBIR based regional optimization approach.

A new and fast image feature selection method for developing an optimal mammographic mass detection scheme

PURPOSE

Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses.

METHODS

An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method.

RESULTS

Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized using other three feature selection methods. In addition, among 271 features, the shape, local morphological features, fat and calcification based features were the most frequently selected features to build ANNs.

CONCLUSIONS

Although conventional GA is a powerful tool in optimizing classifiers used in CAD schemes of medical images, it is very computationally intensive. This study demonstrated that using a new SFFS based approach enabled to significantly improve efficacy of image feature selection for developing CAD schemes.

A five year audit of mammography in a tertiary hospital, North Central Nigeria

Background:

Breast imaging plays a vital role in the multidisciplinary approach to management of breast disease. A baseline data is apt and necessary for collaborative studies.

Materials and Methods:

This is a prospective descriptive study carried out between January 2009 and December 2013 at our institution. Patients who had breast imaging were recruited into the study. Film-screen mammograms were acquired with a General Electric (GE) Senographe DMR machine. Breast ultrasound done using an Aloka Prosound SSD-350+ ultrasound machine equipped with linear and curvilinear 7.5–10 MHz transducer. Findings were categorized using the ACR-BIRADS (American college of Radiologists-Breast imaging reporting and data system). Data was collated and analysed using social statistical package (SPSS) version 17.

Result:

The mammograms of 824 patients were evaluated during this study period. Their age ranged from 40–85 years with a mean age of 50.9 ± 8.1 years. Eight hundred and sixteen (99%) were females and eight (1%) were males. The commonest clinical indication was breast lump (23.9%). The commonest density pattern was BIRADS 2-scattered fibroglandular pattern (43.9%). Mammograms were normal in 266 (32.3%) and positive in 558 (67.7%). The final BIRADS assessment showed BIRADS 0, 1, 2, 3, 4, 5 and 6 constituted 6.6%, 30.1%, 29.7%, 22.2%, 5.9%, 5.0% and 0.5% respectively.

Conclusion:

The level of awareness of breast cancer is quite high with the positive mammographic yield emphasizing the value of a multidisciplinary approach in the management of breast diseases.

Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk

The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first "prior" examination in the series was interpreted as negative (not recalled) during the original image reading. In the second "current" examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative ("cancer-free"). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.

Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework

In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named "Phased Searching with NEAT in a Time-Scaled Framework" was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers - SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework - are performing comparably well in our mammographic mass detection scheme.

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793  ±  0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

PURPOSE

Improving radiologists' performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification.

METHODS

We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest, we performed the study using a tenfold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only.

RESULTS

The area under the receiver operating characteristic curve [Formula: see text] was obtained for the classification task. The results also showed that the most frequently selected features by the SFFS-based algorithm in tenfold iterations were those related to mass shape, isodensity, and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions.

CONCLUSION

In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a "second reader" in future clinical practice.

Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer

The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.

Mammogram retrieval on similar mass lesions

Enormous numbers of digital mammograms have been produced in hospitals and breast screening centers. To exploit those valuable resources in aiding diagnoses and research, content-based mammogram retrieval systems are required to effectively access the mammogram databases. This paper presents a content-based mammogram retrieval system, which allows medical professionals to seek mass lesions that are pathologically similar to a given example. In this retrieval system, shape and margin features of mass lesions are extracted to represent the characteristics of mammographic lesions. To compare the similarity between the query example and any lesion within the databases, this study proposes a similarity measure scheme which involves the hierarchical arrangement of mammographic features and a weighting distance measure. This makes similarity measure of the retrieval system consistent with the way radiologists observe mass lesions. This study used the DDSM dataset to evaluate the effectiveness of the extracted shape feature and margin feature, respectively. Experimental results demonstrate that, when Zernike moments are used, round-shape masses are the most discriminative among four types of shape; the circumscribed-margin masses can be effectively discriminated among the four types of margins. Moreover, the result also shows that, when retrieving round-shape and circumscribed margin masses, this retrieval system can achieve the highest precision among all mass lesion types.

