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Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.

ABSTRACT: BACKGROUND AND OBJECTIVE:This study aims to develop and evaluate a unique global mammographic image feature analysis scheme to predict likelihood of a case depicting the detected suspicious breast mass being malignant for breast cancer. METHODS:From the entire breast area depicting on the mammograms, 59 features were initially computed to characterize the breast tissue properties at both spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. Next, for each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training a support vector machine (SVM) classifier to generate a final score for predicting likelihood of the case being malignant. To test the scheme, we assembled a dataset involving 275 patients who had biopsy due to the suspicious findings on mammograms. Among them, 134 are malignant and 141 are benign. A ten-fold cross validation method was used to train and test the scheme. RESULTS:The classification performance levels measured by the areas under ROC curves are 0.79 ± 0.07 and 0.75 ± 0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. CONCLUSIONS:This study demonstrates feasibility of developing a new global mammographic image feature analysis-based scheme to predict the likelihood of case being malignant without lesion segmentation.

SUBMITTER: Chen X

PROVIDER: S-EPMC7081451 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.

Chen Xuxin X Zargari Abolfazl A Hollingsworth Alan B AB Liu Hong H Zheng Bin B Qiu Yuchen Y

Computer methods and programs in biomedicine 20190729

<h4>Background and objective</h4>This study aims to develop and evaluate a unique global mammographic image feature analysis scheme to predict likelihood of a case depicting the detected suspicious breast mass being malignant for breast cancer.<h4>Methods</h4>From the entire breast area depicting on the mammograms, 59 features were initially computed to characterize the breast tissue properties at both spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio ...[more]

PMID: 31443864

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Project description:BackgroundMachine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems.ObjectiveThis review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks.MethodsIn this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets.ResultsThe analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems.ConclusionsFrom the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.

Project description:Introduction: The loss of nigrosome-1, which is also referred to as the swallow tail sign (STS) in T2*-weighted iron-sensitive magnetic resonance imaging (MRI), has recently emerged as a new biomarker for idiopathic Parkinson's disease (IPD). However, consistent recognition of the STS is difficult due to individual variations and different imaging parameters. Radiomics might have the potential to overcome these shortcomings. Therefore, we chose to explore whether radiomic features of nigrosome-1 of substantia nigra (SN) based on quantitative susceptibility mapping (QSM) could help to differentiate IPD patients from healthy controls (HCs). Methods: Three-dimensional multi-echo gradient-recalled echo images (0.86 × 0.86 × 1.00 mm3) were obtained at 3.0-T MRI for QSM in 87 IPD patients and 77 HCs. Regions of interest (ROIs) of the SN below the red nucleus were manually drawn on both sides, and subsequently, volumes of interest (VOIs) were segmented (these ROIs included four 1-mm slices). Then, 105 radiomic features (including 18 first-order features, 13 shape features, and 74 texture features) of bilateral VOIs in the two groups were extracted. Forty features were selected according to the ensemble feature selection method, which combined analysis of variance, random forest, and recursive feature elimination. The selected features were further utilized to distinguish IPD patients from HC using the SVM classifier with 10 rounds of 3-fold cross-validation. Finally, the representative features were analyzed using an unpaired t-test with Bonferroni correction and correlated with the UPDRS-III scores. Results: The classification results from SVM were as follows: area under curve (AUC): 0.96 ± 0.02; accuracy: 0.88 ± 0.03; sensitivity: 0.89 ± 0.06; and specificity: 0.87 ± 0.07. Five representative features were selected to show their detailed difference between IPD patients and HCs: 10th percentile and median in IPD patients were higher than those in HCs (all p < 0.00125), while Gray Level Run Length Matrix (GLRLM)-Long Run Low Gray Level Emphasis, Gray Level Size Zone Matrix (GLSZM)-Gray Level Non-Uniformity, and volume (all p < 0.00125) in IPD patients were lower than those in HCs. The 10th percentile was positively correlated with UPDRS-III score (r = 0.35, p = 0.001). Conclusion: Radiomic features of the nigrosome-1 region of SN based on QSM could be useful in the diagnosis of IPD and could serve as a surrogate marker for the STS.

Project description:PurposeTo derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data.Materials and methodsClinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways.ResultsEighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes.ConclusionRadiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively. Online supplemental material is available for this article.

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Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.

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Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.

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