A STUDY ON GENETIC ALGORITHM BASED HYBRID SOFTCOMPUTING MODEL FOR BENIGNANCY/MALIGNANCY DETECTION OF MASSES USING DIGITAL MAMMOGRAM

In present works authors have developed a computerized classification procedure for tumor mass in breasts using digital mammogram. The process implements genetic algorithm and hybrid neuro-fuzzy approaches to classify tumor masses into benign and malignant group in order to assist the physicians for treatment planning. The classification process is based on accurate analysis of shape and margin of tumor mass appearing in breast. The shape features using Fourier descriptors introduce a large number of feature vectors. Thus, to classify different boundaries, a standard multilayer preceptor needs large number of inputs. Simultaneously, to train the network, a large number of training cycles and huge memory are also required. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology genetic algorithm (GA) has been used for the searching of effective input feature vectors. Adaptive neuro-fuzzy model has been used for final classification of different boundaries of tumor masses. The proposed technique is an innovative soft computing approach that removes the limitation of conventional neural networks and indicates a promising direction of adaptation in a changing environment. The classification system utilizes a Euclidean distance function to detect the belongingness of masses in benign and in malignant classes along with degree of benignancy/malignancy. Presently 200 digitized mammograms from MIAS and other databases have been considered for the experiment and which have shown an average of approximately 86% correct classification as compared with clinical data with a highest rate of 88.9%.

Characterizing mammographic images by using generic texture features

INTRODUCTION

Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design.

METHODS

A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model.

RESULTS

Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model.

CONCLUSIONS

Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.

Computerized decision support system for mass identification in breast using digital mammogram: a study on GA-based neuro-fuzzy approaches

In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.

Mammogram retrieval through machine learning within BI-RADS standards

A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively.

Hybrid segmentation of mass in mammograms using template matching and dynamic programming

RATIONALE AND OBJECTIVES

Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems. The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems.

MATERIALS AND METHODS

The data set used in this study consisted of 483 regions of interest extracted from 328 patients. A hybrid method for segmenting breast masses was proposed on the basis of the template-matching and dynamic programming techniques. First, a template-matching technique was used to locate and obtain the rough region of masses. Then, on the basis of this rough region, a local cost function for dynamic programming was defined. Finally, the optimal contour was derived by applying dynamic programming as an optimization technique. The performance of this proposed segmentation method was evaluated using area-based and boundary distance-based similarity measures based on radiologists' manually marked annotations. A comparison with three different segmentation algorithms on the data set was provided.

RESULTS

The mean overlap percentage for our proposed hybrid method was 0.727 ± 0.127, whereas those for Timp and Karssemeijer's dynamic programming method, Song et al's plane-fitting and dynamic programming method, and the normalized cut segmentation method were 0.657 ± 0.216, 0.636 ± 0.190, and 0.562 ± 0.199, respectively. All P values for the measure distribution of our proposed method and the other three algorithms were <.001.

CONCLUSIONS

A hybrid method based on the template-matching and dynamic programming techniques was proposed to segment breast masses on mammograms. Evaluation results indicate that the proposed segmentation method can improve the accuracy of mass segmentation compared to three other algorithms. The proposed segmentation method shows better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in interpreting mammograms.

Probing model tumor interfacial properties using piezoelectric cantilevers

Invasive malignant breast cancers are typically branchy and benign breast tumors are typically smooth. It is of interest to characterize tumor branchiness (roughness) to differentiate invasive malignant breast cancer from noninvasive ones. In this study, we examined the shear modulus (G) to elastic modulus (E) ratio, G/E, as a quantity to describe model tumor interfacial roughness using a piezoelectric cantilever capable of measuring both tissue elastic modulus and tissue shear modulus. The piezoelectric cantilever used had two lead zirconate titanate layers to facilitate all-electrical elastic (shear) modulus measurements using one single device. We constructed model tissues with tumors by embedding one-dimensional (1D) corrugated inclusions and three-dimensional (3D) spiky-ball inclusions made of modeling clay in gelatin. We showed that for smooth inclusions, G/E was 0.3 regardless of the shear direction. In contrast, for a 1D corrugated rough inclusion G/E was 0.3 only when the shear was parallel to corrugation and G/E increased with an increasing angle between the shear direction and the corrugation. When the shear was perpendicular to corrugation, G/E became >0.7. For 3D isotropic spiky-ball inclusions we showed that the G/E depended on the degree of the roughness. Using the ratio s/r of the spike length (s) to the overall inclusion radius (r) as a roughness parameter, we showed that for inclusions with s/r larger than or equal to 0.28, the G/E ratio over the inclusions was larger than 0.7 whereas for inclusions with s/r less than 0.28, the G/E decreased with decreasing s/r to around 0.3 at s/r=0. In addition, we showed that the depth limit of the G/E measurement is twice the width of the probe area of the piezoelectric cantilever.

Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI

RATIONALE AND OBJECTIVES

To investigate a multimodality computer-aided diagnosis (CAD) scheme that combines image information from full-field digital mammography (FFDM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for computerized breast cancer classification.

MATERIALS AND METHODS

From a retrospective FFDM database with 432 lesions (255 malignant, 177 benign) and a retrospective DCE-MRI database including 476 lesions (347 malignant, 129 benign), we constructed a multimodality dataset of 213 lesions (168 malignant, 45 benign). Each lesion was present on both FFDM and DCE-MRI images and deemed to be a difficult case given the necessity of having both clinical imaging exams. Using a manually indicated lesion location (ie, a seed point on FFDM images or a region of interest on DCE-MRI images, the computer automatically segmented the mass lesions and extracted lesion features). A subset of features was selected using linear stepwise feature selection and merged by a Bayesian artificial neural network to yield an estimate of the probability of malignancy. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the selected features in distinguishing between malignant and benign lesions.

RESULTS

With leave-one-lesion-out cross-validation on the multimodality dataset, the mammography-only features yielded an area under the ROC curve (AUC) of 0.74 +/- 0.04, and the DCE-MRI-only features yielded an AUC of 0.78 +/- 0.04. The combination of these two modalities, which included a spiculation feature from mammography and two kinetic features from DCE-MRI, yielded an AUC of 0.87 +/- 0.03. The improvement of combining multimodality information was statistically significant as compared to the use of single modality information alone.

CONCLUSIONS

A CAD scheme that combines features extracted from FFDM and DCE-MRI images may be advantageous to single-modality CAD in the task of differentiating between malignant and benign lesions.

Computer-aided diagnosis with temporal analysis to improve radiologists' interpretation of mammographic mass lesions

The purpose of this study was to evaluate the effect of independent reading with computer-aided diagnosis (CAD) and independent double reading on radiologists' performance to characterize mass lesions on serial mammograms. Six radiologists rated 198 cases, 99 benign and 99 malignant. For each case, the mammograms from two consecutive screening rounds were available. The mass was visible on the prior view in 40% of the cases. Independently, a CAD programe also rated each mass lesion making use of information from prior and current views. The following reading situations were compared: single reading, independent reading with CAD, and independent double reading. Independent reading with CAD was implemented by averaging the scaled ratings from each radiologist and the scaled CAD scores. We implemented independent double reading by averaging the scaled scores from two radiologists. Results were evaluated using receiver-operating characteristic (ROC) methodology and multiple reader multiple case analysis. The average performance, measured as the area under the ROC curve (A(z) value), was 0.80 for the single-reading mode. For independent double reading, the average performance improved to 0.81. This improvement was not significant. For independent interpretation with CAD, the average performance significantly increased to 0.83 (P < 0.05). We conclude that CAD technology with temporal analysis has the potential to help radiologists with the task of discriminating between benign and malignant masses.

Multi-modality CADx: ROC study of the effect on radiologists' accuracy in characterizing breast masses on mammograms and 3D ultrasound images

RATIONALE AND OBJECTIVES

To investigate the effect of a computer-aided diagnosis (CADx) system on radiologists' performance in discriminating malignant and benign masses on mammograms and three-dimensional (3D) ultrasound (US) images.

MATERIALS AND METHODS

Our dataset contained mammograms and 3D US volumes from 67 women (median age, 51; range: 27-86) with 67 biopsy-proven breast masses (32 benign and 35 malignant). A CADx system was designed to automatically delineate the mass boundaries on mammograms and the US volumes, extract features, and merge the extracted features into a multi-modality malignancy score. Ten experienced readers (subspecialty academic breast imaging radiologists) first viewed the mammograms alone, and provided likelihood of malignancy (LM) ratings and Breast Imaging and Reporting System assessments. Subsequently, the reader viewed the US images with the mammograms, and provided LM and action category ratings. Finally, the CADx score was shown and the reader had the opportunity to revise the ratings. The LM ratings were analyzed using receiver-operating characteristic (ROC) methodology, and the action category ratings were used to determine the sensitivity and specificity of cancer diagnosis.

RESULTS

Without CADx, readers' average area under the ROC curve, A(z), was 0.93 (range, 0.86-0.96) for combined assessment of the mass on both the US volume and mammograms. With CADx, their average A(z) increased to 0.95 (range, 0.91-0.98), which was borderline significant (P = .05). The average sensitivity of the readers increased from 98% to 99% with CADx, while the average specificity increased from 27% to 29%. The change in sensitivity with CADx did not achieve statistical significance for the individual radiologists, and the change in specificity was statistically significant for one of the radiologists.

CONCLUSIONS

A well-trained CADx system that combines features extracted from mammograms and US images may have the potential to improve radiologists' performance in distinguishing malignant from benign breast masses and making decisions about biopsies.

Comparison of tissue equalization, and premium view post-processing methods in full field digital mammography

OBJECTIVE

To retrospectively evaluate the diagnostic abilities of 2 post-processing methods provided by GE Senographe DS system, tissue equalization (TE) and premium view (PV) in full field digital mammography (FFDM).

MATERIALS AND METHODS

In accordance with the ethical standards of the World Medical Association, this study was approved by regional ethics committee and signed informed patient consents were obtained. We retrospectively reviewed digital mammograms from 101 women (mean age, 47 years; range, 23-81 years) in the modes of TE and PV, respectively. Three radiologists, fully blinded to the post-processing methods, all patient clinical information and histologic results, read images by using objective image interpretation criteria for diagnostic information end points such as lesion border delineation, definition of disease extent, visualization of internal and surrounding morphologic features of the lesions. Also, overall diagnostic impression in terms of lesion conspicuity, detectability and diagnostic confidence was assessed. Between-group comparisons were performed with Wilcoxon signed rank test.

RESULTS

Readers 1, 2, and 3 demonstrated significant overall better impression of PV in 29, 27, and 24 patients, compared with that for TE in 12, 13, and 11 patients, respectively (p<0.05). Significant (p<0.05) better impression of PV was also demonstrated for diagnostic information end points. Importantly, PV proved to be more sensitive than TE while detecting malignant lesions in dense breast rather than benign lesions and malignancy in non-dense breast (p<0.01).

CONCLUSION

PV compared with TE provides marked better diagnostic information in FFDM, particularly for patients with malignancy in dense breast.

Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.

New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data

The automated decision paradigms presented in this work address the false positive (FP) biopsy occurrence in diagnostic mammography. An EP/ES stochastic hybrid and two kernelized Partial Least Squares (K-PLS) paradigms were investigated with following studies: methodology performance comparisonsautomated diagnostic accuracy assessments with two data sets. The findings showed: the new hybrid produced comparable results more rapidlythe new K-PLS paradigms train and operate Essentially in real time for the data sets studied. Both advancements are essential components for eventually achieving the FP reduction goal, while maintaining acceptable diagnostic sensitivities.

Temporal change analysis for characterization of mass lesions in mammography

In this paper, we present a fully automated computer-aided diagnosis (CAD) program to detect temporal changes in mammographic masses between two consecutive screening rounds. The goal of this work was to improve the characterization of mass lesions by adding information about the tumor behavior over time. Towards this goal we previously developed a regional registration technique that finds for each mass lesion on the current view a location on the prior view where the mass was most likely to develop. For the task of interval change analysis, we designed two kinds of temporal features: difference features and similarity features. Difference features indicate the (relative) change in feature values determined on prior and current views. These features may be especially useful for lesions that are visible on both views. Similarity features measure whether two regions are comparable in appearance and may be useful for lesions that are visible on the prior view as well as for newly developing lesions. We evaluated the classification performance with and without the use of temporal features on a dataset consisting of 465 temporal mammogram pairs, 238 benign, and 227 malignant. We used cross validation to partition the dataset into a training set and a test set. The training set was used to train a support vector machine classifier and the test set to evaluate the classifier. The average A(z) value (area under the receiver operating characteristic curve) for classifying each lesion was 0.74 without temporal features and 0.77 with the use of temporal features. The improvement obtained by adding temporal features was statistically significant (P = 0.005). In particular, similarity features contributed to this improvement. Furthermore, we found that the improvement was comparable for masses that were visible and for masses that were not visible on the prior view. These results show that the use of temporal features is an effective approach to improve the characterization of masses.